Hydrology and water resource intelligent analysis and calculation method and system

Abstract

The invention relates to the field of hydrology and water resource analysis, in particular to a hydrology and water resource intelligent analysis and calculation method and system. The method comprises the following steps: acquiring long-term hydrology and water resource monitoring parameters of a target area; performing multi-time-point parameter change calculation and time sequence change trend analysis on the hydrology and water resource monitoring parameters to obtain a hydrology monitoring parameter change trend; acquiring a satellite remote sensing image of the target monitoring area; hydrological region depth semantic analysis is carried out on the satellite remote sensing image, and hydrogeological structure quantitative analysis is carried out, so that hydrogeological structure features are obtained; hydrologic flow topological logic analysis is carried out on the hydrogeological structure features, multi-parameter iterative calculation is carried out based on the hydrologic monitoring parameter change trend, and a real-time hydrologic monitoring virtual model is constructed; performing short-term hydrological monitoring evolution simulation on the real-time hydrological monitoring virtual model to obtain short-term hydrological simulation parameters; therefore, future hydrological parameter conditions can be effectively predicted.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of hydrology and water resources analysis, and in particular to a hydrology and water resources intelligent analysis and calculation method and system. BACKGROUND

[0002] As an important part of natural resources, hydrology and water resources play a crucial role in maintaining ecological balance and promoting social and economic development. With the increasing severity of global climate change and water resource shortages, scientifically and effectively analyzing and managing hydrology and water resources has become an important issue for water conservancy, environmental protection and sustainable development. Traditional hydrology and water resources analysis methods often rely on manual calculation and empirical prediction, which can meet the demand to some extent, but when dealing with complex and variable hydro-meteorological conditions, sudden extreme weather events and changing water demand, there are often problems of low precision and low efficiency, making it difficult to accurately predict the trend of water resources in real time, affecting the rational scheduling and efficient use of water resources.

[0003] With the rapid development of information technology, artificial intelligence and big data technology, intelligent hydrology and water resources analysis methods have gradually become the key to solving the above problems. By introducing modern technical means such as machine learning, deep learning and remote sensing monitoring, a large amount of hydro-meteorological data can be collected and processed more efficiently, and the spatio-temporal variation of water resources can be analyzed in depth to predict the dynamic changes of hydrology and water resources in real time, providing scientific basis for decision-makers. At the same time, the intelligent algorithm-based hydrology and water resources analysis method not only improves the prediction accuracy, but also effectively deals with the complex changes in different regions and different time scales, improving the intelligent level of water resources management. In order to meet the needs of modern water resources management, an intelligent hydrology and water resources analysis and calculation method integrating multiple technologies and having self-adaptive ability is urgently needed. SUMMARY

[0004] The present application is to solve the above technical problems, and proposes a hydrology and water resources intelligent analysis and calculation method and system to solve at least one of the above technical problems.

[0005] To achieve the above purpose, the present application provides a hydrology and water resources intelligent analysis and calculation method, comprising the following steps:

[0006] Step S1: Obtain long-term hydrology and water resources monitoring parameters of the target area; perform multi-time point parameter change calculation and time series trend analysis on the hydrology and water resources monitoring parameters to obtain the hydrology monitoring parameter change trend;

[0007] Step S2: Obtain satellite remote sensing images of the target monitoring area; perform hydrology area depth semantic analysis on the satellite remote sensing images, and perform hydrogeological structure quantitative analysis to obtain the hydrogeological structure characteristics;

[0008] Step S3: hydrological flow topology logic analysis is performed on the hydrogeological structure characteristics, and multi-parameter iterative calculation is performed based on the change trend of the hydrological monitoring parameters, and a real-time hydrological monitoring virtual model is constructed;

[0009] Step S4: short-term hydrological monitoring evolution simulation is performed on the real-time hydrological monitoring virtual model to obtain short-term hydrological simulation parameters;

[0010] Step S5: hydrological simulation deviation verification is performed on the short-term hydrological simulation parameters, and global parameter optimization is performed to construct a hydrological monitoring optimization model;

[0011] Step S6: target time point step-by-step dynamic prediction calculation is performed on the hydrological monitoring optimization model, and hydrological situation visualization processing is performed, thereby generating a hydrological situation prediction visualization result.

[0012] The present application can systematically accumulate hydrological data by obtaining long-term hydrological water resource monitoring parameters, thereby identifying long-term trends in hydrological changes, which is crucial for predicting future hydrological changes and long-term sustainable use of water resources. Multi-time point parameter change calculation enables the system to track fluctuations in hydrological parameters over time, thereby discovering potential seasonal, periodic changes and abnormal events. Through analysis of time series change trends, the regularity of hydrological processes can be better understood, providing a scientific basis for subsequent water resource management and scheduling. Satellite remote sensing images can provide high-resolution spatial data for a wide area, helping to conduct comprehensive spatial analysis of the hydrogeological environment of the target area. Through deep semantic analysis, key hydrological features in satellite images can be extracted to help reveal the change patterns and potential hydrological risks (such as floods, droughts, etc.) in the hydrological region. Quantifying hydrogeological structure characteristics can accurately identify key elements such as hydrological flow paths and groundwater level distribution, providing strong support for subsequent analysis. Hydrological flow topology logic analysis helps reveal the complex relationship of water flow, optimizes the management and scheduling of water resources, and can predict the impact of water flow changes on different regions. Multi-parameter iterative calculation based on hydrological monitoring parameter change trends can improve the accuracy and adaptability of the model by considering multiple factors (such as precipitation, evaporation, groundwater level, etc.), thereby more accurately reflecting the actual situation. The constructed virtual model can reflect the hydrological state of the target area in real time, facilitating dynamic monitoring and adjustment, and optimizing the use of water resources. Hydrological simulation bias verification helps to find the differences between the model and the actual hydrological situation, ensuring the accuracy of the model and providing feedback for subsequent optimization. Adjusting the key parameters of the model through global optimization techniques can improve the prediction accuracy of the hydrological monitoring model and ensure better control of hydrological changes. Through step-by-step dynamic prediction, real-time updates and adjustments can be made based on actual data at the target time point, ensuring the timeliness and accuracy of the prediction results. Visualization of hydrological conditions enables complex hydrological data to be presented in the form of graphs or maps, facilitating understanding, analysis, and use by decision-makers and the public. The visualization results provide intuitive support for hydrological monitoring and management, helping to implement effective emergency management, resource allocation, and disaster prevention.

[0013] In the present specification, an intelligent analysis and calculation system for hydrological water resources is provided for performing the intelligent analysis and calculation method for hydrological water resources as described above, comprising:

[0014] A hydrological parameter trend module is configured to obtain long-term hydrological water resource monitoring parameters of a target area, and perform multi-time point parameter change calculation and time series change trend analysis on the hydrological water resource monitoring parameters to obtain hydrological monitoring parameter change trends.

[0015] The hydrological element analysis module is configured to acquire a satellite remote sensing image of a target monitoring area; perform hydrological area depth semantic analysis on the satellite remote sensing image, and perform hydrogeological structure quantitative analysis, so as to obtain hydrogeological structure characteristics;

[0016] The hydrological topology logic module is configured to perform hydrological flow topology logic analysis on the hydrogeological structure characteristics, perform multi-parameter iterative calculation based on a hydrological monitoring parameter change trend, and construct a real-time hydrological monitoring virtual model;

[0017] The hydrological simulation module is configured to perform short-term hydrological monitoring evolution simulation on the real-time hydrological monitoring virtual model, so as to obtain short-term hydrological simulation parameters;

[0018] The deviation verification module is configured to perform hydrological simulation deviation verification on the short-term hydrological simulation parameters, perform global parameter optimization, and construct a hydrological monitoring optimization model;

[0019] The hydrological situation prediction module is configured to perform step-by-step dynamic prediction calculation on the hydrological monitoring optimization model at a target time point, perform hydrological situation visualization processing, and thus generate a hydrological situation prediction visualization result.

[0020] The application can extract the long-term change trend of hydrological monitoring parameters by analyzing long-term hydrological data, providing forward-looking reference data for water resource management and decision-making. Time series trend analysis can help detect subtle changes in hydrological parameters (such as water level, flow, rainfall, etc.), timely detect abnormal fluctuations, and warn potential floods, droughts, and other problems. With the change trend of hydrological monitoring parameters, managers can make dynamic adjustments in actual water resource scheduling to avoid the negative impact of uncertainty factors on water resource allocation. Through satellite remote sensing images, large-scale data can be accurately analyzed to provide a comprehensive geographical and environmental background for regional water resource management. Compared with traditional ground monitoring methods, remote sensing image processing can efficiently and comprehensively quantify hydrological elements, improving monitoring accuracy, especially for large-scale regional hydrological monitoring. Satellite images provide real-time updating capabilities, helping managers understand real-time changes in the monitoring area, such as changes in vegetation coverage and fluctuations in soil moisture. Through hydrological topology logic analysis, the regional hydrological structure and its flow characteristics can be understood from a system perspective, helping to discover complex interaction relationships between hydrological elements. Based on the iterative calculation of hydrological parameter change trends, the hydrological virtual model can be updated in real time in a changing hydrological environment, maintaining the efficiency and accuracy of the model. After building the virtual model, it can provide prediction capabilities in actual water resource management, helping decision-makers more accurately predict future hydrological conditions and their trends. Short-term hydrological simulation can effectively capture the dynamic changes of the hydrological system in the short term, predicting changes in water level and flow in the next few hours or days. Through real-time calculation of hydrological simulation results, the change trend of water resources in the short term can be understood in time, which helps emergency scheduling, such as flood warning and drought response. Short-term simulation results can be used for rational allocation and optimization of water resources to ensure the most appropriate resource management measures in uncertain hydrological environments. Through deviation detection and correction of simulation results, the accuracy of the hydrological monitoring model can be gradually optimized, reducing errors and ensuring more reliable prediction results. By continuously verifying and adjusting the deviation of the model, the robustness of the model can be enhanced to adapt to different hydrological conditions and changes. The deviation verification and optimization process enables the system to have adaptive adjustment capabilities, automatically optimizing parameters according to actual hydrological environmental changes, improving system flexibility. Through gradual dynamic prediction, the hydrological situation at the target time point can be more accurately predicted, avoiding the lag or limitations of traditional prediction methods. Visualization of the hydrological situation provides clear and intuitive information for decision-makers to quickly and accurately understand future hydrological conditions and make scientific management decisions. Through visual display of the hydrological situation, not only can managers make decisions, but also can provide transparent information on water resource conditions to the public. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1A hydrological water resource intelligent analysis and calculation method step flow diagram of the present application;

[0022] Figure 2 A detailed implementation step flow diagram of step S1;

[0023] Figure 3 A detailed implementation step flow diagram of step S2;

[0024] Figure 4 A detailed implementation step flow diagram of step S3. DETAILED DESCRIPTION

[0025] It should be understood that the specific embodiments described herein are merely illustrative of the present application and do not limit the present application.

[0026] The present application provides a hydrological water resource intelligent analysis and calculation method and system. The execution subject of the hydrological water resource intelligent analysis and calculation method and system includes but is not limited to the following: mechanical equipment, data processing platform, cloud server node, network upload device, etc. which can be regarded as general computing nodes of the present application, and the data processing platform includes but is not limited to: at least one of an audio image management system, an information management system, and a cloud data management system.

[0027] Please refer to Figures 1 to 4 The present application provides a hydrological water resource intelligent analysis and calculation method, which includes the following steps:

[0028] Step S1: Obtain long-term hydrological water resource monitoring parameters of a target area; perform multi-time point parameter change calculation and time series change trend analysis on the hydrological water resource monitoring parameters to obtain a hydrological monitoring parameter change trend;

[0029] Step S2: Obtain satellite remote sensing images of a target monitoring area; perform hydrological region depth semantic analysis on the satellite remote sensing images, and perform hydrogeological structure quantitative analysis to obtain hydrogeological structure characteristics;

[0030] Step S3: Perform hydrological flow topology logic analysis on the hydrogeological structure characteristics, and perform multi-parameter iterative calculation based on the hydrological monitoring parameter change trend to construct a real-time hydrological monitoring virtual model;

[0031] Step S4: Perform short-term hydrological monitoring evolution simulation on the real-time hydrological monitoring virtual model to obtain short-term hydrological simulation parameters;

[0032] Step S5: Perform hydrological simulation deviation verification on the short-term hydrological simulation parameters, and perform global parameter optimization to construct a hydrological monitoring optimization model;

[0033] Step S6: Perform step-by-step dynamic prediction calculation on the hydrological monitoring optimization model at target time points, and perform hydrological situation visualization processing to generate hydrological situation prediction visualization results.

[0034] The present application can systematically accumulate hydrological data by obtaining long-term hydrological and water resource monitoring parameters, thereby identifying long-term trends in hydrological changes, which is crucial for predicting future hydrological changes and long-term sustainable use of water resources. Multi-point parameter change calculation enables the system to track fluctuations in hydrological parameters over time, thereby discovering potential seasonal, periodic changes and abnormal events. Through analysis of time series trends, the regularity of hydrological processes can be better understood, providing a scientific basis for subsequent water resource management and scheduling. Satellite remote sensing images can provide high-resolution spatial data for a wide area, helping to conduct comprehensive spatial analysis of the hydrogeological environment of the target area. Through deep semantic analysis, key hydrological features in satellite images can be extracted to help reveal the change patterns and potential hydrological risks (such as floods, droughts, etc.) of the hydrological region. Quantifying hydrogeological structure characteristics can accurately identify key elements such as hydrological flow paths and groundwater level distribution, providing strong support for subsequent analysis. Hydrological flow topology logic analysis helps to reveal the complex relationship of water flow, optimize the management and scheduling of water resources, and predict the impact of water flow changes on different regions. Multi-parameter iterative calculation based on hydrological monitoring parameter change trends can improve the accuracy and adaptability of the model by considering multiple factors (such as precipitation, evaporation, groundwater level, etc.), thereby more accurately reflecting the real situation. The constructed virtual model can reflect the hydrological state of the target area in real time, facilitating dynamic monitoring and adjustment, and optimizing the use of water resources. Hydrological simulation bias verification helps to find the differences between the model and the actual hydrological situation, ensuring the accuracy of the model and providing feedback for subsequent optimization. By adjusting the key parameters of the model through global optimization techniques, the prediction accuracy of the hydrological monitoring model can be improved, ensuring better control of hydrological changes. Through step-by-step dynamic prediction, real-time updates and adjustments can be made at the target time point based on actual data, ensuring the timeliness and accuracy of the prediction results. Visualization of the hydrological situation enables complex hydrological data to be presented in the form of graphs or maps, facilitating understanding, analysis and use by decision-makers and the public. The visualization results provide intuitive support for hydrological monitoring and management, helping to implement effective emergency management, resource allocation and disaster prevention.

[0035] In the embodiment of the present application, referring to Figure 1 The steps of the hydrological and water resource intelligent analysis and calculation method in the present application are shown in the flowchart, and in this example, the steps of the hydrological and water resource intelligent analysis and calculation method include:

[0036] Step S1: Obtain long-term hydrological and water resource monitoring parameters in the target area; perform multi-time point parameter change calculation and time series trend analysis on the hydrological and water resource monitoring parameters to obtain the change trend of the hydrological monitoring parameters;

[0037] In this embodiment, obtaining long-term hydrological and water resource monitoring parameters in the target area is the basis for hydrological analysis. These parameters usually include water level, flow, precipitation, evaporation, soil moisture, etc., and can reflect the changes in regional hydrological conditions. Long-term monitoring data helps to identify the trend and periodic characteristics of hydrological changes, providing scientific basis for subsequent water resource management and decision-making. Determine the data source, which can usually be obtained through hydrological monitoring stations, meteorological stations, satellite remote sensing, environmental monitoring agencies, etc. The data should cover multiple monitoring points in the target area to ensure the comprehensiveness and representativeness of the data. When collecting data, pay attention to the time range of the data to ensure that the time span covered by the data is long enough (such as ten years or more) to conduct long-term trend analysis. After obtaining the data, perform data organization and standardization processing to ensure consistency of data format from different sources. Use Excel, database or data processing tools to integrate the data into a unified platform for subsequent analysis. Check the completeness of the data and eliminate missing values and outliers. Interpolation method can be used to fill in missing values to ensure the continuity and accuracy of the data. The purpose of multi-time point change calculation of hydrological and water resource monitoring parameters is to identify the changes between different time points to understand the dynamic characteristics of hydrological resources. Through analysis, the trend, seasonality and periodicity of hydrological changes can be revealed, which will provide important data support for subsequent water resource management and decision-making. Use appropriate statistical analysis methods such as percentage change, annual average change, trend analysis, etc. Time series analysis methods such as ARIMA model, seasonal decomposition, etc. can be used to analyze the change trend of monitoring parameters. Set analysis parameters such as calculation period (such as month, season, year) and required statistical indicators (such as mean, standard deviation, maximum, minimum, etc.). According to the set time period and calculation method, calculate the sorted hydrological monitoring parameters step by step. Calculate the change amount and rate of each time period. Use visualization tools such as time series chart and line chart to visually display the change trend of each hydrological parameter. Analyze the changes in different seasons and years to identify long-term trends and short-term fluctuations. Record the observed key trends and anomalies during the analysis process for further analysis. For example, if it is found that the precipitation in a particular year has significantly decreased, it may be related to climate change, land use change, etc.

[0038] Step S2: Obtain satellite remote sensing images of the target monitoring area; perform hydrological area depth semantic analysis on the satellite remote sensing images and perform hydrogeological structure quantitative analysis to obtain the hydrogeological structure characteristics;

[0039] In this example, satellite remote sensing images are an important data source for hydrological analysis, providing real-time and large-scale surface information to help study hydrological characteristics, geological structures, and environmental changes. Obtaining high-resolution remote sensing images helps identify surface features such as water bodies, vegetation cover, and soil types, providing basic data for subsequent hydrological regional depth semantic analysis and geological structure analysis. Select appropriate satellite remote sensing data sources, commonly used satellites include Landsat, Sentinel-2, MODIS, etc. These satellites provide remote sensing images with different resolutions and frequencies, and select data suitable for target areas and research needs. Download the required satellite images through various remote sensing data platforms (such as USGS Earth Explorer, Copernicus Open Access Hub). Ensure that the data covers the required time period and area, usually choose multi-temporal data to facilitate change analysis. After obtaining satellite remote sensing images, pre-process the data, including image radiation correction, atmospheric correction, and geometric correction, etc. The correction process ensures the accuracy and comparability of the image, eliminating environmental interference and sensor errors. Crop the image to extract the image data of the target monitoring area for subsequent analysis. Ensure that the processed image has consistent spatial resolution and color performance to facilitate deep semantic analysis. The purpose of hydrological regional depth semantic analysis is to extract hydrological-related surface features such as water bodies, vegetation, soil, and landforms through image processing and analysis techniques, thereby identifying and classifying the hydrological elements of the target area. This analysis provides a basis for subsequent quantitative analysis of hydrogeological structures, ensuring the accuracy and scientificity of the data. Use deep learning methods for semantic segmentation, commonly used network architectures include U-Net, DeepLab, etc. These models can automatically identify different surface types through training and perform pixel-level classification. Prepare the training data set, use existing labeled data or manually labeled sample images to improve the accuracy and reliability of the model. Train the selected deep learning model and infer the obtained satellite remote sensing images to generate classification results of hydrological elements. Ensure that the spatial resolution of the analysis results is consistent with the original image. Record the extracted hydrological features and related parameters such as water body area, vegetation coverage, soil type distribution, etc. to form a detailed hydrological element data set to support subsequent analysis. Through quantitative analysis of the extracted hydrological features, understand the hydrogeological structure of the target area and evaluate its impact on the hydrological process. This analysis can reveal the mutual relationship and dynamic change of hydrological elements. Quantitative analysis results will provide important scientific basis for water resource management and environmental protection. Use GIS (Geographic Information System) tools for quantitative analysis of hydrogeological structures. You can calculate the spatial distribution, area, shape, and mutual relationship of each hydrological feature. Set analysis parameters such as analysis area, buffer radius, etc. to ensure the comprehensiveness and representativeness of the analysis.The extracted hydrological feature data is analyzed using GIS software to calculate the area, distribution density, and mutual superposition of various hydrological elements. The generated analysis results should include a detailed report on the hydrological structure characteristics. Statistical data for each hydrological element is recorded to facilitate subsequent comprehensive analysis and decision support. The results can be visualized to generate maps and charts, providing an intuitive display of the hydrogeological structure characteristics.

[0040] Step S3: Perform hydrological flow topology logic analysis on the hydrogeological structure characteristics, and conduct multi-parameter iterative calculation based on the change trend of hydrological monitoring parameters to build a real-time hydrological monitoring virtual model.

[0041] In this embodiment, the hydrological flow topology logic analysis aims to reveal the flow relationship and interaction between hydrological elements, which can help understand the dynamic characteristics of hydrological system, identify the water flow path, catchment area and mutual influence between hydrological elements. By establishing a topology logic model, the hydrological process can be effectively simulated, and the change of hydrological elements under different conditions can be predicted, providing a scientific basis for water resources management. According to the extracted hydrogeological structure characteristics, the hydrological flow topology logic model is constructed. In the model, hydrological elements are regarded as nodes, and water flow relationship is regarded as the edge connecting these nodes. The characteristic parameters of each node are determined, such as the water level, flow rate, soil moisture of water body, etc., and the rules of flow relationship (such as flow direction, flow rate, etc.) are set. Ensure that the model can truly reflect the regional hydrological movement process. The topology logic model of hydrological flow is analyzed by using graph theory method, and the connection relationship between hydrological elements is identified. By analyzing the water flow path and catchment area, the corresponding topology structure diagram is generated. Record the analysis results, including the connection of each node, the flow intensity and the mutual influence degree, so as to carry out the subsequent multi-parameter iterative calculation and model optimization. Before carrying out the multi-parameter iterative calculation, firstly analyze the change trend of the hydrological monitoring parameters, including precipitation, evaporation, flow, etc. Through statistical analysis of historical data, the long-term change rule is identified. Using time series analysis, trend analysis and other methods, the periodicity, seasonality and sudden change of monitoring parameters are determined, which provides basic data for subsequent iterative calculation. The multi-parameter iterative calculation aims to update the parameter values in the hydrological flow topology model based on the change trend of monitoring parameters, so as to simulate the change of hydrological state in real time. Determine the initial conditions and boundary conditions of iterative calculation to ensure the stability and accuracy of calculation process. Set appropriate time step to capture the dynamic process of hydrological change in detail. In the established hydrological flow topology logic model, gradually carry out the iterative calculation of parameters. According to the change of hydrological monitoring data, gradually update the hydrological parameters in the model, such as adjusting precipitation, evaporation, etc. Record the calculation results of each iteration, analyze the influence of parameter change on model output, and ensure that the finally generated hydrological monitoring virtual model can truly reflect the regional hydrological characteristics. Real-time hydrological monitoring virtual model is a dynamic simulation system, which can predict the change of hydrological elements in real time according to real-time monitoring data and historical data. The model provides support for water resources management and decision-making, helps to predict extreme hydrological events such as flood and drought. Combined with topology logic analysis and multi-parameter iterative calculation, an integrated hydrological monitoring virtual model is constructed to ensure the real-time and accuracy of the model. The established hydrological flow topology logic model and the results of multi-parameter iterative calculation are integrated to ensure that the model can dynamically respond to the change of input data. According to the output results of the model, the model parameters are adjusted and the model performance is optimized. Model verification is carried out, and the virtual model is verified by using real hydrological monitoring data to ensure that the output of the model is consistent with the actual situation. Analyze the prediction accuracy of the model, record the error and adjust it.Record the parameter settings, running results, and validation indicators of the completed real-time hydrological monitoring virtual model for future use and reference. Use visualization tools to display the model's running conditions and hydrological state changes, providing intuitive decision support for water resource management. Finally, ensure that the constructed model can effectively serve regional hydrological monitoring and management.

[0042] Step S4: Perform short-term hydrological monitoring evolution simulation on the real-time hydrological monitoring virtual model to obtain short-term hydrological simulation parameters.

[0043] In this embodiment, short-term hydrological monitoring evolution simulation is a further application of the real-time hydrological monitoring virtual model, aiming to simulate the changes of hydrological elements (such as water level, flow, precipitation, etc.) in the short term. This process can help decision-makers understand the dynamic changes of hydrological state in a timely manner, so as to effectively respond to possible hydrological events (such as floods, droughts). Through short-term simulation, potential hydrological risks can be identified, and scientific basis can be provided for water resource management to ensure sustainable use of water resources. Before performing short-term hydrological monitoring evolution simulation, the time range of simulation needs to be determined, usually selecting a period of 1 to 7 days in the future to capture the short-term dynamics of hydrological changes. Collect input parameters related to simulation, such as recent precipitation forecasts, air temperature, wind speed, and other meteorological data, as well as historical hydrological monitoring data, to ensure the accuracy and reliability of input data. Based on the previously constructed real-time hydrological monitoring virtual model, establish a short-term hydrological evolution simulation model. Ensure that the structure of the model can reflect the dynamic characteristics of the hydrological process, including hydrological cycle, confluence process, etc. Perform necessary parameter configuration on the model, set initial conditions and boundary conditions to ensure that the model can effectively simulate within the specified time range. Start the simulation model, input the collected meteorological data and historical hydrological data, and perform short-term hydrological parameter evolution simulation. The model will dynamically update the state of hydrological elements according to the input data, simulating the hydrological changes in the short term. During the simulation process, record the changes of hydrological parameters at each time step, including water level, flow, precipitation, etc., and ensure the accuracy and integrity of the data. After the simulation is completed, analyze the generated short-term hydrological simulation parameters. Compare the differences between the simulation results and the actual monitoring data to evaluate the prediction accuracy of the model. If significant deviations are found, analyze the reasons and adjust the model. Record key short-term hydrological simulation parameters such as predicted maximum water level, flow trend, etc. to support subsequent water resource management decisions.

[0044] Step S5: Perform hydrological simulation deviation verification on the short-term hydrological simulation parameters and perform global parameter optimization to construct a hydrological monitoring optimization model.

[0045] In this embodiment, hydrological simulation bias verification is to evaluate the accuracy of short-term hydrological simulation parameters, to judge the difference between model prediction results and actual monitoring data, and this process can identify potential systematic bias in model simulation, ensuring the effectiveness of subsequent optimization. By comparing actual hydrological monitoring data (such as water level, flow, etc.) with simulation results, the bias can be quantified, and then the basis for global parameter optimization is provided. Collect short-term hydrological simulation parameters and corresponding actual monitoring data. Ensure that the time range of the data is consistent, usually choose the daily average value during the simulation period for comparison to ensure the representativeness of the data. Calculate the bias, the commonly used indicators include absolute error (AE), relative error (RE) and root mean square error (RMSE). For example, the bias calculation formula is: bias = actual monitoring value - simulation value, record the bias value at each time point, and calculate the overall bias index to evaluate the prediction accuracy of the model. Analyze the calculated hydrological simulation bias to identify time points and hydrological parameters with large bias. By analyzing the source of the bias, such as the uncertainty of meteorological data, the bias of model assumptions, etc., understand the shortcomings of the model performance. Visualize the bias results, including line charts or bar charts, to visually display the comparison between actual monitoring data and simulation results. According to the bias analysis results, perform global parameter optimization to improve the prediction ability of the model. Global optimization aims to adjust the key parameters of the model to better adapt to the actual hydrological environment. Determine the parameters to be optimized, such as the conversion coefficient of precipitation, the discharge coefficient of flow, etc., to ensure that these parameters have a significant impact on the model output. Use appropriate optimization algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO) or simulated annealing (SA), to perform global parameter optimization. These algorithms can effectively explore the parameter space to find the optimal solution. Determine the optimization objective function, usually choose to minimize the bias index (such as minimizing RMSE) to ensure the scientificity and effectiveness of the optimization process. Start the global parameter optimization algorithm, according to the set objective function and constraint conditions, iteratively calculate the model parameters. After each iteration, run the simulation model again according to the new parameter values, and update the hydrological simulation results. Record the results of each iteration, analyze the impact of parameter changes on model output, and ensure that the final optimized model can significantly improve the prediction accuracy. After completing the global parameter optimization, organize the optimized model parameters to build a hydrological monitoring optimization model. Ensure that the model structure is reasonable and can truly reflect the hydrological dynamic process. Perform model verification, test the optimized model using new monitoring data to ensure its stability and accuracy under different conditions.

[0046] Step S6: Perform step-by-step dynamic prediction calculation on the hydrological monitoring optimization model at the target time point, and perform hydrological situation visualization processing to generate hydrological situation prediction visualization results.

[0047] In this embodiment, before performing the step-by-step dynamic prediction calculation, the target time points for prediction need to be determined first. These time points should be set according to actual needs, usually choosing key moments in the future (such as flood peak period, drought season, etc.) to effectively monitor hydrological changes. Ensure that the selected time points cover different hydrological states and weather conditions, making it easier to capture diverse hydrological dynamic characteristics. To perform dynamic prediction, real-time input parameters are needed, which usually include precipitation, air temperature, evaporation, flow, etc., reflecting the current hydrological state. According to historical monitoring data and weather forecasts, generate input parameter sequences for a certain period in the future to ensure that the model can make predictions based on the latest information. Start the hydrological monitoring optimization model, input the prepared real-time parameters, and perform step-by-step dynamic prediction. The model will calculate the hydrological state at each future time point based on the input parameters. At each time step, the model updates the state of hydrological elements (such as water level, flow, etc.) and records the prediction results at each time point. This process usually requires setting appropriate time steps (such as hours or days) to capture detailed hydrological changes. After completing the prediction calculation at each time step, record the prediction results in detail, including key hydrological parameters such as water level, flow, precipitation, etc. Ensure the systematicity and traceability of the data. Compare the prediction results at different time points to analyze the trend of hydrological state changes. For example, observe the water level changes before and after a specific precipitation event to identify potential flood risks. Hydrological situation visualization is to display the prediction results in an intuitive way to help decision-makers quickly understand the dynamic changes of hydrology. Visualization results can improve data readability, support scientific analysis and decision-making. Through visualization, the spatial distribution, temporal changes and mutual relationships of hydrological elements can be displayed to identify key trends and potential risks. Choose appropriate data visualization tools, such as GIS software, data analysis platforms (such as Tableau, Power BI) or programming languages (such as Matplotlib and Seaborn in Python) for data visualization. According to the characteristics of the prediction results, choose appropriate visualization methods such as time series chart, heat map, streamline chart, etc. to ensure effective display of hydrological situation changes. Import the hydrological parameters calculated by step-by-step dynamic prediction into the visualization tool for data processing and display. The generated visualization results should include the distribution of hydrological parameters, change trends and their performance in geographical space. Ensure that the visualization results are clear and easy to understand, add necessary legends, labels and annotations to help the audience quickly obtain information. Highlight the hydrological changes at key time points to help decision-makers focus on important events.

[0048] In this embodiment, refer to Figure 2 The detailed implementation steps of step S1 include:

[0049] Step S11: obtaining long-term hydrological water resource monitoring parameters of a target area based on a multi-sensor array;

[0050] Step S12: dividing the hydrological water resource monitoring parameters into time sequence stages to obtain first-stage hydrological water resource monitoring parameters and second-stage hydrological water resource monitoring parameters;

[0051] Step S13: performing multi-time-point parameter change calculation on the first-stage hydrological water resource monitoring parameters to extract hydrological monitoring parameter change values at multiple time points;

[0052] Step S14: performing time sequence change trend analysis according to the hydrological monitoring parameter change values at the multiple time points to obtain a hydrological monitoring parameter change trend.

[0053] In this embodiment, a variety of sensors suitable for hydrological monitoring are selected, including water level sensors, flow meters, temperature sensors, and water quality sensors. These sensors should have high precision and good anti-interference ability, and be able to work stably under different environmental conditions. In the target area, the sensor array should be arranged reasonably to ensure that the key hydrological monitoring points are covered. The spacing and arrangement of the sensors should be optimized according to the flow characteristics of the water body and the topography, and a grid layout is usually used to improve the representativeness of the monitoring area. The sampling frequency of the sensors is set, and it is recommended to choose a sampling frequency of once an hour to capture the trend of changes in hydrological parameters. In special cases (such as heavy rain, floods, etc.), the sampling frequency can be encrypted to once every 10 or 15 minutes. The data collected by the sensors should be transmitted to the data management platform in real time, and the Internet of Things technology should be used to ensure the stability and security of data transmission. At the same time, a data storage mechanism should be set up to ensure that all collected data can be saved for a long time for subsequent analysis. After data collection, data verification should be carried out to ensure the accuracy and consistency of the data. Check the data integrity of each sensor, eliminate outliers and missing values, and usually set a threshold range to identify abnormal data. For monitoring parameters with missing values, interpolation methods such as linear interpolation or spline interpolation can be used to fill in the missing values to ensure data continuity and reliability, thereby laying a foundation for subsequent analysis. The obtained hydrological monitoring parameter data is sorted and arranged in chronological order to form a time series data table. Each time point data should include water level, flow, temperature, and water quality parameters to ensure the comprehensiveness of the data. Set a reasonable time period, usually recommend dividing the data into the first stage (such as the dry season) and the second stage (such as the rainy season) to compare and analyze the monitoring parameters under different hydrological conditions. According to the climate characteristics and hydrological period of the target area, set the stage division standard. It can be divided according to meteorological factors (such as precipitation, temperature change) and hydrological characteristics (such as water level change, flow fluctuation). Ensure that the time period of each stage is representative and consistent, for example, the first stage can be set as a continuous dry period, and the second stage as a rainfall period. Record the start and end time of each stage for subsequent analysis. According to the division of stages, select the hydrological monitoring parameter data of each stage and store them in different data tables. Ensure that the data of each stage is clear and explicit, which is convenient for subsequent analysis and comparison. Establish the corresponding database in the data management platform to facilitate user query and use, and ensure the safety and traceability of the data. In the first stage of hydrological monitoring parameters, select multiple key time points for change calculation, which should be representative, such as selecting the starting point, intermediate point, and ending point to fully reflect the trend of changes in hydrological parameters in the first stage. Record the hydrological monitoring parameters at each time point, including water level, flow, temperature, and water quality indicators to ensure data integrity. Calculate the changes of hydrological monitoring parameters at multiple selected time points, usually using the difference method to calculate the change value of each parameter between different time points.For example, the change in water level can be represented as ΔH = Ht2 - Ht1, where Ht2 and Ht1 are the water level values at time point 1 and time point 2 respectively. The same calculation method is used for flow rate, temperature, and water quality parameters. Record the change in each parameter for subsequent analysis. Organize the calculated hydrological monitoring parameter change values into a data table, ensuring that it includes time points, parameter names, and change values. Use Excel or a database management tool for organization to facilitate subsequent queries and analysis. Ensure that the data table is clear in format to facilitate subsequent visualization and report generation. At the same time, record the methods and parameter settings used during calculation to facilitate subsequent verification and analysis. Visualize the hydrological monitoring parameter change values to facilitate the analysis of time series trends. Use chart types such as line charts and bar charts to visually display parameter changes at different time points. During the visualization process, ensure that the trend of each parameter is clearly presented, and mark the specific values and changes at each time point. Use statistical analysis methods to analyze the trend of hydrological monitoring parameters. Linear regression analysis can be used to identify the trend line of parameter changes, calculate the slope and correlation coefficient, and evaluate the significance of parameter changes. In addition, moving average method can be used to smooth the data to reduce the impact of short-term fluctuations on trend analysis, which helps to reveal long-term trends and periodic changes. According to the results of trend analysis, interpret the trend of hydrological monitoring parameters and identify potential influencing factors (such as climate change, human activities, etc.). Record the key trends and change rules found during analysis to provide a basis for subsequent decision-making. Generate a detailed analysis report that summarizes the analysis results, visualized charts, and interpretation instructions. Ensure that the report content is clear and easy to understand, and can provide effective information support for relevant decision-makers.

[0054] In this embodiment, referring to Figure 3 For the detailed implementation step flowchart of step S2, in this embodiment, the detailed implementation steps of step S2 include:

[0055] Step S21: Obtain satellite remote sensing images of the target monitoring area;

[0056] Step S22: Perform global brightness optimization on the satellite remote sensing images to construct a global brightness optimization image;

[0057] Step S23: Perform hydrological region depth semantic analysis on the global brightness optimization image to obtain region depth semantic features;

[0058] Step S24: Perform quantitative analysis of hydrogeological structure according to the region depth semantic features to obtain hydrogeological structure features.

[0059] In this example, we select appropriate satellite remote sensing image data sources. After obtaining authorization from relevant platforms and units, we can choose satellites such as Landsat, Sentinel-2, or MODIS. These satellites have different spatial resolutions and spectral bands, which can meet different monitoring needs. According to the characteristics of the research target and the monitoring area, we determine the required image resolution and time range. For example, for hydrological monitoring, we usually choose images with high spatial resolution (such as 10 meters or 20 meters) to capture detailed features. We access the corresponding remote sensing data platform (such as NASA Earthdata or ESA Copernicus Open Access Hub) and search for satellite remote sensing images that meet the conditions according to the set time and spatial range. We pay attention to selecting images with less cloud cover to improve the accuracy of subsequent analysis. During the download process, we record the image acquisition time, satellite name, sensor type, and spectral band metadata for subsequent use and analysis. We perform necessary preprocessing on the acquired satellite remote sensing images, including geometric correction, radiation correction, and atmospheric correction. This step is crucial to ensure image quality and data accuracy, especially when conducting hydrological analysis, as it must eliminate the effects of atmospheric and lighting changes. We use professional remote sensing image processing software (such as ENVI or ERDAS Imagine) for preprocessing to ensure that the final image accurately reflects the surface features of the target area. Satellite remote sensing images often have uneven brightness due to factors such as lighting conditions and cloud shadows, which can affect the effectiveness of subsequent analysis. Therefore, global brightness optimization is extremely important to ensure uniform brightness distribution in the image. Brightness optimization helps enhance the contrast between water bodies, vegetation, and geological features, making them easier to identify in subsequent analysis. We choose appropriate brightness optimization algorithms, such as histogram equalization, contrast-limited adaptive histogram equalization (CLAHE), or Gamma correction, which can effectively improve the contrast and brightness distribution of the image. Taking CLAHE as an example, this algorithm limits the enhancement of local contrast to avoid noise amplification caused by excessive enhancement, making it suitable for image processing in hydrological monitoring. We use image processing software (such as QGIS or MATLAB) to perform brightness optimization on satellite remote sensing images. First, we calculate the histogram of the image to analyze the brightness distribution. Then, we apply the selected optimization algorithm to adjust the brightness and contrast, generating a globally brightness-optimized image. After processing, we evaluate the optimization effect by comparing the histograms before and after processing to ensure that the brightness distribution is more uniform and that the hydrological features are more identifiable. Deep semantic analysis aims to extract feature information of hydrological areas from the globally brightness-optimized image, including water bodies, wetlands, soil types, and vegetation coverage. This process helps identify and classify different hydrological areas, providing basic data for subsequent hydrogeological structure analysis.To select an appropriate deep learning model for semantic analysis of hydrological regions, commonly used models include U-Net, DeepLab, and Mask R-CNN, which can effectively handle image segmentation tasks and extract features of different regions. When selecting a model, consider the computational resources and task complexity to ensure that the selected model can complete the analysis within a reasonable time. If there is an existing labeled dataset, you can directly use it to train the model; if not, you need to create a labeled dataset and manually label the hydrological regions. Use the existing globally brightness-optimized image for training to ensure that the model can learn effective features. After training, apply the model to the globally brightness-optimized image for deep semantic analysis, generating a classification map of hydrological regions and extracting regional deep semantic features. Save the analysis results for subsequent data processing and analysis. Based on the regional deep semantic features extracted from deep semantic analysis, define hydrogeological structure features, including water body distribution, wetland area, soil type, and vegetation coverage, which are crucial for understanding the hydrological characteristics and geological conditions of the region. Use quantitative analysis methods to evaluate hydrogeological structure features, such as spatial analysis techniques such as buffer analysis, overlay analysis, and spatial statistical analysis, which can help quantify the distribution of different hydrological features in the region. For example, you can calculate the soil moisture and vegetation coverage within a 1-kilometer range around the water body using buffer analysis to determine the impact of the water body on the surrounding environment. Use GIS software (such as ArcGIS or QGIS) to perform quantitative analysis of hydrogeological structure features, load the results of deep semantic analysis, and perform spatial analysis. Calculate the area, distribution, and relationship of each hydrological feature, generate a quantitative analysis report, and record the specific values and spatial distribution characteristics of each feature.

[0060] In this embodiment, the specific steps of step S22 are:

[0061] Perform atmospheric radiation blur detection on the satellite remote sensing image and mark the blurred areas;

[0062] Perform distortion degree evaluation on the blurred areas to generate an image blur area distortion evaluation value;

[0063] Calculate the solar elevation angle and atmospheric transmittance based on the satellite remote sensing image;

[0064] Calculate the magnitude correction parameter for the image blur area distortion evaluation value based on the solar elevation angle and atmospheric transmittance, thereby generating an image blur magnitude correction parameter;

[0065] Perform magnitude correction compensation on the blurred areas based on the image blur magnitude correction parameter, thereby obtaining a magnitude-corrected remote sensing image;

[0066] Perform global brightness optimization on the magnitude-corrected remote sensing image to construct a globally brightness-optimized image.

[0067] In this embodiment, satellite remote sensing images are affected by atmospheric scattering and absorption during acquisition, which may cause image blurring, affecting the accuracy of subsequent analysis, so blur detection is needed to identify and mark the blurred areas. Selecting appropriate blur detection algorithms, commonly used methods include Laplacian operator, Sobel operator and gradient amplitude detection, etc. These methods can effectively identify blurred areas by analyzing image edge intensity and contrast. For example, use the Laplacian operator to calculate the second derivative of the image, if the derivative value is lower than the set threshold, the area is marked as a blurred area. Use image processing software (such as MATLAB or OpenCV) to detect blur in satellite remote sensing images. First, convert the image to a grayscale image to simplify processing, then apply the selected blur detection algorithm to calculate the gradient and edge intensity. According to the calculation results, set the blur threshold to mark the blurred areas. Binary processing can be used to display the blurred areas on the original image with a specific marker (such as black) to facilitate subsequent processing and analysis. For the marked blurred areas, distortion degree assessment is needed to quantify the severity of the blur effect, which will provide the basis for subsequent correction and compensation. Select appropriate evaluation indicators, commonly used indicators include structural similarity index (SSIM), peak signal-to-noise ratio (PSNR) and blur measurement (such as Brenner gradient), these indicators can effectively evaluate image quality and distortion degree. For example, use PSNR to calculate the difference between the blurred area and the clear area, the lower the PSNR value, the higher the blur degree. Perform distortion degree assessment on the marked blurred areas, first extract the data of the blurred areas from the original image, then compare it with the corresponding clear area to calculate the PSNR and SSIM values. Record the evaluation results of each blurred area, generate the distortion evaluation value of the blurred area, and visualize the results for subsequent analysis. The solar elevation angle is an important parameter that affects the radiation characteristics of satellite remote sensing images. Calculating the solar elevation angle needs to consider factors such as observation time, geographical location and solar position. Use the solar position algorithm (such as SPA algorithm) to calculate the solar elevation angle at a specific time and location. This algorithm can provide accurate solar position, ensuring the reliability of the calculation results. Atmospheric transmittance reflects the degree of attenuation of satellite remote sensing signals by the atmosphere, which is usually estimated by meteorological data or empirical formula. Use the atmospheric transmittance model (such as MODTRAN) to calculate the transmittance under specific conditions. Obtain the meteorological data (such as temperature, humidity and pressure) of the target area, substitute it into the transmittance model to calculate the atmospheric transmittance under the specific solar elevation angle. Record the calculated solar elevation angle and atmospheric transmittance in the database to ensure that subsequent correction and compensation calculations can be used. A table containing time, location, solar elevation angle and atmospheric transmittance can be generated for future reference.Since the atmospheric transmittance and solar elevation angle affect the radiometric properties of the image, these factors need to be considered when correcting the hazy areas, and the corresponding amplitude correction parameters need to be calculated. Linear regression or empirical formulas can be used to calculate the amplitude correction parameters of the hazy areas based on the solar elevation angle and atmospheric transmittance. A relationship model can be set up, A = k1 · transmittance + k2 · elevation angle + C, where A is the amplitude correction parameter, k1 and k2 are undetermined coefficients, and C is a constant. The calculated solar elevation angle and atmospheric transmittance are substituted into the amplitude correction formula to calculate the correction parameters. Record the correction parameters of each hazy area for subsequent application. Generate a table containing hazy areas and corresponding amplitude correction parameters to ensure systematicity and traceability of data. According to the calculated amplitude correction parameters, correct and compensate the hazy areas to restore the clarity of the image, which is crucial for improving the accuracy of subsequent analysis. Choose appropriate amplitude correction algorithms, such as linear enhancement or contrast limited adaptive histogram equalization (CLAHE), which can enhance the contrast of hazy areas while preserving image details. For example, use linear enhancement to achieve amplitude correction compensation by multiplying the pixel values of hazy areas by correction parameters. According to the amplitude correction parameters, compensate the marked hazy areas. Multiply the original pixel values by the correction parameters to generate new image data. After the operation is completed, save the amplitude-corrected remote sensing image to ensure significant compensation effect and perform visual comparison to intuitively display the effect changes before and after correction. The amplitude-corrected remote sensing image may still have non-uniform brightness problems, so global brightness optimization is needed to ensure uniform brightness distribution and facilitate subsequent analysis. Choose effective global brightness optimization algorithms such as histogram equalization or Gamma correction, which can enhance image contrast and improve information visibility. Histogram equalization can adjust the gray value distribution of the image to make the brightness distribution more uniform, thereby improving the overall quality of the image. Use image processing software such as QGIS or MATLAB to perform global brightness optimization on the amplitude-corrected remote sensing image. First, calculate the histogram of the image to analyze the brightness distribution. Apply the selected optimization algorithm to adjust the brightness and contrast of the image to generate a globally brightness-optimized image. After processing, evaluate the optimization effect and check the histogram changes to ensure more uniform brightness distribution.

[0068] In this embodiment, the specific steps of step S24 are:

[0069] Based on the regional depth semantic features, the regional hydrological elements are marked, and the surface runoff data, vegetation cover data, and landform structure data are extracted.

[0070] The surface runoff data is analyzed for spatial distribution of surface runoff to generate spatial distribution characteristics of surface runoff.

[0071] The vegetation coverage data is subjected to regional coverage change calculation to generate regional coverage change features;

[0072] The geomorphologic structure data is subjected to multi-geomorphologic form analysis to generate multi-geomorphologic form features in the region;

[0073] Based on the surface runoff spatial distribution features, the regional coverage change features and the multi-geomorphologic form features in the region, quantitative analysis is performed on the hydrogeological structure to obtain hydrogeological structure features.

[0074] In this embodiment, the deep semantic analysis results obtained in the previous steps are used to identify and label hydrological features within the target area, including surface runoff, vegetation cover, and landform structure. Semantic segmentation algorithms can be used to accurately extract the spatial distribution of various hydrological features. Specifically, a trained deep learning model (such as U-Net or DeepLab) is used to process high-resolution remote sensing images to generate labeled maps containing surface runoff, vegetation, and landform structure. Through pixel-level labeling, the accuracy of each type of hydrological feature is ensured. The labeled hydrological feature data is extracted and organized into a database, ensuring that the spatial information and attribute information of each type of feature are clearly recorded. The data should include the location, area, type, and related meteorological parameters (such as precipitation, evaporation, etc.) of each feature. This process can be achieved through GIS software, which loads remote sensing images and related labeled maps, extracts spatial information of features, and calculates statistical indicators such as area and distribution density for each type of feature. Collect and organize the extracted surface runoff data, including its spatial location, flow, flow rate, and other parameters. These data can be obtained through remote sensing monitoring, ground measurement, or hydrological model simulation. Record the characteristic parameters of each watershed, such as area, slope, soil type, etc., for subsequent analysis. Use spatial analysis methods to analyze the distribution characteristics of surface runoff data. Methods such as Kriging interpolation, inverse distance weighting (IDW), or spatial autocorrelation analysis can be used to generate spatial distribution maps of surface runoff. For example, Kriging interpolation can effectively handle spatially correlated flow data to generate smooth watershed runoff distribution maps. Use GIS software to import and apply the selected spatial analysis method to the surface runoff data. During this process, set appropriate analysis parameters, such as interpolation range and search radius, to ensure that the generated spatial distribution map is representative. The analysis results should include the spatial distribution map of surface runoff within the watershed and calculate the runoff characteristic indicators within the watershed, such as runoff collection area and flow distribution characteristics, providing quantitative information to support subsequent analysis. Use the previously extracted vegetation cover data to record its spatial distribution and coverage. Vegetation cover data can be extracted from remote sensing images or verified in combination with ground survey data. Calculate the vegetation cover rate of each plot, usually expressed as a percentage, to ensure data accuracy and completeness. Select change detection methods, such as difference image analysis, normalized vegetation index (NDVI) change analysis, etc., to evaluate changes in vegetation cover. NDVI is a commonly used vegetation index that effectively reflects vegetation growth conditions. Set the time period for change detection to ensure the timeliness and relevance of the comparison. Use remote sensing images and NDVI calculation methods to obtain vegetation cover data at different time points and calculate the change in coverage. During the analysis process, use GIS software to generate change maps to visually display the changes in vegetation cover within the region. Record the change results and generate a regional coverage change feature report, including the increase or decrease in coverage and its possible influencing factors.Collect topographic data for the target area, typically including elevation data, slope, soil type, and surface features. Obtain the data through remote sensing techniques, ground measurements, or existing topographic maps. Organize the data to ensure that spatial and attribute information for each topographic feature is clearly recorded. Choose appropriate topographic analysis methods, such as digital elevation model (DEM) analysis, slope analysis, and watershed division, which can effectively extract and analyze the spatial distribution of topographic features. Use GIS software tools, such as topographic analysis functions, to calculate the area, distribution, and characteristics of different topographic types. Analyze the collected topographic data using GIS software to generate spatial distribution maps of various topographic features. During the analysis, record the characteristic parameters of each topographic type, such as area, distribution density, and their relationship with other hydrological elements. The generated topographic feature report should include the distribution of major topographic types in the region and their impact on hydrological processes, providing basic data for subsequent analysis. By integrating surface runoff, vegetation coverage, and topographic features, conduct quantitative analysis of hydrogeological structure to reveal the relationships between different hydrological elements and their impact on hydrogeological structure. Use statistical analysis methods, such as correlation analysis, regression analysis, and multiple linear regression models, to evaluate the relationships between elements. Use spatial regression models to consider the impact of spatial effects on analysis results. Set reasonable analysis parameters to ensure the scientificity and accuracy of the analysis. Integrate the collected surface runoff spatial distribution characteristics, regional coverage change characteristics, and topographic features for quantitative analysis. Use statistical software (such as SPSS or R) to perform correlation tests on each hydrological element and generate corresponding statistical results.

[0075] In this embodiment, referring to Figure 4 For the detailed implementation step flowchart of step S3, in this embodiment, the detailed implementation steps of step S3 include:

[0076] Step S31: Perform hydrological element spatial position relationship analysis on the hydrogeological structure characteristics, and extract the spatial position relationship of multiple hydrological elements;

[0077] Step S32: Perform hydrological flow topology logic analysis on the hydrogeological structure characteristics to generate regional hydrological flow topology logic;

[0078] Step S33: Perform hydrological spatial topology evolution based on the spatial position relationship of multiple hydrological elements and the regional hydrological flow topology logic to construct a regional hydrological topology relationship graph;

[0079] Step S34: Perform multi-parameter iterative calculation on the regional hydrological topology relationship graph based on the change trend of the hydrological monitoring parameters to construct a real-time hydrological monitoring virtual model.

[0080] In this example, the hydrological element data extracted in the previous steps, including surface runoff, vegetation cover, soil type, and other related hydrological parameters, are collected and organized. These data should have clear spatial coordinate information to facilitate spatial position relationship analysis. Ensure data integrity and accuracy, remove outliers and missing values during processing, and if necessary, perform interpolation to maintain data continuity. Use GIS spatial analysis tools to analyze the spatial position relationship of hydrological elements. Common methods include proximity analysis, buffer analysis, and spatial overlay analysis. Through these analyses, the spatial relationships and mutual influences between different hydrological elements can be determined. For example, buffer analysis can be used to determine the vegetation cover around water bodies and assess the impact of vegetation on hydrological elements. Use GIS software (such as ArcGIS or QGIS) to analyze the spatial position relationship of the collected hydrological element data. First, set the analysis parameters, such as buffer radius, analysis scale, etc., to ensure the representativeness of the analysis results. Generate a spatial position relationship diagram of hydrological elements, marking the spatial relationships between different elements. Record the characteristic data of each relationship, such as distance, overlapping area, and mutual influence degree, for subsequent use. The hydrological flow topology logic analysis aims to reveal the flow relationship and interaction between hydrological elements, and generate a regional hydrological flow topology logic diagram. This analysis helps to understand the hydrological cycle process and the distribution characteristics of water resources. Select an appropriate topology logic model, usually using graph theory to represent the relationship between hydrological elements. Each hydrological element can be regarded as a node in the graph, while the hydrological flow relationship is represented as the edge between nodes. Set the rules of flow relationship, such as water flow direction, flow rate and flow, to ensure that the model can truly reflect the hydrological movement in the region. Use graph theory to analyze the topology of hydrological elements and establish the hydrological flow topology logic diagram. Through the analysis of the connection relationship between hydrological elements, the corresponding topology structure diagram is generated. Record the analysis results, including flow path, flow intensity and mutual influence, to ensure that the generated topology logic diagram is scientific and practical. The purpose of hydrological spatial topology evolution analysis is to explore how hydrological elements change in time and space, and thus understand their impact on water resources management. This analysis can reveal the dynamic change relationship between hydrological elements. Based on the spatial position relationship and topology logic analysis of the previous two steps, construct a hydrological spatial topology evolution model. Dynamic system models can be used to treat hydrological elements as state variables in the system and analyze their time-varying rules. Set the initial conditions and boundary conditions of the evolution model to ensure the accuracy and stability of the model. Use mathematical modeling tools to simulate and analyze the hydrological spatial topology evolution model. Through iterative calculation, predict the future spatial distribution and change trend of hydrological elements. Generate a hydrological topology relationship diagram to visually display the evolution process of hydrological elements. Record the changes at each time step to ensure that the analysis results reflect the actual situation. Collect regional hydrological monitoring data and analyze its change trend.The data should include parameters such as water level, flow rate, precipitation, and evaporation, to ensure the timeliness and accuracy of the data. Statistical analysis methods (such as time series analysis) are used to evaluate the trends of hydrological monitoring parameters and identify potential influencing factors. Based on the results of trend analysis, a multi-parameter iterative calculation model is constructed. The model should consider the mutual relationship and dynamic changes between various hydrological parameters to ensure the scientificity of the calculation process. The iteration rules and convergence conditions of the model are set to ensure the stability and accuracy of the calculation process. Through numerical simulation methods, the multi-parameter iterative calculation model is solved. The values of hydrological monitoring parameters are updated through continuous iterative calculation, and a real-time hydrological monitoring virtual model is generated. The results of each iteration are recorded, and the changes in hydrological elements are analyzed to ensure that the final virtual model can truly reflect the hydrological characteristics of the region.

[0081] In this embodiment, step S4 includes the following steps:

[0082] Step S41: Extract the timestamps of the second-stage hydrological monitoring parameters and the first-stage hydrological monitoring parameters;

[0083] Step S42: Calculate the stage interval according to the timestamps to obtain the inter-stage time interval;

[0084] Step S43: Calculate the actual simulation time based on the inter-stage time interval to obtain the simulation time window;

[0085] Step S44: Perform short-term hydrological monitoring evolution simulation on the real-time hydrological monitoring virtual model according to the simulation time window to obtain short-term hydrological simulation parameters.

[0086] In this embodiment, before timestamp extraction, first ensure that the hydrological monitoring parameter data of the first and second stages is complete, which includes various parameters such as water level, flow, precipitation, etc., and ensure that the data format is consistent for subsequent processing. Check if there are missing values or outliers in the data, and if necessary, perform data cleaning. Interpolation method can be used to fill in missing values to ensure the continuity and availability of timestamps. Extract timestamps from the first and second stage hydrological monitoring parameter data. Timestamps are usually represented in the format of date and time, which need to be extracted from the dataset and arranged into a unified time series. Store the extracted timestamps in a data structure (such as a list or data frame) for subsequent calculation and analysis. During the extraction of timestamps, record the start and end times of each stage for subsequent stage interval calculation. These time information will serve as the basis for analysis to ensure the accuracy of subsequent steps. Ensure that the extracted timestamp data is accurate and perform visual display to intuitively understand the monitoring time distribution of each stage. The stage interval refers to the difference between the monitoring times of the first and second stages, which will provide basic data for subsequent simulation time calculation. Determine the start and end times of the stages, such as the difference between the end time of the first stage and the start time of the second stage. Use the time difference calculation method, usually in date time format. You can use the datetime library in Python or other data processing tools to achieve this. Stage interval = second stage start time - first stage end time, ensure that the calculation result is positive, if the result is negative, you need to check if there is an error in the extraction of timestamps. Record the calculated stage interval and ensure that its unit (such as hours, days) meets the needs of subsequent analysis. The simulation time window refers to the time range for simulation in the hydrological model, which is usually based on the stage interval and appropriately extended or shortened to better reflect the actual hydrological changes. Determine the start and end times of the simulation time window, which can usually be selected as the midpoint of the stage interval as the simulation start time. According to the calculated stage interval, set the specific time period of the simulation time window. You can choose to divide the stage interval into several parts (such as short-term, medium-term, long-term) and simulate them respectively. For example, if the stage interval is 10 days, you can set the simulation time window to 5 days (half of the stage interval) and calculate from the start time of the second stage. Record the start and end times of the simulation time window in the data structure and ensure that their format is consistent with the previous timestamps for subsequent analysis. Visualize the relationship between the simulation time window and the actual monitoring time to help understand the hydrological evolution process. Through short-term hydrological monitoring evolution simulation, simulate the changes of hydrological elements within the set simulation time window, so as to evaluate the dynamic characteristics and response mechanism of the hydrological system. Choose a suitable hydrological model for short-term simulation, commonly used models include SWAT, HEC-HMS, etc. These models can simulate the dynamic changes of the hydrological system according to the input hydrological data and meteorological parameters.Configure the model parameters to ensure the model adapts to the hydrological characteristics of the target area and input relevant data within the simulation time window (such as precipitation, evaporation, etc.). Run the hydrological model within the set simulation time window to perform short-term hydrological monitoring evolution simulation. Record the model output results, including the changes in water level, flow, runoff, and other hydrological parameters. Generate short-term hydrological simulation parameters and analyze the results to evaluate the effectiveness and accuracy of the model.

[0087] In this embodiment, step S5 includes the following steps:

[0088] Step S51: Perform hydrological simulation bias verification on the short-term hydrological simulation parameters based on the second-stage hydrological monitoring parameters, and extract the hydrological simulation parameters with verification bias;

[0089] Step S52: Calculate the bias of the hydrological simulation parameters with verification bias to obtain the hydrological bias value;

[0090] Step S53: Perform local bias compensation calculation on the hydrological bias value to obtain the local bias compensation value;

[0091] Step S54: Perform global parameter optimization on the real-time hydrological monitoring virtual model based on the local bias compensation value, and construct a hydrological monitoring optimization model.

[0092] In this embodiment, before performing hydrological simulation bias verification, first ensure that the second-stage hydrological monitoring parameter data is complete and accurate. These parameters usually include water level, flow, precipitation, etc., and ensure that the timestamp of the data corresponds to the previous simulation parameters. Organize the second-stage and short-term hydrological simulation parameters to ensure that their data formats are the same, facilitating subsequent comparison. The purpose of hydrological simulation bias verification is to evaluate the differences between short-term hydrological simulation parameters and actual monitoring parameters. Through this process, systematic biases that may exist in the simulation process can be identified. Determine the key indicators for verification, such as water level, flow, etc., and select an appropriate time period for comparison to ensure the reliability of the results. Compare the second-stage hydrological monitoring parameters with the short-term hydrological simulation parameters and calculate the bias of each hydrological element. Absolute error and relative error can be used as evaluation criteria, and the calculation formula is as follows: Bias = Actual hydrological monitoring value of the second stage - Simulation value Extract the hydrological simulation parameters with verification bias, record the bias value of each parameter, and ensure the systematicity and traceability of the data to facilitate subsequent analysis. The purpose of hydrological bias calculation is to quantify the differences between short-term hydrological simulation parameters and actual monitoring parameters, thereby providing basic data for subsequent compensation calculation. Bias values can reflect the prediction accuracy of the model, identify potential systematic errors, and help optimize the model. Choose appropriate bias calculation methods, including absolute bias, root mean square error (RMSE), and mean absolute error (MAE), which can effectively evaluate the accuracy of simulation results. .

[0093] According to the selected deviation calculation method, the extracted hydrological simulation parameters are calculated one by one, and the deviation value of each parameter is recorded. Ensure that the calculation process adopts unified standards to reflect the true deviation situation. Summarize all the calculation results, generate a deviation analysis report, record the deviation value of each hydrological element, and perform visual display for easy understanding and analysis. Local deviation compensation aims to correct the systematic errors in the simulation results, improve the accuracy and reliability of the model, and this process helps to improve the credibility of the hydrological monitoring results. By compensating for the deviation value, the model parameters can be optimized to better adapt to the actual hydrological environment. Choose the appropriate compensation calculation method, commonly including linear compensation, proportional compensation and weighted average method, etc. These methods can effectively adjust the output of the model to reduce the deviation. The linear compensation method can be expressed as: compensation value=k⋅deviation, where k is the compensation coefficient, which is set according to the actual situation. According to the calculated hydrological deviation value, use the selected compensation method to calculate the local deviation compensation. Ensure that the parameter setting in the compensation process is reasonable and scientific, reflecting the changes in the hydrological environment. Apply the compensation value to the short-term hydrological simulation parameters, record the results after compensation, and ensure the accuracy and consistency of the data. The purpose of global parameter optimization is to adjust the parameters of the hydrological monitoring virtual model according to the local deviation compensation value, improve the adaptability and prediction ability of the model to the actual hydrological environment. By optimizing the model parameters, the hydrological process can be better reflected, and the overall performance of the model can be improved. Choose the appropriate optimization method, including genetic algorithm, particle swarm optimization (PSO) or gradient descent method, etc. These methods can effectively adjust the model parameters to find the best parameter combination. Determine the optimization objective function, such as minimizing the deviation value, minimizing the RMSE, etc., as the evaluation standard for the optimization process. Use the selected optimization algorithm to perform global parameter optimization on the real-time hydrological monitoring virtual model. Through multiple iterations of calculation, gradually approach the optimal solution. Record the results of each optimization, analyze the changes in model performance before and after optimization, and ensure that the final generated hydrological monitoring optimization model can effectively reflect the hydrological characteristics.

[0094] In this embodiment, step S6 includes the following steps:

[0095] Step S61: define a preset hydrological monitoring prediction target time point;

[0096] Step S62: input the preset hydrological monitoring prediction target time point into the hydrological monitoring optimization model, and perform step-by-step dynamic prediction calculation to obtain the hydrological state prediction situation of the target time point;

[0097] Step S63: based on the hydrological state prediction situation, perform hydrological situation visualization processing to generate a hydrological situation prediction visualization result.

[0098] In this example, in hydrological monitoring, it is crucial to define clear prediction target time points, which will serve as the basis for model prediction, helping us understand the dynamic process of hydrological changes. The prediction target time points can be specific dates and times in the future, usually set according to specific monitoring needs and the changing cycle of hydrological phenomena. Based on the analysis of historical hydrological data, select appropriate prediction time points. For example, you can choose key moments before and after floods, or specific times during droughts, to better capture changes in hydrological conditions. When selecting time points, consider seasonal factors, weather changes, and historical events to ensure the relevance and importance of the selected time points. Record the selected prediction target time points in the system, ensuring consistency in time format (such as ISO 8601 format). You can store the time points in a database or data structure for subsequent steps of calling and using. You can also generate a list of prediction time points to facilitate comparison and analysis of changes in hydrological conditions at different time points. Input the prediction target time points into the hydrological monitoring optimization model, which is the first step in dynamic prediction calculation. By inputting these time points, the model can generate future hydrological trend predictions based on historical data and current conditions. Ensure that the input data format is consistent with the model's requirements to avoid calculation errors due to format inconsistencies. Use the hydrological monitoring optimization model to perform step-by-step dynamic prediction calculations. The model usually uses time series analysis, regression analysis, or dynamic system models to simulate the changes of hydrological elements in the time dimension. You can set the time step (such as hours, days) to gradually advance the prediction, ensuring that the calculation results of each step provide the basis data for the next time step. After inputting the prediction target time points, start the model for dynamic calculation. The model will generate hydrological state prediction trends for the corresponding time points based on input parameters (such as precipitation, evaporation, flow, etc.) and historical hydrological data. Record the results of each calculation step, including the predicted water level, flow, and other hydrological parameters, to ensure systematicity and traceability of the data. Visualization of hydrological trends can visually display the prediction results, helping decision-makers and relevant personnel better understand hydrological changes and their potential impacts. Visualization not only enhances data readability but also effectively communicates complex hydrological information, supporting scientific analysis and decision-making. Choose appropriate visualization tools, such as GIS software (ArcGIS, QGIS), data visualization platforms (Tableau, Power BI), etc., to ensure effective display of hydrological state changes. According to the characteristics of the prediction results, choose appropriate visualization methods, such as time series graphs, heat maps, three-dimensional terrain maps, etc., to display the dynamic changes of hydrological trends. Import the predicted hydrological state data into the visualization tool and process it according to the set visualization method. The generated visualization results should include the distribution of hydrological parameters, change trends, and their performance in geographical space. Ensure that the visualization results are clear and easy to understand, and add necessary legends, labels, and annotations to help the audience quickly access information.Finally, a report of the hydrological situation prediction visualization result is generated, recording the observed key trends and potential risks, providing support for subsequent water resource management and decision-making.

[0099] In the embodiment, a hydrological water resource intelligent analysis and calculation system is provided for executing the hydrological water resource intelligent analysis and calculation method as described above, comprising:

[0100] a hydrological parameter trend module for acquiring long-term hydrological water resource monitoring parameters of a target area; performing multi-time point parameter change calculation and time series change trend analysis on the hydrological water resource monitoring parameters to obtain hydrological monitoring parameter change trends;

[0101] a hydrological element analysis module for acquiring satellite remote sensing images of a target monitoring area; performing hydrological area depth semantic analysis on the satellite remote sensing images and performing hydrogeological structure quantitative analysis to obtain hydrogeological structure characteristics;

[0102] a hydrological topology logic module for performing hydrological flow topology logic analysis on the hydrogeological structure characteristics and performing multi-parameter iterative calculation based on the hydrological monitoring parameter change trends to construct a real-time hydrological monitoring virtual model;

[0103] a hydrological simulation module for performing short-term hydrological monitoring evolution simulation on the real-time hydrological monitoring virtual model to obtain short-term hydrological simulation parameters;

[0104] a bias verification module for performing hydrological simulation bias verification on the short-term hydrological simulation parameters and performing global parameter optimization to construct a hydrological monitoring optimization model;

[0105] a hydrological situation prediction module for performing target time point step-by-step dynamic prediction calculation on the hydrological monitoring optimization model and performing hydrological situation visualization processing to generate a hydrological situation prediction visualization result.

[0106] The application can extract the long-term change trend of hydrological monitoring parameters by analyzing long-term hydrological data, providing forward-looking reference data for water resource management and decision-making. Time series change trend analysis can help detect subtle changes in hydrological parameters (such as water level, flow, rainfall, etc.), timely detect abnormal fluctuations, and warn potential floods, droughts, and other problems. With the change trend of hydrological monitoring parameters, managers can make dynamic adjustments in actual water resource scheduling to avoid the negative impact of uncertainty factors on water resource allocation. Through satellite remote sensing images, large-scale data can be accurately analyzed to provide a comprehensive geographical and environmental background for regional water resource management. Compared with traditional ground monitoring methods, remote sensing image processing can efficiently and comprehensively quantify hydrological elements, improving monitoring accuracy, especially for large-scale regional hydrological monitoring. Satellite images provide real-time updating capabilities, helping managers understand real-time changes in the monitoring area, such as changes in vegetation coverage and fluctuations in soil moisture. Through hydrological topology logic analysis, the regional hydrological structure and its flow characteristics can be understood from a system perspective, helping to discover complex interaction relationships between hydrological elements. Based on the iterative calculation of hydrological parameter change trends, the hydrological virtual model can be updated in real time in a changing hydrological environment, maintaining the efficiency and accuracy of the model. After building the virtual model, it can provide prediction capabilities in actual water resource management, helping decision-makers more accurately predict future hydrological conditions and their change trends. Short-term hydrological simulation can effectively capture the dynamic changes of the hydrological system in the short term, predicting changes in water level and flow in the next few hours or days. Through real-time calculation of hydrological simulation results, the change trend of water resources in the short term can be understood in time, which helps emergency scheduling such as flood warning and drought response. Short-term simulation results can be used for rational allocation and optimization of water resources to ensure the most appropriate resource management measures in uncertain hydrological environments. Through deviation detection and correction of simulation results, the accuracy of the hydrological monitoring model can be gradually optimized, reducing errors and ensuring more reliable prediction results. By continuously verifying and adjusting the deviation of the model, the robustness of the model can be enhanced to adapt to different hydrological conditions and changes. The deviation verification and optimization process enables the system to have adaptive adjustment capabilities, automatically optimizing parameters according to actual hydrological environmental changes, improving system flexibility. Through gradual dynamic prediction, the hydrological situation at the target time point can be more accurately predicted, avoiding the lag or limitations of traditional prediction methods. The visualization of hydrological situation provides clear and intuitive information for decision-makers, helping them quickly and accurately understand future hydrological conditions and make scientific management decisions. Through visual display of hydrological situation, not only can managers make decisions, but also can provide transparent information on water resource conditions to the public.

[0107] Therefore, the embodiments should be regarded, at all points, as illustrative and non-restrictive, the scope of the present application being defined by the appended claims and not by the above description, and it is therefore intended that all changes falling within the meaning and range of equivalency of the elements of the patent file be embraced within the present application.

[0108] The foregoing merely illustrates the principles of the application and application of its principles. Various modifications and alterations to this implementation will occur to those skilled in the art. The described embodiments are to be considered in all respects only as illustrative and not restrictive, and all changes coming within the meaning and equivalency range of the appended claims are intended to be embraced therein.

Claims

1. A method for intelligent analysis and calculation of hydrology and water resources, characterized in that, Includes the following steps: Step S1: Obtain long-term hydrological and water resources monitoring parameters for the target area; The hydrological and water resources monitoring parameters are calculated for changes at multiple time points and their time-series changes are analyzed to obtain the changing trends of the hydrological monitoring parameters. Step S2: Acquire satellite remote sensing images of the target monitoring area; perform deep semantic analysis of the hydrological region on the satellite remote sensing images, and perform quantitative analysis of the hydrogeological structure to obtain the hydrogeological structure characteristics; Step S3: Perform hydrological flow topology logic analysis on the hydrogeological structural characteristics, and perform multi-parameter iterative calculation based on the changing trends of hydrological monitoring parameters to construct a real-time hydrological monitoring virtual model; Step S4: Perform short-term hydrological monitoring evolution simulation on the real-time hydrological monitoring virtual model to obtain short-term hydrological simulation parameters; Step S5: Verify the hydrological simulation deviation of the short-term hydrological simulation parameters, optimize the global parameters, and construct an optimized hydrological monitoring model; Step S6: Perform stepwise dynamic prediction calculations for the target time point on the hydrological monitoring optimization model, and perform hydrological situation visualization processing to generate hydrological situation prediction visualization results.

2. The intelligent analysis and calculation method for hydrology and water resources according to claim 1, characterized in that, The specific steps of step S1 are as follows: Step S11: Acquire long-term hydrological and water resource monitoring parameters of the target area based on a multi-sensor array; Step S12: Divide the hydrological and water resources monitoring parameters into time-series stages to obtain the first-stage hydrological and water resources monitoring parameters and the second-stage hydrological and water resources monitoring parameters; Step S13: Perform multi-time point parameter change calculations on the hydrological and water resources monitoring parameters of the first stage to extract the hydrological monitoring parameter change values ​​at multiple time points; Step S14: Perform time-series trend analysis on the changes in hydrological monitoring parameters at multiple time points to obtain the trend of hydrological monitoring parameter changes.

3. The intelligent analysis and calculation method for hydrology and water resources according to claim 1, characterized in that, The specific steps of step S2 are as follows: Step S21: Acquire satellite remote sensing images of the target monitoring area; Step S22: Perform global brightness optimization on the satellite remote sensing image to construct a globally brightness-optimized image; Step S23: Perform hydrological region depth semantic analysis on the global brightness optimization image to obtain regional depth semantic features; Step S24: Quantitative analysis of hydrogeological structure is performed based on regional depth semantic features to obtain hydrogeological structure characteristics.

4. The intelligent analysis and calculation method for hydrology and water resources according to claim 3, characterized in that, The specific steps of step S22 are as follows: Atmospheric radiation blur detection is performed on the satellite remote sensing image, and blurred areas are marked; The degree of distortion in blurred areas is evaluated, and a distortion evaluation value for blurred areas of the image is generated. Calculate the solar altitude angle and atmospheric transmittance based on the satellite remote sensing images; The amplitude correction parameters are calculated based on the distortion evaluation value of the blurred area of ​​the image using the solar elevation angle and atmospheric transmittance, thereby generating the image blur amplitude correction parameters. Amplitude correction compensation is performed on the blurred area based on the image blur amplitude correction parameters to obtain an amplitude-corrected remote sensing image; Global brightness optimization is performed on the amplitude-corrected remote sensing image to construct a globally brightness-optimized image.

5. The intelligent analysis and calculation method for hydrology and water resources according to claim 3, characterized in that, The specific steps of step S24 are as follows: Regional hydrological elements are labeled based on regional deep semantic features, and surface runoff data, vegetation cover data and geomorphic structure data are extracted. Surface runoff data are analyzed for spatial distribution to generate spatial distribution characteristics of surface runoff. Calculate regional coverage changes from vegetation cover data and generate regional coverage change characteristics; Multi-landform morphology analysis is performed on geomorphic structure data to generate various geomorphic features within the region; Based on the spatial distribution characteristics of surface runoff, the regional coverage variation characteristics, and the various geomorphological features within the region, a quantitative analysis of the hydrogeological structure is conducted to obtain the hydrogeological structure characteristics.

6. The intelligent analysis and calculation method for hydrology and water resources according to claim 1, characterized in that, Step S3 is as follows: Step S31: Analyze the spatial relationship of hydrological elements based on the hydrogeological structural characteristics, and extract the spatial relationship of multiple hydrological elements; Step S32: Perform hydrological flow topology analysis based on hydrogeological structural characteristics to generate regional hydrological flow topology. Step S33: Based on the spatial location relationships of multiple hydrological elements and the topological logic of regional hydrological flow, perform hydrological spatial topology evolution and construct a regional hydrological topology map; Step S34: Perform multi-parameter iterative calculations on the regional hydrological topology map based on the changing trends of hydrological monitoring parameters, and construct a real-time hydrological monitoring virtual model.

7. The intelligent analysis and calculation method for hydrology and water resources according to claim 1, characterized in that, The specific steps of step S4 are as follows: Step S41: Extract the timestamps of the hydrological monitoring parameters in the second stage and the hydrological monitoring parameters in the first stage; Step S42: Calculate the stage interval based on the timestamp to obtain the time interval between stages; Step S43: Calculate the actual simulation time based on the inter-stage time interval to obtain the simulation time window; Step S44: Perform short-term hydrological monitoring evolution simulation on the real-time hydrological monitoring virtual model according to the simulation time window to obtain short-term hydrological simulation parameters.

8. The intelligent analysis and calculation method for hydrology and water resources according to claim 1, characterized in that, The specific steps of step S5 are as follows: Step S51: Verify the hydrological simulation deviation of the short-term hydrological simulation parameters based on the hydrological monitoring parameters of the second stage, and extract the hydrological simulation parameters with the verification deviation. Step S52: Calculate the deviation of the hydrological simulation parameters for the verification deviation to obtain the hydrological deviation value; Step S53: Perform local deviation compensation calculation on the hydrological deviation value to obtain the local deviation compensation value; Step S54: Optimize the global parameters of the real-time hydrological monitoring virtual model based on the local deviation compensation value to construct an optimized hydrological monitoring model.

9. The intelligent analysis and calculation method for hydrology and water resources according to claim 1, characterized in that, The specific steps of step S6 are as follows: Step S61: Define the preset target time points for hydrological monitoring and forecasting; Step S62: Input the preset hydrological monitoring prediction target time point into the hydrological monitoring optimization model, and perform step-by-step dynamic prediction calculations to obtain the hydrological state prediction situation at the target time point; Step S63: Perform hydrological situation visualization processing based on the predicted hydrological situation to generate a hydrological situation prediction visualization result.

10. A hydrological and water resources intelligent analysis and calculation system, characterized in that, The method for performing intelligent hydrological and water resource analysis and calculation as described in claim 1 includes: The hydrological parameter trend module is used to acquire long-term hydrological and water resources monitoring parameters of the target area; to perform multi-time point parameter change calculation and time series change trend analysis on the hydrological and water resources monitoring parameters, and to obtain the hydrological monitoring parameter change trend. The hydrological element analysis module is used to acquire satellite remote sensing images of the target monitoring area; perform deep semantic analysis of the hydrological region on the satellite remote sensing images, and perform quantitative analysis of the hydrogeological structure to obtain the hydrogeological structure characteristics; The hydrological topology logic module is used to perform hydrological flow topology logic analysis on hydrogeological structural characteristics, and to perform multi-parameter iterative calculations based on the changing trends of hydrological monitoring parameters to build a real-time hydrological monitoring virtual model. The hydrological simulation module is used to simulate the evolution of short-term hydrological monitoring on a real-time hydrological monitoring virtual model in order to obtain short-term hydrological simulation parameters. The deviation verification module is used to verify the deviation of short-term hydrological simulation parameters, optimize global parameters, and build an optimized hydrological monitoring model. The hydrological situation prediction module is used to perform step-by-step dynamic prediction calculations of the hydrological monitoring optimization model at target time points, and to perform hydrological situation visualization processing to generate hydrological situation prediction visualization results.

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