Water resource management method and system based on salt tide upstream prediction model

Abstract

The invention relates to the technical field of water resource management, in particular to a water resource management method and system based on a salt tide upstream prediction model. The method comprises the following steps: carrying out estuary line tidal water upstream change analysis on salt tide upstream hydrological data, and carrying out tidal water upstream water quantity analysis to obtain tidal water upstream water quantity data; performing upstream diffusion trend analysis on the tide water upstream water volume data to obtain upstream diffusion trend data; carrying out salinity gradient space distribution structure analysis on the upstream diffusion trend data, and carrying out salt tide upstream retardation effect analysis by considering the upstream water discharge amount to obtain salt tide upstream retardation effect data; and performing water resource management long-term influence effect analysis based on the salt tide upstream blocking effect data to obtain water resource management long-term influence effect data. According to the invention, the water resource management technology is optimized, so that the water resource management is more perfect.

Description

Technical Field

[0001] The present invention relates to the technical field of water resource management, and in particular to a water resource management method and system based on a saltwater tide upstream prediction model. Background Art

[0002] With the intensification of global climate change and human activities, saltwater tides have become increasingly serious in coastal water resource management, especially in river estuary areas. The upstream movement of saltwater tides not only affects the quality and availability of freshwater resources, but also has a profound impact on agricultural irrigation, drinking water supply and ecosystems. In some coastal areas, the frequency and intensity of saltwater tides have increased, leading to the salinization of groundwater levels, which directly affects agricultural production and ecological balance. Traditional water resource management methods often fail to effectively respond to this challenge, and new technologies and strategies are urgently needed to predict and mitigate the impact of saltwater tides on water resources. By real-time monitoring of water quality changes, tidal level changes, etc., the latest data support can be provided for the model to improve the accuracy of prediction. In addition, combined with GIS technology, the spatial distribution of saltwater tides can be visualized, providing an intuitive basis for management decisions. The adoption of this comprehensive management method can not only improve the utilization efficiency of water resources, but also play a positive role in responding to the impact of climate change and human activities on water resources. However, a traditional water resource management method based on the saltwater upstream prediction model has the problem of inaccurate analysis of the salinity diffusion of water bodies and the inability to accurately analyze its impact range. Summary of the invention

[0003] Based on this, it is necessary to provide a water resources management method and system based on a saltwater tide upstream prediction model to solve at least one of the above technical problems.

[0004] To achieve the above object, a water resources management method based on a saltwater tide upstream prediction model is provided, the method comprising the following steps:

[0005] Step S1: collecting the saltwater upstream hydrological data of the estuary through the high-precision sensors deployed in the monitoring station to obtain the saltwater upstream hydrological data; analyzing the upstream changes of the tide along the estuary of the saltwater upstream hydrological data to obtain the upstream change data of the tide along the estuary; analyzing the upstream water volume of the tide according to the tidal level of the tidal upstream change data along the estuary to obtain the upstream water volume data of the tide;

[0006] Step S2: analyzing the upstream diffusion trend of the tidal water volume data to obtain the upstream diffusion trend data; analyzing the spatial distribution structure of the salinity gradient of the upstream diffusion trend data to obtain the spatial distribution data of the salinity gradient;

[0007] Step S3: performing bottom water salinity stratification diffusion analysis on the salinity gradient spatial distribution data to obtain bottom water salinity stratification diffusion data; performing saltwater tide upstream blocking effect analysis based on the bottom water salinity stratification diffusion data to obtain saltwater tide upstream blocking effect data;

[0008] Step S4: Based on the saltwater tide upstream blocking effect data and the bottom water salinity stratification diffusion data, the long-term impact effect of water resources management is analyzed to obtain the long-term impact effect data of water resources management.

[0009] The present invention can monitor the salt tide upward phenomenon of seawater in real time by deploying high-precision sensors at the estuary. The data collected by these sensors include hydrological parameters such as tidal level, flow velocity, and salinity, which constitute the basis of the salt tide upward hydrological data. These data can not only help scientists understand the intensity and impact range of the salt tide, but also provide important references for water resource management in the areas along the line. After analyzing the data, the changes in the tidal level along the estuary are systematized, and the key points and change trends of the tide upward are identified. Through this technology, the impact of the salt tide can be effectively predicted, and a scientific basis can be provided for the ecological environment protection, agricultural irrigation, and urban water supply in coastal areas in a timely manner, thereby enhancing the prevention ability and response strategy. After obtaining the tidal upward change data, it is crucial to further analyze the tidal upward water volume. By calculating the tidal upward water volume, the degree of influence of the salt tide on the distribution of water resources can be evaluated. This analysis result provides a quantitative basis for understanding how the salt tide affects the interaction between fresh water and salt water, and can reveal the dynamic process of water mixing. Subsequently, the upward diffusion trend analysis of the tidal upward water volume data further clarifies the diffusion mechanism of the salt tide. This analysis not only helps to establish a spatial model of saltwater impact, but also lays the foundation for the analysis of the spatial distribution structure of salinity gradient. By identifying the changes in salinity gradient, the ecological health of water bodies can be effectively monitored, providing scientific guidance for fishery resource management and environmental protection, thereby achieving sustainable water resource utilization and ecological protection strategies. The analysis of the salinity stratification and diffusion of bottom water bodies based on the spatial distribution data of salinity gradient is an important part of understanding the changes in seawater salinity. By analyzing the salinity stratification of bottom water bodies, the salinity distribution characteristics and diffusion dynamics in water bodies at different depths can be identified. This analysis not only reveals the law of salinity change with depth, but also provides basic data for further research on the ecological impact of saltwater tides. Especially in the phenomenon of saltwater tide tracing, the salinity stratification of bottom water bodies directly affects the mixing of water bodies and the health of the ecosystem. By obtaining the salinity stratification and diffusion data of bottom water bodies, it can provide an important basis for the subsequent analysis of the blocking effect of saltwater tide tracing, help identify which factors are preventing the saltwater tide from tracing further, and thus promote the formulation of strategies for water resource management and ecological protection. Based on the saltwater upstream blocking effect data and the bottom water salinity stratification diffusion data obtained in the first two steps, the long-term impact analysis of water resources management can comprehensively evaluate the potential risks of saltwater on water resources utilization. By combining these two types of data, analysts can gain a deep understanding of the interference of saltwater on water resources and its long-term changing trends. This analysis helps reveal the impact of saltwater on the supply of freshwater resources, especially during drought seasons or extreme weather events. The long-term impact data of water resources management obtained in this way can provide a scientific basis for policymakers to optimize water resource allocation and management strategies and ensure sustainable water resource utilization.Therefore, the present invention optimizes a traditional water resource management method based on a saltwater tide upstream prediction model, solves the problems of inaccurate analysis of water body salinity diffusion and inability to accurately analyze its impact range in the traditional water resource management method based on a saltwater tide upstream prediction model, improves the accuracy of water body salinity diffusion analysis, and improves the accuracy of its impact range.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: collecting the upstream hydrological data of the saltwater tide at the estuary through the high-precision sensors deployed in the monitoring station to obtain the upstream hydrological data of the saltwater tide;

[0012] Step S12: Fill missing values ​​in the saltwater upstream hydrological data to obtain optimized saltwater upstream hydrological data;

[0013] Step S13: analyzing the tidal upstream change along the estuary based on the saltwater upstream prediction model on the saltwater upstream hydrological optimization data to obtain the tidal upstream change data along the estuary;

[0014] Step S14: analyzing the tidal upstream water volume on the saltwater upstream hydrological optimization data according to the tidal upstream change data along the estuary to obtain the tidal upstream water volume data.

[0015] The present invention is an important basis for studying the impact of saltwater tides by deploying high-precision sensors at the estuary to collect hydrological data of saltwater tides. These sensors can monitor multiple hydrological parameters such as water level, flow velocity, salinity, etc. in real time, thereby generating comprehensive hydrological data of saltwater tides. The collection of this data not only provides necessary raw data support for subsequent analysis, but also provides important information for real-time early warning and management decisions. Under the influence of changing climatic conditions and human activities, the saltwater tide phenomenon in the estuary area presents a new trend of change. Real-time monitoring can help scientists identify these changes in time, assess their potential impact on coastal ecological environment, fishery resources and water resources management, and provide a scientific basis for more effective resource protection and management. In the data collection process, due to various reasons (such as sensor failure, environmental interference, etc.), missing values ​​will appear in the saltwater tide hydrological data. To ensure the accuracy of subsequent analysis, it is crucial to fill the missing values. By applying statistical methods (such as interpolation, time series analysis, etc.), these missing values ​​can be effectively filled, thereby generating saltwater tide hydrological optimization data. The implementation of this step not only improves the integrity of the data, but also improves the reliability of the analysis model. The optimized data provides a solid foundation for the subsequent analysis of tidal level changes and water volume, ensuring that the conclusions drawn can more accurately reflect the actual situation and provide strong support for scientific decision-making. After obtaining the optimized hydrological data of saltwater upwelling, it is a key link to analyze the tidal upwelling changes along the estuary based on the prediction model. This analysis can reveal the trend of tidal level changes over time and help identify the impact of saltwater on the dynamics of water bodies. Through the operation of the model, the changes in tidal levels under different conditions can be predicted, including the peak and trough of tidal levels and their frequency and time period. This understanding of tidal level changes not only helps scientists assess the intensity and scope of saltwater tides, but also provides a scientific basis for managers to formulate corresponding countermeasures. In addition, this analysis can promote the protection of ecosystems and the rational use of water resources, laying a good foundation for sustainable development. After obtaining the tidal upwelling change data along the tidal estuary, it is necessary to conduct tidal upwelling water volume analysis. This analysis will calculate the actual flow and water volume of the water body through the change of tidal levels, providing a quantitative basis for understanding the impact of saltwater tides. The generation of tidal upstream water volume data can not only reveal the flow characteristics of water bodies, but also provide support for the effective management of water resources. By assessing the impact of saltwater tides on water volume, managers can better deal with the problems of saltwater intrusion and freshwater scarcity, and ensure the balance of water supply and demand in coastal areas. The results of this analysis will provide an important reference for future water resource management, ecological restoration and regional planning, thereby enhancing the ability and resilience to cope with climate change.

[0016] Preferably, step S2 comprises the following steps:

[0017] Step S21: Calculate the upstream impact area of ​​the tidal upstream change data along the estuary to obtain the tidal upstream impact area data;

[0018] Step S22: analyzing the upstream diffusion trend of the tidal upstream water volume data according to the tidal upstream impact area data to obtain the upstream diffusion trend data;

[0019] Step S23: simulating the salinity gradient superposition effect on the upstream diffusion trend data to obtain salinity gradient superposition effect data;

[0020] Step S24: performing salinity gradient spatial distribution structure analysis on the upstream diffusion trend data according to the salinity gradient superposition effect data to obtain salinity gradient spatial distribution data.

[0021] The present invention can clarify the actual impact range of saltwater on the surrounding areas by calculating the upstream impact area of ​​the tidal water upstream change data along the tidal water level estuary. This calculation not only involves the spatial distribution of tidal water level changes, but also takes into account the topographic characteristics of the coastline and the flow path of the water body. The obtained tidal water level and tidal upstream impact area data lays the foundation for subsequent water volume and ecological analysis. The validity of this data directly affects the assessment of the ecological impact of saltwater tides, helps to identify which areas are most significantly affected by saltwater tides, and thus provides a basis for formulating targeted management strategies. Through quantitative analysis, the potential threat of saltwater tides to agricultural irrigation, freshwater resource supply and ecosystems can be better understood. After obtaining the tidal water upstream impact area data, the upstream diffusion trend analysis of the tidal upstream water volume based on these data can reveal the diffusion pattern of the saltwater water body. This analysis reveals the flow and distribution characteristics of water bodies under different conditions through mathematical models and statistical methods, helping researchers predict the changing trend of saltwater tides in different seasons and climate change. Upstream diffusion trend data can not only reveal the dynamics of water salinity changes, but also provide managers with important information for dealing with future water shortages and saltwater intrusion. By deeply understanding the diffusion trend. By simulating the salinity gradient superposition effect of upstream diffusion trend data, the interaction between different salinity levels can be explored in depth. The superposition effect of salinity gradient not only affects the physical and chemical properties of water bodies, but also has an important impact on the health of ecosystems. Through accurate simulation, the potential impact of salinity changes on aquatic organisms, such as the habitats and growth environments of fish and other aquatic organisms, can be obtained. The obtained salinity gradient superposition effect data provides an important scientific basis for ecological protection and water resources management. By understanding the spatial variation and superposition effect of salinity, relevant management agencies can more effectively design intervention measures to protect the integrity of coastal ecosystems and ensure the sustainable use of water resources. After obtaining the salinity gradient superposition effect data, it is a crucial step to analyze the spatial distribution structure of the salinity gradient. This analysis aims to identify and describe the spatial distribution characteristics of salinity and help scientists understand how salinity changes with geographical location. Through detailed analysis of the salinity gradient, potential ecological risks and challenges in water resources management can be revealed. For example, abnormal changes in salinity can threaten the survival of freshwater organisms, thereby affecting fisheries and agriculture. Therefore, this analysis not only provides basic data for ecological monitoring, but also provides a scientific basis for the rational allocation of water resources and the formulation of management strategies. Ultimately, these data will provide support for responding to the impacts of climate change and human activities, thereby promoting the realization of sustainable development goals.

[0022] Preferably, step S23 includes the following steps:

[0023] Step S231: defining the diffusion zone orientation of the upstream diffusion trend data to obtain the upstream diffusion zone orientation data;

[0024] Step S232: performing tidal impact superposition analysis on the upstream diffusion trend data according to the upstream diffusion zone azimuth data to obtain tidal impact superposition effect data;

[0025] Step S233: Based on the tidal influence superposition effect data, the salinity gradient benchmarks of different diffusion zones are calculated for the upstream diffusion zone azimuth data to obtain the diffusion zone salinity gradient benchmark data;

[0026] Step S234: performing cross-water area superposition analysis on the diffusion zone salinity gradient benchmark data according to the upstream diffusion zone azimuth data and the tidal influence superposition effect data, to obtain cross-water area salinity superposition effect data;

[0027] Step S235: Simulate the salinity gradient superposition effect based on the diffusion zone salinity gradient baseline data and the cross-water salinity superposition effect data to obtain the salinity gradient superposition effect data.

[0028] The present invention defines the diffusion zone orientation of the upstream diffusion trend data, which is an important step in understanding the impact of saltwater tide. This process involves analyzing the main direction of water flow and determining how the saltwater tide spreads upstream. By using a geographic information system (GIS) and a fluid dynamics model, the diffusion paths of water bodies in different orientations can be accurately identified. This data not only provides a basis for spatial positioning for subsequent analysis, but also helps scientists assess the extent to which different regions are affected by saltwater tides. Accurate orientation definition is essential for formulating corresponding ecological protection and water resource management strategies. By analyzing the orientation of the diffusion zone, it is possible to identify which areas need to be monitored and managed first, thereby optimizing the allocation of resources and reducing potential ecological risks. After obtaining the upstream diffusion zone orientation data, performing tidal impact superposition analysis is an important step in further understanding the dynamics of saltwater tides. Tidal changes can significantly affect the salinity distribution and flow pattern of the water body. Therefore, superimposing the tidal effect on the upstream diffusion trend data can reveal the actual diffusion of saltwater tides under different tidal water level conditions. By analyzing the tidal cycle, the changing characteristics of salinity in different time periods can be identified. The results of this study provide a more comprehensive perspective for water resource managers, helping to consider tidal factors when formulating measures to deal with saltwater tides, thereby improving the effectiveness of management strategies. At the same time, this analysis can also provide guidance for future ecological monitoring plans to ensure that ecosystems in coastal areas can better adapt to tidal changes. The calculation of the diffusion zone salinity gradient benchmark based on the tidal impact superposition effect data is a key link in deeply understanding the impact of saltwater tides on water salinity. Through this calculation, a salinity benchmark value can be established in different diffusion zones to provide a reference for subsequent comparison and analysis. This process not only helps researchers identify the changing characteristics of salinity in different regions, but also reveals the ecological characteristics of different diffusion zones. For example, some diffusion zones show different salinity gradients due to factors such as topography and flow rate, which directly affects the biodiversity in the water body. Understanding the salinity gradient benchmark will help managers formulate corresponding ecological protection measures and provide data support for predicting future saltwater tide diffusion trends. After obtaining the diffusion zone salinity gradient benchmark data, the cross-water superposition analysis can be carried out to explore the mutual influence between different salinity areas. This analysis focuses on the salinity changes in different diffusion zones in the intersection area, revealing the compound effect of salinity in the cross-water area. By analyzing the salinity superposition of cross-waters, researchers can identify potential ecological risks and impacts, such as biological mortality or habitat loss caused by excessive salinity. This analysis provides important information for water resource management and ecological protection, enabling managers to better understand the comprehensive impact of saltwater tides on waters, thereby providing a scientific basis for formulating more effective response strategies. Simulating the salinity gradient superposition effect based on the diffusion zone salinity gradient baseline data and the cross-water salinity superposition data is the final step in studying the impact of saltwater tides.By establishing a mathematical model, the ecological response under different salinity conditions can be simulated, which not only includes changes in water salinity, but also predicts the impact on aquatic organisms and ecosystems. The simulation results can provide decision-making support for water resource managers and help formulate targeted management strategies to meet the challenges brought about by salinity changes. Through this simulation, researchers can understand the ecological dynamics under different scenarios, thereby providing a scientific basis for improving the resilience and sustainability of coastal ecosystems. At the same time, this research result can also provide support for future policy formulation and implementation, and promote the healthy development of regional ecology.

[0029] Preferably, step S3 comprises the following steps:

[0030] Step S31: performing a salinity-tidal level nonlinear correlation analysis on the tidal upstream variation data along the estuary based on the salinity gradient spatial distribution data to obtain salinity-tidal level nonlinear correlation data;

[0031] Step S32: performing bottom water body salinity stratification diffusion analysis on the salinity gradient spatial distribution data according to the tidal level salinity-tidal level nonlinear correlation data to obtain bottom water body salinity stratification diffusion data;

[0032] Step S33: Acquire upstream water flow data; perform periodic fluctuation analysis on the upstream water flow data to obtain periodic fluctuation data of upstream water flow;

[0033] Step S34: Simulating the salinity desalination of the downstream water volume according to the upstream downstream flow periodic fluctuation data on the salinity stratification diffusion data of the bottom water body to obtain the downstream water volume salinity desalination data;

[0034] Step S35: Based on the tidal level salinity-tidal level nonlinear correlation data, the bottom water body salinity stratification diffusion data and the downstream water salinity desalination data, the salt tide upstream blocking effect analysis is performed to obtain the salt tide upstream blocking effect data.

[0035] The present invention can reveal the complex relationship between salinity and tidal level by performing salinity-tidal level nonlinear correlation analysis on the tidal level of tidal upstream change data along the tidal level estuary based on salinity gradient spatial distribution data. Tidal level changes directly affect the salinity of the water body, and nonlinear correlation analysis can capture the nonlinear characteristics of this influence, thereby obtaining more accurate tidal level salinity-tidal level nonlinear correlation data. This data provides an important basis for understanding the impact of saltwater on the estuary ecosystem, and helps scientists identify the law of salinity changes under different tidal levels. This in-depth analysis helps to optimize water resource management strategies, especially in preventing saltwater intrusion and protecting freshwater resources. Using tidal level salinity-tidal level nonlinear correlation data, the salinity gradient spatial distribution data is further analyzed for the salinity stratification and diffusion of the bottom water body, which can deeply explore the salinity distribution and diffusion characteristics of the bottom water body. Through analysis, the salinity differences between water bodies at different depths can be identified, revealing the propagation mode of salinity in the water body stratification. This analysis provides important information for understanding the changes in the physical and chemical properties of water bodies, and also provides a scientific basis for evaluating the impact of bottom water bodies on ecosystems. The salinity stratification diffusion data of bottom water bodies can help managers formulate targeted ecological protection measures to maintain the biodiversity and overall health of water bodies. The upstream downstream water volume data was obtained and analyzed for periodic fluctuations. This analysis aims to identify and extract the fluctuation characteristics of the downstream water volume, including the pattern and influencing factors of its periodic changes. Through in-depth analysis of the downstream volume data, we can understand the changing trend of water flow in different time periods, which is crucial for subsequent salinity desalination and water body regulation. The periodic fluctuation of the downstream water volume directly affects the change of salinity. By processing these data, the upstream downstream volume periodic fluctuation data can be formed to provide basic information for subsequent simulations and provide a reference for the reasonable regulation of water resources management. The simulation of downstream water salinity desalination is based on the upstream downstream volume periodic fluctuation data. By introducing the upstream downstream volume changes into the salinity stratification diffusion data, the changes in salinity in the bottom water body can be simulated more accurately. This process not only takes into account the actual fluctuation of the downstream water volume, but also combines it with the salinity diffusion model to achieve dynamic simulation of salinity. The final salinity desalination data of the downstream water volume provides a specific basis for evaluating the changes in water salinity under different downstream conditions. These simulation results can help us better understand the impact of human activities and natural factors on water quality, and provide important decision-making support for water resources management and ecological protection. The saltwater tide upstream blocking effect analysis based on the tidal salinity-tidal level nonlinear correlation data and the bottom water salinity stratified diffusion data can effectively evaluate the degree of restriction of the saltwater tide in the process of upstream water. This analysis helps to understand how the saltwater tide is affected by topography, tides and water flow under different conditions, thereby affecting the diffusion of the saltwater tide and the formation of biological habitats.The data on the upstream blocking effect of saltwater tides provide important reference information for decision makers, enabling them to better deal with the problem of saltwater intrusion. By scientifically assessing the impact of saltwater tides, management agencies can formulate more effective water resource management policies and protect the stability of coastal ecosystems.

[0036] Preferably, step S32 includes the following steps:

[0037] Step S321: extracting the tidal upstream flow velocity from the tidal level salinity-tidal level nonlinear correlation data tidal level to obtain tidal upstream flow velocity data;

[0038] Step S322: Calculate the velocity shear force field of the upstream tidal velocity data to obtain velocity shear force field data;

[0039] Step S323: Calculate the vertical water section momentum according to the upstream tidal velocity data and the velocity shear force field data, and obtain the vertical water section momentum data;

[0040] Step S324: performing layered structural analysis on the salinity gradient spatial distribution data according to the vertical water body section momentum data to obtain salinity spatial layered data;

[0041] Step S325: performing bottom water salinity stratification diffusion analysis on the salinity gradient spatial distribution data according to the vertical water body section momentum data and the salinity spatial stratification data to obtain bottom water salinity stratification diffusion data.

[0042] The present invention extracts the upstream velocity of the tide based on the nonlinear correlation data of tidal salinity-tidal level, and obtains the upstream velocity data of the tide. This process can not only reveal the influence of tidal level changes on the velocity, but also provide important information about the flow direction and intensity. Understanding the influence of the upstream velocity of the tide on the salinity distribution of the water body lays the foundation for subsequent fluid dynamics analysis. Accurate velocity data can help scientists predict the diffusion pattern of salt tides, support the decision-making of water resources management and ecological protection, and make management measures more scientific and effective. After obtaining the upstream velocity data of the tide, the velocity shear force field calculation is performed to obtain the velocity shear force field data. This calculation can reveal the shear force distribution caused by the change of velocity in the water body, which is crucial to understanding the flow characteristics inside the water body. Changes in velocity shear force can affect the mixing and salinity distribution of the water body, thereby directly affecting the health of the ecosystem. By analyzing the velocity shear force field, researchers can identify the areas that cause changes in biological habitats, so as to formulate corresponding protection strategies and improve the pertinence and effectiveness of management measures. The calculation of the momentum of the vertical water section based on the upstream tidal velocity data and the velocity shear force field data can deeply explore the momentum transfer between different water layers. This process is crucial to understanding the dynamic characteristics of the water body, and can reveal how the momentum is distributed in the water body at different depths and its changing trend. By obtaining the momentum data of the vertical water section, researchers can analyze the behavior of the water body under the change of tides and flow velocity, which is of great significance for assessing the spread of saltwater tides and their potential impact on the ecosystem. These data provide strong support for subsequent salinity stratification analysis and management decisions. According to the vertical water section momentum data, the spatial distribution data of salinity gradient is analyzed by layered structure to obtain salinity spatial layered data. This step can help researchers identify the hierarchical structure of salinity distribution in the water body and understand the salinity differences between different layers and their formation mechanism. Through the analysis of layered data, it can be revealed how salinity shows significant changes in water bodies at different depths, thereby affecting the habitat and distribution of aquatic organisms. This analysis provides a scientific basis for subsequent ecological assessment and management, enabling managers to better cope with the impact of salinity changes on the ecosystem. In this step, the spatial distribution data of salinity gradients are analyzed for the stratified diffusion of salinity in the bottom water based on the momentum data of the vertical water section and the spatial stratification data of salinity, and finally the stratified diffusion data of salinity in the bottom water is obtained. This analysis can provide an in-depth understanding of how salinity diffuses in the bottom water over time and space, and provides important information on the characteristics of salinity propagation in the water body. Through the study of the stratified diffusion of salinity in the bottom water, managers can more effectively formulate strategies to deal with saltwater tides and water pollution, and ensure the health and stability of coastal ecosystems. These data provide important support for protecting water resources and biodiversity and promote sustainable management.

[0043] Preferably, step S33 includes the following steps:

[0044] Step S351: Acquire estuary topographic data;

[0045] Step S352: Calculate the riverbed elevation based on the estuary terrain data to obtain riverbed elevation data;

[0046] Step S353: performing water impact azimuth analysis on the riverbed elevation data based on the tidal level salinity-tidal level nonlinear correlation data to obtain water impact azimuth data;

[0047] Step S354: simulating the water body recirculation behavior of the bottom water body salinity stratification diffusion data according to the water body impact azimuth data and the riverbed elevation data to obtain the water body stratification recirculation behavior data;

[0048] Step S355: analyzing the saltwater upwelling blocking effect on the bottom water salinity stratification diffusion data according to the water body stratification recirculation behavior data to obtain the saltwater upwelling blocking effect data.

[0049] The present invention obtains the topographic data of the estuary, which is an important basis for analyzing the movement of water bodies and salinity distribution. Through high-precision topographic measurement, the geographical features of the estuary, including the slope, width and depth of the riverbed, can be understood in detail. These data provide necessary information for the subsequent riverbed elevation calculation, and can effectively reveal the flow path and changes of the water body at the estuary. Comprehensive topographic data helps to predict the interaction between salt tides and freshwater flows, and thus provide a scientific basis for water resource management and ecological protection. By calculating the riverbed elevation of the estuary topographic data, the riverbed elevation data obtained provides important parameters for subsequent analysis. This process reveals the height change of the riverbed and its impact on the water flow, which can help researchers understand how the riverbed morphology affects the flow of water bodies and the distribution of salinity. Under the changes in salinity and tidal water level, the riverbed elevation has a significant impact on the impact and recirculation behavior of the water body. Accurate riverbed elevation data provides a basis for evaluating the movement of water bodies and their impact on the ecological environment, and helps to formulate effective management strategies. Based on the tidal salinity-tidal nonlinear correlation data, the water impact direction analysis of the riverbed elevation data can identify the impact direction of the water flow in the estuary area and its degree of influence. This analysis can reveal how the water body interacts with the riverbed under different tidal levels and flow rates, thereby affecting the distribution and mixing process of salinity. Understanding the impact direction of the water body helps to predict the diffusion and recirculation behavior of saltwater tides, thereby providing an important reference for ecosystem protection and water resources management. This analysis is of great value in optimizing management measures in the estuary area. Based on the water impact direction data and riverbed elevation data, the water recirculation behavior simulation of the bottom water salinity stratification diffusion data can deeply explore the behavior patterns of water bodies under different conditions. This simulation can show how water bodies form recirculation phenomena in the estuary area and affect the stratified distribution of salinity. Through the study of recirculation behavior, researchers can identify the interaction between water bodies at different depths and provide a basis for the prediction of salinity changes. These data help to assess the potential impact of saltwater tides on the bottom water ecosystem and provide scientific support for management decisions. After obtaining the water stratification and recirculation behavior data, the saltwater tide upstream blocking effect analysis of the bottom water salinity stratification diffusion data can effectively evaluate the degree of restriction of the saltwater tide in the upstream process of the estuary. This analysis helps to understand how the saltwater tide is affected by factors such as riverbed morphology, water flow speed and direction, and then affects the salinity distribution. By accurately evaluating the blocking effect of the saltwater tide upstream, managers can formulate more effective water resource protection strategies, reduce the negative impact of the saltwater tide on the coastal ecosystem, and thus ensure the sustainable development of the ecological environment.

[0050] Preferably, simulating the water body recirculation behavior based on the water body impact azimuth data and the riverbed elevation data on the bottom water body salinity stratification diffusion data comprises the following steps:

[0051] Calculate the slope angle of the riverbed cross section according to the water impact azimuth data and the riverbed elevation data to obtain the slope angle data of the riverbed cross section;

[0052] According to the riverbed cross-section slope angle data, the bottom water body salinity stratification diffusion data is matched with the slope angle to obtain the bottom water body stratification slope angle data;

[0053] Based on the bottom water layer slope angle data and riverbed cross-section slope angle data, the impact longitudinal vortex structure analysis is carried out to obtain the water impact longitudinal vortex structure data;

[0054] The water recirculation behavior is simulated based on the water impact longitudinal vortex structure data and the salinity stratification diffusion data of the bottom water body to obtain the water stratification recirculation behavior data.

[0055] The present invention calculates the slope angle of the riverbed cross section according to the water impact azimuth data and the riverbed elevation data, which can deeply understand the influence of the riverbed morphology on the water flow. Through this calculation, researchers can obtain the slope angle data of each riverbed section at different positions, thereby providing key parameters for the subsequent simulation of water body recirculation behavior. These slope angle data not only help to predict the dynamic changes of water flow at the estuary, but also reveal the flow characteristics of water bodies under different tidal water conditions, thereby providing an important basis for the management of water resources and ecosystems in estuary areas. According to the slope angle data of the riverbed cross section, the slope angle matching of the salinity stratification diffusion data of the bottom water body can further reveal the relationship between the stratified structure of the water body and the riverbed morphology. Through matching analysis, researchers can identify the salinity distribution characteristics of different water layers under specific slopes, providing support for understanding the dynamic behavior of the bottom water body. This analysis helps to confirm the interaction between the salinity layer and the riverbed characteristics, thereby providing a scientific basis for optimizing water management and ecological protection measures, making the research results more practical. The analysis of the impact longitudinal vortex structure based on the bottom water layered slope angle data and the riverbed cross-section slope angle data can effectively reveal the vortex characteristics in the water flow and its impact on the salinity distribution. Through this analysis, researchers can obtain the water impact longitudinal vortex structure data, which provides an important perspective for understanding the complex behavior of water flow in the estuary area. Studying the changes in vortex structure helps to predict the mixing and salinity diffusion of water bodies under different environmental conditions, and thus provide a scientific basis for protecting water ecology and formulating management strategies. The water body recirculation behavior simulation of the bottom water salinity stratification diffusion data based on the water body impact longitudinal vortex structure data can deeply understand the recirculation phenomenon of water bodies under different conditions and its impact on salinity distribution. Through this simulation, researchers can obtain water body stratification recirculation behavior data, which provides important information for evaluating the mixing and salinity gradient inside the water body. Accurate recirculation behavior simulation not only helps to predict the upstream and diffusion patterns of salt tides, but also provides effective support for ecological protection and water resources management in estuary areas, thereby ensuring the health and sustainable development of the ecosystem.

[0056] Preferably, step S4 comprises the following steps:

[0057] Step S41: Based on the saltwater tide upstream blocking effect data and the bottom water salinity stratification diffusion data, a water body ecological progressive impact assessment is performed to obtain water body ecological progressive impact data;

[0058] Step S42: performing progressive water diversion volume adjustment according to the progressive water body ecological impact data to obtain progressive water diversion volume adjustment data;

[0059] Step S43: Analyze the long-term impact of progressive water diversion regulation data on water resources management to obtain long-term impact data on water resources management.

[0060] The present invention conducts a progressive impact assessment of water ecology based on saltwater tide upstream blocking effect data and bottom water salinity stratification diffusion data, which is an important link in understanding the dynamic changes of the ecosystem. This assessment can reveal the changing trend of water ecology under different environmental conditions by comprehensively considering the upstream impact of saltwater tide and its effect on the salinity stratification of bottom water. The final progressive impact data of water ecology provides a scientific basis for evaluating the long-term impact of saltwater tide on the health of water ecology, and helps to formulate more reasonable ecological protection strategies when managing and protecting water resources. According to the progressive impact data of water ecology, progressive water diversion adjustment is helpful to achieve effective management and sustainable utilization of water resources. By analyzing the changes in water ecology, researchers can formulate water diversion schemes that adapt to different hydrological and salinity conditions, thereby optimizing the interaction between the introduction of freshwater resources and saltwater tide. This adjustment process can not only improve the salinity distribution of bottom water bodies, but also effectively mitigate the negative impact of saltwater tide on the ecological environment. The obtained progressive water diversion adjustment data provides a basis for subsequent management measures to ensure the stability and health of the ecosystem. The long-term impact analysis of water resource management on the progressive water diversion regulation data can evaluate the long-term effect of water diversion regulation on water resource management. Through long-term monitoring and simulation, this analysis can reveal the impact of water diversion measures on water salinity changes, ecosystem health and water resource sustainability, and ensure the effectiveness of management measures. The long-term impact data of water resource management obtained provides an important reference for decision makers, making water resource management more scientific and systematic. This not only helps to protect the ecological environment of water bodies, but also promotes the rational use of regional water resources and ensures the coordinated development of ecology and economy.

[0061] Preferably, the present invention further provides a water resource management system based on a saltwater tide upward prediction model, which is used to execute the water resource management method based on a saltwater tide upward prediction model as described above. The water resource management system based on a saltwater tide upward prediction model comprises:

[0062] The tidal upstream analysis module is used to collect the saltwater upstream hydrological data of the estuary through the high-precision sensors deployed in the monitoring station to obtain the saltwater upstream hydrological data; analyze the tidal upstream changes along the estuary of the saltwater upstream hydrological data to obtain the tidal upstream change data along the estuary; analyze the tidal upstream water volume according to the tidal upstream change data along the estuary to obtain the tidal upstream water volume data;

[0063] The salinity gradient spatial distribution module is used to analyze the upstream diffusion trend of tidal water volume data to obtain upstream diffusion trend data; analyze the salinity gradient spatial distribution structure of the upstream diffusion trend data to obtain the tidal level of salinity gradient spatial distribution data;

[0064] The saltwater tide upward blocking effect analysis module is used to analyze the salinity stratification diffusion of the bottom water body based on the salinity gradient spatial distribution data to obtain the salinity stratification diffusion data of the bottom water body; the saltwater tide upward blocking effect analysis is performed based on the salinity stratification diffusion data of the bottom water body to obtain the saltwater tide upward blocking effect data;

[0065] The water resources management module is used to analyze the long-term impact of water resources management based on the saltwater tide upstream blocking effect data and the bottom water salinity stratification diffusion data, and obtain the long-term impact data of water resources management.

[0066] The beneficial effect of the present invention is that by deploying high-precision sensors at the estuary, the saltwater tide phenomenon of seawater can be monitored in real time. The data collected by these sensors include hydrological parameters such as tidal level, flow velocity, and salinity, which constitute the basis of the saltwater tide traceability hydrological data. These data can not only help scientists understand the intensity and scope of influence of the saltwater tide, but also provide important references for water resource management in the areas along the line. After analyzing the data, the changes in the tidal level along the estuary are systematized, and the key points and changing trends of the tide traceability are identified. Through this technology, the impact of the saltwater tide can be effectively predicted, and a scientific basis can be provided in time for the ecological environment protection, agricultural irrigation, and urban water supply in coastal areas, thereby enhancing the prevention ability and response strategy. After obtaining the tidal traceability change data, it is crucial to further analyze the tidal traceability water volume. By calculating the tidal traceability water volume, the degree of influence of the saltwater tide on the distribution of water resources can be evaluated. This analysis result provides a quantitative basis for understanding how the saltwater tide affects the interaction between freshwater and saltwater, and can reveal the dynamic process of water mixing. Subsequently, the upstream diffusion trend analysis of the upstream water volume data further clarified the diffusion mechanism of saltwater. This analysis not only helped to establish a spatial model of saltwater impact, but also laid the foundation for the analysis of the spatial distribution structure of salinity gradient. By identifying the changes in salinity gradient, the ecological health of water bodies can be effectively monitored, and scientific guidance can be provided for fishery resource management and environmental protection, so as to achieve sustainable water resource utilization and ecological protection strategies. The analysis of the salinity stratification diffusion of bottom water bodies based on the spatial distribution data of salinity gradient is an important part of understanding the changes in seawater salinity. By analyzing the salinity stratification of bottom water bodies, the salinity distribution characteristics and diffusion dynamics in water bodies at different depths can be identified. This analysis not only reveals the law of salinity change with depth, but also provides basic data for further research on the ecological impact of saltwater. Especially in the phenomenon of saltwater upstream, the salinity stratification of bottom water bodies directly affects the mixing of water bodies and the health of the ecosystem. By obtaining the salinity stratification diffusion data of bottom water bodies, it can provide an important basis for the subsequent analysis of the blocking effect of saltwater upstream, help identify which factors are preventing the further upstream movement of saltwater, and thus promote the formulation of strategies for water resource management and ecological protection. Based on the saltwater upstream blocking effect data and the bottom water salinity stratification diffusion data obtained in the first two steps, the long-term impact analysis of water resources management can comprehensively evaluate the potential risks of saltwater on water resources utilization. By combining these two types of data, analysts can gain a deep understanding of the interference of saltwater on water resources and its long-term changing trends. This analysis helps reveal the impact of saltwater on the supply of freshwater resources, especially during drought seasons or extreme weather events. The long-term impact data of water resources management obtained in this way can provide a scientific basis for policymakers to optimize water resource allocation and management strategies and ensure sustainable water resource utilization.Therefore, the present invention optimizes a traditional water resource management method based on a saltwater tide upstream prediction model, solves the problems of inaccurate analysis of water body salinity diffusion and inability to accurately analyze its impact range in the traditional water resource management method based on a saltwater tide upstream prediction model, improves the accuracy of water body salinity diffusion analysis, and improves the accuracy of its impact range. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A schematic diagram of the steps of a water resources management method based on a saltwater tide upstream prediction model;

[0068] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0069] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0070] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0071] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0072] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0073] To achieve this, please refer to Figure 1 to Figure 2 , a water resources management method based on a saltwater tide upstream prediction model, the method comprising the following steps:

[0074] Step S1: collecting the saltwater upstream hydrological data of the estuary through the high-precision sensors deployed in the monitoring station to obtain the saltwater upstream hydrological data; analyzing the upstream changes of the tide along the estuary of the saltwater upstream hydrological data to obtain the upstream change data of the tide along the estuary; analyzing the upstream water volume of the tide according to the tidal level of the upstream change data of the tide along the estuary to obtain the upstream water volume data of the tide;

[0075] Step S2: analyzing the upstream diffusion trend of the tidal water volume data to obtain the upstream diffusion trend data; analyzing the spatial distribution structure of the salinity gradient of the upstream diffusion trend data to obtain the spatial distribution data of the salinity gradient;

[0076] Step S3: performing bottom water salinity stratification diffusion analysis on the salinity gradient spatial distribution data to obtain bottom water salinity stratification diffusion data; performing saltwater tide upstream blocking effect analysis based on the bottom water salinity stratification diffusion data to obtain saltwater tide upstream blocking effect data;

[0077] Step S4: Based on the saltwater tide upstream blocking effect data and the bottom water salinity stratification diffusion data, the long-term impact effect of water resources management is analyzed to obtain the long-term impact effect data of water resources management.

[0078] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a water resources management method based on a saltwater tide upstream prediction model of the present invention. In this example, the water resources management method based on a saltwater tide upstream prediction model includes the following steps:

[0079] Step S1: collecting the saltwater upstream hydrological data of the estuary through the high-precision sensors deployed in the monitoring station to obtain the saltwater upstream hydrological data; analyzing the upstream changes of the tide along the estuary of the saltwater upstream hydrological data to obtain the upstream change data of the tide along the estuary; analyzing the upstream water volume of the tide according to the tidal level of the upstream change data of the tide along the estuary to obtain the upstream water volume data of the tide;

[0080] In an embodiment of the present invention, high-precision sensors are deployed in the estuary area to collect hydrological data of saltwater tide traceability in real time. These sensors are configured at key monitoring points to ensure that the water flow changes of the entire estuary are covered, and the monitoring frequency is set to once an hour. The collected data include water level, flow rate, temperature and salinity, etc. The data transmission adopts a wireless network mode and is uploaded to the data processing platform in real time. Subsequently, a hydrological analysis method (cause analysis method, regional induction method, mathematical statistics method (hydrological statistics method), random hydrological analysis technology, etc.) is used to analyze the tidal traceability changes for the collected saltwater tide traceability hydrological data. The specific method is to calculate the tidal level change rate of each monitoring point and perform spatial interpolation to generate a tidal traceability change curve, and finally obtain the tidal traceability change data along the tidal level estuary. These data can be used to evaluate the changing trend of the tidal level in different time periods.

[0081] Step S2: analyzing the upstream diffusion trend of the tidal water volume data to obtain the upstream diffusion trend data; analyzing the spatial distribution structure of the salinity gradient of the upstream diffusion trend data to obtain the spatial distribution data of the salinity gradient;

[0082] In an embodiment of the present invention, when analyzing the upstream water volume data of the tide, the data interpolation technology is first applied to spatially process the water volume data of different monitoring points to obtain a continuous water volume distribution map. Then, a trend analysis method is used to determine the changing trend of the upstream water volume of the tide, and the upstream diffusion trend data is obtained by combining linear regression analysis and time series analysis. Further, based on these data, the spatial distribution structure analysis of the salinity gradient is performed. The specific operation is to calculate the salinity difference between different points and draw a salinity distribution map, and the Kriging interpolation method is used to ensure the accuracy and reliability of the data. This analysis result will form the spatial distribution data of the salinity gradient, providing a basis for subsequent water resource management decisions.

[0083] Step S3: performing bottom water salinity stratification diffusion analysis on the salinity gradient spatial distribution data to obtain bottom water salinity stratification diffusion data; performing saltwater tide upstream blocking effect analysis based on the bottom water salinity stratification diffusion data to obtain saltwater tide upstream blocking effect data;

[0084] In the embodiment of the present invention, when performing stratified diffusion analysis of salinity in the bottom water body on the spatial distribution data of salinity gradient, the stratified analysis method is first used to divide the water body into several layers according to the depth and salinity data. In each layer, the rate of change of salinity is calculated, and the diffusion trend of salinity in the vertical direction is analyzed by the principle of fluid dynamics. A multivariate linear regression model is used to establish the relationship between the salinity of the bottom water body and variables such as time, flow rate, and temperature, so as to obtain the stratified diffusion data of the salinity of the bottom water body. The results show the distribution of salinity at different depths, which is helpful to evaluate the impact of salt tide on the salinity of water bodies and provide a scientific basis for subsequent analysis.

[0085] Step S4: Based on the saltwater tide upstream blocking effect data and the bottom water salinity stratification diffusion data, the long-term impact effect of water resources management is analyzed to obtain the long-term impact effect data of water resources management.

[0086] In an embodiment of the present invention, when analyzing the long-term impact of water resource management based on the saltwater upstream blocking effect data and the bottom water salinity stratification diffusion data, the two sets of data are first integrated, and a composite effect model is established to analyze the long-term impact of saltwater on water resource management. The regression analysis method is applied to examine the degree of influence of different salinity layers on the availability of water resources, and the water resource status under different saltwater intensities in the future is simulated through scenario analysis. In the specific implementation, the spatial and temporal distribution of water resources is visualized in combination with geographic information system (GIS) technology to ensure the intuitiveness and readability of the data, and finally form the long-term impact effect data of water resource management to provide quantitative support for decision-making.

[0087] Preferably, step S1 comprises the following steps:

[0088] Step S11: collecting the upstream hydrological data of the saltwater tide at the estuary through the high-precision sensors deployed in the monitoring station to obtain the upstream hydrological data of the saltwater tide;

[0089] Step S12: Fill missing values ​​in the saltwater upstream hydrological data to obtain optimized saltwater upstream hydrological data;

[0090] Step S13: analyzing the tidal upstream change along the estuary based on the saltwater upstream prediction model on the saltwater upstream hydrological optimization data to obtain the tidal upstream change data along the estuary;

[0091] Step S14: analyzing the tidal upstream water volume on the saltwater upstream hydrological optimization data according to the tidal upstream change data along the estuary to obtain the tidal upstream water volume data.

[0092] In an embodiment of the present invention, in the estuary area, the collection of saltwater tide upstream hydrological data is carried out through a high-precision sensor network. These sensors are installed at multiple key monitoring points and can accurately record various hydrological parameters such as water level, flow rate, salinity, etc. The sensor is set to automatically collect data every hour and upload the data to the central database in real time through wireless communication technology. After data collection, quality control is first performed to exclude abnormal values ​​to ensure the accuracy and reliability of the data. This process provides detailed saltwater tide upstream hydrological data for subsequent analysis and forms an effective data basis. In the processing of saltwater tide upstream hydrological data, the missing values ​​in the data are first identified. For missing values, interpolation is used to fill them. In the specific implementation, a combination of linear interpolation and polynomial interpolation is used to reasonably infer the missing hydrological parameter values ​​based on the observed values ​​of adjacent time points. When processing, priority is given to using adjacent data in the time series to ensure the coherence and rationality of the filled data. After completing the missing value filling, the saltwater tide upstream hydrological optimization data is formed to provide complete data information for further analysis. Based on the saltwater tide upstream prediction model, the saltwater tide upstream hydrological optimization data is used to analyze the tidal upstream changes along the estuary. At this time, the model input parameters are first determined, including the tidal level, flow velocity, salinity, etc. of the hydrological optimization data, and the nonlinear regression analysis method is used to establish the relationship model between the tidal level and other hydrological variables. The model predicts the changes in tidal levels under different conditions. In the specific implementation, the model parameters are calibrated using historical data to improve the prediction accuracy. Finally, the tidal upstream change data along the tidal estuary are obtained, providing a scientific basis for water resources management. When analyzing the tidal upstream change data along the tidal estuary, the corresponding water volume is first calculated based on the tidal level data. The specific operation is to use the relationship between the water level and the cross-sectional area of ​​the water body and use the physical formula to calculate the water volume change of the tidal level water upstream. The total tidal upstream water volume data is obtained by integrating the tidal upstream changes of each monitoring point. In addition, combined with the tidal level change rate and time factors, the influencing factors are comprehensively considered to ensure that the obtained data are accurate and representative. These data provide important support for subsequent water resources management decisions.

[0093] Preferably, step S2 comprises the following steps:

[0094] Step S21: Calculate the upstream impact area of ​​the tidal upstream change data along the estuary to obtain the tidal upstream impact area data;

[0095] Step S22: analyzing the upstream diffusion trend of the tidal upstream water volume data according to the tidal upstream impact area data to obtain the upstream diffusion trend data;

[0096] Step S23: simulating the salinity gradient superposition effect on the upstream diffusion trend data to obtain salinity gradient superposition effect data;

[0097] Step S24: performing salinity gradient spatial distribution structure analysis on the upstream diffusion trend data according to the salinity gradient superposition effect data to obtain salinity gradient spatial distribution data.

[0098] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0099] Step S21: Calculate the upstream impact area of ​​the tidal upstream change data along the estuary to obtain the tidal upstream impact area data;

[0100] In an embodiment of the present invention, when analyzing the data of tidal upstream changes along the tidal estuary, it is first necessary to calculate the area affected by the tidal upstream. In the specific implementation process, the tidal upstream data is first spatially interpolated to generate an elevation contour map to intuitively display the affected area under different tidal levels. Then, using geographic information system (GIS) technology, based on the relationship between tidal level changes and the terrain along the line, the actual area affected by the tidal upstream is calculated. By superimposing the affected area and the terrain data along the line, the scope of the tidal upstream affected area is determined, and finally the tidal level tidal upstream affected area data is obtained, which will provide a basis for subsequent water volume analysis.

[0101] Step S22: analyzing the upstream diffusion trend of the tidal upstream water volume data according to the tidal upstream impact area data to obtain the upstream diffusion trend data;

[0102] In an embodiment of the present invention, the upstream diffusion trend of the tidal upstream water volume data is analyzed based on the tidal upstream impact area data. In this process, the tidal upstream water volume data is first combined with the impact area data to calculate the water volume change trend under different tidal water levels. Using the trend analysis method and the polynomial regression analysis method, a relationship model between water volume and impact area is established. By calculating the water volume changes under different time periods and tidal levels, the upstream diffusion trend data is formed. This data provides an important basis for understanding the diffusion of water volume in space and reveals the law of hydrological changes.

[0103] Step S23: simulating the salinity gradient superposition effect on the upstream diffusion trend data to obtain salinity gradient superposition effect data;

[0104] In an embodiment of the present invention, when simulating the salinity gradient superposition effect on the upstream diffusion trend data, a salinity gradient model must first be established. In specific operations, historical hydrological data are used to determine the salinity distribution under different tidal levels. By superimposing salinity data at different levels, the vertical distribution of salinity is analyzed using numerical simulation methods. The finite element method or finite difference method is used to simulate the superposition effect of salinity under different tidal conditions to form salinity gradient superposition effect data. This simulation process will help understand the distribution of salinity in water bodies and its impact on water resource management.

[0105] Step S24: performing salinity gradient spatial distribution structure analysis on the upstream diffusion trend data according to the salinity gradient superposition effect data to obtain salinity gradient spatial distribution data.

[0106] In the embodiment of the present invention, when performing the spatial distribution structure analysis of the salinity gradient based on the salinity gradient superposition effect data, the salinity data must first be spatially processed. The salinity data of different monitoring points are interpolated using the Kriging interpolation method to generate a continuous salinity distribution map. Then, by analyzing the salinity differences in different regions, the principal component analysis method is used to determine the main influencing factors of the salinity distribution. Finally, the spatial distribution data of the salinity gradient is formed to provide a scientific basis for water resources management, so as to better formulate targeted management measures and reduce the impact of salt tides on water resources.

[0107] Preferably, step S23 includes the following steps:

[0108] Step S231: defining the diffusion zone orientation of the upstream diffusion trend data to obtain the upstream diffusion zone orientation data;

[0109] Step S232: performing tidal impact superposition analysis on the upstream diffusion trend data according to the upstream diffusion zone azimuth data to obtain tidal impact superposition effect data;

[0110] Step S233: Based on the tidal influence superposition effect data, the salinity gradient benchmarks of different diffusion zones are calculated for the upstream diffusion zone azimuth data to obtain the diffusion zone salinity gradient benchmark data;

[0111] Step S234: performing cross-water area superposition analysis on the diffusion zone salinity gradient benchmark data according to the upstream diffusion zone azimuth data and the tidal influence superposition effect data, to obtain cross-water area salinity superposition effect data;

[0112] Step S235: Simulate the salinity gradient superposition effect based on the diffusion zone salinity gradient baseline data and the cross-water salinity superposition effect data to obtain the salinity gradient superposition effect data.

[0113] In the embodiment of the present invention, when defining the diffusion zone orientation of the upstream diffusion trend data, it is first necessary to analyze the relationship between the tidal level change and the water flow direction. By adopting the vector analysis method, the main flow direction of the diffusion zone is determined in combination with the historical hydrological data and the real-time tidal level data. In the specific implementation, the vector diagram of the flow direction is drawn using parameters such as wind speed, flow velocity, and salinity change. By analyzing the flow vectors at different time points, the main orientation of the diffusion zone is extracted to form the upstream diffusion zone orientation data. This data provides a spatial basis for the subsequent analysis of tidal influences and ensures a comprehensive understanding of the water body diffusion phenomenon. According to the upstream diffusion zone orientation data, the upstream diffusion trend data is subjected to tidal influence superposition analysis. In the specific implementation process, the tidal level change data is first superimposed with the diffusion zone orientation data to analyze the degree of influence of tidal changes on water body diffusion. In the superposition analysis method, the relative weight of tidal influence is calculated by comparing the relationship between the change of tidal level and the diffusion trend in each time period. Finally, the tidal influence superposition effect data is obtained, which can reveal the specific influence of tidal changes on the upstream diffusion of saltwater tides and provide a basis for subsequent salinity analysis. Based on the tidal influence superposition effect data, the salinity gradient benchmark of different diffusion zones is calculated for the upstream diffusion zone azimuth data. In this process, it is first necessary to combine the salinity data at different tidal levels with the diffusion zone azimuth data, and use the hierarchical analysis method to determine the salinity benchmark of each diffusion zone. In specific implementation, by calculating the salinity values ​​at different depths in each diffusion zone, a salinity distribution map is drawn, and the salinity gradient benchmark of each diffusion zone is determined by the comparative analysis method. Finally, the diffusion zone salinity gradient benchmark data is formed, which provides important parameters for subsequent cross-water analysis. According to the upstream diffusion zone azimuth data and the tidal influence superposition effect data, the cross-water superposition effect analysis is performed on the diffusion zone salinity gradient benchmark data. In specific implementation, the spatial information of the cross-water is first combined with the diffusion zone data, and the spatial superposition analysis method is used to evaluate the superposition effect of salinity in different regions. By using the weighted average method, the salinity gradients of different regions are combined with the diffusion zone data to calculate the overall salinity change of the cross-water. The cross-water salinity superposition effect data is formed to reveal the complex changes in salinity in the cross-water, providing support for subsequent salinity simulation. The salinity gradient superposition effect is simulated based on the diffusion zone salinity gradient benchmark data and the cross-water salinity superposition data. First, the salinity data of the diffusion zone and the cross-water are integrated by establishing a salinity distribution model. The numerical simulation method is combined with the historical salinity change data and the finite difference method is used to simulate the spatiotemporal changes of salinity. The salinity gradient superposition effect data are obtained by dynamically simulating the salinity changes under different tidal levels and flow conditions. These data provide an important reference for water resources management decisions and help optimize the use and management of water resources.

[0114] Preferably, step S3 comprises the following steps:

[0115] Step S31: Based on the salinity gradient spatial distribution data, a nonlinear correlation analysis of salinity and tidal level is performed on the tidal level of the upstream tidal change data along the estuary to obtain nonlinear correlation data of tidal level salinity and tidal level;

[0116] Step S32: performing bottom water body salinity stratification diffusion analysis on the salinity gradient spatial distribution data according to the tidal level salinity-tidal level nonlinear correlation data to obtain bottom water body salinity stratification diffusion data;

[0117] Step S33: Acquire upstream water flow data; perform periodic fluctuation analysis on the upstream water flow data to obtain periodic fluctuation data of upstream water flow;

[0118] Step S34: Simulating the salinity desalination of the downstream water volume according to the upstream downstream flow periodic fluctuation data on the salinity stratification diffusion data of the bottom water body to obtain the downstream water volume salinity desalination data;

[0119] Step S35: analyzing the saltwater upwelling blocking effect based on the tidal level salinity-tidal level nonlinear correlation data and the bottom water body salinity stratified diffusion data to obtain the saltwater upwelling blocking effect data.

[0120] In the embodiment of the present invention, when performing a nonlinear correlation analysis of salinity and tidal level on the tidal upstream change data along the tidal estuary, it is first necessary to collect time series data of salinity and tidal level. These data will be used to establish a nonlinear relationship between salinity and tidal level. In the specific implementation process, a polynomial regression analysis method is used to fit polynomials of different orders by the least squares method to determine the best fitting curve. First, the salinity values ​​under different tidal levels are calculated, and the tidal level is used as the independent variable and the salinity is used as the dependent variable for fitting. By calculating the coefficient of determination (R 2) is used to evaluate the goodness of fit of the model, and finally the nonlinear correlation data of tidal salinity-tidal level are obtained. This data provides a key relationship basis for the subsequent salinity stratification analysis and reveals the complex interaction mechanism between salinity and tidal level. According to the nonlinear correlation data of tidal salinity-tidal level, the spatial distribution data of salinity gradient is analyzed for the salinity stratification diffusion of the bottom water body. In this process, the salinity gradient data must first be spatially interpolated to obtain the salinity distribution at different depths of the bottom water body. In the specific implementation, the Kriging interpolation method is used to convert the salinity data of the observation point into a continuous salinity field. Then, combined with the nonlinear correlation model, the hierarchical analysis method is applied to determine the salinity hierarchy of the bottom water body. By calculating the salinity values ​​of different depth layers and their changing trends, the salinity stratification diffusion data of the bottom water body are finally formed. These data will help to evaluate the impact of salinity on the water ecosystem and provide the necessary scientific basis. Water level meters and flow monitoring equipment are installed at key hydrological stations in the upstream waters to continuously monitor the changes in water flow and downstream water volume. These devices connect to satellite and ground sensor networks to ensure accurate acquisition of upstream water volume time series data, covering hydrological changes in different seasons throughout the year, especially the significant differences between the rainy season and the dry season. In order to ensure the reliability of the data, the collected raw data needs to be preprocessed, including removing outliers and noise data, and smoothing the water volume curve by sliding average method to reduce the impact of external sudden factors. Next, the Fourier transform is used to perform periodic fluctuation analysis on the time series data to identify the main periodic components of the upstream downstream water volume and reveal its changing patterns in different seasons or time periods. Frequency domain analysis can help extract the periodic characteristics of the downstream water volume, and further quantify its fluctuation range through spectrum analysis. Finally, by establishing a periodic fluctuation model of upstream water volume, "upstream downstream volume periodic fluctuation data" is generated to provide key input data for subsequent salinity desalination simulation. In this step, a comprehensive analysis is first conducted based on the "upstream downstream volume periodic fluctuation data" and the "bottom water body salinity stratification diffusion data" to evaluate the impact of upstream water volume changes on the salinity distribution in the freshwater and saltwater interface area of ​​Haikou. Specific operations include using fluid dynamics models (such as Delft3D) to simulate the dilution effect of different downstream water volumes on salinity layer diffusion. The key to the simulation is to reveal how upstream water volume fluctuations affect the salinity distribution of the bottom water body, especially the dilution effect of upstream water flow at low tide. In the analysis, it is necessary to combine the periodic fluctuation characteristics of upstream water volume to test the range and depth changes of salinity layer diffusion under different flow conditions. Through regression analysis methods, a mathematical model between the amount of downstream water and the degree of salinity dilution is established to reveal how the upstream water volume affects the change of water body salinity in different time periods. Finally, numerical simulation tools (such as COMSOL Multiphysics) are used to further refine the dynamic relationship between salinity and water volume, construct a multidimensional dynamic desalination model, and simulate the long-term impact of periodic fluctuations of upstream water volume on salinity diffusion.The final generated "downstream water salinity desalination data" will provide a scientific basis for the detailed analysis of the saltwater blocking effect. When analyzing the saltwater upstream blocking effect, it is first necessary to integrate the tidal salinity-tidal level nonlinear correlation data, the bottom water salinity stratification diffusion data, and the downstream water salinity desalination data. In the specific implementation, the "salinity-tidal level nonlinear correlation data" and "bottom water salinity stratification diffusion data" are firstly fused to construct a comprehensive analysis framework. By integrating these two data sets, the dynamic impact of tidal level changes on the diffusion of bottom water salinity can be effectively analyzed, especially the behavior of saltwater during the upstream process. In order to reveal the saltwater upstream blocking effect, a multi-field coupling simulation is first performed using dynamic simulation software (such as MIKE 21) to simulate the change process of salinity with the rise of tidal level. The simulation process combines the spatiotemporal changes of tidal level, salinity gradient and water flow velocity, focusing on how saltwater forms a blocking effect on water flow under different tidal conditions. During the specific implementation process, firstly, a profile of the upstream and downstream salt-fresh water junction area is established in the model, and the changes of salinity in different water depths during the saltwater tide are calculated using multi-level distributed salinity data. Through iterative simulation, the possible blocking effect area during the saltwater tide is identified. In further operations, the "particle tracking method" is used to simulate the dynamic process of salinity tracing back with the tide. The particle tracking method can track the movement path of salt molecules in the water body and help identify the key nodes of salinity diffusion in the water flow. By analyzing the diffusion speed and direction of these nodes, the main blocking effect of the saltwater tide tracing back is identified. In addition, the blocking effect of the saltwater tide tracing back is also monitored for a long time, and the dynamic data of the saltwater tide tracing back and salinity diffusion are obtained in real time using a buoy observation system and multi-point underwater sensors. These real-time data will be input into the dynamic analysis model to further calibrate the blocking effect analysis results. Next, the nonlinear regression analysis method is used to correlate the blocking effect of the saltwater tide with multiple factors such as water flow rate, tidal level changes, and salinity diffusion. By calculating the salinity change rate of different water layers under the blocking effect, the specific impact of saltwater tide on water diffusion and flow rate is quantified. Finally, the multidimensional nonlinear correlation model is used to generate "saltwater upstream blocking effect data", which provides the blocking behavior of saltwater tide under specific flow and tidal conditions. This data will provide an important reference for subsequent water resources management, especially when formulating saltwater upstream prevention plans along the estuary, and provide a scientific basis for optimizing freshwater supply strategies.

[0121] Preferably, step S32 includes the following steps:

[0122] Step S321: extracting the tidal upstream flow velocity from the tidal level salinity-tidal level nonlinear correlation data tidal level to obtain tidal upstream flow velocity data;

[0123] Step S322: Calculate the velocity shear force field of the upstream tidal velocity data to obtain velocity shear force field data;

[0124] Step S323: Calculate the vertical water section momentum according to the upstream tidal velocity data and the velocity shear force field data, and obtain the vertical water section momentum data;

[0125] Step S324: performing layered structural analysis on the salinity gradient spatial distribution data according to the vertical water body section momentum data to obtain salinity spatial layered data;

[0126] Step S325: performing bottom water salinity stratification diffusion analysis on the salinity gradient spatial distribution data according to the vertical water body section momentum data and the salinity spatial stratification data to obtain bottom water salinity stratification diffusion data.

[0127] In the embodiment of the present invention, when extracting the tidal upstream flow velocity of the tidal level of the tidal level salinity-tidal level nonlinear correlation data, it is first necessary to construct the relationship between the tidal level change and the flow velocity. By performing correlation analysis on the tidal level and flow velocity data over the years, a linear regression method is used to determine the flow velocity response caused by the tidal level change. During the specific operation, the corresponding tidal level data and flow velocity data in the time series are selected, and the least squares method is used for fitting to obtain the linear relationship equation between the tidal level and the flow velocity. The equation is applied to the current tidal level data to calculate the corresponding tidal upstream flow velocity data. This data provides a basis for the subsequent calculation of the flow velocity shear force, and can accurately reflect the influence of the tidal level change on the flow velocity. When calculating the flow velocity shear force field of the tidal upstream flow velocity data, it is first necessary to establish a fluid dynamics model to describe the motion characteristics of the fluid under different conditions. The Navier-Stokes equation is used to describe the movement of the fluid and determine the influence of the flow velocity distribution on the shear force. In the specific implementation process, the upstream tidal velocity data is used as input, and the velocity shear force field data is obtained by calculating the shear force exerted by the fluid on the unit area. When calculating the momentum of the vertical water section based on the upstream tidal velocity data and the velocity shear force field data, it is necessary to first define the vertical section of the calculation area. By determining the water flow distribution in the section, the momentum of the water body on the section is calculated by applying the principle of conservation of momentum. In the specific implementation process, the velocity, density and thickness of each small section of the water body in the vertical section are calculated, and these parameters are combined to obtain the momentum data of the vertical water section. When performing layered structural analysis on the spatial distribution data of the salinity gradient based on the momentum data of the vertical water section, it is first necessary to clarify the salinity distribution of the water body at each layer. In the specific implementation, the multi-level analysis method is applied to stratify the water body, and based on the previously obtained vertical water section momentum data, the influence of the momentum of each layer on the salinity distribution is determined. By comparing the salinity values ​​of different depth layers, the relationship between salinity and momentum is established using the layered analysis method, and finally the salinity spatial layered data is obtained. This data can intuitively reflect the salinity distribution of water bodies at different levels, providing support for subsequent analysis. When analyzing the salinity stratification diffusion of the bottom water body based on the salinity gradient spatial distribution data based on the vertical water body section momentum data and salinity spatial stratification data, it is necessary to first integrate the above two data to establish a salinity diffusion model. In the specific operation process, the diffusion equation is used to calculate the diffusion rate of salinity, and the obtained salinity spatial stratification data is used to perform diffusion analysis on the salinity of different layers, and calculate the salinity changes of each layer at different times. Finally, the stratified diffusion data of the salinity of the bottom water body is obtained. This data will provide the necessary basis for the assessment of the impact of saltwater tides in water resources management, ensuring the scientificity and accuracy of decision-making.

[0128] Preferably, step S33 includes the following steps:

[0129] Step S351: Acquire estuary topographic data;

[0130] Step S352: Calculate the riverbed elevation based on the estuary terrain data to obtain riverbed elevation data;

[0131] Step S353: performing water impact azimuth analysis on the riverbed elevation data based on the tidal level salinity-tidal level nonlinear correlation data to obtain water impact azimuth data;

[0132] Step S354: simulating the water body recirculation behavior of the bottom water body salinity stratification diffusion data according to the water body impact azimuth data and the riverbed elevation data to obtain the water body stratification recirculation behavior data;

[0133] Step S355: analyzing the saltwater upwelling blocking effect on the bottom water salinity stratification diffusion data according to the water body stratification recirculation behavior data to obtain the saltwater upwelling blocking effect data.

[0134] In the embodiment of the present invention, in the process of obtaining the estuary terrain data, it is first necessary to clarify the data source. A method combining high-resolution remote sensing images and field measurements is used to ensure the accuracy and integrity of the terrain data. The specific operation includes using satellite remote sensing technology to obtain the terrain image of the area, and using GPS equipment to conduct field measurements at the estuary to record the elevation information of key terrain points. Combined with the topographic map and hydrological data, the acquired terrain information is sorted out, and finally a three-dimensional terrain model of the estuary is constructed. This model will provide basic data support for subsequent riverbed elevation calculation and water body behavior analysis. When calculating the riverbed elevation of the estuary terrain data, the acquired three-dimensional terrain model must first be processed to extract the elevation information of the riverbed area. In the specific implementation, the digital elevation model (DEM) technology is used to grid the terrain data to obtain the elevation values ​​of different points. Through interpolation methods, such as Kriging interpolation, the elevation gaps between data points are filled to ensure the continuity and accuracy of the elevation data. Finally, the generated riverbed elevation data will clearly show the changes in the water depth in the estuary area, providing the necessary basis for the subsequent water impact azimuth analysis. When analyzing the water impact direction of riverbed elevation data based on the nonlinear correlation data of tidal salinity-tidal level, it is first necessary to integrate the salinity and tidal level data obtained in the previous steps and analyze the relationship between them and the riverbed elevation. By establishing a multivariate regression model, the nonlinear relationship between riverbed elevation and water impact direction is found. In specific implementation, the riverbed elevation data under different water level conditions are analyzed using a nonlinear fitting algorithm to calculate the impact direction of the water body under specific tidal level conditions. The generated water impact direction data will provide important direction information for the subsequent water recirculation behavior simulation. When simulating the water recirculation behavior of the bottom water body salinity stratified diffusion data based on the water impact direction data and riverbed elevation data, it is necessary to first establish a physical model of water movement to describe the dynamic characteristics of the water flow. In the specific implementation process, the Navier-Stokes equation in fluid dynamics is used, combined with the acquired water impact direction and riverbed elevation data, to simulate the recirculation behavior of the water body after the impact. By calculating the velocity distribution and flow direction of the water flow, the recirculation dynamics of water bodies at different levels are obtained. The water stratification and reflow behavior data finally generated will provide a dynamic basis for the analysis of the saltwater tide upstream blocking effect. When analyzing the saltwater tide upstream blocking effect on the bottom water salinity stratification diffusion data based on the water stratification and reflow behavior data, it is first necessary to integrate the dynamic characteristics of the water stratification and reflow and the salinity stratification data. By establishing a physical model of saltwater tide upstream, the hydrodynamics and salinity diffusion equations are used to analyze the changes in salinity under different reflow conditions. In the specific implementation, the relationship between water flow momentum and salinity concentration is used to evaluate the impact of saltwater tide upstream on salinity stratification. The calculated saltwater tide upstream blocking effect data will provide a scientific basis for water resources management and ensure a comprehensive assessment of the impact of saltwater tides and the formulation of response strategies.

[0135] Preferably, simulating the water body recirculation behavior based on the water body impact azimuth data and the riverbed elevation data on the bottom water body salinity stratification diffusion data comprises the following steps:

[0136] Calculate the slope angle of the riverbed cross section according to the water impact azimuth data and the riverbed elevation data to obtain the slope angle data of the riverbed cross section;

[0137] According to the riverbed cross-section slope angle data, the bottom water body salinity stratification diffusion data is matched with the slope angle to obtain the bottom water body stratification slope angle data;

[0138] Based on the bottom water layer slope angle data and riverbed cross-section slope angle data, the impact longitudinal vortex structure analysis is carried out to obtain the water impact longitudinal vortex structure data;

[0139] The water recirculation behavior is simulated based on the water impact longitudinal vortex structure data and the salinity stratification diffusion data of the bottom water body to obtain the water stratification recirculation behavior data.

[0140] In an embodiment of the present invention, when calculating the slope angle of the riverbed cross section according to the water impact azimuth data and the riverbed elevation data, the geometric characteristics of the riverbed must first be determined. A three-dimensional terrain model is used to obtain the elevation data of the riverbed through the digital elevation model (DEM) technology. In the specific implementation process, the slope of the riverbed at different points is calculated using a terrain analysis tool, and the slope calculation formula is used to combine the elevation change of the riverbed to obtain the slope angle of the cross section in the direction of the water flow. This calculation method relies on the elevation difference of the local area in order to accurately reflect the actual terrain characteristics of the riverbed. The final riverbed cross-section slope angle data will provide the necessary basis for subsequent water behavior analysis. When matching the slope angle of the bottom water body salinity stratification diffusion data according to the riverbed cross-section slope angle data, the bottom water body salinity stratification data obtained must first be integrated. This process includes classifying the salinity data according to different depth levels to ensure the hierarchy and integrity of the data. Subsequently, by comparing the salinity stratification data of the bottom water body with the calculated riverbed cross-section slope angle, an interpolation algorithm, such as spline interpolation, is used to match the slope angle to ensure that the salinity data of each depth layer is consistent with the corresponding slope angle. This matching process will lay the foundation for analyzing the stratification characteristics of the water body and generate the bottom water stratification slope angle data. When analyzing the impact longitudinal vortex structure based on the bottom water stratification slope angle data and the riverbed cross-section slope angle data, it is necessary to first establish a dynamic model of water flow. In the specific implementation process, the vortex structure generated by the water impact is simulated and analyzed by the Navier-Stokes equation using the fluid dynamics theory. In this analysis, the bottom water stratification slope angle is combined with the riverbed cross-section slope angle to calculate the velocity changes of the water flow at different depths and directions, thereby deducing the vortex formation mechanism. The final water impact longitudinal vortex structure data will provide an important basis for the subsequent water recirculation behavior simulation. When simulating the water body recirculation behavior based on the salinity stratification diffusion data of the bottom water body according to the water body impact longitudinal vortex structure data, it is necessary to integrate the vortex characteristics and salinity stratification data obtained in the previous steps. In the specific implementation, the energy conservation and momentum conservation principles in fluid dynamics are used to establish a physical model of water body movement and analyze the impact of vortices on water body recirculation. By calculating the reflow speed and direction of different water layers under the action of vortices, the dynamic change characteristics of the water body in different time periods are obtained. The final generated water body stratification recirculation behavior data will fully reflect the water body movement characteristics during the salt tide upstream process and provide a scientific basis for water resources management.

[0141] Preferably, step S4 comprises the following steps:

[0142] Step S41: Based on the saltwater tide upstream blocking effect data and the bottom water salinity stratification diffusion data, a water body ecological progressive impact assessment is performed to obtain water body ecological progressive impact data;

[0143] Step S42: performing progressive water diversion volume adjustment according to the progressive water body ecological impact data to obtain progressive water diversion volume adjustment data;

[0144] Step S43: Analyze the long-term impact of progressive water diversion regulation data on water resources management to obtain long-term impact data on water resources management.

[0145] In an embodiment of the present invention, when a progressive impact assessment of water ecology is performed based on saltwater tide upstream blocking effect data and bottom water salinity stratification diffusion data, the previous data must first be integrated. In this process, the standard method of environmental impact assessment is used to construct a water ecosystem model to consider the impact of water salinity changes on the ecosystem. In a specific implementation, a statistical analysis method is used to classify salinity data to assess the impact of different salinity levels on aquatic biological populations and ecosystem functions. By calculating indicators such as biodiversity index and species abundance at each salinity level, combined with saltwater tide upstream blocking effect data, a systematic ecological impact assessment data is formed. This assessment will provide a scientific basis for water resources management to guide subsequent water diversion regulation. When a progressive water diversion is performed based on the progressive impact data of water ecology, it is necessary to first analyze the impact of different water diversion amounts on water salinity and ecosystems. In a specific implementation, an optimization algorithm, such as a linear programming method, is used to determine the optimal water diversion amount to minimize ecological impact and ensure the sustainable use of water resources. In this process, the ecological progressive impact data is compared with the water diversion demand to identify the water diversion range with the least impact on the ecosystem. According to the results of this analysis, the water diversion implementation plan is adjusted to ensure the stability and healthy development of the water ecosystem. The resulting progressive water diversion regulation data will provide necessary support for management decisions. When analyzing the long-term impact of progressive water diversion regulation data on water resources management, it is first necessary to build a long-term water resources management model, which includes the dynamic relationship between water diversion, ecological health and water quality changes. In the specific implementation process, the time series analysis method is used to evaluate the impact of different water diversion schemes on the sustainability of water resources and the health of the ecosystem in the long term. This analysis should take into account seasonal changes and climate factors, simulate the trend of water body changes after water diversion regulation through historical data, and evaluate the resilience and stability of the ecosystem. The resulting long-term impact data on water resources management will provide decision makers with long-term management strategies to ensure the effective use of water resources and the protection of the ecological environment.

[0146] Preferably, the present invention further provides a water resource management system based on a saltwater tide upward prediction model, which is used to execute the water resource management method based on a saltwater tide upward prediction model as described above. The water resource management system based on a saltwater tide upward prediction model comprises:

[0147] The tidal upstream analysis module is used to collect the saltwater upstream hydrological data of the estuary through the high-precision sensors deployed in the monitoring station to obtain the saltwater upstream hydrological data; analyze the tidal upstream changes along the estuary of the saltwater upstream hydrological data to obtain the tidal upstream change data along the estuary; analyze the tidal upstream water volume according to the tidal upstream change data along the estuary to obtain the tidal upstream water volume data;

[0148] The salinity gradient spatial distribution module is used to analyze the upstream diffusion trend of tidal water volume data to obtain upstream diffusion trend data; analyze the salinity gradient spatial distribution structure of the upstream diffusion trend data to obtain salinity gradient spatial distribution data;

[0149] The saltwater tide upward blocking effect analysis module is used to analyze the salinity stratification diffusion of the bottom water body based on the salinity gradient spatial distribution data to obtain the salinity stratification diffusion data of the bottom water body; the saltwater tide upward blocking effect analysis is performed based on the salinity stratification diffusion data of the bottom water body to obtain the saltwater tide upward blocking effect data;

[0150] The water resources management module is used to analyze the long-term impact of water resources management based on the saltwater tide upstream blocking effect data and the bottom water salinity stratification diffusion data, and obtain the long-term impact data of water resources management.

[0151] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0152] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A water resources management method based on a saltwater tide upstream prediction model, characterized in that: The following steps are involved: Step S1: collecting the saltwater upstream hydrological data of the estuary through the high-precision sensors deployed in the monitoring station to obtain the saltwater upstream hydrological data; analyzing the upstream changes of the tide along the estuary of the saltwater upstream hydrological data to obtain the upstream change data of the tide along the estuary; analyzing the upstream water volume of the tide according to the tidal level of the tidal upstream change data along the estuary to obtain the upstream water volume data of the tide; Step S2: analyzing the upstream diffusion trend of the tidal water volume data to obtain the upstream diffusion trend data; The spatial distribution structure of salinity gradient is analyzed on the upstream diffusion trend data to obtain the spatial distribution data of salinity gradient; Step S3: performing bottom water salinity stratification diffusion analysis on the salinity gradient spatial distribution data to obtain bottom water salinity stratification diffusion data; performing saltwater tide upstream blocking effect analysis based on the bottom water salinity stratification diffusion data to obtain saltwater tide upstream blocking effect data; Step S4: Based on the saltwater tide upstream blocking effect data and the bottom water salinity stratification diffusion data, the long-term impact effect of water resources management is analyzed to obtain the long-term impact effect data of water resources management.

2. The water resources management method based on the saltwater tide upstream prediction model according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: collecting the upstream hydrological data of the saltwater tide at the estuary through the high-precision sensors deployed in the monitoring station to obtain the upstream hydrological data of the saltwater tide; Step S12: Fill missing values ​​in the saltwater upstream hydrological data to obtain optimized saltwater upstream hydrological data; Step S13: analyzing the tidal upstream change along the estuary based on the saltwater upstream prediction model on the saltwater upstream hydrological optimization data to obtain the tidal upstream change data along the estuary; Step S14: analyzing the tidal upstream water volume on the saltwater upstream hydrological optimization data according to the tidal upstream change data along the estuary to obtain the tidal upstream water volume data.

3. The water resources management method based on the saltwater upstream prediction model according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Calculate the upstream impact area of ​​the tidal upstream change data along the estuary to obtain the tidal upstream impact area data; Step S22: analyzing the upstream diffusion trend of the tidal upstream water volume data according to the tidal upstream impact area data to obtain the upstream diffusion trend data; Step S23: simulating the salinity gradient superposition effect on the upstream diffusion trend data to obtain salinity gradient superposition effect data; Step S24: performing salinity gradient spatial distribution structure analysis on the upstream diffusion trend data according to the salinity gradient superposition effect data to obtain salinity gradient spatial distribution data.

4. The water resources management method based on the saltwater upstream prediction model according to claim 3 is characterized in that: Step S23 includes the following steps: Step S231: defining the diffusion zone orientation of the upstream diffusion trend data to obtain the upstream diffusion zone orientation data; Step S232: performing tidal impact superposition analysis on the upstream diffusion trend data according to the upstream diffusion zone azimuth data to obtain tidal impact superposition effect data; Step S233: Based on the tidal influence superposition effect data, the salinity gradient benchmarks of different diffusion zones are calculated for the upstream diffusion zone azimuth data to obtain the diffusion zone salinity gradient benchmark data; Step S234: performing cross-water area superposition analysis on the diffusion zone salinity gradient benchmark data according to the upstream diffusion zone azimuth data and the tidal influence superposition effect data, to obtain cross-water area salinity superposition effect data; Step S235: Simulate the salinity gradient superposition effect based on the diffusion zone salinity gradient baseline data and the cross-water salinity superposition effect data to obtain the salinity gradient superposition effect data.

5. The water resources management method based on the saltwater tide upstream prediction model according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: performing a salinity-tidal level nonlinear correlation analysis on the tidal upstream variation data along the estuary based on the salinity gradient spatial distribution data to obtain salinity-tidal level nonlinear correlation data; Step S32: performing bottom water salinity stratification diffusion analysis on the salinity gradient spatial distribution data according to the tidal salinity-tidal level nonlinear correlation data to obtain bottom water salinity stratification diffusion data; Step S33: Acquire upstream water flow data; perform periodic fluctuation analysis on the upstream water flow data to obtain periodic fluctuation data of upstream water flow; Step S34: Simulating the salinity desalination of the downstream water volume according to the upstream downstream flow periodic fluctuation data on the salinity stratification diffusion data of the bottom water body to obtain the downstream water volume salinity desalination data; Step S35: Based on the tidal level salinity-tidal level nonlinear correlation data, the bottom water body salinity stratification diffusion data and the downstream water salinity desalination data, the salt tide upstream blocking effect analysis is performed to obtain the salt tide upstream blocking effect data.

6. The water resources management method based on the saltwater upstream prediction model according to claim 5 is characterized in that: Step S32 The following steps are involved: Step S321: extracting the tidal upstream flow velocity from the tidal level salinity-tidal level nonlinear correlation data tidal level to obtain tidal upstream flow velocity data; Step S322: Calculate the velocity shear force field of the upstream tidal velocity data to obtain velocity shear force field data; Step S323: Calculate the vertical water section momentum according to the upstream tidal velocity data and the velocity shear force field data, and obtain the vertical water section momentum data; Step S324: performing layered structural analysis on the salinity gradient spatial distribution data according to the vertical water body section momentum data to obtain salinity spatial layered data; Step S325: performing bottom water salinity stratification diffusion analysis on the salinity gradient spatial distribution data according to the vertical water body section momentum data and the salinity spatial stratification data to obtain bottom water salinity stratification diffusion data.

7. The water resources management method based on the saltwater tide upstream prediction model according to claim 6 is characterized in that: Step S33 includes the following steps: Step S351: Acquire estuary topographic data; Step S352: Calculate the riverbed elevation based on the estuary terrain data to obtain riverbed elevation data; Step S353: performing water impact azimuth analysis on the riverbed elevation data based on the tidal level salinity-tidal level nonlinear correlation data to obtain water impact azimuth data; Step S354: simulating the water body recirculation behavior of the bottom water body salinity stratification diffusion data according to the water body impact azimuth data and the riverbed elevation data to obtain the water body stratification recirculation behavior data; Step S355: analyzing the saltwater upwelling blocking effect on the bottom water salinity stratification diffusion data according to the water body stratification recirculation behavior data to obtain the saltwater upwelling blocking effect data.

8. The water resources management method based on the saltwater tide upstream prediction model according to claim 7 is characterized in that: The simulation of water recirculation behavior based on the water impact azimuth data and riverbed elevation data for the salinity stratification diffusion data of the bottom water body includes the following steps: Calculate the slope angle of the riverbed cross section according to the water impact azimuth data and the riverbed elevation data to obtain the slope angle data of the riverbed cross section; According to the riverbed cross-section slope angle data, the bottom water body salinity stratification diffusion data is matched with the slope angle to obtain the bottom water body stratification slope angle data; Based on the bottom water layer slope angle data and riverbed cross-section slope angle data, the impact longitudinal vortex structure analysis is carried out to obtain the water impact longitudinal vortex structure data; The water recirculation behavior is simulated based on the water impact longitudinal vortex structure data and the salinity stratification diffusion data of the bottom water body to obtain the water stratification recirculation behavior data.

9. The water resources management method based on the saltwater upstream prediction model according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: Based on the saltwater tide upstream blocking effect data and the bottom water salinity stratification diffusion data, a water body ecological progressive impact assessment is performed to obtain water body ecological progressive impact data; Step S42: performing progressive water diversion volume adjustment according to the progressive water body ecological impact data to obtain progressive water diversion volume adjustment data; Step S43: Analyze the long-term impact of progressive water diversion regulation data on water resources management to obtain long-term impact data on water resources management.

10. A water resources management system based on a saltwater tide upstream prediction model, characterized in that: Used to execute the water resource management method based on the saltwater tide upstream prediction model as claimed in claim 1, the water resource management system based on the saltwater tide upstream prediction model comprises: The tidal upstream analysis module is used to collect the saltwater upstream hydrological data of the estuary through the high-precision sensors deployed in the monitoring station to obtain the saltwater upstream hydrological data; analyze the tidal upstream changes along the estuary of the saltwater upstream hydrological data to obtain the tidal upstream change data along the estuary; analyze the tidal upstream water volume according to the tidal upstream change data along the estuary to obtain the tidal upstream water volume data; The salinity gradient spatial distribution module is used to analyze the upstream diffusion trend of tidal water volume data to obtain upstream diffusion trend data; analyze the salinity gradient spatial distribution structure of the upstream diffusion trend data to obtain the tidal level of salinity gradient spatial distribution data; The saltwater tide upward blocking effect analysis module is used to analyze the salinity stratification diffusion of the bottom water body based on the salinity gradient spatial distribution data to obtain the salinity stratification diffusion data of the bottom water body; the saltwater tide upward blocking effect analysis is performed based on the salinity stratification diffusion data of the bottom water body to obtain the saltwater tide upward blocking effect data; The water resources management module is used to analyze the long-term impact of water resources management based on the saltwater tide upstream blocking effect data and the bottom water salinity stratification diffusion data, and obtain the long-term impact data of water resources management.

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