Intelligent early warning and prevention and control emergency system for flood, drought and salt tide disasters

Abstract

The invention relates to the technical field of information technology and environmental science, in particular to an intelligent early warning, prevention and control emergency system for flood, drought and salt tide disasters, which comprises a data monitoring and acquisition subsystem, a data processing and optimization subsystem, a disaster assessment and analysis subsystem and a disaster early warning and emergency prevention and control subsystem, the data monitoring and acquisition subsystem is used for detecting the monitored drainage basin to obtain multi-source dynamic monitoring information of the monitored drainage basin; a time sequence reconstruction model is established in the data processing and optimizing subsystem, and a target information set of the monitored drainage basin is obtained after model processing; a flood-drought-salt tide disaster prediction model is constructed in the disaster evaluation and analysis subsystem, and a disaster prediction and evaluation result of the monitored drainage basin is obtained based on the model; and the disaster early warning and emergency prevention and control subsystem sends out a disaster early warning signal and matches emergency prevention and control measures in combination with the prediction and evaluation result and the information set. According to the invention, monitoring and early warning, data optimization processing, disaster assessment and analysis and emergency prevention and control decision making are realized.

Description

Technical Field

[0001] The present invention relates to the fields of information technology and environmental science and technology, and in particular to an intelligent early warning and prevention and control emergency system for flood, drought and saltwater tide disasters. Background Art

[0002] Natural disasters such as floods, droughts, and saltwater tides have brought severe challenges to human society, threatening not only the safety of life and property, but also having a significant impact on social and economic development. Such disasters are characterized by suddenness, uncertainty, and strong destructiveness. Although the existing disaster early warning system can provide early warning information to a certain extent, there are still deficiencies. The existing system mostly relies on a single data source and lacks the ability to integrate and analyze multi-source data, resulting in limited accuracy and timeliness of early warning information: the early warning algorithms of the existing systems are mostly based on simple statistical models or empirical formulas, lacking intelligent algorithm support, and are difficult to cope with complex and changeable disaster scenarios; in terms of emergency response, the existing system has a lag and cannot provide effective prevention and control measures and decision-making basis in a timely manner.

[0003] In order to address the shortcomings of the existing early warning system and conduct in-depth analysis and understanding of the causes, mechanisms and impacts of natural disasters such as floods, droughts and saltwater intrusions, it is necessary to build an intelligent early warning and prevention and control emergency system for flood, drought and saltwater intrusion disasters based on technologies such as big data, cloud computing, the Internet of Things and artificial intelligence, which will help improve the accuracy and timeliness of disaster warnings and provide technical support for disaster prevention and control and emergency response. Summary of the invention

[0004] In view of the shortcomings of existing methods and the needs of practical applications, in order to solve the limitations of early warning systems in warning of natural disasters such as floods, droughts, and saltwater tides, we will deeply analyze the causes, mechanisms, and potential impacts of different disasters, improve the early warning capabilities and emergency response efficiency of natural disasters, reduce the losses caused by disasters, and provide technical guarantees for the sustainable development of society. The present invention provides an intelligent early warning and prevention and control emergency system for flood, drought and saltwater tide disasters, the system comprising: a data monitoring and acquisition subsystem, a data processing and optimization subsystem, a disaster assessment and analysis subsystem and a disaster early warning and emergency prevention and control subsystem; the data monitoring and acquisition subsystem is used to perform real-time detection on the monitoring basin to obtain multi-source dynamic monitoring information of the monitoring basin; a time series reconstruction model is established in the data processing and optimization subsystem, and the multi-source dynamic monitoring information is processed based on the time series reconstruction model to obtain a target information set of the monitoring basin; a flood, drought and saltwater tide disaster prediction model is constructed in the disaster assessment and analysis subsystem, and the disaster prediction and evaluation results of the monitoring basin are obtained through the flood, drought and saltwater tide disaster prediction model and the target information set; the disaster early warning and emergency prevention and control subsystem combines the disaster prediction and evaluation results and the target information set to send out a disaster early warning signal and match emergency prevention and control measures. The present invention can collect multi-source dynamic information of the monitoring basin in real time to ensure the timeliness and comprehensiveness of the information, buy time for intelligent identification, early warning and prevention and control of disasters, shorten the analysis cycle, and improve the response speed and practical performance of the intelligent early warning and prevention and control emergency system.

[0005] Optionally, the establishing of the time series reconstruction model in the data processing and optimization subsystem includes: obtaining the time series characteristics of the data processing and optimization subsystem based on the data processing and optimization subsystem and the multi-source dynamic monitoring information; and establishing the time series reconstruction model in the data processing and optimization subsystem according to the time series characteristics. The present invention can effectively process and optimize raw data, reduce the impact of noise and outliers, and improve the accuracy and reliability of system data.

[0006] Optionally, establishing a time series reconstruction model in the data processing and optimization subsystem includes: The time series reconstruction model satisfies the following relationship: in, represents the reconstructed time series node, Indicates the position mark value of the time series node. represents the embedding dimension of the time series of the data monitoring and acquisition subsystem, represents the quantitative coefficient corresponding to the monitored watershed, Represents the time series embedding lag of the data monitoring and acquisition subsystem.

[0007] The present invention can accurately describe and reconstruct time series data, help reduce errors in data processing, improve data accuracy, and provide a basis for disaster warning and prevention and control.

[0008] Optionally, the processing of the multi-source dynamic monitoring information based on the time series reconstruction model to obtain a target information set for monitoring the watershed includes: obtaining a reconstructed time series node set based on the time series reconstruction model and the multi-source dynamic monitoring information; obtaining a target information set for monitoring the watershed based on the reconstructed time series node set; The reconstructed time series node set satisfies the following relationship: in, Represents the reconstructed time series node set, Represents the reconstructed , Represents the reconstructed , Represents the space after reconstruction .

[0009] The present invention converts the original multi-source dynamic monitoring information into a reconstructed time series node set through a time series reconstruction model, further ensuring the system data quality and providing technical support for the acquisition of target information.

[0010] Optionally, the construction of the flood, drought and saltwater tide disaster prediction model in the disaster assessment and analysis subsystem includes: establishing a flood prediction model in the disaster assessment and analysis subsystem based on the multidimensional hydraulic analysis model and the target information set; obtaining the evapotranspiration of the monitored basin according to the target information set, and establishing a drought prediction model in the disaster assessment and analysis subsystem based on the evapotranspiration; selecting saltwater tide prediction parameters according to the target information set, and establishing a saltwater tide prediction model in the disaster assessment and analysis subsystem in combination with the saltwater tide prediction parameters; and obtaining a flood, drought and saltwater tide disaster prediction model in combination with the flood prediction model, the drought prediction model and the saltwater tide prediction model. Based on the multidimensional hydraulic analysis model and the target information set, the present invention can more comprehensively consider various factors affecting floods, such as rainfall, topography, river characteristics, etc., thereby improving the accuracy of disaster prediction results, helping to formulate flood control measures in advance, and reducing losses from flood disasters.

[0011] Optionally, the flood prediction model satisfies the following relationship: in, represents the movement coefficient in the x direction of the monitoring basin, represents the average flow velocity in the x direction The partial differential of represents the partial differential with respect to time t, represents the acceleration due to gravity, represents the average flow velocity in the x direction of the monitored basin, represents the turbulent viscosity coefficient of the monitoring basin, represents the component of the vertical average velocity in the x direction, represents the roughness coefficient of the monitored watershed, It represents the average water depth in the monitored basin; in, represents the movement coefficient in the y direction of the monitoring basin, represents the average flow velocity in the y direction The partial differential of represents the partial differential with respect to time t, represents the acceleration due to gravity, represents the average flow velocity in the y direction of the monitoring basin, represents the turbulent viscosity coefficient of the monitoring basin, represents the component of the vertical average velocity in the y direction, represents the roughness coefficient of the monitored watershed, Indicates the average water depth of the monitored basin.

[0012] The relevant parameters of the model of the present invention reflect the change of flow velocity over time, so that the model can dynamically simulate the propagation process of floods in the basin, thereby more accurately predicting the change of floods and the degree of impact.

[0013] Optionally, the drought prediction model satisfies the following relationship: in, It represents the difference between precipitation and evapotranspiration in the monitoring basin in month i. It indicates the precipitation detection value of the monitoring basin in month i. It represents the slope of the change of the temperature in the monitoring basin with the saturated water vapor pressure. represents the soil heat flux density in the monitoring basin in month i, It represents the average wind speed at a height of 2m above the horizontal plane of the monitoring basin in month i. Indicates the saturated vapor pressure in the monitoring basin in month i, It indicates the actual vapor pressure in the monitoring basin in month i. represents the average temperature in the monitoring basin in month i, express The corresponding weight coefficient is, express The corresponding weight coefficient.

[0014] The model of the present invention takes into account multiple factors such as precipitation, evapotranspiration, temperature, soil density, wind speed, vapor pressure, and corresponding weight coefficients, so that the model can more accurately predict the trend and intensity of drought in the basin.

[0015] Optionally, the disaster prediction and assessment results of the monitored basin obtained through the flood, drought and saltwater tide disaster prediction model and the target information set include: obtaining hydraulic prediction results in different directions of the monitored basin based on the flood prediction model and the target information set; obtaining precipitation and evapotranspiration analysis results of the monitored basin according to the drought prediction model and the target information set; and obtaining saltwater tide upstream prediction results of the monitored basin according to the saltwater tide prediction model and the target information set. The present invention obtains disaster prediction and assessment results of the monitored basin based on the flood, drought and saltwater tide disaster prediction model and the target information set, which is conducive to comprehensively grasping disaster information, improving the disaster warning capability of the system, and optimizing disaster response strategies.

[0016] Optionally, the intelligent early warning and prevention and control emergency system for flood, drought and saltwater tide disasters also includes: introducing historical disaster data of the monitoring basin into the disaster assessment and analysis subsystem; intelligently analyzing the monitoring basin based on the historical disaster data, the hydraulic prediction results, precipitation and evapotranspiration analysis results and saltwater tide upstream prediction results, and obtaining disaster prediction and assessment results for the monitoring basin. The historical disaster data of the present invention provides key information such as the time, location and intensity of the disaster, and the relevant information can be used as a reference for future disaster predictions, thereby verifying and calibrating the accuracy of the prediction model.

[0017] Optionally, the disaster warning and emergency prevention and control subsystem sends out a disaster warning signal and matches emergency prevention and control measures in combination with the disaster prediction and assessment results and the target information set, including: setting up an emergency prevention and control database and configuring a safety alarm device in the disaster warning and emergency prevention and control subsystem; the disaster warning and emergency prevention and control subsystem controls the safety alarm device according to the disaster prediction and assessment results and the target information set to send out a disaster warning signal for the monitored basin; the disaster warning and emergency prevention and control subsystem matches the emergency prevention and control measures for the monitored basin based on the disaster warning signal and the emergency prevention and control database; and the intelligent prediction and prevention and control of disasters in the monitored basin are realized through the disaster warning signal and the emergency prevention and control measures. The present invention sends out a disaster warning signal through an intelligent module and matches the corresponding emergency prevention and control measures, which can improve the timeliness and accuracy of disaster warnings, optimize the matching and execution of emergency prevention and control measures, and enhance the intelligence and automation level of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of the intelligent early warning and prevention and control emergency system for flood, drought and saltwater tide disasters of the present invention; Figure 2It is a schematic diagram of the analysis of the x-direction and y-direction of the monitoring basin in the intelligent early warning and prevention and control emergency system for flood, drought and saltwater tide disasters of the present invention; Figure 3 This is a structural diagram of the intelligent early warning and prevention and control emergency system for flood, drought and saltwater tide disasters of the present invention. DETAILED DESCRIPTION

[0019] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0020] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0021] See also Figure 1 In order to monitor the watershed conditions in real time, issue disaster warning signals in time, match reasonable disaster emergency prevention and control measures, improve the timeliness and automation level of the disaster warning system, and provide technical support for intelligent prevention and response to disasters. The present invention provides an intelligent early warning and prevention and control emergency system for flood, drought and saltwater disasters, and the system includes the following steps: The intelligent early warning and prevention and control emergency system for flood, drought and saltwater disasters includes a data monitoring and collection subsystem, a data processing and optimization subsystem, a disaster assessment and analysis subsystem and a disaster early warning and emergency prevention and control subsystem.

[0022] S1. Use the data monitoring and collection subsystem to conduct real-time detection of the monitoring basin to obtain multi-source dynamic monitoring information of the monitoring basin. The specific implementation steps and related contents are as follows: A data monitoring and collection subsystem is deployed and designed in the embodiment. The subsystem can capture key indicators such as hydrological information and meteorological data of the monitored basin in real time, and predict the probability and potential impact of flood, drought and saltwater disasters based on the above data. This subsystem not only has data collection functions, but also can integrate data from multiple sources and in different formats to form a comprehensive and real-time multi-source information library.

[0023] The data monitoring and collection subsystem can collect data in real time, focusing on real-time monitoring and collection of data closely related to flood, drought and saltwater tide disasters such as rainfall, water level, flow, soil moisture, seawater salinity, etc. in the monitoring basin; then, it integrates multi-source heterogeneous data through big data technology, and aggregates and organizes multi-source heterogeneous data such as hydrology, hydraulics, meteorology, and engineering facilities to ensure the comprehensiveness and accuracy of the monitoring data, and provide an information basis and basis for the rapid and accurate analysis of the algorithm model and the prediction of disaster conditions.

[0024] The above data monitoring and collection subsystem is mainly composed of various sensors, monitoring stations and automatic collection equipment. The relevant equipment is distributed in key locations of the monitoring basin to ensure the accuracy and timeliness of the system data. The sensors are responsible for real-time capture of environmental parameters of the monitoring basin, the monitoring stations are responsible for the initial processing and transmission of dynamic monitoring information, and the automatic collection equipment can further ensure the continuity and integrity of the system information, thereby ensuring the accuracy of multi-source dynamic monitoring information in the monitoring basin.

[0025] In this embodiment, the data monitoring and acquisition subsystem is a comprehensive platform that integrates sensor technology, big data processing and intelligent analysis algorithms. It can collect multi-source dynamic monitoring information in the monitoring basin in real time and accurately, which not only improves the efficiency and accuracy of data collection, but also realizes data analysis and mining through big data technology and intelligent algorithms, providing strong technical support for water resources management and disaster warning. By integrating real-time data from different monitoring points through the above system, the actual situation of the monitoring basin can be fully and accurately reflected, which helps to timely discover potential disaster problems, improve monitoring accuracy, and then more accurately grasp the hydrological and meteorological changes in the monitoring basin, thereby minimizing the impact of flood, drought and saltwater disasters.

[0026] Furthermore, the method for collecting multi-source dynamic monitoring information in this embodiment is only an optional condition of the present invention. In one or some other embodiments, the method for collecting multi-source dynamic monitoring information can be adjusted according to the actual situation of the monitored basin and the data monitoring and collection requirements, which can more reasonably allocate monitoring resources and avoid unnecessary waste, thereby helping to reduce monitoring costs and improve resource utilization efficiency.

[0027] S2. Establish a time series reconstruction model in the data processing and optimization subsystem, and process the multi-source dynamic monitoring information based on the time series reconstruction model to obtain the target information set of the monitoring basin. The specific implementation content is as follows: Firstly, the time series characteristics of the data processing and optimization subsystem are obtained based on the data processing and optimization subsystem and multi-source dynamic monitoring information.

[0028] The phase space of the monitored basin is analyzed and reconstructed based on the multi-source dynamic monitoring information of the data monitoring and acquisition subsystem. In this process, the reconstructed phase space theorem is introduced, and chaos theory is integrated into the analysis and reconstruction process of the time series. In the embodiment, based on the multi-source dynamic monitoring information of the data monitoring and acquisition subsystem, the characteristics of the system time series are extracted and clarified, and the total number of system time series nodes is set to m. Then the set of time series nodes corresponding to the data monitoring and acquisition subsystem satisfies the following relationship: in, Represents a set of time series nodes of the data monitoring and collection subsystem, represents the first time series node, Represents the second time series node, Represents the mth time series node.

[0029] in Represents the first time node in the time series, represents the second time node immediately following it, and so on, until It represents the last one in the time series, that is, the mth time node. Based on this, we can analyze the distribution of system information and the characteristics of time series. We can further analyze the time series distribution of the data monitoring and acquisition subsystem, extract the data distribution and time series characteristics, and then accurately collect the dynamic change information of the monitoring basin. At the same time, it can reveal the dynamic characteristics and structure of system information, which is helpful to improve the overall performance and application level of the monitoring system.

[0030] Furthermore, a time series reconstruction model was established in the data processing and optimization subsystem based on the above time series characteristics and time series node sets.

[0031] According to the time series characteristics and time series node set of the data monitoring and collection subsystem, a time series reconstruction model is constructed in the data processing and optimization subsystem. The core of this model is to rearrange and optimize the structure of the time series node set. In order to improve the model computing power, the time series embedding dimension and embedding lag are set as reconstruction model parameters. The above parameters play an important role in the information time series space reconstruction model.

[0032] The embedding dimension represents the minimum number of dimensions required to describe the dynamic behavior of the system in the reconstructed phase space, which is helpful for understanding the complex structure of the system in high-dimensional space; while the embedding lag reflects the time interval between adjacent data points in the time series, which is helpful for analyzing the dynamic characteristics of the system. Then, combined with the time series data of the data monitoring and acquisition subsystem, an information time series space reconstruction model was established. The model reveals the dynamic change law of the monitored basin and provides a theoretical basis for subsequent disaster warning, water resources scheduling and other decisions.

[0033] The above time series reconstruction model satisfies the following relationship: in, represents the reconstructed time series node, Indicates the position mark value of the time series node. represents the embedding dimension of the time series of the data monitoring and acquisition subsystem, represents the quantitative coefficient corresponding to the monitored watershed, Represents the time series embedding lag of the data monitoring and acquisition subsystem.

[0034] The above quantitative coefficients mainly depend on the monitoring purpose, content and requirements. The calculation method of the quantitative coefficients needs to be characterized and optimized according to the actual situation of the monitored basin, monitoring purpose and monitoring requirements, so as to fully understand the actual situation of the monitored basin and provide a scientific basis for the sustainable development of the basin.

[0035] Then, a reconstructed time series node set is obtained according to the time series reconstruction model and multi-source dynamic monitoring information; and a target information set of the monitoring basin is obtained based on the reconstructed time series node set.

[0036] Based on the time series reconstruction model and multi-source dynamic monitoring information, a new time series node set is obtained. This set is re-arranged and structurally optimized, and can more accurately reflect the dynamic changes of the monitored basin within a specific time range. The reconstructed time series node set satisfies the following relationship: in, Represents the reconstructed time series node set, Represents the reconstructed , Represents the reconstructed , Represents the space after reconstruction .

[0037] The node set is composed of a series of rearranged and optimized time series nodes, each of which carries specific time information and monitoring data. The reconstructed time series node set not only retains the time order in the original time series, but also further enhances the spatiotemporal relationship between nodes through spatial reconstruction technology. At the same time, the dynamic characteristics of the monitored basin are mastered through the time series reconstruction model, providing an information basis for subsequent disaster analysis and prediction.

[0038] Finally, based on the spatially reconstructed time series node set and the data monitoring and collection subsystem, the target information set of the monitored basin is obtained.

[0039] The target information set refers to the target information set of the intelligent early warning and prevention and control emergency system for flood, drought and saltwater disasters, which is composed of m information subsets arranged in chronological order, which together constitute the information set of the monitored basin in the time series.

[0040] The target information set of the monitoring basin satisfies the following relationship: in, Represents the target information set of the monitoring basin, express The corresponding information subset, express The corresponding subset of information, express The corresponding information subset.

[0041] The target information set can ensure the integrity of the monitored basin information. Each information subset corresponds to a specific moment in the time series, thus ensuring the timing and continuity of the information. Since each information subset is associated with a specific time point, it is possible to clearly understand the state changes of the monitored basin at different time points and the temporal and spatial relationship between different changes.

[0042] The data processing and optimization subsystem optimizes the monitored basin information, which can simplify the data analysis and processing process. It can select specific information subsets for analysis as needed, or uniformly process the entire set to reveal the potential laws and changing trends of the monitored basin, thereby better understanding the actual situation of the monitored basin, and thus formulating more scientific and reasonable decision-making plans and prevention and control measures, providing technical support for the sustainable development of the monitored basin.

[0043] Furthermore, the processing and optimization method of multi-source dynamic monitoring information in the present embodiment is only an optional condition of the present invention. In one or some other embodiments, the processing and optimization method of multi-source dynamic monitoring information can be adjusted according to the actual situation of the monitored basin and the data requirements of the early warning and prevention and control system to ensure the comprehensiveness and accuracy of the monitoring information, further improve the efficiency and reliability of system data collection, and thereby ensure the effectiveness of the intelligent early warning and prevention and control emergency system for flood, drought and saltwater disasters.

[0044] S3. Construct a flood, drought and saltwater disaster prediction model in the disaster assessment and analysis subsystem. Obtain disaster prediction and assessment results for the monitored basin through the flood, drought and saltwater disaster prediction model and target information set. The specific implementation contents are as follows: In an optional embodiment, a flood prediction model is established in the disaster assessment and analysis subsystem based on the multi-dimensional hydraulic analysis model and the target information set.

[0045] A flood prediction model was established based on the dynamic balance principle and target information set in the multidimensional hydraulic analysis model. During the model establishment process, key parameters of the monitored basin were extracted from the target information set, including but not limited to the basic properties of the basin, bed resistance coefficient, calculation parameters of the moving boundary, and eddy viscosity coefficient, thus providing an information basis for the flood prediction model.

[0046] The dynamic balance principle refers to the water balance that changes over time in the monitored basin, which is analyzed through partial differential equations. The dynamic balance principle in the above multidimensional hydraulic analysis model needs to satisfy the following relationship: in, The sign of the partial derivative, represents the partial differential with respect to time t, represents the average flow velocity in the x direction The partial differential of represents the average flow velocity in the y direction The partial differential of represents the average water depth of the monitored basin, represents the average flow velocity in the x direction of the monitored basin, It represents the average flow velocity in the y direction of the monitoring basin.

[0047] The above partial differential at time t represents the partial derivative at time t, which describes the rate of change of water volume in the monitored basin over time; and They represent the rate of change of water volume due to water flow movement (average flow velocity) in the x and y directions respectively.

[0048] The average flow velocity in different directions in the monitored basin reflects the movement state of water in the basin.

[0049] The average water depth and the average flow velocity in different directions further indicate the amount of water (i.e. flow rate) passing through unit width per unit time in different directions. The average water depth refers to the average water depth of the monitored basin, based on which a preliminary analysis can be made of the topography and water distribution of the monitored basin.

[0050] The above equation sets the monitoring basin as a closed system, analyzes the dynamic equilibrium relationship between the change of water volume over time and the movement state of water flow in space, and provides a theoretical basis for the construction of flood prediction models. By solving the above partial differential equations, the change of water volume over time in the monitoring basin and the movement law of water flow in the x and y directions can be obtained, thus providing an accurate hydrodynamic calculation basis for flood prediction and environmental simulation.

[0051] A flood prediction model is constructed based on the above target information set and combined with the dynamic balance principle in the multidimensional hydraulic analysis model.

[0052] Based on the target information set analysis, the movement characteristics, hydraulic conditions and other factors of the monitoring basin are monitored, and a flood prediction model is established based on the dynamic balance principle of the multidimensional hydraulic analysis model. Based on this, flood disasters can be accurately predicted, and the water balance relationship in the process of flood evolution can be accurately reflected, providing a scientific basis for disaster assessment and response. The above flood prediction model satisfies the following relationship: in, represents the movement coefficient in the x direction of the monitoring basin, represents the average flow velocity in the x direction The partial differential of represents the partial differential with respect to time t, represents the acceleration due to gravity, represents the average flow velocity in the x direction of the monitored basin, represents the turbulent viscosity coefficient of the monitoring basin, represents the component of the vertical average velocity in the x direction, represents the roughness coefficient of the monitored watershed, It represents the average water depth in the monitored basin; in, represents the movement coefficient in the y direction of the monitoring basin, represents the average flow velocity in the y direction The partial differential of represents the partial differential with respect to time t, represents the acceleration due to gravity, represents the average flow velocity in the y direction of the monitoring basin, represents the turbulent viscosity coefficient of the monitoring basin, represents the component of the vertical average velocity in the y direction, represents the roughness coefficient of the monitored watershed, Indicates the average water depth of the monitored basin.

[0053] In this embodiment, the operation analysis is mainly carried out in the x-direction and y-direction of the monitoring basin to explore the internal mechanism of water balance in the process of flood evolution. That is, the operation state of the monitoring basin is divided into two orthogonal directions, the x-direction and the y-direction, and the dynamic changes of water flow are examined in two dimensions respectively, so as to fully grasp the water balance relationship when the flood occurs. By analyzing the key parameters such as the velocity, flow direction, and water depth of the water flow in the x-direction and the y-direction, the behavior characteristics of the flood at different time and space scales can be simulated and predicted more accurately, thereby providing strong scientific support for flood prevention and disaster reduction.

[0054] See the schematic diagram of the x-direction and y-direction decomposition of the monitoring basin in the embodiment. Figure 2 .

[0055] The movement coefficient of the monitored basin in different directions reflects the dynamic characteristics of water flow movement in different directions in the monitored basin.

[0056] The partial differential of the average flow velocity in different directions with time indicates the rate of change of the flow velocity in different directions with time.

[0057] Gravitational acceleration is the ratio of the gravity acting on an object near the Earth's surface to the object's mass, and affects the movement and distribution of fluids in fluid dynamics.

[0058] The average flow velocity in different directions of the monitored basin indicates the average movement speed of water in different directions.

[0059] The turbulent viscosity coefficient is an important parameter to describe the turbulent characteristics of fluid. It reflects the momentum exchange capacity caused by turbulence in the monitored basin and is closely related to the turbulence intensity of the water area.

[0060] The components of the vertical average flow velocity in different directions indicate the average value of the flow velocity in different directions in the vertical direction.

[0061] The roughness coefficient of the monitored basin can describe the resistance encountered by water when flowing on the surface of a river or basin. It is related to the surface morphology, vegetation coverage, riverbed material, etc. of the monitored basin. It is helpful in estimating parameters such as water velocity and flow rate in hydraulic calculations, as well as predicting flood processes.

[0062] The average water depth of the monitored basin indicates the average depth of the water body in the basin, which can further describe the topography and water body distribution of the monitored basin.

[0063] The above parameters together constitute the basis of the flood prediction model. Through the interaction between the parameters, the flood prediction model can simulate and predict the dynamic evolution of floods in the monitored basin.

[0064] In an optional embodiment, the evapotranspiration of the monitored basin is obtained according to the target information set, and a drought prediction model is established in the disaster assessment and analysis subsystem based on the evapotranspiration.

[0065] In the process of building a drought prediction model, it is necessary to fully integrate the multi-dimensional data of the monitoring basin covered by the target information set. The relevant data not only includes long-term climate change trends and regional water resources conditions, but also comprehensively considers a series of key meteorological, hydrological and soil factors such as precipitation, temperature, evapotranspiration and soil moisture. In the embodiment, based on the calculation principle of precipitation evapotranspiration, the precipitation and evapotranspiration of the monitoring basin are used as the core variables of the drought prediction model, and then the drought conditions of the monitoring basin are quantitatively analyzed and evaluated.

[0066] Basin evapotranspiration is a key link in the water cycle, representing the total amount of evaporation of surface water and soil moisture in the monitored basin and transpiration of plants. In order to accurately analyze the evapotranspiration, a calculation formula for evapotranspiration was introduced, which can comprehensively consider the influence of multiple factors such as precipitation, temperature, soil conditions and vegetation coverage. On this basis, an in-depth analysis was conducted on the difference between monthly precipitation and monthly evapotranspiration in the monitored basin, which is conducive to revealing the internal connection and dynamic balance relationship of the monitored basin.

[0067] In this embodiment, evapotranspiration data of the monitored basin in different months are obtained according to the target information set. The above data are helpful to derive the evapotranspiration calculation formula applicable to this embodiment, which can quantitatively reflect the relationship between evapotranspiration and multiple key parameters, including but not limited to the temperature, soil density, vapor pressure and corresponding coefficients of the monitored basin.

[0068] By comprehensively considering relevant factors, accurate estimation of evapotranspiration in the monitored basin in different months can be achieved. The calculation formula of evapotranspiration in this embodiment satisfies the following relationship: in, represents the evapotranspiration of the monitored basin in month i, It represents the slope of the change of the temperature in the monitoring basin with the saturated water vapor pressure. represents the soil heat flux density in the monitoring basin in month i, It represents the average wind speed at a height of 2m above the horizontal plane of the monitoring basin in month i. Indicates the saturated vapor pressure in the monitoring basin in month i, It indicates the actual vapor pressure in the monitoring basin in month i. represents the average temperature in the monitoring basin in month i, express The corresponding weight coefficient is, express The corresponding weight coefficient.

[0069] Monitoring the evapotranspiration of a watershed in different months is an important indicator to measure the loss of water in the watershed to the atmosphere through evaporation and plant transpiration.

[0070] The slope of the change in the monitored basin temperature with the saturated water vapor pressure reflects the influence of temperature on the saturated water vapor pressure and is a key parameter for considering the temperature effect when calculating evapotranspiration.

[0071] The soil heat flux density in the basin is monitored in different months, which refers to the rate of energy exchange between the soil and the atmosphere. The heat transferred from the soil to the atmosphere has an important influence on the evapotranspiration process.

[0072] The average wind speed at a height of 2 meters above the horizontal plane of the monitoring basin in different months refers to the average value of the wind speed measured at a height of 2 meters above the water surface in a specific monitoring basin for each month. This can reflect the overall level and changing trend of wind speed in different time periods. The above wind speed is one of the important factors affecting evapotranspiration, which determines the speed of air flow and thus affects the rate at which water is lost from the surface to the atmosphere.

[0073] The saturated vapor pressure in different months of the monitoring basin refers to the saturated water vapor pressure corresponding to each month in a specific monitoring basin. Saturated vapor pressure is also called saturated water vapor pressure or saturated vapor pressure, which can describe the maximum amount of water vapor that can be contained in the air at a certain temperature. When the water vapor in the air reaches this maximum value, the air is called saturated air. The water vapor pressure at this time is the saturated vapor pressure, which is one of the basic data for calculating evapotranspiration.

[0074] The actual vapor pressure in different months of the monitoring basin refers to the pressure generated by the actual water vapor in the air in each month in the specific monitoring basin. The actual vapor pressure is different from the saturated vapor pressure. The saturated vapor pressure is the maximum amount of water vapor that can be contained in the air at a certain temperature, which is the theoretical upper limit; while the actual vapor pressure is the actual observed pressure of water vapor in the air, which is affected by many factors, including but not limited to temperature, humidity, air pressure, wind speed, and geographical and meteorological conditions in the basin. The difference between the actual amount of water vapor in the air and the saturated vapor pressure reflects the degree to which the air is close to saturation, which has a direct impact on the evaporation process.

[0075] The average temperature of different months in the monitoring basin refers to the average value of the temperature data measured in each month in a specific monitoring basin. In the embodiment, the temperature is the air temperature at a certain height above the water surface, which reflects the climate conditions in the basin. In the embodiment, the arithmetic mean method is used for average calculation, that is, all observations are added and divided by the number of observations, and the result is the average temperature of the month, where the temperature is one of the key factors affecting the evapotranspiration rate. The higher the temperature, the faster the evapotranspiration rate is usually.

[0076] The weight coefficient corresponding to the average temperature can be used to adjust the influence of the average temperature in calculating evapotranspiration, further reflecting the sensitivity of temperature to changes in time, location or other factors.

[0077] The weight coefficient corresponding to the average wind speed can be used to adjust the influence of wind speed in calculating evapotranspiration, taking into account the volatility of wind speed or its nonlinear effect on the evapotranspiration process.

[0078] The evapotranspiration calculation formula takes into account a variety of meteorological and soil factors, and estimates evapotranspiration through mathematical relationships, providing key input for drought prediction models and helping to more accurately assess future drought risks in the basin.

[0079] The drought prediction model can analyze the difference between precipitation and evapotranspiration in the monitored basin in any month, and evaluate the drought conditions in the monitored basin based on the difference between precipitation and evapotranspiration in each month.

[0080] Based on the evapotranspiration of the monitoring basin in different months, the drought prediction model needs to satisfy the following relationship: in, It represents the difference between precipitation and evapotranspiration in the monitoring basin in month i. It indicates the precipitation detection value of the monitoring basin in month i. It represents the evapotranspiration of the monitored basin in month i.

[0081] Furthermore, in this embodiment, the drought prediction model satisfies the following relationship: in, It represents the difference between precipitation and evapotranspiration in the monitoring basin in month i. It indicates the precipitation detection value of the monitoring basin in month i. It represents the slope of the change of the temperature in the monitoring basin with the saturated water vapor pressure. represents the soil heat flux density in the monitoring basin in month i, It represents the average wind speed at a height of 2m above the horizontal plane of the monitoring basin in month i. Indicates the saturated vapor pressure in the monitoring basin in month i, It indicates the actual vapor pressure in the monitoring basin in month i. represents the average temperature in the monitoring basin in month i, express The corresponding weight coefficient is, express The corresponding weight coefficient.

[0082] Based on the drought prediction model, the difference between precipitation and evapotranspiration in the monitored basin in different months is calculated, which can effectively evaluate the water balance of the monitored basin and further reflect the dynamic relationship between precipitation and evapotranspiration in the monitored basin in different months.

[0083] When the difference is positive, it means that precipitation exceeds evapotranspiration, and water accumulates in the basin, which helps to maintain or increase the water resource reserves in the monitored basin. On the contrary, when the difference is negative, it means that evapotranspiration exceeds precipitation, and the water in the monitored basin is in a state of deficit, which may lead to drought phenomena such as dry soil and reduced river runoff.

[0084] In an optional embodiment, saltwater tide prediction parameters are selected according to the target information set, and a saltwater tide prediction model is constructed in the disaster assessment and analysis subsystem in combination with the above saltwater tide prediction parameters.

[0085] The key parameters required for saltwater tide prediction and analysis are screened according to the target information set, and a saltwater tide prediction model is constructed in the disaster assessment and analysis subsystem in combination with relevant parameters. This saltwater tide prediction model is mainly suitable for monitoring basins in coastal areas. The risk of saltwater intrusion in the monitoring basin is predicted by in-depth analysis of the marine meteorological conditions, tidal variation patterns and characteristics of saltwater intrusion in the monitoring basin.

[0086] When constructing a saltwater tide prediction model, the hydrological condition information and the overall water resource status of the monitored basin are first fully analyzed. In order to more accurately predict the possibility of saltwater tide upstream, multiple prediction parameters such as topography, hydrology, hydraulics and tidal currents are introduced, thereby obtaining a comprehensive saltwater tide upstream prediction model, i.e., the saltwater tide prediction model of the embodiment.

[0087] A saltwater tide prediction model was established based on the basic principle of saltwater tide formation, namely, the gravitational effect of the moon (and the sun) on the surface seawater causes seawater to flow upstream and mix with fresh water when the flow of freshwater rivers is insufficient, thereby making the water in the upstream river channel salty and eventually forming a saltwater tide.

[0088] By analyzing the key parameters and their interrelationships in the saltwater tide prediction model, it can be seen that the plane shape, widening rate, river depth and degree of riverbed incision of the estuary all affect the hydrodynamic conditions of the estuary waters, and thus affect the confluence and transfer of fresh and salt water; runoff is the core factor that determines the intensity of saltwater tides, and its seasonal changes (such as flood season and dry season) will directly affect the strength of saltwater tide activities; the water flow speed and direction in the hydraulic parameters have an impact on the advancement speed and diffusion range of the saltwater tide; the tidal velocity and direction in the tidal parameters determine the intensity and speed of seawater backflow, as well as the range of saltwater tides.

[0089] Based on the target information set of the monitoring basin, the topographic map of the monitoring basin and hydrological observation data such as precipitation, runoff, hydraulic parameters and tidal parameters are obtained. At the same time, the parameters of the saltwater tide prediction model are calibrated and verified using historical data and actual conditions to ensure the prediction accuracy and reliability of the model.

[0090] The collected data are input into the saltwater prediction model, and the saltwater prediction model is run to predict the saltwater upstream. According to the output of the saltwater prediction model, the saltwater upstream intensity of the monitored basin can be predicted, reflecting the severity of the saltwater upstream. The greater the intensity, the deeper the seawater backflows into the river channel, and the greater the threat to freshwater resources. The analysis of the saltwater upstream range, including the length and width of the affected river channel and the possible affected areas, helps the relevant departments and personnel understand the affected areas of the saltwater disaster and take corresponding countermeasures. The estimated saltwater upstream time, including the predicted start time, duration and possible peak period, has a certain guiding role in early warning, formulation of emergency plans and reduction of disaster losses. In-depth analysis of the key characteristics of the saltwater upstream intensity, range and time provides a scientific basis for relevant decision-making. In summary, the saltwater prediction model not only comprehensively considers a variety of key parameters and their interrelationships, but also calibrates and adjusts the model parameters to ensure the accuracy and practicality of the prediction results.

[0091] By combining the above-mentioned flood prediction model, drought prediction model and saltwater tide prediction model, the flood, drought and saltwater tide disaster prediction model in the embodiment can be obtained, wherein the hydraulic prediction results in different directions of the monitored basin are obtained based on the flood prediction model and the target information set; the precipitation and evaporation analysis results of the monitored basin are obtained according to the drought prediction model and the target information set; and the saltwater tide upstream prediction results of the monitored basin are obtained according to the saltwater tide prediction model and the target information set.

[0092] In an optional embodiment, a comprehensive flood, drought and saltwater tide disaster prediction model is constructed by integrating flood prediction models, drought prediction models and saltwater tide prediction models. The flood, drought and saltwater tide disaster prediction model fully utilizes the advantages and pertinence of each prediction model to achieve targeted prediction and effective analysis of different types of disasters in the monitoring basin.

[0093] Based on the flood prediction model and combined with the target information set, the hydraulic conditions in different directions of the monitored basin are predicted. The movement coefficient of the monitored basin in different directions helps to understand the possible area, intensity and propagation direction of the flood, providing data support for flood prevention and disaster reduction work in the monitored basin.

[0094] At the same time, the drought prediction model and target information set are used to analyze the precipitation and evapotranspiration in the monitored basin. By calculating the difference between precipitation and evapotranspiration, the water balance in the basin can be evaluated, and the drought disaster risk and possible duration of the monitored basin can be predicted.

[0095] In addition, based on the saltwater tide prediction model and target information set, the saltwater tide upstream situation in the monitored basin is predicted. Taking into account various factors such as the topography, hydrology, hydraulics and tidal currents of the monitored basin, the intensity, range and affected area of ​​the saltwater tide upstream can be accurately predicted, providing a scientific basis for disaster prevention and mitigation work in the monitored basin.

[0096] In summary, the flood, drought and saltwater tide disaster prediction model not only covers various disaster types such as floods, droughts and saltwater tides, but also makes full use of the advantages of different disaster prediction models to achieve a comprehensive assessment of disaster risks in the monitored basin, which helps relevant departments and personnel to take timely measures to reduce disaster losses and is conducive to the sustainable development of the monitored basin.

[0097] Furthermore, the historical disaster data of the monitored basin is introduced into the disaster assessment and analysis subsystem, and then the monitored basin is intelligently analyzed based on the above historical disaster data, hydraulic prediction results, precipitation and evapotranspiration analysis results and saltwater tide upstream prediction results, and the disaster prediction and assessment results of the monitored basin are obtained.

[0098] The historical disaster data of the monitored basin is integrated into the disaster assessment and analysis subsystem. Based on this, combined with the hydraulic prediction results provided by the flood prediction model, the precipitation and evaporation analysis results obtained by the drought prediction model, and the saltwater tide prediction results given by the saltwater tide prediction model, a comprehensive and in-depth intelligent analysis of the monitored basin is carried out. The above process not only takes into account the current and future disaster risks, but also fully draws on historical disaster experiences and lessons.

[0099] The comprehensive use of the above multivariate data and analysis results can more accurately predict the type, intensity, scope and potential impact of disasters that the monitored basin may face, and thus obtain reliable disaster prediction and assessment results. The above results not only provide reference information for decision makers, but also help to formulate effective disaster prevention and mitigation strategies, but also provide a scientific basis for basin management and water resources protection.

[0100] In the embodiment, historical disaster data and multiple prediction models are used in a coordinated manner in the disaster assessment and analysis subsystem to achieve intelligent assessment and accurate prediction of disaster risks in the monitored river basin, providing a reference basis and technical support for sustainable development and disaster management in the monitored river basin.

[0101] Furthermore, in the present embodiment, the analysis method and implementation steps of the disaster prediction and assessment results are merely an optional condition of the present invention. In one or some other embodiments, the analysis method and implementation steps of the disaster prediction and assessment results may be optimized according to the disaster prediction requirements and the structural conditions of the disaster assessment and analysis system, and may be closely combined with the specific disaster types and characteristics, thereby improving the feasibility of the system prediction results.

[0102] S4. The above-mentioned disaster warning and emergency prevention and control subsystem combines the disaster prediction and assessment results and the target information set to issue disaster warning signals and match emergency prevention and control measures. The specific implementation contents are as follows: An emergency prevention and control database is set up and a safety alarm device is configured in the disaster warning and emergency prevention and control subsystem to comprehensively improve the system's emergency response capabilities and prevention and control efficiency.

[0103] An emergency prevention and control database is added as a component of the disaster warning and emergency prevention and control subsystem, which stores a large amount of key information related to disaster emergency prevention and control, including but not limited to emergency plans, distribution of rescue resources, historical disaster cases, expert advice and guidance materials, etc. The above database not only provides a way to quickly obtain the required information, but also supports data analysis and mining functions, which helps to discover potential risk points in the monitored basin and optimize emergency measures.

[0104] At the same time, a safety alarm device is also configured in the disaster warning and emergency prevention and control subsystem. The relevant device can receive and store the warning signals of the monitored river basin disasters in real time, and immediately trigger the alarm when receiving the disaster danger signal, ensuring that the relevant departments and personnel can respond quickly and accurately and timely convey the warning information in an emergency.

[0105] Through the emergency prevention and control database and safety alarm device, the disaster warning and emergency prevention and control subsystem not only has powerful data support and intelligent analysis capabilities, but also has an efficient emergency response mechanism, which helps relevant departments and personnel to make decisions quickly and take effective measures when disasters occur to minimize disaster losses.

[0106] In an optional embodiment, first, the disaster warning and emergency prevention and control subsystem controls the safety alarm device based on the disaster prediction and assessment results and the target information set to issue a disaster warning signal for the monitored basin; then, the disaster warning and emergency prevention and control subsystem matches the emergency prevention and control measures for the monitored basin based on the disaster warning signal and the emergency prevention and control database; finally, the intelligent prediction and prevention of disasters in the monitored basin are realized through disaster warning signals and emergency prevention and control measures.

[0107] First, the disaster warning and emergency prevention and control subsystem will intelligently control the safety alarm device based on the disaster prediction and assessment results provided by the disaster prediction and assessment subsystem, as well as the target information set. The above steps enable the system to quickly and accurately issue disaster warning signals when the monitored basin faces disaster risks, thereby buying time for relevant emergency response and prevention.

[0108] Next, the disaster warning and emergency prevention and control subsystem will use a matching algorithm, combined with the characteristics of the disaster warning signal and the emergency measures resources stored in the emergency prevention and control database, to match the most appropriate emergency prevention and control plan for the monitored basin. This process not only takes into account key factors such as the type, intensity, and impact range of the disaster, but also fully combines the geographical, climatic, socio-economic and other characteristics of the monitored basin to ensure the effectiveness and pertinence of emergency measures.

[0109] Ultimately, through the timely release of disaster warning signals and the effective matching of emergency prevention and control measures, the disaster warning and emergency prevention and control subsystem has achieved intelligent prediction and effective prevention and control of disasters in the monitored river basins, which not only improved the efficiency and accuracy of disaster response, but also provided more comprehensive and reliable guarantees for disaster prediction in the monitored river basins, thereby improving the intelligence level of the intelligent warning and prevention and control emergency system for flood, drought and saltwater disasters, and providing technical support for disaster prediction and prevention.

[0110] In an intelligent early warning and prevention and control emergency system for flood, drought and saltwater tide disasters, a data monitoring and collection subsystem, a data processing and optimization subsystem, a disaster assessment and analysis subsystem, and a disaster early warning and emergency prevention and control subsystem work closely together to form an efficient and intelligent disaster early warning and prevention and control system.

[0111] First, the data monitoring and collection subsystem can monitor various dynamic information of the basin in real time and comprehensively, including but not limited to multi-source data such as hydrology, meteorology, and geology. The relevant data provide an information basis for subsequent disaster prediction and assessment.

[0112] Next, the data processing and optimization subsystem uses the time series reconstruction model to process and optimize the system's original data (multi-source dynamic monitoring information) to obtain the target information set of the monitored basin. The above process not only improves the accuracy and availability of the data, but also provides more accurate and comprehensive input information for the disaster prediction model.

[0113] Then, the disaster assessment and analysis subsystem uses the flood, drought and saltwater tide disaster prediction model and combines the target information set to predict and assess the disaster risks in the monitored basin. This step not only reveals the type, intensity and impact range of the disaster, but also provides a decision-making basis for subsequent emergency prevention and control.

[0114] Finally, the disaster warning and emergency prevention and control subsystem quickly issues disaster warning signals for the monitored basin based on disaster prediction and assessment results and combines target information sets, and intelligently matches the most appropriate emergency prevention and control measures. The above links enable relevant departments and personnel to respond in the first time, take effective prevention and control measures, and minimize disaster losses.

[0115] The intelligent early warning and prevention and control emergency system for flood, drought and saltwater tide disasters can more accurately predict the occurrence type, change trend and impact range of flood, drought and saltwater tide disasters through real-time monitoring and multi-source data fusion; the system can quickly match the most appropriate emergency prevention and control measures according to the disaster prediction results, provide timely and effective emergency guidance for relevant departments and personnel, and improve the efficiency and accuracy of emergency response; through the intelligent early warning and prevention and control system, it can realize the full-chain management of flood, drought and saltwater tide disasters, from monitoring, prediction to early warning and prevention and control, forming a complete and efficient disaster prevention and control mechanism, providing more comprehensive and reliable protection measures for the monitored basin. The above system not only promotes the development of disaster early warning and prevention and control technology, but also helps to improve the relevant technical level and innovation ability.

[0116] See also Figure 3 In an optional embodiment, the present invention further provides an intelligent early warning and prevention and control emergency system for flood, drought and saltwater tide disasters, the system includes a data monitoring and collection subsystem, a data processing and optimization subsystem, a disaster assessment and analysis subsystem and a disaster early warning and emergency prevention and control subsystem, the data monitoring and collection subsystem, the data processing and optimization subsystem, the disaster assessment and analysis subsystem and the disaster early warning and emergency prevention and control subsystem are interconnected, and the specific steps of the relevant embodiments of the intelligent early warning and prevention and control emergency system for flood, drought and saltwater tide disasters provided by the present invention are implemented. The intelligent early warning and prevention and control emergency system for flood, drought and saltwater tide disasters of the present invention has a complete structure, objective stability, and improves the overall applicability and practical application ability of the present invention.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. An intelligent early warning and prevention and control emergency system for flood, drought and saltwater disasters, characterized in that: The intelligent early warning and prevention and control emergency system for flood, drought and saltwater tide disasters includes: a data monitoring and collection subsystem, a data processing and optimization subsystem, a disaster assessment and analysis subsystem and a disaster early warning and emergency prevention and control subsystem; The data monitoring and collection subsystem is used to perform real-time detection on the monitoring basin to obtain multi-source dynamic monitoring information of the monitoring basin; Establishing a time series reconstruction model in the data processing and optimization subsystem, and processing the multi-source dynamic monitoring information based on the time series reconstruction model to obtain a target information set for the monitoring basin; Constructing a flood, drought, and saltwater tidal disaster prediction model in the disaster assessment and analysis subsystem, and obtaining disaster prediction and assessment results of the monitored basin through the flood, drought, and saltwater tidal disaster prediction model and the target information set; The disaster warning and emergency prevention and control subsystem combines the disaster prediction and assessment results and the target information set to issue a disaster warning signal and match emergency prevention and control measures.

2. The intelligent early warning and prevention and control emergency system for flood, drought and saltwater disasters according to claim 1 is characterized in that: The establishing of a time series reconstruction model in the data processing and optimization subsystem includes: Obtaining time series characteristics of the data processing and optimization subsystem based on the data processing and optimization subsystem and the multi-source dynamic monitoring information; A time series reconstruction model is established in the data processing and optimization subsystem according to the time series characteristics.

3. The intelligent early warning and prevention and control emergency system for flood, drought and saltwater disasters according to claim 2 is characterized in that: The establishing of a time series reconstruction model in the data processing and optimization subsystem includes: The time series reconstruction model satisfies the following relationship: in, represents the reconstructed time series node, Indicates the position mark value of the time series node. represents the embedding dimension of the time series of the data monitoring and acquisition subsystem, represents the quantitative coefficient corresponding to the monitored watershed, Represents the time series embedding lag of the data monitoring and acquisition subsystem.

4. The intelligent early warning and prevention and control emergency system for flood, drought and saltwater disasters according to claim 2 is characterized in that: The processing of the multi-source dynamic monitoring information based on the time series reconstruction model to obtain a target information set for monitoring the watershed includes: Obtaining a reconstructed time series node set according to the time series reconstruction model and the multi-source dynamic monitoring information; Obtaining a target information set for monitoring the watershed based on the reconstructed time series node set; The reconstructed time series node set satisfies the following relationship: in, Represents the reconstructed time series node set, Represents the reconstructed , Represents the reconstructed , Represents the space after reconstruction .

5. The intelligent early warning and prevention and control emergency system for flood, drought and saltwater disasters according to claim 1 is characterized in that: The construction of the flood, drought and saltwater tide disaster prediction model in the disaster assessment and analysis subsystem includes: Establishing a flood prediction model in the disaster assessment and analysis subsystem based on the multidimensional hydraulic analysis model and the target information set; Obtaining the evapotranspiration of the monitored basin according to the target information set, and establishing a drought prediction model in the disaster assessment and analysis subsystem based on the evapotranspiration; Selecting saltwater tide prediction parameters according to the target information set, and establishing a saltwater tide prediction model in the disaster assessment and analysis subsystem in combination with the saltwater tide prediction parameters; The flood prediction model, the drought prediction model and the saltwater tide prediction model are combined to obtain a flood, drought and saltwater tide disaster prediction model.

6. The intelligent early warning and prevention and control emergency system for flood, drought and saltwater disasters according to claim 5 is characterized in that: The flood prediction model satisfies the following relationship: in, represents the movement coefficient in the x direction of the monitoring basin, represents the average flow velocity in the x direction The partial differential of represents the partial differential with respect to time t, represents the acceleration due to gravity, represents the average flow velocity in the x direction of the monitored basin, represents the turbulent viscosity coefficient of the monitoring basin, represents the component of the vertical average velocity in the x direction, represents the emission coefficient of the monitoring basin, It represents the average water depth in the monitored basin; in, represents the movement coefficient in the y direction of the monitoring basin, represents the average flow velocity in the y direction The partial differential of represents the partial differential with respect to time t, represents the acceleration due to gravity, represents the average flow velocity in the y direction of the monitoring basin, represents the turbulent viscosity coefficient of the monitoring basin, represents the component of the vertical average velocity in the y direction, represents the emission coefficient of the monitoring basin, Indicates the average water depth of the monitored basin.

7. The intelligent early warning and prevention and control emergency system for flood, drought and saltwater tide disasters according to claim 5 is characterized in that: The drought prediction model satisfies the following relationship: in, It represents the difference between precipitation and evapotranspiration in the monitoring basin in month i. It indicates the precipitation detection value of the monitoring basin in month i. It represents the slope of the change of the temperature in the monitoring basin with the saturated water vapor pressure. represents the soil heat flux density in the monitoring basin in month i, It represents the average wind speed at a height of 2m above the horizontal plane of the monitoring basin in month i. Indicates the saturated vapor pressure in the monitoring basin in month i, It indicates the actual vapor pressure in the monitoring basin in month i. represents the average temperature in the monitoring basin in month i, express The corresponding weight coefficient is, express The corresponding weight coefficient.

8. The intelligent early warning and prevention and control emergency system for flood, drought and saltwater tide disasters according to claim 5 is characterized in that: The disaster prediction and assessment results of the monitored basin obtained by the flood, drought and saltwater tide disaster prediction model and the target information set include: Based on the flood prediction model and the target information set, hydraulic prediction results in different directions of the monitored basin are obtained; Obtaining precipitation and evapotranspiration analysis results of the monitored basin according to the drought prediction model and the target information set; The saltwater tide prediction result of the monitored basin is obtained according to the saltwater tide prediction model and the target information set.

9. The intelligent early warning and prevention and control emergency system for flood, drought and saltwater tide disasters according to claim 8 is characterized in that: The intelligent early warning and prevention and control emergency system for flood, drought and saltwater tide disasters also includes: Introducing historical disaster data of the monitoring basin into the disaster assessment and analysis subsystem; Based on the historical disaster data, the hydraulic prediction results, the precipitation and evapotranspiration analysis results and the saltwater tide upstream prediction results, the monitored basin is intelligently analyzed to obtain disaster prediction and assessment results for the monitored basin.

10. The intelligent early warning and prevention and control emergency system for flood, drought and saltwater tide disasters according to claim 9 is characterized in that: The disaster warning and emergency prevention and control subsystem sends out disaster warning signals and matches emergency prevention and control measures in combination with the disaster prediction and assessment results and the target information set, including: Setting up an emergency prevention and control database and configuring a safety alarm device in the disaster warning and emergency prevention and control subsystem; The disaster warning and emergency prevention and control subsystem controls the safety alarm device according to the disaster prediction and assessment results and the target information set to issue a disaster warning signal for the monitored basin; The disaster warning and emergency prevention and control subsystem matches the emergency prevention and control measures of the monitored basin based on the disaster warning signal and the emergency prevention and control database; Intelligent prediction and prevention of disasters in the monitored river basin can be achieved through the disaster warning signals and the emergency prevention and control measures.

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