Method for Dynamically Predicting Surface Water Resources under Digital Twin

Abstract

The present application provides a method for dynamically predicting surface water resources under digital twins, which relates to the technical field of water resources management and includes: obtaining hydrogeological monitoring data; transmitting it to the digital twin module to construct a hydrogeological model; obtaining surface water resources scheduling instructions; introducing a surface water resources scheduling risk prediction module to conduct water resources scheduling risk prediction by combining the hydrogeological model and the surface water resources scheduling plan; if the water resources scheduling risk prediction result does not meet the water resources scheduling risk constraint, optimizing and adjusting the surface water resources scheduling plan; and based on the future time zone, executing surface water resources scheduling according to the water resources scheduling optimization strategy. Through the present application, the technical problem in the prior art that due to the inability of water resources scheduling to adapt to the rapidly changing natural environment, over-exploitation or waste phenomena occur, resulting in low efficiency of water resources management can be solved. By combining digital twins for water resources scheduling, the accuracy and efficiency of surface water resources management are improved.

Description

Technical Field

[0001] This application relates to the technical field of water resources management, and particularly to a method for dynamically predicting surface water resources under digital twins. Background Art

[0002] The dynamic prediction of surface water resources refers to comprehensively analyzing various natural factors such as hydrology, meteorology, and geology, and establishing a model to predict the change trend of surface water resources in the future for a period of time. It can not only reasonably allocate water resources and reduce resource waste, but also respond to extreme climate events (such as droughts, floods, etc.) in advance. Existing water resource allocation methods mostly rely on static models or historical data, and it is difficult to reflect hydrological and meteorological changes in real time. When facing extreme weather or sudden hydrological events, timely and accurate predictions cannot be provided, and the allocation plan may deviate or be unsuitable for the rapidly changing environment, resulting in waste or shortage of water resources. In addition, existing water resource allocation methods lack a comprehensive assessment of the risks of the allocation plan. Especially when facing unpredictable extreme events (such as floods, droughts, etc.), it is difficult to carry out effective risk control, which may lead to unreasonable allocation and even resource conflicts or waste.

[0003] In summary, there is a technical problem in the prior art that due to the inability of water resource allocation to adapt to the rapidly changing natural environment, overdevelopment or waste occurs, resulting in low efficiency of water resource management. Summary of the Invention

[0004] The purpose of this application is to provide a method for dynamically predicting surface water resources under digital twins to solve the technical problem in the prior art that due to the inability of water resource allocation to adapt to the rapidly changing natural environment, overdevelopment or waste occurs, resulting in low efficiency of water resource management.

[0005] In view of the above problems, the present application provides a method for dynamically predicting surface water resources under digital twins. Among them, the method for dynamically predicting surface water resources under digital twins includes: obtaining hydrogeological monitoring data of a region according to a multi-source monitoring module; transmitting the hydrogeological monitoring data to a digital twin module to construct a hydrogeological model; obtaining a surface water resources scheduling instruction for the region, where the surface water resources scheduling instruction includes a surface water resources scheduling plan corresponding to a future time zone; introducing a surface water resources scheduling risk prediction module, combining the hydrogeological model and the surface water resources scheduling plan to predict the water resources scheduling risk of the region, and obtaining a water resources scheduling risk prediction result; judging whether the water resources scheduling risk prediction result meets the water resources scheduling risk constraint; if the water resources scheduling risk prediction result does not meet the water resources scheduling risk constraint, optimizing and adjusting the surface water resources scheduling plan according to the water resources scheduling risk constraint and the surface water resources scheduling risk prediction module to generate a water resources scheduling optimization strategy; based on the future time zone, performing surface water resources scheduling for the region according to the water resources scheduling optimization strategy.

[0006] The technical solution provided in the present application has at least the following technical effects or advantages:

[0007] By obtaining hydrogeological monitoring data of a region according to a multi-source monitoring module; transmitting the hydrogeological monitoring data to a digital twin module to construct a hydrogeological model; obtaining a surface water resources scheduling instruction for the region, where the surface water resources scheduling instruction includes a surface water resources scheduling plan corresponding to a future time zone; introducing a surface water resources scheduling risk prediction module, combining the hydrogeological model and the surface water resources scheduling plan to predict the water resources scheduling risk of the region, and obtaining a water resources scheduling risk prediction result; judging whether the water resources scheduling risk prediction result meets the water resources scheduling risk constraint; if the water resources scheduling risk prediction result does not meet the water resources scheduling risk constraint, optimizing and adjusting the surface water resources scheduling plan according to the water resources scheduling risk constraint and the surface water resources scheduling risk prediction module to generate a water resources scheduling optimization strategy; based on the future time zone, performing surface water resources scheduling for the region according to the water resources scheduling optimization strategy. That is to say, by collecting hydrogeological monitoring data of a region and transmitting it to a digital twin module, a detailed hydrogeological model is constructed, surface water resources scheduling instructions are obtained and scheduling risk prediction is carried out, and optimization adjustment is made according to the risk prediction result, reducing losses caused by emergencies or unreasonable scheduling, and improving the accuracy and efficiency of surface water resources management.

[0008] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application, it can be implemented according to the content of the specification. In order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the specific implementation manners of this application are given below. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of this application, nor is it used to limit the scope of this application. Other features of this application will become easily understandable through the following description of the specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in this application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings described below are only exemplary. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0010] Figure 1 It is a schematic flowchart of the dynamic prediction method of surface water resources under the digital twin of this application;

[0011] Figure 2 It is a schematic flowchart of obtaining the optimal water resources scheduling strategy in the dynamic prediction method of surface water resources under the digital twin of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0012] By providing the dynamic prediction method of surface water resources under the digital twin, this application solves the technical problem in the prior art that due to the inability of water resources scheduling to adapt to the rapidly changing natural environment, overdevelopment or waste phenomena occur, resulting in low efficiency of water resources management. By collecting the hydrogeological monitoring data of the region and transmitting it to the digital twin module, a detailed hydrogeological model is constructed, surface water resources scheduling instructions are obtained and scheduling risk prediction is carried out, and optimization adjustment is made according to the risk prediction results, reducing the losses caused by emergencies or unreasonable scheduling and improving the accuracy and efficiency of surface water resources management.

[0013] Next, the technical solutions in this application will be clearly and completely described with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited by the exemplary embodiments described here. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application. Additionally, it should be noted that for the sake of convenience of description, only the parts related to this application are shown in the drawings rather than all of them.

[0014] Embodiments, please refer to the appendix Figure 1, this application provides a dynamic prediction method for surface water resources under digital twins. Specifically, the dynamic prediction method for surface water resources under digital twins includes the following steps:

[0015] S100: Obtain the hydrogeological monitoring data of the region according to the multi-source monitoring module.

[0016] Specifically, comprehensive real-time data on the hydrogeological conditions of the target area are obtained through various monitoring means, including at least hydrological data (such as precipitation, runoff, groundwater level, river water level, evaporation, etc.), geological data (such as groundwater permeability, soil moisture content, rock type, etc.), and ecological data (such as vegetation cover, wetland water level, ecological water volume, etc.). The hydrogeological monitoring data involves the distribution, flow, quality, and related geological characteristics of surface water and groundwater. The region refers to the specific geographical area that needs to be concerned during the management or dispatching of surface water resources, which may be a river basin, a reservoir basin, or a specific water resource supply and demand area. Different target areas have different hydrogeological conditions, climate characteristics, and water use demands, so targeted monitoring and management are required.

[0017] The multi-source monitoring module refers to a module that integrates multiple monitoring means and data sources, including meteorological data, remote sensing data, hydrological monitoring data, soil moisture data, basin water use data, etc. For example, ground meteorological stations provide rainfall and temperature data, satellite remote sensing provides soil moisture and vegetation cover conditions, groundwater monitoring wells are used to provide groundwater level change data, and river flow meters provide river flow information. The hydrogeological monitoring data obtained through the multi-source monitoring module provides accurate basic data support for the entire dynamic prediction of surface water resources, enabling an accurate understanding of the hydrographic and geological conditions of the target area, thereby improving the accuracy of water resource prediction.

[0018] S200: Transmit the hydrogeological monitoring data to the digital twin module to construct a hydrogeological model.

[0019] Specifically, the hydrogeological monitoring data is preprocessed to remove errors, missing values, duplicates, and inconsistent data, making it meet specific quality standards and usage requirements, which can be accurately recognized by the digital twin module. The preprocessed data is input into the digital twin module through a data transmission system (such as wireless communication, satellite communication, fiber optic communication, etc.). The hydrological data (such as precipitation, flow rate, water level, etc.) in the hydrogeological monitoring data is used to complete the modeling of the hydrological model through hydrological modeling tools (such as HEC-HMS, SWAT model, etc.), simulating processes such as water flow, evaporation, precipitation, and runoff in the region; the geological data (such as soil permeability, groundwater level, rock formation structure, etc.) in the hydrogeological monitoring data is used to complete the modeling of the geological model through geological modeling tools (such as groundwater modeling software like MODFLOW, Feflow, etc.), simulating how water flow penetrates different soil layers and how it is affected by geological conditions.

[0020] The digital twin module integrates the regional hydrological model and the regional geological model to generate a comprehensive hydrogeological model, taking into account the impacts of hydrological and geological factors on water resources management. Once constructed, the hydrogeological model will output prediction results, including hydrological process prediction, groundwater flow prediction, etc. The hydrogeological model generated by the digital twin module accurately simulates and predicts the dynamic changes of water resources, providing a comprehensive basis for water resources scheduling decisions, avoiding overdevelopment or resource waste, and improving the utilization efficiency and management level of water resources.

[0021] S300: Obtain the surface water resources scheduling instruction for the region, where the surface water resources scheduling instruction includes the surface water resources scheduling plan corresponding to the future time zone.

[0022] Specifically, obtaining the surface water resources scheduling instruction for the region includes the surface water resources scheduling plan for a future period of time. The surface water resources scheduling instruction is the decision of the management department on the use, allocation, and scheduling of water resources in a certain area, involving water resources allocation plans, water use priorities, scheduling strategies, etc. for different time periods. The surface water resources scheduling plan determines the water resources allocation strategy according to various factors such as water use requirements, ecological requirements, and emergency requirements in different regions, and is used to guide the management and allocation of surface water resources in the future time zone, including how to allocate water resources, when to release water from reservoirs, how to respond to possible droughts or floods, control the flow direction of water, regulate the water volume of irrigation systems, etc.

[0023] By simulating the future water resource situation, calculate the water demand in each water use area during the future period, and match it with factors such as the current water resource reserve and precipitation prediction, etc., in order to formulate a reasonable scheduling plan. The future time zone refers to the time period that has not yet arrived but requires advance planning and decision-making. It is the prediction and simulation of the hydrogeological and environmental conditions in a certain area for a future period (such as a week, a month or a season). By simulating the changes in water resources under different scenarios, automatically generate water resource scheduling instructions for the future time zone to avoid excessive waste or shortage of resources and optimize the water resource use efficiency.

[0024] S400: Introduce a surface water resource scheduling risk prediction module, and combine the hydrogeological model and the surface water resource scheduling plan to predict the water resource scheduling risk in the area, and obtain the water resource scheduling risk prediction result.

[0025] Specifically, first conduct regional environmental prediction, that is, predict the changes in the environment in the future period based on historical meteorological data and environmental trends. According to the regional environmental prediction results, combine the predicted environmental prediction results with the hydrogeological model to conduct water resource scheduling simulation. Through computer simulation, combine the hydrogeological model to predict the effect of the water resource scheduling plan, especially evaluate the performance in aspects such as water resource allocation, water supply guarantee, water quality status and disaster risk under the scheduling plan. Input environmental prediction data (such as precipitation, temperature, evaporation, etc.) into the hydrogeological model to simulate the flow and use of water resources under the scheduling plan, such as reservoir scheduling, river flow regulation, etc., and evaluate the stability and reliability of the scheduling plan under different time periods and climate conditions.

[0026] Input the water resource scheduling simulation results into the surface water resource scheduling risk prediction module to predict the risk of the scheduling plan, including water supply guarantee risk, water quality risk and hydrogeological disaster risk. According to the simulation results, judge whether the scheduling plan can guarantee sufficient water supply, focusing on the changes in future water sources and whether the scheduling plan can meet the water use needs in all aspects. Judge whether there is a risk of water quality pollution in the scheduling plan. For example, excessive reservoir water release will cause eutrophication of water bodies or diffusion of pollutants, affecting water quality. Evaluate whether the water resource scheduling plan may trigger hydrogeological disasters, especially in disaster-prone areas. For example, excessive scheduling of reservoirs or rivers may lead to disasters such as dam breaks, floods, and landslides.

[0027] After predicting the water supply guarantee risk, water quality risk and hydrogeological disaster risk, obtain the water resource scheduling risk prediction result, provide risk assessment information of the scheduling plan, and determine the existing risks. Through the water resource scheduling risk prediction module, identify and prevent potential water supply guarantee, water quality and disaster risks in advance, effectively avoid water quality pollution and hydrogeological disasters caused by the scheduling plan, and ensure the stability of the ecological environment of the water source area.

[0028] S500: Determine whether the predicted result of the water resources scheduling risk meets the water resources scheduling risk constraint.

[0029] S600: If the predicted result of the water resources scheduling risk does not meet the water resources scheduling risk constraint, optimize and adjust the surface water resources scheduling plan according to the water resources scheduling risk constraint and the surface water resources scheduling risk prediction module to generate a water resources scheduling optimization strategy.

[0030] Specifically, to determine whether the predicted result of the water resources scheduling risk meets the water resources scheduling risk constraint, obtain the water supply security risk coefficient, water quality risk coefficient, and hydrogeological disaster risk coefficient from the water resources scheduling risk prediction module, and compare the water supply security risk coefficient, water quality risk coefficient, and hydrogeological disaster risk coefficient with the water supply security risk constraint, water quality risk constraint, and hydrogeological disaster risk constraint in the water resources scheduling risk constraint to determine whether there are risk indicators that are all lower than or equal to the specified safety threshold. If all risk indicators meet the constraint conditions, the scheduling plan is considered feasible; if any one risk indicator exceeds the threshold, the scheduling plan is considered to have unacceptable risks and needs to be adjusted.

[0031] When the predicted result of the water resources scheduling risk does not meet the water resources scheduling risk constraint, further optimize the existing scheduling plan through optimization algorithms (such as particle swarm optimization, genetic algorithm, etc.) to optimize its various risk indicators. By adjusting scheduling parameters, optimizing resource allocation, modifying scheduling strategies, etc., until all risk indicators are within the tolerance range. Obtain the water resources scheduling objective of the surface water resources scheduling plan, retrieve the scheduling plan within the region, analyze the triggering characteristics, and determine which conditions (such as the reservoir water level exceeding the warning line) will trigger plan adjustment. Define a spatial range according to the triggering characteristics, and adjust the surface water resources scheduling plan that does not meet the water resources scheduling risk constraint to generate an adjusted plan set.

[0032] Perform risk prediction on the adjusted plans through the surface water resources scheduling risk prediction module, judge the prediction results in combination with the water resources scheduling risk constraint, and retain all the plan sets that meet the water resources scheduling risk constraint. Assign weights to the water supply security risk, water quality risk, and hydrogeological disaster risk, and construct a multi-factor risk assessment framework to comprehensively evaluate the impact of different types of risks on water resources scheduling. Input the relevant data of each plan in the second space of the water resources scheduling adjustment into the comprehensive risk assessment channel, calculate the comprehensive risk value of each plan, which is the weighted average of each individual risk coefficient and its corresponding weight, and use optimization algorithms (such as genetic algorithm, particle swarm optimization, multi-objective optimization, etc.) to search for the plan with the minimum comprehensive risk in the second space, and continuously adjust the plan parameters through the iterative process to find the plan that minimizes the comprehensive risk value.

[0033] After optimization and adjustment, a new water resource scheduling plan is obtained, called the water resource scheduling optimization strategy. Under the premise of ensuring that the water resource scheduling risk constraints are met, the use and allocation of water resources are optimized to achieve the best scheduling effect. The water resource scheduling optimization strategy finally generated needs to re-risk prediction to verify whether it meets all water resource scheduling risk constraints. If the new scheduling plan still cannot meet the constraints, optimization and adjustment will continue until an optimal plan that meets the risk constraints is generated. By optimizing the water resource scheduling plan, the scheduling strategy can be dynamically adjusted according to real-time environmental changes and water resource conditions to ensure the rational allocation of resources and minimize risks. By continuously optimizing the scheduling plan, the risks of water supply security, water pollution and hydrogeological disasters can be reduced, thereby improving the safety of water resource management.

[0034] S700: Based on the future time zone, perform surface water resource scheduling in the area according to the water resource scheduling optimization strategy.

[0035] Specifically, according to the water resource scheduling optimization strategy, specific scheduling decisions are executed in the future time zone, including water resource allocation, scheduling plan and risk control measures. When executing the scheduling, the goal is to ensure that various needs are met in the future time zone without exceeding the risk constraints by adjusting the allocation of various water resources. According to the data of climate, water demand, existing water resources and other data in the future time zone, they are input into the water resource scheduling system, and real-time prediction is performed based on the input data. According to the current water resource status and prediction results, a reasonable water resource allocation plan is determined.

[0036] According to the water resource demand of different demand areas (such as drinking water, agricultural water, industrial water, etc.), water resource scheduling optimization strategy is used to allocate, control facilities such as reservoirs, rivers and irrigation systems, and dispatch water sources on demand. During the implementation of water resource scheduling, the use of water sources, rainfall, changes in water demand, etc. are continuously monitored and adjusted in time. If it is found that water resource scheduling deviates from the predetermined target, feedback adjustments are made to ensure that the scheduling plan always meets the set risk constraints. By executing surface water resource scheduling according to the water resource scheduling optimization strategy in future time zones, planning and responding to possible water supply challenges such as droughts or floods in advance will help improve the efficiency and effectiveness of water resource management and reduce the risks caused by poor water resource management.

[0037] Further, the present application S200 includes:

[0038] S210: Perform data cleaning on the hydrogeological monitoring data to obtain standard hydrographic monitoring data and standard geological monitoring data; S220: Input the standard hydrographic monitoring data into the digital twin module to obtain a regional hydrographic model; S230: Based on the digital twin module, perform modeling according to the standard geological monitoring data to generate a regional geological model; S240: Integrate the regional hydrographic model and the regional geological model to generate the hydrogeological model.

[0039] Specifically, the hydrogeological monitoring data may have problems such as missing values, outliers, or inconsistent formats. Data cleaning is performed on the collected hydrogeological monitoring data to remove errors, missing, duplicate, and inconsistent data, making it meet specific quality standards and usage requirements. For example, assume that in the data of a hydrographic monitoring station, the water level data for a certain period is missing, or there are obvious errors in some data points (such as negative water level values). Through data cleaning, these outliers can be deleted, and the missing values can be filled (such as using surrounding data for interpolation or prediction) to ensure the integrity and accuracy of the data.

[0040] The cleaned hydrogeological monitoring data is classified into standard hydrographic monitoring data and standard geological monitoring data according to the data type. The standard hydrographic monitoring data refers to the hydrographic data after data cleaning and processing, which meets certain quality standards and can accurately reflect the hydrographic conditions of the region, such as precipitation, flow rate, water level, etc. The standard geological monitoring data refers to the geological data that meets the quality standards after data cleaning, such as groundwater level, soil permeability, rock and soil layer distribution, etc.

[0041] By inputting the standard hydrographic monitoring data into the digital twin module and using simulation techniques (such as hydrographic simulation algorithms, statistical models, physical models, etc.), a regional hydrographic model is generated. After inputting the standard hydrographic monitoring data into the digital twin module, a regional hydrographic model is generated according to the standard hydrographic monitoring data and the corresponding hydrographic physical processes (such as precipitation, evaporation, runoff, soil infiltration, etc.). Usually, certain hydrographic simulation algorithms (such as precipitation-runoff models, hydrographic cycle models, etc.) are used to simulate the hydrographic processes in the region. The generated regional hydrographic model can reflect the hydrographic conditions in the region in real time, not only can evaluate the current water resource conditions, but also can predict the dynamic changes of future water resources, such as changes in flow rate, river water level, precipitation and evaporation in the region, etc. The precipitation-runoff model is used to simulate the process of precipitation turning into runoff, such as the SCS-CN model, the HEC-HMS model, etc.; the hydrographic cycle model is used to simulate the cycle process of water in the atmosphere, soil, and water bodies; the distributed hydrographic model is a hydrographic model that considers spatial distribution differences for more refined simulation.

[0042] Meanwhile, using the collected geological monitoring data and the digital twin module, a virtual geological model is generated. By inputting standard geological data (such as soil type, groundwater level, etc.), the digital twin module creates a virtual geological model to simulate the flow of groundwater, soil permeability, the distribution of rock and soil layers, etc. The regional geological model is a virtual model constructed in the digital twin module based on standard geological monitoring data. By simulating different characteristics of the geological environment (such as soil layers, rock and soil structures, groundwater flow, etc.), it describes the geological characteristics of a region and is used to analyze geological factors such as changes in groundwater resources and the impact of soil permeability on water flow. The regional geological model shows the distribution of different soil layers and rock layers, the flow path of groundwater, and the way of interaction between water and soil. It not only describes the soil type, groundwater level, and the distribution of rock and soil layers, but also simulates how these geological factors interact with each other to affect the distribution and flow of water resources. The regional geological model can not only generate static geological maps, but also achieve dynamic updates. That is to say, when new monitoring data (such as changes in groundwater level, new soil test data, etc.) is input into the digital twin module, the model can be updated in real time and reflect the latest geological state, capable of coping with rapidly changing environmental conditions, such as sudden precipitation events, changes in soil type, etc.

[0043] Fuse the hydrological model and the geological model to generate a hydrogeological model, comprehensively considering the impact of hydrological processes and geological conditions on water resources, providing more comprehensive support for the management and scheduling of water resources, and simultaneously considering the changes in groundwater, soil permeability, and the impact of geological conditions on water flow when predicting water resources. Through the fusion of the hydrological model and the geological model, the dynamic changes of water resources in the region can be comprehensively understood, and the prediction accuracy can be improved.

[0044] Furthermore, step S400 of the present application includes:

[0045] S410: Based on the future time zone, conduct environmental prediction on the region to obtain a regional environmental prediction result; S420: Based on the regional environmental prediction result, simulate water resource scheduling for the hydrogeological model according to the surface water resource scheduling plan to obtain water resource scheduling simulation data; S430: Input the water resource scheduling simulation data into the surface water resource scheduling risk prediction module to generate the water resource scheduling risk prediction result.

[0046] Specifically, according to the future time zone, environmental prediction is carried out for the region to obtain the regional environmental prediction results. That is to say, based on the meteorological, hydrological, soil and other data in the future time zone, the changes in the environment within a certain period in the future are predicted through models. Environmental prediction models are usually based on meteorological principles and are trained and predicted using statistical regression models, physical process models (such as precipitation-runoff models), machine learning algorithms, etc. According to historical data (such as historical precipitation, temperature, humidity data), future meteorological changes are predicted through regression analysis, and temperature, precipitation, etc. for a certain period in the future are predicted. The model is trained using historical data, and the predicted environmental data for the next time period (predicted based on the data for a certain time period) is compared with the real data of the historical data (the real environmental data for the next time period), and the model parameters are adjusted according to the difference to minimize the prediction error.

[0047] The regional environmental prediction results are obtained, that is, the environmental prediction results for the future time zone of the region, including the changes in factors such as precipitation, temperature, humidity, wind speed, etc. within a certain period in the future. According to the obtained regional environmental prediction results and combined with the surface water resources scheduling plan, the water resources scheduling simulation of the hydrogeological model is carried out. The hydrogeological model is a multi-physical field model integrating a hydrological model and a geological model, which is a mathematical model that can describe hydrological processes such as groundwater flow, precipitation, evaporation, evapotranspiration, groundwater recharge, and soil moisture movement.

[0048] Based on the water resources scheduling plan, the hydrogeological model will simulate hydrogeological processes such as precipitation, evaporation, flow, water storage, etc., and predict the water resources allocation situation after the implementation of the scheduling plan. The water resources scheduling plan is input into the hydrogeological model, including various water use allocations (agricultural, industrial, domestic, ecological, etc.) and basin scheduling (such as reducing irrigation water use and giving priority to urban water supply). Then, in the hydrogeological model, the environmental prediction results (such as precipitation, temperature, humidity, etc.) are input, and different water resources use scenarios are simulated according to the scheduling plan. The hydrogeological model will output the changes in various hydrogeological parameters (such as reservoir water level, basin flow, groundwater level, etc.), reflecting the dynamic changes of water resources under the scheduling plan.

[0049] After completing the simulation of the hydrogeological model, the water resources scheduling simulation data is obtained, including reservoir water level, basin flow, groundwater level, water demand, water supply situation, etc., reflecting the hydrological changes after the implementation of the scheduling plan and helping to evaluate the feasibility and effectiveness of the scheduling plan.

[0050] Input the water resources scheduling simulation data into the water resources scheduling risk prediction module to generate risk prediction results. The water resources scheduling risk prediction module is a module for evaluating the risks of water resources scheduling plans, including a water supply guarantee risk prediction model, a water quality risk prediction model, and a hydrogeological disaster risk prediction model, to obtain the water supply guarantee risk coefficient, the water quality risk coefficient, and the hydrogeological disaster risk coefficient, evaluate the overall risk of the existing scheduling plan, and ensure that the risks in the water resources scheduling process are controllable. By accurately adjusting the water resources scheduling plan based on the environmental prediction results in the future time zone, improving the flexibility and efficiency of scheduling, the water resources scheduling risk prediction module can identify possible water resources risks in advance, thereby taking measures to reduce the risks of water resources shortage and overdevelopment and ensuring the sustainable utilization of water resources.

[0051] Furthermore, the present application further includes the following steps:

[0052] S431: The surface water resources scheduling risk prediction module includes a water supply guarantee risk prediction model, a water quality risk prediction model, and a hydrogeological disaster risk prediction model; S432: Input the water resources scheduling simulation data into the water supply guarantee risk prediction model to obtain the water supply guarantee risk coefficient; S433: Input the water resources scheduling simulation data into the water quality risk prediction model to obtain the water quality risk coefficient; S434: Input the water resources scheduling simulation data into the hydrogeological disaster risk prediction model to obtain the hydrogeological disaster risk coefficient; S435: Output the water supply guarantee risk coefficient, the water quality risk coefficient, and the hydrogeological disaster risk coefficient as the water resources scheduling risk prediction results.

[0053] Specifically, the surface water resources scheduling risk prediction module is an integrated model for evaluating the potential risks of water resources scheduling plans. By analyzing the water resources scheduling simulation data, different types of risk coefficients are calculated, including risks such as water supply guarantee, water quality, and hydrogeological disasters, to help identify and avoid potential risks and ensure the safety of the water resources supply, quality, and ecological environment during the water resources scheduling process.

[0054] Collect historical water supply data, water demand data, climate data, environmental impact data, hydrological data of reservoirs and rivers, etc. Select features that have a significant impact on water supply security from the collected data, such as rainfall, evaporation, reservoir water level, water consumption, etc. Select appropriate mathematical models to construct a water supply security risk prediction model, such as linear regression, machine learning algorithms (such as random forest, support vector machine, etc.). For example, construct a neural network model, including an input layer, a hidden layer, and an output layer. The input layer receives the preprocessed data, usually including multiple input features, and each input feature serves as a neuron. The hidden layer is used to learn the non-linear relationships in the data. In the water supply security risk prediction model, the number of hidden layers and the number of neurons in each layer need to be adjusted according to the complexity of the data. The output layer is used to predict the risk of water supply security. The output is usually a continuous value representing the water supply security risk coefficient.

[0055] Use historical data to train the model and adjust the model parameters to minimize the prediction error. Use the data that was not involved in the training to validate and test the model, and evaluate the accuracy and reliability of the model. Through the backpropagation algorithm, calculate the contribution of each neuron to the output error and adjust the network weights. Through multiple iterations of training, the neural network will gradually learn the relationship between the input features and the water supply security risk coefficient, and then provide more accurate risk predictions. Use the trained water supply security risk prediction model to predict the water supply security risk coefficient, which indicates whether the water resource supply and demand are balanced and whether there is a risk of insufficient water supply in the future for a certain period. Similarly, the training processes of the water quality risk prediction model and the hydrogeological disaster risk prediction model are similar to that of the water supply security risk prediction model, and will not be elaborated here.

[0056] The water supply security risk prediction model is used to evaluate whether the water resource scheduling plan can meet the water resource demands of various users (such as agriculture, cities, industry, ecology, etc.). By simulating the matching degree of water resource supply and demand, calculate the water supply security risk coefficient to measure the risk of insufficient water supply or excessive water supply under a specific scheduling plan. In the dry season, the water resource supply decreases and the water supply security risk increases. Input the data such as reservoir water level, basin flow, and water demand generated during the simulation process into the water supply security risk prediction model, and calculate the water supply security risk coefficient based on the current water supply volume and the predicted demand.

[0057] The water quality risk prediction model is used to evaluate the possible water quality pollution risks during the water resource scheduling process. Water quality changes are usually affected by factors such as water flow, pollution sources, temperature, etc. According to the hydrological data of the water resource scheduling plan, predict whether the water quality will be polluted and calculate the water quality risk coefficient. Input the data such as water flow and temperature changes in the simulation results into the water quality risk prediction model, and evaluate whether the water quality will be affected based on factors such as pollutant concentration, flow, and temperature changes.

[0058] The hydrogeological disaster risk prediction model is used to predict the risks of hydrogeological disasters that may be triggered by water resource scheduling, such as floods, droughts, landslides, debris flows, etc. By inputting hydrogeological data and scheduling plans, the model evaluates the likelihood of natural disasters occurring and their impact levels. Inputting the simulated data of the hydrogeological model (such as basin flow, precipitation, groundwater level, etc.) into the hydrogeological disaster risk prediction model to evaluate whether various hydrogeological changes will lead to geological disasters. For example, heavy precipitation may trigger floods, and long-term droughts may cause land subsidence or landslides, and evaluate whether water resource scheduling exacerbates these risks.

[0059] The three independently calculated risk coefficients (water supply security risk coefficient, water quality risk coefficient, hydrogeological disaster risk coefficient) are combined and output as the overall risk prediction result of water resource scheduling, providing a comprehensive risk assessment of the scheduling plan and helping to optimize the water resource scheduling plan according to different types of risks. Through comprehensive risk assessments of the three categories of water supply security, water quality, and hydrogeological disasters, a comprehensive water resource scheduling risk analysis is provided, which helps to identify and respond to various possible risks in advance and avoid neglecting risks in a single dimension.

[0060] Further, as shown in Figure 2 the following, step S600 of this application includes:

[0061] S610: Adjust the surface water resource scheduling plan to establish a first space for water resource scheduling adjustment; S620: Based on the surface water resource scheduling risk prediction module, perform an optimization analysis on the first space for water resource scheduling adjustment according to the water resource scheduling risk constraints to obtain a second space for water resource scheduling adjustment; S630: Perform an optimization for minimizing the comprehensive risk of water resource scheduling according to the second space for water resource scheduling adjustment to obtain the water resource scheduling optimization strategy.

[0062] Specifically, according to the water resource scheduling objectives of the region, including ensuring water supply security, maintaining ecological flow, flood control and drought resistance, etc., guide the formulation and implementation of the water resource scheduling plan. According to the water resource scheduling objectives, a set of potentially applicable water resource scheduling plans retrieved from historical plans or preset plans. Analyze the retrieved plans to identify the key features that may be triggered under the current conditions and determine which plans may be triggered or implemented under the current conditions. According to the triggering features, determine the range of conditions that may trigger water resource scheduling. Within the triggering space, preliminarily adjust the water resource scheduling plan to form a set of adjusted plans, that is, the first space for water resource scheduling adjustment.

[0063] Perform risk prediction on all the scenarios in the first water resources scheduling adjustment space through the surface water resources scheduling risk prediction module to obtain the risk prediction results of the scenarios. Compare the scheduling risk prediction results of each scenario with the water resources scheduling risk constraints to determine whether the water supply guarantee risk constraints, water quality risk constraints, and hydrogeological disaster risk constraints are met. Eliminate all the scenarios in the first water resources scheduling adjustment space that do not meet the water resources scheduling risk constraints. All the scenarios that meet the water resources scheduling risk constraints constitute the second water resources scheduling adjustment space.

[0064] Construct a comprehensive risk assessment model, comprehensively considering multiple factors such as water supply guarantee risk, water quality risk, and hydrogeological disaster risk, including the weights of water supply guarantee risk, water quality risk, and hydrogeological disaster risk. According to the comprehensive risk assessment model, search for the optimal scenario in the second water resources scheduling adjustment space through genetic algorithms, particle swarm optimization, or other optimization methods, and finally obtain the water resources scheduling optimization strategy, which is the optimal water resources scheduling scenario with the minimum comprehensive risk. By comprehensively considering multiple risk factors such as water supply guarantee, water quality protection, and hydrogeological disasters, balance the requirements of all aspects to ensure the comprehensiveness and scientificity of the scheduling scenario. Through trigger feature analysis and risk prediction, dynamically adjust according to the actual situation to more flexibly respond to environmental changes. Use optimization algorithms to optimize the scheduling scenario to improve the quality and optimization efficiency of the scenario.

[0065] Furthermore, this application also includes the following steps:

[0066] S611: Obtain the water resources scheduling objective of the surface water resources scheduling scenario; S612: Retrieve the water resources scheduling scenario according to the water resources scheduling objective for the region to obtain the water resources scheduling scenario retrieval set; S613: Perform trigger feature analysis according to the water resources scheduling scenario retrieval set to obtain the water resources scheduling trigger space; S614: Adjust the surface water resources scheduling scenario according to the water resources scheduling trigger space to generate the first water resources scheduling adjustment space.

[0067] Specifically, obtain the water resources scheduling objective of the surface water resources scheduling scenario, which is determined through documents, water resources management plans, or user requirements, including regional water use requirements, water source protection and ecological requirements, climate and environmental changes, etc. The purpose is to maximize the rational use of water resources and ensure sustainability. For example, in an arid area, the water resources scheduling objective may be to ensure the priority supply of agricultural irrigation and urban water use, while taking measures to protect the ecological environment of the water source and avoid water pollution.

[0068] According to the determined water resources scheduling objectives, retrieve water resources scheduling plans that meet the objectives within the region to obtain a series of retrieved sets of water resources scheduling plans that meet the water resources scheduling objectives. Depending on different climate conditions and reservoir water storage levels, the retrieved set of water resources scheduling plans may contain multiple scheduling plans, each of which is optimized according to different environmental, climate, and water resources conditions and can meet water resources demands and ecological protection requirements to a certain extent. For example, some plans focus on reducing industrial water use, while others focus on optimizing agricultural irrigation water use.

[0069] Trigger feature analysis refers to, based on the retrieved set of water resources scheduling plans, analyzing the key features and factors in different scheduling plans to identify which factors (such as precipitation, changes in reservoir water levels, fluctuations in water use demands, etc.) will have a significant impact on water resources scheduling under what conditions, thereby triggering further adjustments to the scheduling plans. Trigger features usually include weather changes, fluctuations in water use demands, changes in basin water volumes, etc. For example, if the reservoir water level drops by more than 30%, trigger feature analysis is triggered, and the current water resources scheduling plan needs to be adjusted to prioritize ensuring residential water use and agricultural irrigation demands. The water resources scheduling trigger space refers to, based on trigger feature analysis, identifying the specific regions or condition ranges for adjusting the water resources scheduling plan, defining which regions or hydrological conditions need to adjust the current water resources scheduling plan under different circumstances to ensure the achievement of the objectives.

[0070] According to the water resources scheduling trigger space, preliminarily adjust the surface water resources scheduling plan, including reallocating water resources, reducing unnecessary water use, or optimizing the water discharge volume according to new hydrological conditions, etc., to ensure that the scheduling objectives can be achieved. The first water resources scheduling adjustment space refers to adjusting the surface water resources scheduling plan according to changes in the water resources scheduling trigger space to form a set of adjusted plans. Through target-based scheduling plan retrieval and trigger feature analysis, quickly respond to environmental changes such as precipitation and basin water volume, timely adjust the scheduling plan, and reduce water resources waste and overdevelopment.

[0071] Furthermore, this application also includes the following steps:

[0072] S621: Adjust the first space according to the water resource scheduling, and extract the first water resource scheduling adjustment plan; S622: Based on the surface water resource scheduling risk prediction module, conduct water resource scheduling risk prediction on the first water resource scheduling adjustment plan to obtain the first plan scheduling risk prediction result; S623: Judge whether the first plan scheduling risk prediction result meets the water resource scheduling risk constraint; S624: If the first plan scheduling risk prediction result meets the water resource scheduling risk constraint, add the first water resource scheduling adjustment plan to the second water resource scheduling adjustment space; S625: If the first plan scheduling risk prediction result does not meet the water resource scheduling risk constraint, eliminate the first water resource scheduling adjustment plan; S626: Continue to perform optimization analysis on the first water resource scheduling adjustment space according to the surface water resource scheduling risk prediction module and the water resource scheduling risk constraint to generate the second water resource scheduling adjustment space.

[0073] Specifically, select a plan arbitrarily from the first water resource scheduling adjustment space obtained after preliminary adjustment as the first water resource scheduling adjustment plan. Evaluate the selected first water resource scheduling adjustment plan through the water resource scheduling risk prediction module, including water supply guarantee risk prediction, water quality risk prediction, and hydrogeological disaster risk prediction. The water supply guarantee risk prediction is used to evaluate whether the water resource supply demand in the future can be met under the scheduling plan and whether there is a risk of water resource shortage; the water quality risk prediction is used to predict whether the scheduling plan will cause water quality pollution in the water source area and whether water quality problems are likely to occur; the hydrogeological disaster risk prediction is used to evaluate whether the scheduling plan will trigger hydrogeological disasters such as floods, droughts, or debris flows.

[0074] After the water resource scheduling risk prediction is completed, the first plan scheduling risk prediction result is obtained, including the first plan water supply guarantee risk coefficient, the first plan water quality risk coefficient, and the first plan hydrogeological disaster risk coefficient. Compare the first plan scheduling risk prediction result with the water resource scheduling risk constraint to judge whether the risk prediction result of the scheduling plan meets the water resource scheduling risk constraint, that is, whether it meets the following conditions: water supply guarantee risk constraint, water quality risk constraint, and hydrogeological disaster risk constraint. If the first plan scheduling risk prediction result meets the water resource scheduling risk constraint, add the first water resource scheduling adjustment plan to the second water resource scheduling adjustment space. On the contrary, if the first plan scheduling risk prediction result does not meet the water resource scheduling risk constraint, then eliminate the plan and remove the plan that does not meet the requirements.

[0075] For other solutions within the first space of water resource scheduling adjustment, continue the optimization analysis. Through the surface water resource scheduling risk prediction module, predict the water resource scheduling risk, and determine whether the prediction results meet the water resource scheduling risk constraints. Eliminate all solutions that do not meet the water resource scheduling risk constraints, and finally obtain the second space of water resource scheduling adjustment. The second space of water resource scheduling adjustment includes all solutions that meet the triggering characteristics and water resource scheduling risk constraints. By predicting and judging the risk of each water resource scheduling solution, ensure that the finally selected scheduling solution achieves the best results in terms of water supply guarantee, water quality protection, and disaster prevention and mitigation, and reduce risks and potential problems.

[0076] Furthermore, this application also includes the following steps:

[0077] S631: Construct a comprehensive risk assessment channel for water resource scheduling. Among them, the comprehensive risk assessment channel for water resource scheduling includes a water supply guarantee risk weight, a water quality risk weight, and a hydrogeological disaster risk weight; S632: According to the comprehensive risk assessment channel for water resource scheduling, perform optimization for minimizing the comprehensive risk of the second space of water resource scheduling adjustment, and generate the water resource scheduling optimization strategy.

[0078] Specifically, construct a comprehensive risk assessment channel for water resource scheduling to comprehensively evaluate various risks in the water resource scheduling solution. By performing weighted analysis on the water supply guarantee risk, water quality risk, and hydrogeological disaster risk, determine the importance of each risk in the comprehensive assessment, and make optimization decisions accordingly. The comprehensive risk assessment channel for water resource scheduling helps to evaluate and quantify the performance of each solution in different risk dimensions, providing a basis for decision-making. According to the specific situation of the region, assign different weights (water supply guarantee risk weight, water quality risk weight, and hydrogeological disaster risk weight) to each risk, reflecting the importance of different risk types to the water resource scheduling goal. Integrate the weights into the evaluation model to form a comprehensive risk assessment channel for water resource scheduling.

[0079] Input the relevant data of each solution in the second space of water resource scheduling adjustment into the comprehensive risk assessment channel, and calculate the comprehensive risk value of each solution according to the water supply guarantee risk weight, water quality risk weight, and hydrogeological disaster risk weight, that is, the weighted average of each individual risk coefficient and its corresponding weight. By adjusting the scheduling parameters (such as the water source allocation ratio, the discharge of different reservoirs, the change in water demand, etc.), minimize the comprehensive value of the water supply guarantee risk, water quality risk, and hydrogeological disaster risk.

[0080] Taking particle swarm optimization as an example, it is a swarm intelligence optimization algorithm that simulates the foraging behavior of bird flocks. It continuously adjusts its own position through communication with other particles to find the global optimal solution. Each particle represents a possible water source allocation scheme, and the position of the particle corresponds to the various parameters of water resources scheduling. By continuously updating the parameters of the particles, the particles gradually approach the optimal scheduling scheme. The positions and velocities of the initial particles are randomly generated from the parameter range of the scheduling scheme. According to the position of the particle (i.e., the water resources scheduling scheme), the comprehensive risk (fitness) of this scheme is calculated. The fitness function usually includes multiple risk factors, such as water supply guarantee risk, water quality risk, hydrogeological disaster risk, etc. The velocity and position of the particle are updated according to the current velocity of the particle, the historical optimal position, and the global optimal position. When all the particles in the particle swarm converge to a certain position, or when the maximum number of iterations is reached, the search process ends.

[0081] The optimal water resources scheduling strategy is the optimal scheduling scheme generated through the optimization process of minimizing the comprehensive risk of water resources scheduling. It can not only ensure that the water supply demand is met, but also minimize the risks in aspects such as water supply guarantee, water quality, and hydrogeological disasters. On the basis of ensuring the water supply demand, the goal of minimizing the comprehensive risk is achieved by reasonably allocating water resources, optimizing water quality protection measures, and avoiding disaster risks. The comprehensive risk evaluation channel of water resources scheduling can comprehensively evaluate the potential risks of water resources scheduling schemes by weighting different risks, ensuring the reasonable weights and control of various risks in the schemes. Through the optimization process of minimizing the comprehensive risk, the water resources allocation scheme can be optimized, achieving a balance among water supply guarantee, water quality protection, and disaster prevention, and avoiding overdevelopment or water resource waste.

[0082] In summary, the dynamic prediction method of surface water resources under digital twin provided by this application has the following technical effects:

[0083] Obtain the hydrogeological monitoring data of the region according to the multi-source monitoring module; transmit the hydrogeological monitoring data to the digital twin module to construct a hydrogeological model; obtain the surface water resource scheduling instruction for the region, where the surface water resource scheduling instruction includes the surface water resource scheduling plan corresponding to the future time zone; introduce the surface water resource scheduling risk prediction module, and combine the hydrogeological model and the surface water resource scheduling plan to predict the water resource scheduling risk for the region to obtain the water resource scheduling risk prediction result; determine whether the water resource scheduling risk prediction result meets the water resource scheduling risk constraint; if the water resource scheduling risk prediction result does not meet the water resource scheduling risk constraint, optimize and adjust the surface water resource scheduling plan according to the water resource scheduling risk constraint and the surface water resource scheduling risk prediction module to generate a water resource scheduling optimization strategy; based on the future time zone, execute the surface water resource scheduling for the region according to the water resource scheduling optimization strategy. That is to say, by collecting the hydrogeological monitoring data of the region and transmitting it to the digital twin module, a detailed hydrogeological model is constructed, the surface water resource scheduling instruction is obtained and the scheduling risk is predicted, and the optimization adjustment is made according to the risk prediction result, reducing the losses caused by emergencies or unreasonable scheduling and improving the accuracy and efficiency of surface water resource management.

[0084] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0085] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A method for dynamically predicting surface water resources under digital twins, characterized in that, Including: Obtain the hydrogeological monitoring data of the region according to the multi-source monitoring module; Transmit the hydrogeological monitoring data to the digital twin module to construct a hydrogeological model; Obtain the surface water resources scheduling instruction of the region, where the surface water resources scheduling instruction includes the surface water resources scheduling scheme corresponding to the future time zone; Introduce a surface water resources scheduling risk prediction module, and combine the hydrogeological model and the surface water resources scheduling scheme to predict the water resources scheduling risk of the region, and obtain the water resources scheduling risk prediction result; Judge whether the water resources scheduling risk prediction result meets the water resources scheduling risk constraint; If the water resources scheduling risk prediction result does not meet the water resources scheduling risk constraint, optimize and adjust the surface water resources scheduling scheme according to the water resources scheduling risk constraint and the surface water resources scheduling risk prediction module to generate a water resources scheduling optimization strategy; Based on the future time zone, perform surface water resources scheduling of the region according to the water resources scheduling optimization strategy; If the water resources scheduling risk prediction result does not meet the water resources scheduling risk constraint, optimize and adjust the surface water resources scheduling scheme according to the water resources scheduling risk constraint and the surface water resources scheduling risk prediction module to generate a water resources scheduling optimization strategy, including: Adjust the surface water resources scheduling scheme to establish the first space for water resources scheduling adjustment; Based on the surface water resources scheduling risk prediction module, perform optimization analysis on the first space for water resources scheduling adjustment according to the water resources scheduling risk constraint to obtain the second space for water resources scheduling adjustment; Perform optimization for minimizing the comprehensive risk of water resources scheduling according to the second space for water resources scheduling adjustment to obtain the water resources scheduling optimization strategy.

2. The dynamic prediction method of surface water resources under the digital twin as described in claim 1, characterized in that, Introduce a surface water resources scheduling risk prediction module, and combine the hydrogeological model and the surface water resources scheduling scheme to predict the water resources scheduling risk of the region, and obtain the water resources scheduling risk prediction result, including: Based on the future time zone, perform environmental prediction on the region to obtain the regional environmental prediction result; Based on the regional environmental prediction result, perform simulated water resources scheduling on the hydrogeological model according to the surface water resources scheduling scheme to obtain water resources scheduling simulation data; Input the water resources scheduling simulation data into the surface water resources scheduling risk prediction module to generate the water resources scheduling risk prediction result.

3. The dynamic prediction method of surface water resources under digital twin as described in claim 2, wherein, Input the water resources scheduling simulation data into the surface water resources scheduling risk prediction module to generate the water resources scheduling risk prediction result, including: The surface water resources scheduling risk prediction module includes a water supply guarantee risk prediction model, a water quality risk prediction model, and a hydrogeological disaster risk prediction model; Input the water resources scheduling simulation data into the water supply guarantee risk prediction model to obtain the water supply guarantee risk coefficient; Input the water resources scheduling simulation data into the water quality risk prediction model to obtain the water quality risk coefficient; Input the water resources scheduling simulation data into the hydrogeological disaster risk prediction model to obtain the hydrogeological disaster risk coefficient; Output the water supply guarantee risk coefficient, the water quality risk coefficient, and the hydrogeological disaster risk coefficient as the water resource scheduling risk prediction result.

4. The dynamic prediction method of surface water resources under the digital twin as described in claim 1, characterized in that, Adjust the surface water resource scheduling plan and establish the first space for water resource scheduling adjustment, including: Obtain the water resource scheduling objective of the surface water resource scheduling plan; Retrieve the water resource scheduling plan for the region according to the water resource scheduling objective to obtain a water resource scheduling plan retrieval set; Analyze the triggering features according to the water resource scheduling plan retrieval set to obtain a water resource scheduling triggering space; Adjust the surface water resource scheduling plan according to the water resource scheduling triggering space to generate the first space for water resource scheduling adjustment.

5. The dynamic prediction method of surface water resources under the digital twin as claimed in claim 1, wherein, Based on the surface water resource scheduling risk prediction module, perform an optimization analysis on the first space for water resource scheduling adjustment according to the water resource scheduling risk constraint to obtain the second space for water resource scheduling adjustment, including: Extract the first water resource scheduling adjustment plan according to the first space for water resource scheduling adjustment; Based on the surface water resource scheduling risk prediction module, perform a water resource scheduling risk prediction on the first water resource scheduling adjustment plan to obtain the scheduling risk prediction result of the first plan; Judge whether the scheduling risk prediction result of the first plan meets the water resource scheduling risk constraint; If the scheduling risk prediction result of the first plan meets the water resource scheduling risk constraint, add the first water resource scheduling adjustment plan to the second space for water resource scheduling adjustment; If the scheduling risk prediction result of the first plan does not meet the water resource scheduling risk constraint, eliminate the first water resource scheduling adjustment plan; Continue to perform an optimization analysis on the first space for water resource scheduling adjustment according to the surface water resource scheduling risk prediction module and the water resource scheduling risk constraint to generate the second space for water resource scheduling adjustment.

6. The dynamic prediction method of surface water resources under the digital twin as described in claim 1, characterized in that, Perform an optimization for minimizing the comprehensive risk of water resource scheduling according to the second space for water resource scheduling adjustment to obtain the water resource scheduling optimization strategy, including: Construct a comprehensive risk evaluation channel for water resource scheduling, where the comprehensive risk evaluation channel for water resource scheduling includes a water supply guarantee risk weight, a water quality risk weight, and a hydrogeological disaster risk weight; Perform an optimization for minimizing the comprehensive risk of water resource scheduling on the second space for water resource scheduling adjustment according to the comprehensive risk evaluation channel for water resource scheduling to generate the water resource scheduling optimization strategy.

7. The dynamic prediction method of surface water resources under the digital twin as described in claim 1, wherein Transmit the hydrogeological monitoring data to the digital twin module to construct a hydrogeological model, including: Perform data cleaning on the hydrogeological monitoring data to obtain standard hydrographic monitoring data and standard geological monitoring data; Input the standard hydrographic monitoring data into the digital twin module to obtain a regional hydrographic model; Based on the digital twin module, perform modeling according to the standard geological monitoring data to generate a regional geological model; Fuse the regional hydrographic model and the regional geological model to generate the hydrogeological model.

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