A water conservancy informatization management method and management system based on digital twinning technology

Abstract

The application relates to the technical field of water conservancy management, in particular to a water conservancy informatization management method and management system based on digital twinning technology. The method comprises the following steps: a digital twinning model covering a target region is constructed; through an Internet of Things sensing network and remote sensing and other means, multi-source data is collected in real time and is synchronized with the model; the multi-source data is used to calculate the regional flood storage capacity composed of soil water storage capacity, river channel storage capacity and surface water body storage capacity; flood evolution simulation deduction is carried out based on regional weather forecasts and the flood storage capacity; and multi-level early warnings are generated and issued according to forward-looking indexes such as predicted flood storage capacity breakthrough time and inundation range. The application solves the problems of the prior art, such as dependence on static capacity evaluation and early warning reaction lag, quantifies the real flood storage capacity of the region dynamically and integrally, significantly improves the accuracy of flood forecasting and the advance amount of early warning, and provides strong scientific support for accurate flood control decision-making.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of water conservancy management, in particular to a water conservancy informatization management method and system based on digital twinning technology. BACKGROUND

[0002] Under the background of global climate change and accelerated urbanization, extreme rainfall events occur frequently, and floods have become one of the natural disasters with the widest influence and the most serious losses. Therefore, it is crucial to establish a precise and efficient flood warning system. The traditional flood control system mainly relies on physical engineering such as building reservoirs and dikes to regulate surface water, and combines historical data or simplified models for prediction. However, such traditional methods have significant technical defects: first, the evaluation of the flood storage capacity of the basin only considers fixed parameters such as reservoir design capacity and river standard flood discharge capacity, ignoring key dynamic factors such as soil initial moisture content, resulting in a large deviation between the evaluation results and the actual situation; second, the warning mechanism mainly uses "threshold triggering", that is, the warning is issued only when the water level of the monitoring point exceeds the warning level, and the response is lagging, leaving a very limited time window for emergency response and personnel evacuation.

[0003] To address the above challenges, digital twinning technology, as a cutting-edge method of interactive mapping between the physical world and the digital space, provides a new solution. Digital twinning creates a highly accurate virtual copy for a physical entity by integrating technologies such as the Internet of Things, big data, and artificial intelligence. This virtual copy can reflect the state of the physical entity in real time and simulate and predict its behavior throughout its life cycle. Combined with digital twinning technology, it is possible to realize real-time, dynamic, and overall quantification of the comprehensive flood storage capacity of the entire catchment area, thereby issuing more accurate and more advanced flood warnings. SUMMARY

[0004] To overcome the above-mentioned defects in the prior art, the application provides a water conservancy informatization management method and system based on digital twinning technology.

[0005] In one aspect, the application provides a water conservancy informatization management method based on digital twinning technology, comprising the following steps:

[0006] Obtaining historical geographic information data, hydrological parameters, and infrastructure models of a target area;

[0007] Based on the historical geographic information data, hydrological parameters, and infrastructure models, a digital twinning model that is real-time mapped with the target area is constructed;

[0008] Through a sensor network deployed in the target area, real-time monitoring data of the target area is collected, and the real-time monitoring data is bidirectionally synchronized with the digital twinning model;

[0009] Based on the synchronized real-time monitoring data, the flood storage capacity of the target area at the current moment is calculated;

[0010] Acquire rainfall forecast data for the region and use this rainfall forecast data as input. On the digital twin model, with the current flood storage capacity as the initial condition, drive the hydrological and hydrodynamic model to perform simulation and predict the hydrological state in the future time series.

[0011] Based on the prediction results obtained from the simulation, a corresponding flood warning signal is generated according to a set of predefined predictive triggering criteria, and then released to the target users through multiple channels.

[0012] The flood storage capacity is calculated using the following formula:

[0013] ;

[0014] In the formula, for The flood storage capacity of the target area at all times. for The remaining rain-holding capacity of the soil in the target area at any given time. for The remaining water level in the river channel safety reservoir in the target area at any given time. for Available flood storage capacity in the target area at any given time.

[0015] Preferably, the real-time monitoring data includes: river water level and flow velocity data collected by IoT sensors, soil saturation data collected by soil moisture sensors, and vegetation cover and surface water range data interpreted from remote sensing satellite images.

[0016] Preferably, the method for calculating the remaining rainwater carrying capacity of the soil is as follows:

[0017] Acquire soil type data within the target area and determine the saturated water content of different soil types;

[0018] The soil plots in the target area are discretized into multiple soil units, and the soil moisture data of each soil unit is determined based on the soil saturation data collected by the soil moisture sensor.

[0019] Based on the soil moisture data and saturated water content data of each soil unit, the remaining rainwater carrying capacity of the soil in the area is calculated.

[0020] The formula for calculating the remaining rainwater carrying capacity of the soil is as follows:

[0021] ;

[0022] In the formula, numbering of discretized soil units in the target area, number of soil units in the target area, unit saturated water content, unit water content at time, unit soil layer thickness, unit area.

[0023] Preferably, the soil plots in the target area are discretized into a plurality of soil units, each of which belongs to the same soil type.

[0024] Preferably, the calculation method of the riverway safety groove storage capacity is:

[0025] discretizing the river network system in the target area into a sequence composed of a plurality of river reaches;

[0026] for each individual river reach, obtaining its dike safety water level and cross-section geometric information;

[0027] determining the safety cross-section area corresponding to the water level according to the dike safety water level and cross-section geometric information;

[0028] determining the real-time water level data of each river reach according to the riverway water level data collected by the Internet of Things sensors;

[0029] calculating the real-time cross-section area at the current time according to the real-time water level of each river reach and the cross-section geometric information;

[0030] taking the difference between the safety cross-section area and the real-time cross-section area of each river reach as the safety groove storage capacity of the river reach;

[0031] determining the riverway safety groove storage capacity of the target area based on the safety groove storage capacities of each river reach;

[0032] the calculation formula of the riverway safety groove storage capacity is:

[0033] ;

[0034] in the formula, numbering of discretized river reaches in the target area, number of river reaches, safety cross-section area of river reach real-time cross-section area of river reach at time number of river reaches ​​​length of the target region.

[0035] Preferably, the calculation method of the available flood storage capacity is:

[0036] Obtain information of all natural and artificial water bodies with flood regulation function in the target region, the information including water level-storage capacity relationship curve, flood control limiting water level and its corresponding flood control storage capacity;

[0037] For each flood regulation water body, obtain real-time storage capacity at the current time according to the real-time water level at the current time and the water level-storage capacity relationship curve;

[0038] The difference between the flood control storage capacity and the real-time storage capacity of the flood regulation water body is taken as the available flood storage capacity of the water body;

[0039] Sum the available flood storage capacities of all flood regulation water bodies to obtain the available flood storage capacity in the target region.

[0040] Preferably, the simulation and deduction of the flood evolution process driven by the flood storage capacity at the current time as the initial condition, the prediction of the hydrological state of the catchment area at the future time sequence, specifically includes:

[0041] The rainfall prediction data is taken as the input of the hydrological and hydrodynamic model;

[0042] Based on the flood storage capacity, the initial soil moisture content of each ground surface unit and the initial water level of each river section and water body are determined as the initial boundary conditions of the hydrological and hydrodynamic model;

[0043] Using the hydrological and hydrodynamic model, the water balance equation and the fluid dynamics equation are solved step by step in the prediction period to obtain the hydrological state parameters of each calculation grid or section at the future time sequence, the parameters including water level, flow velocity, inundation range and inundation depth.

[0044] Preferably, the predefined predictive trigger criterion includes the remaining time when the predicted comprehensive dynamic flood storage capacity of the catchment area is completely exhausted, and the predicted flood inundation range and depth.

[0045] Another aspect of the present application also provides a water conservancy informatization management system based on digital twin technology, comprising a data acquisition and synchronization module, a digital twin model module, an evaluation and simulation module and an early warning generation module;

[0046] The data acquisition and synchronization module is used to acquire real-time monitoring data from the target region and to be bidirectionally synchronized with a digital twin model;

[0047] The digital twin model module is used to store a digital twin model which is real-time mapped with the target region;

[0048] The evaluation and simulation module is connected with the data acquisition and synchronization module and the digital twin model module, and comprises:

[0049] An evaluation unit is configured to calculate the flood storage capacity of the target region at the current time based on the synchronized real-time monitoring data.

[0050] A simulation and deduction unit is configured to drive a hydrological and hydrodynamic model on the digital twin model to simulate the flood evolution process, with the rainfall forecast data as input and the flood storage capacity as initial condition.

[0051] The early warning generation module is connected with the evaluation and simulation module, and is configured to generate and issue multi-level flood warning signals based on a set of predefined predictive trigger criteria according to the prediction results of the simulation and deduction.

[0052] Compared with the prior art, the present application has the following advantages:

[0053] By integrating the capacities of multiple dynamically changing subsystems such as soil and river channels in real time, the evaluation of the current flood storage capacity of the watershed is changed from post-event statistics and static estimation to real-time, dynamic and accurate quantification, improving the accuracy of the initial conditions of flood forecasting.

[0054] By performing deduction on the digital twin model, the present application can not only predict the probability of flood occurrence, but also show the evolution process of the flood, including key information such as the flooded area, water depth and arrival time. This visual risk assessment provides a scientific and intuitive decision support platform for the development of emergency plans.

[0055] Moreover, the present application adopts a warning mechanism combining "predictive simulation" and "prospective trigger criteria", which can simulate the whole process of the flood according to the forecast and the current real flood storage capacity of the watershed before the rainfall occurs. This can greatly shorten the time of issuing the warning, and thus saves valuable golden time for disaster prevention and mitigation decisions and personnel safety relocation. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 A method flowchart of a water conservancy information management method based on digital twin technology is provided. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0058] REFERENCE Figure 1The application discloses a water conservancy informatization management method based on a digital twin technology.

[0059] S1, historical geographic information data, hydrological parameters and infrastructure models of a target area are acquired, and a digital twin model that is real-time mapped with the target area is constructed based on the historical geographic information data, hydrological parameters and infrastructure models.

[0060] The historical geographic information data comprises high-precision digital elevation models (DEM), digital surface models (DSM), and river network vector data, soil type distribution maps, geological structure maps and the like.

[0061] The infrastructure model is a building information model, and comprises information such as geometric dimensions, materials, design parameters, control operation rules and the like of key water conservancy projects such as reservoirs, dikes, gates, pumping stations and bridges.

[0062] The constructed digital twin model comprises:

[0063] A geometric model: based on DEM, BIM, oblique photography and the like data, a three-dimensional visual scene of a basin is constructed, and 1:1 real reproduction of a physical world is realized.

[0064] A physical model: mechanism models (such as runoff yield models and soil erosion models) that describe hydrological physical processes are encapsulated into digital components, and correct physical parameters (such as roughness and infiltration rate) are given to the models.

[0065] A behavior model: dispatching operation rules of water conservancy projects (such as reservoirs and gates) are defined, for example, automatic opening logic of a gate based on upstream inflow and downstream water level.

[0066] S2, real-time monitoring data of the target area is collected through a sensing network deployed in the target area, and the real-time monitoring data is bidirectionally synchronized with the digital twin model.

[0067] The sensing network comprises, but is not limited to, ultrasonic or radar water level gauges and flowmeters installed at key sections of a river and a reservoir; automatic rain gauges distributed in a basin; soil moisture sensors buried at different depths and in different land use types. These sensors transmit data to a data center in real time through wireless communication technologies such as 5G and NB-IoT.

[0068] In addition, the monitoring data further comprises high-resolution satellite remote sensing images (optical and radar), which are used for interpreting a surface water body range and inverting regional soil moisture.

[0069] S3, based on the synchronized real-time monitoring data, a flood storage capacity of the target area at a current time is calculated, and the calculation specifically comprises:

[0070] S301, calculating the residual rainwater storage capacity of the soil in the region:

[0071] According to the soil type distribution map, the soil type data in the region is determined, and the saturated water content of different soil types is determined.

[0072] The soil plots in the region are discretized into a plurality of soil units, and the soil moisture data of each soil unit is determined according to the soil saturation data collected by the soil moisture sensor.

[0073] Based on the soil moisture data of each soil unit and the saturated water content data of the soil unit, the residual rainwater storage capacity of the soil in the region is calculated:

[0074]

[0075] In the formula, is the number of discretized soil units in the region, is the number of soil units in the region, is the saturated water content of the unit is the water content of the unit at the moment, is the soil layer thickness of the unit is the area of the unit

[0076] It should be noted that each soil unit divided above belongs to the same soil type.

[0077] S302, calculating the residual rainwater storage capacity of the soil in the region:

[0078] The river network system in the region is discretized into a sequence composed of a plurality of river sections.

[0079] For each individual river section, its dike safety water level and cross section geometric information are obtained.

[0080] According to the dike safety water level and cross section geometric information, the safety cross section area corresponding to the water level is determined.

[0081] According to the river water level data collected by the Internet of Things sensor, the real-time water level data of each river section is determined.

[0082] According to the real-time water level of each river section and the cross section geometric information, the real-time cross section area at the current moment is calculated.

[0083] The difference between the safety cross section area and the real-time cross section area of each river section is taken as the safety slot storage capacity of the river section.

[0084] ​​​​​The riverway safety groove storage capacity of the region is determined based on the safety groove storage capacities of the respective river sections:

[0085] ;

[0086] wherein, is the number of the discretized river section in the region, is the number of the river sections, is the safety cross-sectional area of the river section, is the real-time cross-sectional area of the river section at the time is the length of the river section. It should be noted that the calculation method of the cross-sectional area is as follows: the river cross-section geometry data defined by a series of "horizontal site-elevation" coordinate points is obtained through a digital elevation model; then, the area below the water surface line and above the cross-section contour line is calculated by a numerical integration method (such as the trapezoidal rule) combined with water level data, that is, the cross-sectional area, and the specific calculation formula is as follows: ;

[0087] wherein,

[0088] is the cross-sectional area of the river section, and are the horizontal site coordinates of the adjacent two measurement points,

[0089] and are the underwater depths of the two points at the current time, respectively. S303, calculate the available flood storage capacity in the region: Obtain information of all natural and artificial water bodies with storage function in the region, including water level-storage capacity relationship curve, flood control limiting water level and its corresponding flood control storage capacity. For each storage water body, the real-time storage capacity at the current time is obtained according to the real-time water level at the current time and the water level-storage capacity relationship curve.

[0090] The difference between the flood control storage capacity and the real-time storage capacity of the storage water body is taken as the available flood storage capacity of the water body.

[0091] Sum the available flood storage capacities of all storage water bodies to obtain the available flood storage capacity in the region.

[0092]

[0093]

[0094]

[0095] ​​​​​S304. Based on the above calculations of the remaining rainwater carrying capacity of the soil in the region, the remaining capacity of the river safety channel, and the available flood storage capacity, calculate the region's flood storage capacity:

[0096] ;

[0097] In the formula, for The flood storage capacity of the area at any given time. for At any given time, the remaining rainwater carrying capacity of the soil in this area for The current water level in the river channel safety reservoir in this area. for The available flood storage capacity in the area at any given time.

[0098] S4. Obtain rainfall forecast data for the region, and use this rainfall forecast data as input. On the digital twin model, with the current flood storage capacity as the initial condition, drive the hydrological and hydrodynamic model to perform simulation and predict the hydrological state in the future time series. Specifically:

[0099] First, a hydrological and hydrodynamic model is integrated or coupled into the digital twin model. Then, quantitative precipitation forecasts from meteorological departments are used as input to the hydrological and hydrodynamic model, while the calculated flood storage capacity is decomposed into initial conditions for each computational unit (such as initial soil moisture content and initial river level), serving as the initial boundary conditions for the model. Next, the coupled model is driven to perform calculations simulating the generation and confluence of rainfall at the surface, as well as the evolution of floods in the river channel. Finally, the predicted hydrological status of the entire basin at a series of future time points is output, including but not limited to water levels and discharges at key cross-sections, as well as the extent and depth of potential inundation zones.

[0100] S5. Based on the prediction results obtained from the simulation, generate corresponding flood warning signals according to a set of predefined predictive triggering criteria, and release them to target users through multiple channels.

[0101] For example, if the simulation results show that "the comprehensive dynamic flood storage capacity of the basin will be exceeded within 12 hours, and the predicted inundation area will affect residential area A," the system will automatically trigger an orange alert. The alert information (including the alert level, expected impact time, impact area, and defense recommendations) will be pushed to emergency management personnel and the public in the affected area through a unified release gateway in various forms such as SMS, mobile APP push, social media, and emergency broadcasts.

[0102] To realize the water conservancy informatization management method based on the digital twin technology, a water conservancy informatization management system based on the digital twin technology is provided, comprising a data acquisition and synchronization module, a digital twin model module, an evaluation and simulation module, and an early warning generation module.

[0103] The data acquisition and synchronization module is configured to acquire real-time monitoring data from a target area and perform bidirectional synchronization with a digital twin model.

[0104] The digital twin model module is configured to store a digital twin model that is mapped in real time with the target area.

[0105] The evaluation and simulation module is connected to the data acquisition and synchronization module and the digital twin model module, and comprises:

[0106] An evaluation unit is configured to calculate the flood storage capacity of the target area at the current time based on the synchronized real-time monitoring data.

[0107] A simulation and deduction unit is configured to drive a hydrological and hydrodynamic model on the digital twin model to simulate the flood evolution process, with the rainfall forecast data as input and the flood storage capacity as initial conditions.

[0108] The early warning generation module is connected to the evaluation and simulation module and is configured to generate and issue multi-level flood warning signals based on a set of predefined predictive trigger criteria according to the prediction results of the simulation and deduction.

[0109] In a preferred embodiment, the method further comprises a model self-optimization step, specifically: after an actual flood event ends, the full-process measured data of this event (such as the water level process line of each monitoring point, the final inundation range, etc.) are automatically collected. Then, the measured data are compared with the prediction results made in advance before the simulation and deduction, and the error is calculated. Finally, using an artificial intelligence algorithm (such as genetic algorithm, deep reinforcement learning, etc.), the key parameters (such as Manning roughness coefficient, soil infiltration parameter, etc.) in the hydrological and hydrodynamic model are automatically adjusted to minimize the prediction error, and the optimized parameters are updated to the model library for use in the next prediction.

[0110] In the description of the present specification, the description of the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0111] The preferred embodiments of the application disclosed above are only to facilitate the elucidation of the application. The preferred embodiments do not describe all the details of the application and limit the application to the specific embodiments described. Obviously, many modifications and variations can be made in light of the teachings above. The description is chosen and described in order to best explain the principles of the application and its practical application to thereby enable others skilled in the art to best utilize the application and get the best results from the application. The application is only limited by the claims and their full scope and equivalents.

Claims

1. A water conservancy informatization management method based on digital twin technology, characterized in that, The method comprises the following steps: obtaining historical geographic information data, hydrological parameters and infrastructure models of a target area; constructing a digital twin model mapped in real time with the target area based on the historical geographic information data, hydrological parameters and infrastructure models; collecting real-time monitoring data of the target area through a sensing network deployed in the target area, and synchronizing the real-time monitoring data with the digital twin model bidirectionally; the real-time monitoring data comprises river water level and flow rate data collected by Internet of Things sensors, soil saturation data collected by soil moisture sensors, and vegetation coverage and surface water range data interpreted from remote sensing satellite images; calculating the flood storage capacity of the target area at the current time based on the synchronized real-time monitoring data; obtaining rainfall forecast data of the area, and taking the rainfall forecast data as input to drive a hydrological and hydrodynamic model on the digital twin model to simulate and deduce the hydrological state at future time sequences under the initial condition of the current flood storage capacity; generating corresponding flood warning signals based on a set of predefined predictive trigger criteria according to the prediction results obtained through the simulation and deduction, and publishing the flood warning signals to target users through multiple channels; the flood storage capacity is calculated by the following formula: ； wherein is the flood storage capacity of the target area at the time, is the soil residual rain storage capacity of the target area at the time, is the river safety groove residual storage of the target area at the time, is the available flood storage capacity of the target area at the time.

2. The water conservancy informatization management method based on digital twin technology according to claim 1, characterized in that, the calculation method of the residual rainfall capacity of the soil is as follows: obtaining soil type data in the target area and determining the saturated water content of different soil types; discretizing the soil plots of the target area into a plurality of soil units, and determining the soil moisture data of each soil unit according to the soil saturation data collected by the soil moisture sensors; calculating the residual rainfall capacity of the soil in the target area based on the soil moisture data of each soil unit and the saturated water content data of the soil unit; the calculation formula of the residual rainfall capacity of the soil is as follows: ； In the formula, Number the discretized soil units within the target area. The number of soil units within the target area. For unit saturated water content, for Time unit The moisture content, For unit Soil layer thickness, For unit The area.

3. The water conservancy informatization management method based on digital twin technology according to claim 2, characterized in that, the soil plots of the target area are discretized into a plurality of soil units, and each soil unit belongs to the same type of soil.

4. The water conservancy informatization management method based on digital twin technology according to claim 1, characterized in that, the calculation method of the residual storage capacity of the river safety tank is as follows: discretizing the river network system of the target area into a sequence composed of a plurality of river sections; for each individual river section, obtaining its dike safety water level and cross section geometric information; determining the safety cross section area corresponding to the water level according to the dike safety water level and cross section geometric information; determining the real-time water level data of each river section according to the river water level data collected by the Internet of Things sensors; calculating the real-time cross section area at the current time according to the real-time water level of each river section and the cross section geometric information; taking the difference between the safety cross section area and the real-time cross section area of each river section as the safety tank residual storage capacity of the river section; determining the river safety tank residual storage capacity of the target area based on the safety tank residual storage capacities of each river section; the calculation formula of the river safety tank residual storage capacity is as follows: ； In the formula, Number the discrete river segments within the target area. The number of river sections For river section The safe cross-sectional area for water passage. For river section In time The real-time cross-sectional area of ​​the water flow. For river section The length.

5. The water conservancy informatization management method based on digital twin technology according to claim 1, characterized in that, the calculation method of the available flood storage capacity is as follows: obtaining information of all natural and artificial water bodies with storage function in the target area, the information including water level-storage capacity relationship curve, flood control limiting water level and corresponding flood control storage capacity; for each storage water body, obtaining the real-time storage capacity at the current time according to the real-time water level at the current time and the water level-storage capacity relationship curve; The difference between the flood control capacity of the water body and the real-time capacity is used as the available flood storage capacity of the water body. The available flood storage capacities of all the water bodies are summed up to obtain the available flood storage capacity in the target region.

6. The water conservancy informatization management method based on digital twin technology according to claim 1, characterized in that, The flood storage capacity at the current time is used as an initial condition to drive a hydrological and hydrodynamic model to simulate and deduce the flood evolution process, and to predict the hydrological state of the catchment area at a future time sequence, which specifically includes: The rainfall prediction data is used as the input of the hydrological and hydrodynamic model; Based on the flood storage capacity, the initial soil moisture content of each surface unit and the initial water level of each river section and water body are determined as the initial boundary conditions of the hydrological and hydrodynamic model; The hydrological and hydrodynamic model is used to solve the water balance equation and the fluid dynamics equation at each time step in the prediction period to obtain the hydrological state parameters of each calculation grid or section at a future time sequence, including water level, flow rate, flooded area and flooded water depth.

7. The water conservancy informatization management method based on digital twin technology according to claim 1, characterized in that, The predefined predictive trigger criteria include the remaining time for the predicted comprehensive dynamic flood storage capacity of the basin to be completely exhausted, and the predicted flood inundation area and depth.

8. A water conservancy informatization management system based on digital twin technology, used to implement a water conservancy informatization management method based on digital twin technology as claimed in claims 1-7, characterized in that, The system comprises a data acquisition and synchronization module, a digital twin model module, an evaluation and simulation module, and an early warning generation module. The data acquisition and synchronization module is used to acquire real-time monitoring data from the target region and to synchronize with a digital twin model in both directions. The digital twin model module is used to store a digital twin model that is real-time mapped with the target region. The evaluation and simulation module is connected with the data acquisition and synchronization module and the digital twin model module, and comprises: An evaluation unit is configured to calculate the flood storage capacity of the target region at the current time based on the synchronized real-time monitoring data. A simulation and deduction unit is configured to drive a hydrological and hydrodynamic model on the digital twin model to simulate the flood evolution process based on rainfall prediction data as input and the flood storage capacity as an initial condition. The early warning generation module is connected with the evaluation and simulation module and is configured to generate and issue multi-level flood warning signals based on a set of predefined predictive trigger criteria according to the prediction results of the simulation and deduction.

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