Flood simulation method and equipment based on digital twinning and medium

Abstract

The invention discloses a flood simulation method and device based on digital twinning and a medium, and belongs to the technical field of flood simulation. The method comprises the following steps: determining a to-be-monitored area, and deploying a sensor group to the to-be-monitored area based on the terrain of the to-be-monitored area; connecting meteorological data and acquiring monitoring data of the to-be-monitored area based on the sensor group so as to determine simulation data; constructing a hydrodynamic model based on the terrain of the to-be-monitored area and the monitoring data; constructing a digital twinborn model based on the hydrodynamic model and the analog data; when the monitoring data triggers a preset boundary condition, processing the simulation data according to a physical engine based on the digital twin model so as to predict the to-be-monitored area, and generating a prediction result; and processing the prediction result based on a preset decision feedback model to generate a feedback suggestion. According to the method, the flood process can be comprehensively and accurately predicted, and an effective decision is generated according to the prediction result.

Description

Technical Field

[0001] The present application relates to the technical field of flood simulation, and in particular to a flood simulation method, device and medium based on digital twins. Background Art

[0002] Flood warning is an important part of flood prevention and disaster reduction. It detects signs of possible floods in advance through monitoring, forecasting and early warning systems, and promptly releases early warning information to relevant departments and the public so that necessary measures can be taken to respond to flood disasters and protect people’s lives and property.

[0003] In existing technologies, flood simulation often relies on empirical models and limited data sources, which makes it difficult to fully and accurately reflect the complexity and dynamics of the flood process, resulting in inaccurate prediction results and difficulty in providing effective flood control and disaster reduction decision-making support in a timely manner.

[0004] Therefore, how to comprehensively and accurately predict the flood process and make effective decisions based on the prediction results has become an urgent problem to be solved. Summary of the invention

[0005] The embodiments of the present application provide a flood simulation method, device and medium based on digital twins to solve the following technical problems: how to comprehensively and accurately predict the flood process and generate effective decisions based on the prediction results.

[0006] In the first aspect, an embodiment of the present application provides a flood simulation method based on digital twins, the method comprising: determining an area to be monitored, and deploying a sensor group to the area to be monitored based on the terrain of the area to be monitored; connecting meteorological data and acquiring monitoring data of the area to be monitored based on the sensor group to determine simulation data; constructing a hydrodynamic model based on the terrain and monitoring data of the area to be monitored; constructing a digital twin model based on the hydrodynamic model and the simulation data; when the monitoring data triggers preset boundary conditions, processing the simulation data according to the physical engine based on the digital twin model to predict the area to be monitored and generate prediction results; processing the prediction results based on a preset decision feedback model to generate feedback suggestions.

[0007] In one implementation of the present application, monitoring data of the area to be monitored is obtained based on a sensor group and connected to meteorological data, specifically including: obtaining monitoring data of the area to be monitored in real time based on the sensor group; wherein the monitoring data includes water level, flow rate and rainfall; connecting to the meteorological department to obtain meteorological data; wherein the meteorological data includes rainfall, wind speed and wind direction.

[0008] In one implementation of the present application, a hydrodynamic model is constructed based on the terrain and monitoring data of the area to be monitored, specifically including: determining terrain data based on the terrain of the area to be monitored, and constructing a terrain model of the area to be monitored based on the terrain data; wherein the terrain data includes elevation, slope and soil type; comparing the monitoring data based on the terrain model, and simulating the water flow conditions in the area to be detected based on a physical engine to construct a hydrodynamic model.

[0009] In one implementation of the present application, a digital twin model is constructed based on a hydrodynamic model and simulation data, specifically including: processing the hydrodynamic model based on a preset digital twin technology; constructing a first digital twin model corresponding to the hydrodynamic model; and processing simulation data based on the first digital twin model to construct a digital twin model.

[0010] In one implementation of the present application, when the monitoring data triggers the preset boundary conditions, the simulation data is processed according to the physical engine based on the digital twin model to predict the monitored area and generate prediction results, specifically including: determining the boundary conditions; wherein the boundary conditions include water level warning lines and rainfall warning thresholds; when the monitoring data is greater than or equal to the preset boundary conditions, the monitoring data is processed based on the digital twin model, and the predicted water level and predicted rainfall of the area to be detected are determined in chronological order; the digital twin model processes and predicts the water level and predicted rainfall according to the physical engine, and when the water level is greater than the preset flood threshold, the simulation data is processed according to the physical engine to simulate the flood process; the prediction results are generated based on the flood process; wherein the prediction results include the flood inundation range, water depth and flow rate.

[0011] In one implementation of the present application, the digital twin model processes and predicts water levels and predicted rainfall according to a physical engine, and processes simulation data according to the physical engine when the water level is greater than a preset flood threshold to simulate the progress of a flood, specifically including: inputting the predicted water level and predicted rainfall into the digital twin model, and simulating the water flow dynamics based on the physical engine; when the water level exceeds the preset flood threshold, processing the simulated water flow dynamics based on the digital twin model to simulate the progress of a flood.

[0012] In one implementation of the present application, the method also includes: comparing the monitoring data acquired in real time with the predicted data of the digital twin model to determine an error set; analyzing and verifying the error set, and adjusting the parameters in the digital twin model based on the error set so that the predicted data fits the monitoring data to generate modified parameters; when the difference between the predicted data and the monitoring data generated by the modified digital twin model is less than a preset error threshold, modifying the digital twin model based on the modified parameters.

[0013] In one implementation of the present application, the prediction results are processed based on a preset decision feedback model to generate feedback suggestions, specifically including: comparing the prediction results with a preset disaster severity table to determine the severity and impact range of the flood disaster; processing the severity and impact range based on the preset decision feedback model to generate feedback suggestions; wherein the feedback suggestions include evacuation routes, emergency measures and material deployment; and displaying the feedback suggestions based on a preset visualization platform.

[0014] In the second aspect, an embodiment of the present application also provides a flood simulation device based on digital twins, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: determine the area to be monitored, and deploy a sensor group to the area to be monitored based on the terrain of the area to be monitored; connect meteorological data and obtain monitoring data of the area to be monitored based on the sensor group to determine simulation data; construct a hydrodynamic model based on the terrain and monitoring data of the area to be monitored; construct a digital twin model based on the hydrodynamic model and the simulation data; when the monitoring data triggers a preset boundary condition, process the simulation data according to the physical engine based on the digital twin model to predict the area to be monitored and generate a prediction result; process the prediction result based on a preset decision feedback model to generate feedback suggestions.

[0015] In the third aspect, the embodiment of the present application also provides a non-volatile computer storage medium for flood simulation based on digital twins, storing computer executable instructions, and the computer executable instructions are set to: determine the area to be monitored, and deploy a sensor group to the area to be monitored based on the terrain of the area to be monitored; connect meteorological data and obtain monitoring data of the area to be monitored based on the sensor group to determine simulation data; build a hydrodynamic model based on the terrain and monitoring data of the area to be monitored; build a digital twin model based on the hydrodynamic model and the simulation data; when the monitoring data triggers preset boundary conditions, process the simulation data according to the physical engine based on the digital twin model to predict the area to be monitored and generate prediction results; process the prediction results based on the preset decision feedback model to generate feedback suggestions.

[0016] The embodiments of the present application provide a flood simulation method, device and medium based on digital twins, which at least include the following technical effects: By accurately determining the area to be monitored and scientifically deploying sensor groups, accurate perception of regional terrain and real-time acquisition of monitoring data are achieved. Combined with meteorological data, a hydrodynamic model that is highly consistent with reality is constructed to generate an accurate digital twin model. When the monitoring data reaches the preset boundary conditions, the model can respond quickly, process the data through the physical engine, and generate accurate flood prediction results. Based on the flood prediction results, the preset decision feedback model can provide effective response suggestions in a timely manner, significantly improve the efficiency and accuracy of flood warning and emergency response, and provide strong support for flood prevention and disaster reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A flow chart of a flood simulation method based on digital twins provided in an embodiment of the present application; Figure 2 A schematic diagram of the internal structure of a flood simulation device based on digital twins provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0019] The embodiments of the present application provide a flood simulation method, device and medium based on digital twins to solve the following technical problems: how to comprehensively and accurately predict the flood process and generate effective decisions based on the prediction results.

[0020] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0021] Figure 1 A flood simulation flow chart based on digital twins is provided in the embodiment of the present application. Figure 1 As shown, a flood simulation method based on digital twins provided in an embodiment of the present application specifically includes the following steps: Step 1: Determine the area to be monitored and deploy the sensor group to the area to be monitored based on the terrain of the area to be monitored. This step is mainly to clarify the specific area for flood monitoring and deploy the sensor group according to the terrain characteristics of the area to ensure accurate and comprehensive acquisition of monitoring data to a certain extent, providing a basis for subsequent flood simulation and prediction.

[0022] First, it is necessary to clarify the geographical scope that needs to be monitored. Usually, river basins prone to flooding, low-lying areas or urban waterlogged areas are selected. Furthermore, the terrain characteristics in the area, including elevation, slope, soil type, etc., are understood to provide a basis for sensor deployment. Furthermore, according to monitoring needs and terrain characteristics, appropriate sensor types are selected, such as water level sensors, flow rate sensors, and rain gauges. Furthermore, based on terrain analysis and sensor types, a sensor deployment plan is formulated so that sensors can cover key monitoring points.

[0023] In a specific case, the middle and lower reaches of a river in a city were selected as the monitoring area. The area has experienced flood disasters many times in history, posing a serious threat to the safety of people's lives and property. Through GIS (Geographic Information System) technology, the terrain analysis of the monitoring area was carried out to obtain detailed terrain data, including elevation maps, slope maps, etc. According to the characteristics of the monitored area, 5 high-precision water level sensors were selected to monitor river water level changes; 2 flow rate sensors were selected to monitor river flow rate; 2 rain gauges were selected to monitor rainfall. Two water level sensors were deployed at key locations upstream and downstream of the river to monitor water level changes; 1 water level sensor and 3 flow rate sensors were deployed in the middle of the river to monitor water flow status; 1 rain gauge was deployed at two different locations in the area to monitor rainfall. It is understandable that all sensors should be connected to the data center via wireless transmission and equipped with sufficient power supply.

[0024] Step 2: Connect the meteorological data and obtain the monitoring data of the area to be monitored based on the sensor group to determine the simulation data.

[0025] First, the monitoring data of the area to be monitored is obtained in real time based on the sensor group; the monitoring data includes water level, flow rate and rainfall. The monitoring data such as water level, flow rate and rainfall of the area to be monitored is obtained in real time and accurately through the sensor group.

[0026] In a specific example, water level sensors, flow rate sensors and rain gauges are deployed at key locations such as upstream and downstream, middle sections and low-lying areas of the river in the monitored area. These sensors send monitoring data to the data center every 5 minutes through wireless transmission.

[0027] Furthermore, the meteorological department is connected to obtain meteorological data, including rainfall, wind speed and wind direction. Obtain meteorological data such as rainfall, wind speed and wind direction in the monitored area and its surrounding areas to provide meteorological background information for flood simulation and prediction.

[0028] In a specific case, a data sharing mechanism is established with the local meteorological department. Through the API interface or data push service provided by the meteorological department, real-time meteorological data of the monitored area and its surrounding areas are obtained. Meteorological data includes daily rainfall, wind speed, wind direction, and weather forecast information for the next few days.

[0029] It is understandable that the acquired meteorological data and monitoring data are integrated, for example, a data integration platform is established in the data center to connect and integrate the monitoring data acquired by the sensor group with the meteorological data. The simulation data is determined through operations such as data cleaning, deduplication, and outlier processing.

[0030] Step 3: Construct a hydrodynamic model based on the topography and monitoring data of the area to be monitored.

[0031] First, determine the terrain data based on the terrain of the area to be monitored, and build a terrain model of the area to be monitored based on the terrain data; the terrain data includes elevation, slope and soil type. Based on the terrain of the area to be monitored, collect and process terrain data such as elevation, slope and soil type to build a detailed terrain model. For example, using GIS technology, extract the elevation data of the area to be monitored from satellite remote sensing images and ground measured data to generate an elevation map. At the same time, combine geological survey data to determine the slope and soil type of the area, and integrate this information into the GIS platform to build a three-dimensional terrain model containing elevation, slope and soil type information.

[0032] Furthermore, the monitoring data is compared with the terrain model, and the water flow conditions in the area to be monitored are simulated based on the physical engine to build a hydrodynamic model. The real-time monitoring data is compared with the terrain model to verify the accuracy of the terrain model and provide a basis for the subsequent construction of the hydrodynamic model. The physical engine (such as water flow simulation software) and the terrain model are used in combination with the monitoring data to simulate the water flow conditions in the area to be monitored. Based on the simulation results of the physical engine, combined with the terrain model and monitoring data, an accurate hydrodynamic model is constructed.

[0033] In a specific embodiment, the monitoring data such as water level, flow rate and rainfall acquired in real time by the sensor group are matched with the corresponding positions in the terrain model. Through comparative analysis, the accuracy of the terrain model in terms of elevation, slope, etc. is verified, and the terrain model is adjusted as needed to ensure that it is consistent with the actual situation. Select water flow simulation software (such as MIKE 21, HEC-RAS) and import the terrain model into the software. Set initial conditions and boundary conditions such as water level, flow rate, rainfall, etc. according to the monitoring data. Then, run the simulation software, calculate the dynamic process of the water flow through the physical engine, including the flow rate, flow direction, water depth, etc. of the water flow, and analyze the distribution of the water flow in the monitored area according to the simulation results of the physical engine, including the flooding range and flow rate changes of the flood. Finally, a hydrodynamic model containing terrain, hydrological and hydrodynamic information is constructed.

[0034] Step 4: Build a digital twin model based on the hydrodynamic model and simulation data.

[0035] First, the hydrodynamic model is processed based on the preset digital twin technology to construct a first digital twin model corresponding to the hydrodynamic model. The hydrodynamic model is digitally processed using the digital twin technology to construct a first digital twin model corresponding to the hydrodynamic model. Further, the simulation data is processed based on the first digital twin model to construct a digital twin model. The simulation data is imported into the first digital twin model, and the simulation data is combined with the digital model through the processing of the digital twin technology to construct a complete digital twin model.

[0036] In one embodiment, the terrain, hydrology and hydrodynamic information in the hydrodynamic model are converted into digital form through the processing of digital twin technology, and a three-dimensional digital model containing this information is constructed, namely the first digital twin model. The model can display the terrain characteristics and water flow conditions of the monitored area in real time. The water flow data (such as water level, flow rate, and flooding range) simulated in step 3 are imported into the first digital twin model. Through the data fusion and processing functions of the digital twin technology, the simulation data is combined with the terrain and hydrological information in the digital model to form a digital twin model containing real-time water flow status. The model can dynamically display the water flow conditions of the monitored area at different time points, providing an intuitive basis for flood warning and emergency response.

[0037] Step 5: When the monitoring data triggers the preset boundary conditions, the digital twin model is used to process the simulated data according to the physical engine to predict the monitored area and generate prediction results.

[0038] First, determine the boundary conditions, including the water level warning line and the rainfall warning threshold. Clarify the boundary conditions that trigger the forecast process, including the water level warning line and the rainfall warning threshold.

[0039] For example, based on the historical flood records and terrain characteristics of the monitored area, the water level warning line is set at the river water level reaching or exceeding 80% of the historical highest water level, and the rainfall warning threshold is the 24-hour rainfall exceeding 100 mm.

[0040] Furthermore, when the monitoring data is greater than or equal to the preset boundary conditions, the monitoring data is processed based on the digital twin model, and the predicted water level and predicted rainfall of the area to be detected are determined in chronological order. Real-time monitoring data, when the data reaches or exceeds the preset boundary conditions, triggers the prediction process, uses the digital twin model to process the monitoring data that triggers the boundary conditions, and predicts the water level and rainfall of the area to be monitored.

[0041] For example, the sensor group can obtain the water level, flow rate and rainfall data of the monitored area in real time. When the water level data reaches or exceeds the water level warning line, or the rainfall data reaches or exceeds the rainfall warning threshold, the prediction process is automatically triggered.

[0042] Furthermore, the digital twin model processes and predicts water levels and rainfall according to the physical engine, and processes simulation data according to the physical engine when the water level is greater than the preset flood threshold to simulate the flood process. The predicted water level and rainfall data are processed by the physical engine to simulate the water flow dynamics and flood process.

[0043] For example, the monitoring data that triggers the boundary conditions is imported into the digital twin model. The model predicts the changes in water level and rainfall in the monitored area in chronological order based on topographic, hydrological and hydrodynamic information combined with real-time monitoring data.

[0044] The predicted water level and predicted rainfall are input into the digital twin model, and the water flow dynamics are simulated based on the physical engine. When the water level exceeds the preset flood threshold, the simulated water flow dynamics are processed based on the digital twin model to simulate the flood process.

[0045] For example, in the digital twin model, a physical engine (such as water flow simulation software) is used to process the predicted water level and rainfall data. By calculating the dynamic process of water flow, the water flow dynamics in the monitored area are simulated. When the predicted water level exceeds the preset flood threshold (such as the river water level reaches or exceeds the historical highest water level), the model further simulates the flood process, including the flooding range, water depth and flow rate.

[0046] Furthermore, a prediction result is generated based on the flood process, wherein the prediction result includes the flood inundation range, water depth and flow rate. Based on the simulation result of the flood process, a detailed prediction result is generated, including the flood inundation range, water depth and flow rate.

[0047] For example, the digital twin model generates a flood forecast report based on the simulation results of the physical engine. The report includes a flood inundation range map, a water depth distribution map, a flow velocity distribution map, and corresponding data analysis and interpretation. These forecast results can provide a scientific basis and decision-making support for flood warning, emergency response, and disaster prevention and mitigation.

[0048] In order to ensure the accuracy of the digital twin model to a certain extent, the embodiment of the present application also includes the following method: First, the monitoring data acquired in real time is compared with the predicted data of the digital twin model to determine the error set. The monitoring data is acquired in real time and compared with the predicted data of the digital twin model to determine the difference between the two, that is, the error set.

[0049] In one embodiment, the monitoring data such as water level, flow rate and rainfall in the monitored area are acquired in real time through the sensor group. At the same time, the prediction data of the corresponding time point is extracted from the digital twin model. The monitoring data is compared with the prediction data, and the difference between the two is calculated to form an error set.

[0050] Further, the error set is analyzed and verified, and the parameters in the digital twin model are adjusted based on the error set to make the predicted data fit the monitoring data to generate modified parameters. The error set is analyzed in depth to verify the source and rationality of the error to determine whether the parameters in the digital twin model need to be adjusted. Based on the analysis results of the error set, the parameters in the digital twin model are adjusted to make the predicted data better fit the monitoring data and generate modified parameters.

[0051] In one embodiment, the error set is statistically analyzed, including calculating the mean, standard deviation, and maximum and minimum values ​​of the error. At the same time, combined with the topographic, hydrological, and hydrodynamic characteristics of the area to be monitored, possible sources of error are analyzed, such as inaccuracy of topographic data, errors in hydrological parameters, limitations of model algorithms, etc. Based on the analysis results, it is determined whether the parameters in the digital twin model need to be adjusted. If the model parameters that need to be adjusted are determined based on the analysis results of the error set, such as terrain elevation, hydrological parameters (such as roughness, permeability), and weight coefficients in the model algorithm, these parameters are adjusted, and the digital twin model is re-run to generate new prediction data.

[0052] Further, when the difference between the predicted data and the monitoring data generated by the modified digital twin model is less than a preset error threshold, the digital twin model is modified based on the modified parameters. When the difference between the predicted data and the monitoring data generated by the modified digital twin model is less than a preset error threshold, the digital twin model is formally modified based on the modified parameters.

[0053] In one embodiment, an error threshold is set, such as a water level error of less than 0.1 m and a flow velocity error of less than 0.05 m / s. When the difference between the predicted data and the monitored data generated by the modified digital twin model is less than this error threshold, it is considered that the prediction accuracy of the model has met the requirements. At this point, the digital twin model is formally modified based on the modified parameters, and the relevant parameters in the model library are updated.

[0054] Step 6: Process the prediction results based on the preset decision feedback model to generate feedback suggestions.

[0055] First, the prediction results are compared with the preset disaster severity table to determine the severity and impact range of the flood disaster. According to the prediction results generated by the digital twin model, the preset disaster severity table is compared to determine the severity and impact range of the flood disaster.

[0056] In one embodiment, the prediction results generated by the digital twin model, including flood inundation range, water depth and flow rate, are compared with the preset disaster severity table. The disaster severity table is formulated based on historical flood records and expert experience, and divides flood disasters into four levels: mild, moderate, severe and extreme, corresponding to different impact ranges and degrees of damage. Through comparison, the severity and impact range of the current flood disaster are determined.

[0057] Furthermore, based on the preset decision feedback model, the severity and impact scope are processed to generate feedback suggestions; wherein the feedback suggestions include evacuation routes, emergency measures and material deployment. Based on the preset decision feedback model, the severity and impact scope of the flood disaster are processed to generate specific feedback suggestions.

[0058] In one embodiment, according to the severity and scope of the flood disaster, the preset decision feedback model will automatically generate corresponding feedback suggestions. For example, for minor disasters, it is recommended to strengthen monitoring and early warning; for moderate disasters, it is recommended to initiate an evacuation plan and prepare emergency materials; for severe and extreme disasters, it is recommended to immediately initiate emergency evacuation, organize emergency rescue, and deploy a large amount of material support. The feedback suggestions specifically include evacuation routes, emergency rescue measures, and material deployment to ensure the safety of personnel and reduce property losses to a certain extent.

[0059] Furthermore, feedback suggestions are displayed based on a preset visualization platform. The generated feedback suggestions are displayed through a preset visualization platform to provide intuitive and easy-to-understand decision support.

[0060] The generated feedback suggestions are imported into the preset visualization platform. The platform uses visualization technology (geographic information system, 3D modeling and virtual reality) to display the feedback suggestions in a graphical way. Through the platform, you can intuitively see the specific situation of evacuation routes, emergency measures and material allocation, so as to make more accurate and efficient decisions.

[0061] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, the embodiment of this application also provides a flood simulation device based on digital twins, whose structure is as follows: Figure 2 shown.

[0062] Figure 2 A schematic diagram of the internal structure of a flood simulation device based on digital twins provided in an embodiment of the present application. Figure 2 As shown, the device includes: at least one processor 201; and, a memory 202 communicatively connected to the at least one processor; The memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 201 to enable at least one processor 201 to: Determine the area to be monitored, and deploy a sensor group to the area to be monitored based on the terrain of the area to be monitored; connect meteorological data and obtain monitoring data of the area to be monitored based on the sensor group to determine simulation data; build a hydrodynamic model based on the terrain and monitoring data of the area to be monitored; build a digital twin model based on the hydrodynamic model and simulation data; when the monitoring data triggers the preset boundary conditions, process the simulation data according to the physical engine based on the digital twin model to predict the area to be monitored and generate prediction results; process the prediction results based on the preset decision feedback model to generate feedback suggestions.

[0063] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for flood simulation based on digital twins, storing computer executable instructions, wherein the computer executable instructions are set to: Determine the area to be monitored, and deploy a sensor group to the area to be monitored based on the terrain of the area to be monitored; connect meteorological data and obtain monitoring data of the area to be monitored based on the sensor group to determine simulation data; build a hydrodynamic model based on the terrain and monitoring data of the area to be monitored; build a digital twin model based on the hydrodynamic model and simulation data; when the monitoring data triggers the preset boundary conditions, process the simulation data according to the physical engine based on the digital twin model to predict the area to be monitored and generate prediction results; process the prediction results based on the preset decision feedback model to generate feedback suggestions.

[0064] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the IoT device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0065] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0066] Those skilled in the art will appreciate that the embodiments of the present application may provide methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0067] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0068] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0069] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0070] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0071] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0072] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0073] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0074] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A flood simulation method based on digital twins, characterized in that: The method comprises: Determine an area to be monitored, and deploy a sensor group to the area to be monitored based on the terrain of the area to be monitored; Connecting meteorological data and acquiring monitoring data of the area to be monitored based on the sensor group to determine simulation data; Constructing a hydrodynamic model based on the topography of the area to be monitored and the monitoring data; Building a digital twin model based on the hydrodynamic model and the simulation data; When the monitoring data triggers a preset boundary condition, the simulation data is processed according to the physical engine based on the digital twin model to predict the area to be monitored and generate a prediction result; The prediction results are processed based on a preset decision feedback model to generate feedback suggestions.

2. A flood simulation method based on digital twins according to claim 1, characterized in that: Acquiring monitoring data of the area to be monitored based on the sensor group and connecting with meteorological data specifically includes: Acquiring monitoring data of the area to be monitored in real time based on the sensor group; wherein the monitoring data includes water level, flow rate and rainfall; Access the meteorological department to obtain meteorological data; wherein the meteorological data includes rainfall, wind speed and wind direction.

3. A flood simulation method based on digital twins according to claim 1, characterized in that: Constructing a hydrodynamic model based on the topography of the area to be monitored and the monitoring data specifically includes: Determine terrain data based on the terrain of the area to be monitored, and construct a terrain model of the area to be monitored based on the terrain data; wherein the terrain data includes elevation, slope and soil type; The monitoring data is compared based on the terrain model, and the water flow conditions in the area to be detected are simulated based on the physical engine to construct the hydrodynamic model.

4. The flood simulation method based on digital twin according to claim 1 is characterized in that: Building a digital twin model based on the hydrodynamic model and the simulation data specifically includes: Processing the hydrodynamic model based on a preset digital twin technology to construct a first digital twin model corresponding to the hydrodynamic model; The simulation data is processed based on the first digital twin model to construct a digital twin model.

5. The flood simulation method based on digital twin according to claim 1, characterized in that: When the monitoring data triggers a preset boundary condition, the simulation data is processed according to the physical engine based on the digital twin model to predict the area to be monitored and generate a prediction result, which specifically includes: Determine boundary conditions; wherein the boundary conditions include water level warning lines and rainfall warning thresholds; When the monitoring data is greater than or equal to a preset boundary condition, the monitoring data is processed based on the digital twin model, and the predicted water level and predicted rainfall of the area to be monitored are determined in chronological order; The digital twin model processes the predicted water level and predicted rainfall according to the physical engine, and processes the simulation data according to the physical engine when the water level is greater than a preset flood threshold to simulate the flood process; Generate prediction results based on the flood process; wherein the prediction results include flood inundation range, water depth and flow rate.

6. A flood simulation method based on digital twins according to claim 5, characterized in that: The digital twin model processes the predicted water level and predicted rainfall according to the physical engine, and processes the simulation data according to the physical engine when the water level is greater than a preset flood threshold to simulate the flood process, specifically including: Inputting the predicted water level and the predicted rainfall into a digital twin model, and simulating water flow dynamics based on the physical engine; When the water level exceeds a preset flood threshold, the simulated water flow dynamics are processed based on the digital twin model to simulate the flood process.

7. The flood simulation method based on digital twin according to claim 1 is characterized in that: The method further comprises: Compare the monitoring data acquired in real time with the predicted data of the digital twin model to determine the error set; Analyzing and verifying the error set, and adjusting parameters in the digital twin model based on the error set so that the predicted data fits the monitoring data to generate modified parameters; When the difference between the predicted data and the monitoring data generated by the modified digital twin model is less than a preset error threshold, the digital twin model is modified based on the modification parameters.

8. The flood simulation method based on digital twin according to claim 1, characterized in that: Processing the prediction results based on a preset decision feedback model to generate feedback suggestions specifically includes: Compare the forecast results with the preset disaster severity table to determine the severity and impact of the flood disaster; Processing the severity and impact scope based on a preset decision feedback model to generate feedback suggestions; wherein the feedback suggestions include evacuation routes, emergency measures and material deployment; The feedback suggestions are displayed based on a preset visualization platform.

9. A flood simulation device based on digital twins, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Determine an area to be monitored, and deploy a sensor group to the area to be monitored based on the terrain of the area to be monitored; Connecting meteorological data and acquiring monitoring data of the area to be monitored based on the sensor group to determine simulation data; Constructing a hydrodynamic model based on the topography of the area to be monitored and the monitoring data; Building a digital twin model based on the hydrodynamic model and the simulation data; When the monitoring data triggers a preset boundary condition, the simulation data is processed according to the physical engine based on the digital twin model to predict the area to be monitored and generate a prediction result; The prediction results are processed based on a preset decision feedback model to generate feedback suggestions.

10. A non-volatile computer storage medium for flood simulation based on digital twins, storing computer executable instructions, characterized in that: The computer executable instructions are configured to: Determine an area to be monitored, and deploy a sensor group to the area to be monitored based on the terrain of the area to be monitored; Connecting meteorological data and acquiring monitoring data of the area to be monitored based on the sensor group to determine simulation data; Constructing a hydrodynamic model based on the topography of the area to be monitored and the monitoring data; Building a digital twin model based on the hydrodynamic model and the simulation data; When the monitoring data triggers a preset boundary condition, the simulation data is processed according to the physical engine based on the digital twin model to predict the area to be monitored and generate a prediction result; The prediction results are processed based on a preset decision feedback model to generate feedback suggestions.

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