Real-time and efficient basin flood inundation simulation method and system

Abstract

The invention discloses a real-time and efficient basin flood inundation simulation method and system, and relates to the technical field of flood simulation. Multi-source data such as hydrology, remote sensing, weather and terrain are collected in real time, and cleaning, interpolation processing and format standardization are carried out; a data fusion technology and a dynamic weight distribution algorithm are utilized, the system integrates multi-source data to generate a high-precision data set, and the weight of each data source is dynamically adjusted according to the real-time performance, the precision and the space coverage degree of the data; factors such as gradient, elevation and land utilization types are considered, correlation coefficients are quantified, the inundation range and water depth distribution in the drainage basin are estimated, and visual display and early warning release are carried out through the GIS technology. In addition, the system assists in decision making, submerging risk index calculation, emergency evacuation path planning and resource scheduling scheme generation; the real-time performance and accuracy of flood simulation are effectively improved, the flood early warning and emergency response capabilities are enhanced, and powerful support is provided for decision makers.

Description

Technical Field

[0001] The present invention relates to the technical field of flood simulation, and in particular to a real-time and efficient basin flood inundation simulation method and system. Background Art

[0002] Traditional flood simulation methods often rely on a single data source, such as water level and flow data from hydrological stations, which have limitations in terms of data real-time and accuracy. In addition, due to the lack of effective data fusion technology and dynamic weight allocation mechanism, these methods are difficult to fully utilize multi-source data, resulting in low accuracy and efficiency of flood inundation simulation. Therefore, existing technologies have obvious deficiencies in terms of real-time and accuracy of flood simulation, and cannot meet the needs of responding to rapidly changing flood events.

[0003] With the development of remote sensing technology, meteorological monitoring and geographic information system (GIS), the integrated application of multi-source data has provided new possibilities for improving the accuracy and efficiency of flood simulation. However, how to effectively integrate these multi-source data, including hydrological, remote sensing, meteorological and topographic data, and accurately assign weights to them is a technical challenge. In addition, flood inundation simulation also needs to consider a variety of influencing factors, such as slope, elevation and land use type, which have a significant impact on flood flow and inundation range. Existing technologies are insufficient in quantifying these influencing factors and dynamically adjusting simulation parameters, resulting in deviations between simulation results and actual conditions.

[0004] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0005] The purpose of the present invention is to solve the problems of insufficient data real-time and precision and lack of effective fusion of multi-source data and dynamic weight allocation in the existing basin flood inundation simulation methods, and to propose a real-time and efficient basin flood inundation simulation method and system.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A real-time and efficient basin flood inundation simulation method includes the following steps: S1: Multi-source data collection and preprocessing, real-time collection of multi-source data including hydrological stations, remote sensing, meteorological and topographic data, and cleaning, interpolation and format standardization; S2: Multi-source data fusion and weight allocation, using data fusion technology to integrate multi-source data to generate a data set, and at the same time, based on the real-time and accuracy of the data, determine the weight of each data source through a dynamic weight allocation algorithm; S3: Flood inundation analysis, based on the fused data, quantifies the correlation coefficient by considering the slope, elevation, and land use type factors, uses the formula to estimate the inundation range and water depth distribution in the basin, and dynamically adjusts and calibrates the calculation formula parameters based on real-time hydrological data and historical flood event data; S4: Visualization display and warning release, using GIS technology to visualize the flooding results, including data conversion, layer creation and loading, and symbolic annotation operations; based on the calculation results, flood warning information containing various information is generated and released in real time through multiple channels and visualization platforms; S5: Assist decision making, calculate the flood risk index based on multiple factors and display it in grades, plan emergency evacuation routes based on GIS and provide detailed guidance, generate resource scheduling plans based on the disaster situation and resource distribution, and display the execution status in real time.

[0007] Furthermore, the process of S2 is as follows: Use data fusion technology to integrate multi-source data to generate flood-related data sets; Using the dynamic weight allocation algorithm, the weight of each data source is dynamically adjusted according to the real-time and accuracy of the data, through the formula: ,in For the The weight of the data source, For real-time scoring, is the accuracy score; n is the total number of data sources; j is the index variable.

[0008] Furthermore, the real-time scoring in S2 The analysis process is as follows: For different data sources, judge based on the frequency of data update and make corrections based on data transmission delay factors to set the data source The update interval is , set a baseline update interval , considering the transmission delay caused by network transmission speed and data transmission protocol factors , then the real-time rating The calculation formula is: , when the update frequency is higher, that is, The smaller it is, the higher the real-time score is, indicating that its real-time performance is better; conversely, the real-time score is lower.

[0009] Furthermore, the accuracy score in S2 The analysis process is as follows: The data provided by the data source is compared with the accurate value of the same region and the same type of data in history, using the root mean square error, the formula is: , where Is the data source In the The measured value, The historically accurate data is The value of times, m is the number of comparison data; then, the accuracy score is set according to the error range. hour, is the first error threshold set, then ;when hour, is the second error threshold, then ;when When ; Consider the reliability of the data source itself, including the accuracy of the data collection equipment and the scientific nature of the data collection method, and give different data sources basic corrections and classifications. , then the calculation formula for the accuracy score is finally , It is an accuracy score calculated based on comparison with historical data.

[0010] Furthermore, the specific operation steps of S3 are as follows: Based on the fused multi-source data, the following formula is used to estimate the flooding range and water depth distribution in the basin; flooding range calculation formula: ,in, is the flooded area, For the Traffic data from multiple data sources, For time, is the coefficient related to topography and land use; Water depth distribution calculation formula: in, For water depth, is the flow rate, is the width of the river; Dynamically adjust the parameters in the calculation formula according to real-time hydrological data to improve calculation accuracy; use historical flood event data to calibrate and verify the parameters to ensure the accuracy and reliability of the formula.

[0011] Furthermore, in the formula in S3 The specific analysis process is as follows: Through different influencing factors The value is quantified, and the influencing factors include: Slope: The steeper the slope, the faster the flood flow and the smaller the inundation area; the gentler the slope, the slower the flood flow and the larger the inundation area; The influence of is obtained by fitting empirical formula or experimental data: , where is the slope, a and b are fitting parameters; Elevation: The lower the elevation, The larger the value, the higher the elevation. The effect of the value is handled by normalization: , where Indicates the maximum elevation value in the study area. This is the point with the highest elevation in the entire watershed or study area; Indicates the actual elevation value of the current calculation point, which is the altitude of the location being evaluated; Indicates the minimum elevation value in the study area; Land use type: Land use type has different effects on flood inundation range. The impact of land use on flood inundation range is analyzed in the following ways: Contribution to the value: Manning coefficient. Different land use types correspond to different Manning coefficients, which are used to indicate the resistance of the surface to flood flow, including: Farmland: Manning coefficient u=0.035 City: Manning coefficient u=0.02 Forest: Manning coefficient u=0.1 Wetland: Manning coefficient u=0.05 Manning's coefficient The influence of the value is expressed by the following formula: Among them, c and d are fitting parameters; The influence of terrain characteristics and land use types is combined through the formula: To get Value, where is the weight coefficient, which indicates the effect of terrain slope, elevation and land use on The contribution ratio of the value is obtained by fitting the historical flood data; Using historical flood event data, regression analysis or machine learning methods to fit The calculation formula of the value is used to determine the value of each parameter and verify it with independent flood event data. The accuracy of the value ensures the reliability of the formula.

[0012] Furthermore, the specific operation steps of S4 are as follows: The flooding range and water depth distribution data obtained from flood inundation calculations are converted into a format that is compatible with GIS software; the data are classified and graded to better display different degrees of flooding in visualization; Create multiple layers in the GIS software to display terrain data, land use data, river network data, and flood inundation data; load the terrain data as the base layer to provide terrain background information; then load the land use layer and the river network layer in sequence; Load the processed flood inundation data into the layer and set the corresponding layer transparency so that the inundated area can be superimposed on the terrain and geographic features; Set different symbols and colors for different geographic features and flooding data; use contour lines and elevation rendering to display terrain; use different colors or pattern fills for land use types; use gradient colors or animation effects to display flooded areas according to water depth levels or flooding time sequence; Add annotations, including place names, river names, and facility locations; the font, size, and color of annotations are adjusted according to the map display effect; Determine the areas and objects threatened by floods based on the calculation results of flood inundation range and water depth distribution, combined with the population distribution, facility location information and flood evolution trend in the basin; Warning information includes the estimated arrival time of flood, inundation depth forecast, impact range description, and hazard level assessment; use language and charts to make warning information easy to understand and accept; Selection of a visualization platform, including a web-based GIS platform, a mobile application, or a dedicated flood warning dissemination system; Integrate the flood warning model with the visualization platform to achieve real-time data transmission and synchronization; push the calculated warning information to the visualization platform through data interface or middleware technology; Distribute flood warning information through multiple channels, including SMS push, email, social media, and TV broadcasts; push warning information to residents, government departments, and rescue agency personnel in affected areas based on user registration information and geographic location information; Warning information is updated in real time on a visual platform, allowing users to view flood warning content through map interactions, including clicking on affected areas to view specific flooding depth and estimated arrival time information, while providing emergency measures and suggestions.

[0013] Furthermore, the specific operation steps of S5 are as follows: The flood risk index is calculated using a risk assessment model, taking into account the flood inundation range, water depth, flow velocity, and topography, land use, population distribution, and infrastructure factors within the basin. Different weights are assigned based on the degree of harm to buildings and personnel caused by water depth and flow velocity, and the flood risk index of each area is calculated using the weighted summation method in combination with land use type and terrain slope factors. The flood risk index is graded into low risk, relatively low risk, medium risk, high risk and extremely high risk, and the risk distribution is displayed in different colors or symbols on the GIS map; risk assessment reports are provided to decision makers, including the location, area, affected population and facility information of high-risk areas; Based on GIS, combined with road network data, terrain data and real-time flood inundation information, the optimal emergency evacuation route is calculated using a path planning algorithm; factors considered include road capacity, flood inundation conditions, and evacuation start and end locations; Display emergency evacuation route planning results on the GIS map and provide route guidance information, including road name, turn instructions, distance and estimated evacuation time; The resource scheduling model is used to generate a resource scheduling plan based on the flood inundation range, the number of affected people, the degree of disaster, and the distribution of emergency rescue resources. The model takes into account factors such as the priority of rescue needs in different regions, resource transportation time and cost, resource reserves, and supply capacity. The resource scheduling recommendations include the number of rescue teams to be dispatched and their deployment locations, the number of rescue equipment to be deployed and their placement locations, and the supply quantity and delivery routes of materials. The implementation of the resource scheduling plan is displayed in real time through a visualization platform, including the location of resource transport vehicles and the amount of material reserves at material distribution points.

[0014] A real-time and efficient basin flood inundation simulation system, including: The data acquisition unit is used to collect multi-source data such as water level and flow of hydrological stations, remote sensing image radar, meteorological rainfall and wind speed, and terrain DEM in real time to provide basic data for the system; Data preprocessing unit, used to clean, interpolate and standardize the collected data to ensure that the data is accurate and usable; Data fusion and weight allocation unit, used to fuse multi-source data using weighted average technology, determine the data source weight according to the real-time and accuracy of the data, and improve data quality; Flood inundation analysis unit, used to quantify the impact of multiple factors on estimating inundation range and water depth distribution based on fused data, dynamically adjust parameters, and calibrate and verify with historical data to improve calculation accuracy; Visualization display unit, used to convert and process data using GIS technology, build multiple layers to display the relationship between flood inundation and geographic information, and help users understand; The warning release unit is used to generate warning information based on the calculation results combined with multiple factors, push it through multiple channels and update it in real time on the visualization platform, and provide emergency measures suggestions; The decision support unit includes a flood risk assessment submodule, which is used to calculate the risk index and display it in a graded manner to provide risk reports for decision-making; an emergency evacuation route planning submodule, which is used to use algorithms to plan the optimal evacuation route based on multiple factors and provide guidance; and a resource scheduling suggestion submodule, which is used to generate plans based on the disaster situation and display the implementation status to assist decision-making.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention significantly improves the accuracy and efficiency of flood simulation by real-time collection and integration of multi-source data such as hydrological stations, remote sensing, meteorology and terrain, and using a dynamic weight allocation algorithm to determine the weight of each data source; it not only enhances the real-time and accuracy of the data, but also quantifies the impact of key factors such as slope, elevation and land use type on flood flow and inundation range by considering them, so that the inundation simulation results are closer to the actual situation; The present invention uses GIS technology to visualize the flooding results, and combines various information to generate flood warning information, which is released in real time through multiple channels, greatly enhancing the flood warning and emergency response capabilities; it can help relevant departments and the public to understand the flood situation in a timely manner, take effective prevention and response measures, and reduce the losses caused by flood disasters; The present invention provides strong support for decision makers by calculating the flood risk index and displaying it in grades, planning emergency evacuation routes, and generating resource scheduling plans based on the disaster situation and resource distribution; these functions enable decision makers to allocate rescue resources more scientifically, optimize emergency response strategies, and improve the efficiency and effectiveness of flood control and disaster relief work. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0019] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations.

[0020] like Figure 1 As shown, a real-time and efficient basin flood inundation simulation method includes the following steps: Multi-source data collection and preprocessing: real-time collection of multi-source data including hydrological stations, remote sensing, meteorology and terrain, and cleaning, interpolation and format standardization to provide accurate and standardized data basis for subsequent analysis; Collect multi-source data in real time, including hydrological station data (such as water level, flow), remote sensing data (such as satellite images, radar data), meteorological data (such as rainfall, wind speed) and terrain data (such as DEM digital elevation model); pre-process the data, including data cleaning, interpolation processing and format standardization.

[0021] Multi-source data fusion and weight allocation: Use data fusion technology to integrate multi-source data to generate high-precision data sets. At the same time, according to the real-time and accuracy of the data, the weight of each data source is determined through a dynamic weight allocation algorithm to ensure the scientific nature of comprehensive data utilization; Data fusion technology (such as weighted average method and principal component analysis method) is used to integrate multi-source data to generate high-precision and high-coverage flood-related data sets; dynamic weight allocation algorithm is used to dynamically adjust the weight of each data source according to the real-time and accuracy of the data, through the formula: ,in For the The weight of the data source, For real-time scoring, is the accuracy score; n is the total number of data sources; j is the index variable; the real-time score The analysis process is as follows: For different data sources, judge based on the frequency of data update and make corrections based on data transmission delay factors. Set the data source. Set the data source The update interval is , set a baseline update interval , considering the transmission delay caused by network transmission speed and data transmission protocol factors , then the real-time rating The calculation formula is: , when the update frequency is higher, (i.e. The smaller the value is), the closer the real-time score is to 1, the better the real-time performance is; conversely, the lower the real-time score is;

[0022] Accuracy score The analysis process is as follows: The data provided by the data source is compared with the accurate value of the same region and the same type of data in history, using the root mean square error, the formula is: , where Is the data source In the The measured value, The historically accurate data is The value of times, m is the number of comparison data; then, the accuracy score is set according to the error range. hour, is the set smaller error threshold, then ;when hour, is a larger error threshold, then ;when When ; Consider the reliability of the data source itself, including the accuracy of the data collection equipment and the scientific nature of the data collection method, and give different data sources basic corrections and classifications. , then the calculation formula for the accuracy score is finally , basic correction score Set between -0.2 and 0.2, It is an accuracy score calculated based on comparison with historical data.

[0023] Flood inundation analysis, based on the fused data, quantifies the correlation coefficient by considering factors such as slope, elevation, and land use type, uses a specific formula to estimate the inundation range and water depth distribution in the basin, and dynamically adjusts and calibrates the calculation formula parameters based on real-time hydrological data and historical flood event data to improve calculation accuracy; Based on the fused multi-source data, the following formula is used to estimate the flooding range and water depth distribution in the basin; flooding range calculation formula: ,in, is the flooded area, For the Traffic data from multiple data sources, For time, is the coefficient related to topography and land use; the specific analysis process is as follows: The value is quantified, and the influencing factors include: Slope: The steeper the slope, the faster the flood flow and the smaller the inundation area; the gentler the slope, the slower the flood flow and the larger the inundation area; The influence of is obtained by fitting empirical formula or experimental data: , where is the slope, a and b are fitting parameters; Elevation: Low-lying areas are more likely to be flooded, so the lower the elevation, The larger the value, the higher the elevation. The effect of the value is handled by normalization: , where Indicates the maximum elevation value in the study area. This is the point with the highest elevation in the entire watershed or study area; Indicates the actual elevation value of the current calculation point, which is the altitude of the location being evaluated; Indicates the minimum elevation value in the study area; Land use type: Land use type (such as farmland, city, forest, wetland) also has different effects on the flood inundation range. The following methods are used to analyze the impact of land use on flood inundation range. Contribution to the value: Manning coefficient. Different land use types correspond to different Manning coefficients, which are used to indicate the resistance of the surface to flood flow, including: Farmland: Manning coefficient u=0.035 City: Manning coefficient u=0.02 Forest: Manning coefficient u=0.1 Wetland: Manning coefficient u=0.05 Manning's coefficient The influence of the value is expressed by the following formula: Among them, c and d are fitting parameters; The influence of terrain characteristics and land use types is combined through the formula: To get Value, where is the weight coefficient, which indicates the effect of terrain slope, elevation and land use on The contribution ratio of the value is obtained by fitting the historical flood data; Using historical flood event data, regression analysis or machine learning methods to fit The calculation formula of the value is used to determine the value of each parameter and verify it with independent flood event data. The accuracy of the value ensures the reliability of the formula.

[0024] Water depth distribution calculation formula: in, For water depth, is the flow rate, is the width of the river; Dynamically adjust the parameters in the calculation formula according to real-time hydrological data (such as , , ) to improve calculation accuracy; use historical flood event data to calibrate and verify parameters to ensure the accuracy and reliability of the formula.

[0025] Visual display and warning release: Use GIS technology to visualize the flooding results, including data conversion, layer creation and loading, symbolic annotation and other operations; Generate flood warning information containing various information based on the calculation results, and release it in real time through multiple channels and visualization platforms to assist relevant personnel to obtain information in a timely manner and make preparations for response; The inundation results were visualized using Geographic Information System (GIS) technology; the process was as follows: The inundation range and water depth distribution data obtained from flood inundation calculations are converted into a format to make them compatible with GIS software; the data are classified and graded to better display different degrees of inundation in visualization; multiple layers are created in GIS software to display terrain data, land use data, river network data, and flood inundation data; terrain data (such as digital elevation model, DEM) is loaded as a basic layer to provide terrain background information; then land use layers (such as farmland, city, forest and other different land use types) and river network layers are loaded in sequence to make geographic information richer and more intuitive; the processed flood inundation data is loaded into a special layer, and the appropriate layer transparency is set so that the inundated area can be clearly superimposed on the terrain and other geographical elements, making it easier to observe the relationship between floods and terrain and land use; Set different symbols and colors for different geographic features and inundation data; use contour lines and elevation rendering to display terrain; use different colors or pattern fills for land use types; use gradient colors or animation effects to display flooded areas according to water depth levels or inundation time sequence (such as initial inundation area, gradually expanding inundation area, etc.) to enhance visualization; add annotations, including place names, river names, and locations of facilities (such as bridges, dams, etc.) to better identify and locate geographic features; adjust the font, size, and color of annotations according to the map display effect to ensure clarity and readability. Generate flood warning information based on the calculation results and publish it in real time through the visualization platform; the process is as follows: Based on the calculation results of flood inundation range and water depth distribution, combined with the population distribution in the basin, the location information of facilities (such as residential areas, schools, hospitals, factories, etc.) and the flood evolution trend, determine the areas and objects threatened by floods; the warning information content includes the estimated arrival time of the flood, the prediction of the inundation depth, the description of the affected area, and the assessment of the danger level (such as low risk, medium risk, high risk); use language and graphics (such as flood evolution animation, risk area map) to make the warning information easy to understand and accept; Select a visualization platform, including a Web-based GIS platform, a mobile application, or a dedicated flood warning release system. Ensure that the platform has real-time data updates, map interactions (such as zooming, panning, querying, etc.), and information push functions to meet the needs of flood warning information release and display; integrate the flood warning model with the visualization platform to achieve real-time data transmission and synchronization; push the calculated warning information to the visualization platform through data interfaces or middleware technology to ensure that users can obtain the latest flood warning information in real time; Flood warning information is released through various channels, including but not limited to SMS push, email, social media release, and TV broadcasts; warning information is pushed to residents in the affected areas, government departments, and rescue agency personnel based on user registration information and geographic location information; warning information is updated in real time on a visualization platform, allowing users to view detailed flood warning content through map interactions, including clicking on the affected area to view the specific flooding depth and estimated arrival time information, while providing emergency measures and suggestions (such as avoidance routes, self-rescue methods, etc.) to help users prepare for floods.

[0026] Assist decision-making by comprehensively calculating the flood risk index based on multiple factors and displaying it in grades, planning emergency evacuation routes based on GIS and providing detailed guidance, generating resource scheduling plans based on the disaster situation and resource distribution and displaying the implementation status in real time, providing comprehensive support for decision-makers in flood response; Provides decision support functions, including flood risk assessment, emergency evacuation route planning and resource dispatch recommendations, including: The flood risk index is calculated using a risk assessment model, taking into account the flood inundation range, water depth, flow rate, and topography, land use, population distribution, and infrastructure factors within the basin. Different weights are assigned according to the degree of harm to buildings and personnel caused by water depth and flow rate. The flood risk index of each area is calculated using the weighted summation method, taking into account factors such as land use type (for example, commercial areas, residential areas, and other densely populated areas have higher weights) and terrain slope (for example, low-lying areas have higher risks). The flood risk index is graded into low risk, relatively low risk, medium risk, high risk, and extremely high risk, and the risk distribution is displayed on the GIS map in different colors or symbols. Provide decision makers with risk assessment reports, including the location, area, affected population and facility information of high-risk areas, to help them understand the extent of the harm caused by floods so as to formulate appropriate response strategies; based on GIS, combined with road network data, terrain data and real-time flood inundation information, use path planning algorithms (including shortest path algorithms and heuristic search algorithms) to calculate the optimal emergency evacuation route; factors to be considered include road capacity (such as road width, road conditions, etc.), flood inundation conditions (avoiding flooded areas and dangerous sections), evacuation starting points (such as residential areas, schools, etc.) and end points (such as safe refuges, such as high ground, gymnasiums, etc.).

[0027] The emergency evacuation route planning results are displayed on the GIS map, providing detailed route guidance information such as road names, turn instructions, distances and estimated evacuation times. The resource scheduling model is used to generate resource scheduling plans based on the flood inundation range, the number of affected people, the degree of disaster, and the distribution of emergency rescue resources (such as rescue teams, rescue equipment, and material reserves). The model considers factors such as the priority of rescue needs in different regions (determined according to the risk of inundation and the density of the affected population), resource transportation time and cost (considering road conditions and traffic flow), resource reserves and supply capacity, etc. The resource scheduling suggestions include the number of dispatched rescue teams and their deployment locations, the number of allocated rescue equipment (such as assault boats, life jackets, pumps, etc.) and their delivery locations, and the supply quantity and distribution routes of materials (such as food, drinking water, tents, etc.). The visualization platform displays the execution of the resource scheduling plan in real time, including the location of resource transportation vehicles and the material reserves at material distribution points, so that decision makers can adjust the scheduling strategy in a timely manner to ensure that rescue resources can be put into flood control and disaster relief work in a timely and effective manner.

[0028] A real-time and efficient basin flood inundation simulation system, including: The data acquisition unit is used to collect multi-source data in real time, including water level and flow data from hydrological stations, satellite images and radar data obtained by remote sensing, meteorological rainfall and wind speed data, and terrain data (such as DEM digital elevation model), providing basic data support for the entire simulation system; The data preprocessing unit is used to clean the collected multi-source data, remove outliers and erroneous data, perform interpolation processing, supplement missing data, and standardize the data format to make it suitable for subsequent data fusion and analysis operations to ensure the accuracy and availability of the data; The data fusion and weight allocation unit is used to integrate multi-source data using data fusion techniques such as weighted average method and principal component analysis method to generate high-precision and high-coverage flood-related data sets. At the same time, based on the real-time and accuracy of the data, the weight of each data source is determined through a dynamic weight allocation algorithm to make the comprehensive use of data more scientific and improve data quality; The flood inundation analysis unit is used to estimate the inundation range and water depth distribution in the basin by quantifying the influence of factors such as slope, elevation, and land use type on the correlation coefficient based on the fused multi-source data using a specific formula. It can also dynamically adjust the parameters in the calculation formula (such as coefficients related to topography and land use, flow velocity, river width, etc.) according to real-time hydrological data, and use historical flood event data to calibrate and verify parameters to improve the accuracy and reliability of the calculation; The visualization unit is used to convert the format of the flood range, water depth distribution and other data obtained from the flood inundation calculation, classify and grade them using the geographic information system (GIS) technology, create multiple layers containing terrain, land use, river network and flood inundation data, set appropriate symbols, colors and labels, and display the relationship between the flood inundation situation and geographic information in an intuitive and clear way to assist users in understanding and analysis; The warning release unit is used to generate warning information including the estimated arrival time of the flood, flood depth prediction, description of the affected area, and hazard level assessment based on the calculation results of the flood inundation range and water depth distribution, combined with the population distribution, facility location and flood evolution trend in the basin. By integrating with the visualization platform, the warning information is accurately pushed to relevant personnel in the affected area through multiple channels (SMS, email, social media, TV broadcasts, etc.), and updated in real time on the visualization platform, providing emergency measures and suggestions to help users prepare for the response; Decision support unit, including: Inundation risk assessment submodule: Taking into account the flood inundation range, water depth, flow velocity, topography, land use, population distribution, and infrastructure factors within the basin, the risk assessment model is used to calculate the inundation risk index, and the risk is classified (low risk, relatively low risk, medium risk, high risk, and extremely high risk). The risk distribution is displayed in different colors or symbols on the GIS map, and a risk assessment report is provided to decision makers to help understand the degree of flood damage and formulate response strategies; Emergency evacuation route planning submodule: Based on GIS, combined with road network, terrain and real-time flood inundation information, the path planning algorithm (shortest path algorithm, heuristic search algorithm, etc.) is used to calculate the optimal emergency evacuation route, considering factors such as road capacity, flood inundation, evacuation starting and end locations, and displaying the path planning results on the GIS map and providing detailed guidance information; Resource scheduling suggestion submodule: Based on the flood inundation scope, the number of affected people, the degree of disaster and the distribution of emergency rescue resources, the resource scheduling model is used to generate a resource scheduling plan, taking into account factors such as the priority of rescue needs in different regions, resource transportation time and cost, resource reserves and supply capacity, etc., to provide deployment suggestions for rescue teams, equipment, materials, etc., and display the execution of the resource scheduling plan in real time through a visualization platform, to assist decision makers in adjusting strategies in a timely manner and ensure that rescue resources are effectively invested in flood control and disaster relief.

[0029] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A real-time and efficient basin flood inundation simulation method, characterized in that: The following steps are involved: S1: Multi-source data collection and preprocessing, real-time collection of multi-source data including hydrological stations, remote sensing, meteorological and topographic data, and cleaning, interpolation and format standardization; S2: Multi-source data fusion and weight allocation, using data fusion technology to integrate multi-source data to generate a data set, and at the same time, based on the real-time and accuracy of the data, determine the weight of each data source through a dynamic weight allocation algorithm; S3: Flood inundation analysis, based on the fused data, quantifies the correlation coefficient by considering the slope, elevation, and land use type factors, uses the formula to estimate the inundation range and water depth distribution in the basin, and dynamically adjusts and calibrates the calculation formula parameters based on real-time hydrological data and historical flood event data; S4: Visualization display and warning release, using GIS technology to visualize the flooding results, including data conversion, layer creation and loading, and symbolic annotation operations; based on the calculation results, flood warning information containing various information is generated and released in real time through multiple channels and visualization platforms; S5: Assist decision making, calculate the flood risk index based on multiple factors and display it in grades, plan emergency evacuation routes based on GIS and provide detailed guidance, generate resource scheduling plans based on the disaster situation and resource distribution, and display the execution status in real time.

2. A real-time and efficient basin flood inundation simulation method according to claim 1, characterized in that: The process of S2 is as follows: Use data fusion technology to integrate multi-source data to generate flood-related data sets; Using the dynamic weight allocation algorithm, the weight of each data source is dynamically adjusted according to the real-time and accuracy of the data, through the formula: ,in For the The weight of the data source, For real-time scoring, is the accuracy score; n is the total number of data sources; j is the index variable.

3. A real-time and efficient basin flood inundation simulation method according to claim 2, characterized in that: The real-time scoring in S2 The analysis process is as follows: For different data sources, judge based on the frequency of data update and make corrections based on data transmission delay factors to set the data source The update interval is , set a baseline update interval , considering the transmission delay caused by network transmission speed and data transmission protocol factors , then the real-time rating The calculation formula is: , when the update frequency is higher, that is The smaller it is, the higher the real-time score is, indicating that its real-time performance is better; conversely, the real-time score is lower.

4. A real-time and efficient basin flood inundation simulation method according to claim 2, characterized in that: The accuracy score in S2 The analysis process is as follows: The data provided by the data source is compared with the accurate value of the same region and the same type of data in history, using the root mean square error, the formula is: , where Is the data source In the The measured value, The historically accurate data is The value of times, m is the number of comparison data; then, the accuracy score is set according to the error range. hour, is the first error threshold set, then ;when hour, is the second error threshold, then ;when When ; Consider the reliability of the data source itself, including the accuracy of the data collection equipment and the scientific nature of the data collection method, and give different data sources basic corrections and classifications. , then the calculation formula for the accuracy score is finally , It is an accuracy score calculated based on comparison with historical data.

5. A real-time and efficient basin flood inundation simulation method according to claim 1, characterized in that: The specific operation steps of S3 are as follows: Based on the fused multi-source data, the following formula is used to estimate the flooding range and water depth distribution in the basin; flooding range calculation formula: ,in, is the flooded area, For the Traffic data from multiple data sources, For time, is the coefficient related to topography and land use; Water depth distribution calculation formula: in, For water depth, is the flow rate, is the width of the river; Dynamically adjust the parameters in the calculation formula according to real-time hydrological data to improve calculation accuracy; use historical flood event data to calibrate and verify the parameters to ensure the accuracy and reliability of the formula.

6. A real-time and efficient basin flood inundation simulation method according to claim 5, characterized in that: In the formula of S3 The specific analysis process is as follows: Through different influencing factors The value is quantified, and the influencing factors include: Slope: The steeper the slope, the faster the flood flow and the smaller the inundation area; the gentler the slope, the slower the flood flow and the larger the inundation area; The influence of is obtained by fitting empirical formula or experimental data: , where is the slope, a and b are fitting parameters; Elevation: The lower the elevation, The larger the value, the higher the elevation. The effect of the value is handled by normalization: , where Represents the maximum elevation value in the study area; this is the point with the highest elevation in the entire watershed or study area; Indicates the actual elevation value of the current calculation point, which is the altitude of the location being evaluated; Indicates the minimum elevation value in the study area; Land use type: Land use type has different effects on flood inundation range. The impact of land use on flood inundation range is analyzed in the following ways: Contribution to the value: Manning coefficient. Different land use types correspond to different Manning coefficients, which are used to indicate the resistance of the surface to flood flow, including: Farmland: Manning coefficient u=0.035 City: Manning coefficient u=0.02 Forest: Manning coefficient u=0.1 Wetland: Manning coefficient u=0.05 Manning's coefficient The influence of the value is expressed by the following formula: Among them, c and d are fitting parameters; The influence of terrain characteristics and land use types is combined through the formula: To get Value, where is the weight coefficient, which indicates the effect of terrain slope, elevation and land use on The contribution ratio of the value is obtained by fitting the historical flood data; Using historical flood event data, regression analysis or machine learning methods to fit The calculation formula of the value is used to determine the value of each parameter and verify it with independent flood event data. The accuracy of the value ensures the reliability of the formula.

7. A real-time and efficient basin flood inundation simulation method according to claim 1, characterized in that: The specific operation steps of S4 are as follows: The flooding range and water depth distribution data obtained from flood inundation calculations are converted into a format that is compatible with GIS software; the data are classified and graded to better display different degrees of flooding in visualization; Create multiple layers in the GIS software to display terrain data, land use data, river network data, and flood inundation data; load terrain data as a base layer to provide terrain background information; Then load the land use layer and river network layer in sequence; Load the processed flood inundation data into the layer and set the corresponding layer transparency so that the inundated area can be superimposed on the terrain and geographic features; Set different symbols and colors for different geographic features and inundation data; For terrain, contour lines and elevation rendering are used for display; for land use types, different colors or pattern fillings are used; for flooded areas, gradient colors or animation effects are used to display them according to the water depth level or flooding time sequence; Add annotations, including place names, river names, and facility locations; the font, size, and color of annotations are adjusted according to the map display effect; Based on the calculation results of flood inundation range and water depth distribution, combined with the population distribution, facility location information and flood evolution trend in the basin, determine the areas and objects threatened by floods; Warning information includes the estimated arrival time of flood, inundation depth forecast, impact range description, and hazard level assessment; Use language and graphics to make warning information easy to understand and accept; Selection of a visualization platform, including a web-based GIS platform, a mobile application, or a dedicated flood warning dissemination system; Integrate the flood warning model with the visualization platform to achieve real-time data transmission and synchronization; push the calculated warning information to the visualization platform through data interface or middleware technology; Disseminate flood warning information through multiple channels, including SMS push, email, social media, and TV and radio broadcasts; According to the user's registration information and geographic location information, the warning information will be pushed to residents, government departments, and rescue agency personnel in the affected area; Warning information is updated in real time on a visual platform, allowing users to view flood warning content through map interactions, including clicking on affected areas to view specific flooding depth and estimated arrival time information, while providing emergency measures and suggestions.

8. A real-time and efficient basin flood inundation simulation method according to claim 1, characterized in that: The specific operation steps of S5 are as follows: The flood risk index is calculated using a risk assessment model, taking into account the flood inundation range, water depth, flow velocity, and topography, land use, population distribution, and infrastructure factors within the basin. Different weights are assigned based on the degree of harm to buildings and personnel caused by water depth and flow velocity, and the flood risk index of each area is calculated using the weighted summation method in combination with land use type and terrain slope factors. The flood risk index is graded into low risk, relatively low risk, medium risk, high risk and extremely high risk, and the risk distribution is displayed in different colors or symbols on the GIS map; risk assessment reports are provided to decision makers, including the location, area, affected population and facility information of high-risk areas; Based on GIS, combined with road network data, terrain data and real-time flood inundation information, the optimal emergency evacuation route is calculated using a path planning algorithm; factors considered include road capacity, flood inundation conditions, and evacuation start and end locations; Display emergency evacuation route planning results on GIS maps and provide route guidance information, including road names, turn instructions, distances, and estimated evacuation times; The resource scheduling model is used to generate a resource scheduling plan based on the flood inundation range, the number of affected people, the degree of disaster, and the distribution of emergency rescue resources. The model takes into account factors such as the priority of rescue needs in different regions, resource transportation time and cost, resource reserves, and supply capacity. The resource dispatch proposal includes the number of rescue teams to be dispatched and their deployment locations, the number of rescue equipment to be deployed and their delivery locations, and the supply quantity and delivery routes of materials; The execution status of the resource scheduling plan is displayed in real time through a visualization platform, including the location of resource transportation vehicles and the amount of material reserves at material distribution points.

9. A real-time and efficient watershed flood inundation simulation system applied to a real-time and efficient watershed flood inundation simulation method according to any one of claims 1 to 8, characterized in that: include: The data acquisition unit is used to collect multi-source data such as water level and flow of hydrological stations, remote sensing image radar, meteorological rainfall and wind speed, and terrain DEM in real time to provide basic data for the system; Data preprocessing unit, used to clean, interpolate and standardize the collected data to ensure that the data is accurate and usable; Data fusion and weight allocation unit, used to fuse multi-source data using weighted average technology, determine the data source weight according to the real-time and accuracy of the data, and improve data quality; Flood inundation analysis unit, used to quantify the impact of multiple factors on estimating inundation range and water depth distribution based on fused data, dynamically adjust parameters, and calibrate and verify with historical data to improve calculation accuracy; Visualization display unit, used to convert and process data using GIS technology, build multiple layers to display the relationship between flood inundation and geographic information, and help users understand; The warning release unit is used to generate warning information based on the calculation results combined with multiple factors, push it through multiple channels and update it in real time on the visualization platform, and provide emergency measures suggestions; The decision support unit includes a flood risk assessment submodule, which is used to calculate the risk index and display it in a graded manner to provide risk reports for decision-making; an emergency evacuation route planning submodule, which is used to use algorithms to plan the optimal evacuation route based on multiple factors and provide guidance; and a resource scheduling suggestion submodule, which is used to generate plans based on the disaster situation and display the implementation status to assist decision-making.

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