Flood forecasting and dispatching model visualization method based on digital twinning

Abstract

This invention relates to the field of flood forecasting and scheduling technology, and particularly to a visualization method for flood forecasting and scheduling models based on digital twins. The method includes the following steps: acquiring hydrodynamic spatial data from flood forecasts based on a pre-set flood forecasting and scheduling model, and determining the spatial distribution range of floods within a watershed based on the hydrodynamic spatial data; dividing flood-affected areas according to the spatial distribution range, and establishing flood-affected area status data; based on the flood-affected area status data, determining the trend of inundation degree changes and temporal differences in flow states in each area, and generating flood status change characteristics; combining the flood status change characteristics of each area, analyzing the propagation direction and continuity of flood changes between different areas within the watershed; this invention uses a digital twin visualization processing mechanism for regional flood evolution relationships to achieve an intuitive and interconnected presentation of flood forecasting results and scheduling decision-making processes.

Description

Technical Field

[0001] This invention relates to the field of flood forecasting and scheduling technology, and in particular to a visualization method for flood forecasting and scheduling models based on digital twins. Background Technology

[0002] Current flood forecasting technologies largely rely on hydrodynamic models to numerically calculate factors such as water level and flow rate. While these methods can provide forecasts with a certain level of accuracy, the output is typically presented as discrete data or two-dimensional charts, making it difficult to intuitively reflect the spatial propagation and evolution of floods across different regions within a watershed. In actual flood control operations, dispatchers need to simultaneously consider changes in flood conditions, propagation paths, and the order of dispatching across multiple regions. Traditional static display methods are insufficient to support a comprehensive understanding and rapid assessment of regional flood risks. Furthermore, some existing digital twin or 3D visualization technologies focus more on scene reconstruction or single-moment status display, often separating flood forecast results from dispatching logic. They lack a systematic characterization of the evolution of flood risks across regions and fail to reflect the continuous evolution of floods from upstream to downstream and from local to global, resulting in a disconnect between visualization results and actual forecasting and dispatching decisions. Summary of the Invention

[0003] Therefore, it is necessary to provide a visualization method for flood forecasting and scheduling models based on digital twins to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a visualization method for flood forecasting and scheduling models based on digital twins is proposed, comprising the following steps: Step S1: Obtain hydrodynamic spatial data from flood forecasts based on the preset flood forecasting and scheduling model, and determine the spatial distribution range of floods within the basin based on the hydrodynamic spatial data; perform flood impact area division according to the spatial distribution range, and establish flood impact area status data; Step S2: Based on the flood-affected area status data, determine the trend of flood inundation degree and the temporal differences of water flow status in each area, and generate flood status change characteristics. Step S3: Combining the flood state change characteristics of each region, analyze the propagation direction and continuity of the flood between different regions within the basin, predict the evolution process of the flood between regions based on the propagation direction and continuity of the flood, and determine the evolution relationship of flood risk between regions based on the evolution process; Step S4: Determine the regional flood control strategy based on the evolution of flood risk between regions; determine the visualization order and display weight of each region in the flood forecasting and control process based on the regional flood control strategy, and drive the preset digital twin visualization platform to perform three-dimensional dynamic presentation.

[0005] The beneficial effects of this invention are as follows: it refines flood forecast results from a single watershed scale into multiple flood-affected areas with clear spatial boundaries, forming regional flood-affected status data, providing a unified data organization basis for subsequent flood evolution analysis and scheduling decisions, and avoiding analytical biases caused by inconsistencies in different data sources and spatial scales.

[0006] By comprehensively characterizing the regional flood state changes from two dimensions—water depth change and flow evolution—the flood state description is no longer limited to a single water level or flow rate indicator, thus improving the completeness and distinguishability of the regional flood evolution characteristics.

[0007] Transforming flood risk from a static regional attribute into an inter-regional evolutionary relationship with a clear sequence and propagation path gives flood risk identification a temporal logical basis, providing a predictable and extrapolable decision-making basis for regional flood control.

[0008] This system enables the linkage between flood forecast results, dispatch strategies, and 3D visualization, allowing the order and weight of visualization to directly reflect the dispatch logic and risk evolution process, thereby enhancing the intuitiveness, understandability, and decision support capabilities of the flood forecast and dispatch process. Attached Figure Description

[0009] Figure 1 A flowchart illustrating the steps of a visualization method for a flood forecasting and scheduling model based on digital twins; Figure 2 This is a schematic diagram of the forecast results from the flood forecasting and dispatching model; Figure 3 A dynamic schematic diagram of a digital twin visualization platform; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0010] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0011] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0012] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0013] To achieve the above objectives, please refer to Figures 1 to 3 A visualization method for flood forecasting and scheduling models based on digital twins includes the following steps: Step S1: Obtain hydrodynamic spatial data from flood forecasts based on the preset flood forecasting and scheduling model, and determine the spatial distribution range of floods within the basin based on the hydrodynamic spatial data; perform flood impact area division according to the spatial distribution range, and establish flood impact area status data; Step S2: Based on the flood-affected area status data, determine the trend of flood inundation degree and the temporal differences of water flow status in each area, and generate flood status change characteristics. Step S3: Combining the flood state change characteristics of each region, analyze the propagation direction and continuity of the flood between different regions within the basin, predict the evolution process of the flood between regions based on the propagation direction and continuity of the flood, and determine the evolution relationship of flood risk between regions based on the evolution process; Step S4: Determine the regional flood control strategy based on the evolution of flood risk between regions; determine the visualization order and display weight of each region in the flood forecasting and control process based on the regional flood control strategy, and drive the preset digital twin visualization platform to perform three-dimensional dynamic presentation.

[0014] All specific values ​​involved in this embodiment are exemplary parameters used to clearly illustrate the technical operation process and are not the only limitation of the present invention.

[0015] In one embodiment, the preset flood forecasting and scheduling model is a two-dimensional hydrodynamic numerical model. The model takes a watershed digital elevation model (DEM), river cross-section parameters, and boundary hydrological conditions as inputs, and outputs hydrodynamic spatial data including water depth, flow velocity, and water level during the forecasting calculation process. This hydrodynamic spatial data is uniformly mapped to the watershed spatial coordinate system. Flood coverage areas are extracted based on spatial units with water depths greater than a preset threshold, determining the spatial distribution range of the flood within the current forecast period. Furthermore, the flood coverage area is regionalized according to the watershed's sub-watershed boundaries, river segment distribution, or administrative units, and the corresponding hydrodynamic spatial data is collected on a regional basis to form flood-affected area status data.

[0016] For each flood-affected area, water depth data for each spatial unit within the area is extracted at consecutive forecast times. The time-varying sequences of the regional average water depth and maximum water depth are calculated, and the increasing, stabilizing, or decreasing trends of the regional inundation level are determined based on these sequences. Simultaneously, flow velocity data within the area is extracted, and a temporal distribution of regional flow velocity over time is constructed to characterize the differences in regional flow states at different forecast times. The trends in inundation levels and the temporal differences in flow states are combined as characteristics of regional flood state changes.

[0017] Based on the spatial adjacency of flood-affected areas and the temporal sequence of flood state changes, the propagation direction of flood state from upstream to downstream areas is analyzed. Simultaneously, the temporal continuity of flood state changes in adjacent areas is compared to determine the continuity of the flood propagation process. Based on this, the propagation path of the flood between regions is extrapolated, the regional evolution of the flood in subsequent moments is predicted, and the evolutionary relationship of flood risk between regions from earliest to latest is determined based on the evolutionary process.

[0018] Based on the evolution of flood risk across regions, areas with the highest risk are designated as priority areas for flood control, and regional-level flood control strategies are generated accordingly. Furthermore, the display order of each region in the visualization process is determined based on the control strategies, and corresponding display weight parameters are assigned to different regions. The display order and display weights are input into the digital twin visualization platform, driving the platform to dynamically present the flood evolution process in different regions in three dimensions.

[0019] Please refer to [link / reference needed] for further information. Figure 2 The graph marks real-time water level and flow data for key water conservancy projects. The blue areas represent the flood inundation range predicted by the model, vividly simulating the spatial distribution and evolution trend of water potential within the basin. By overlaying abstract forecast results with the real geographical environment, it provides decision-makers with a three-dimensional visualization of flood risk propagation paths and key prevention and control areas.

[0020] Please refer to [link / reference needed] for further information. Figure 3 The diagram illustrates the four core steps from flood data to inundation range analysis. The data acquisition section shows the original water depth distribution and geographic information. The spatial unit division refines the region into computable units through gridding. The variation relationship analysis in the middle right section reveals the correlation and differences of water depth values ​​at different locations through curve graphs. The expansion feature determination uses the blue area to visually present the predictive expansion pattern of the flood inundation range.

[0021] Of particular importance, step S1 includes: Based on the pre-set flood forecasting and scheduling model, the hydrodynamic spatial data output during the flood forecasting calculation process is obtained, and the spatial coordinates of the hydrodynamic spatial data are unified to form a standardized hydrodynamic dataset. Based on standardized hydrodynamic datasets, spatial distribution information of floods within the watershed is extracted to determine the spatial coverage of floods during the current forecast period; Based on the spatial coverage of floods, the watershed space is divided into zones to generate multiple flood-affected areas; The hydrodynamic spatial data of each flood-affected area are collected to form the state data of the flood-affected area.

[0022] In one embodiment, during the operation of the flood forecasting and scheduling model, hydrodynamic spatial data output for the current forecast period is directly obtained from the model calculation module. This hydrodynamic spatial data includes at least water depth, water level, and flow velocity data at different spatial locations, along with corresponding spatial coordinate information. The obtained hydrodynamic spatial data undergoes a coordinate reference check, and spatial coordinates from different sources are uniformly converted to the same watershed coordinate reference system to eliminate coordinate offsets and form a standardized hydrodynamic dataset.

[0023] After standardization, the standardized hydrodynamic dataset is spatially traversed. Based on the spatial locations where the water depth or water level is not zero, the spatial distribution information of the flood within the watershed is extracted. The continuously distributed spatial locations are then aggregated to determine the spatial coverage of the flood within the current forecast period.

[0024] Based on the determined flood spatial coverage, the watershed space within the coverage area is divided into zones according to preset watershed spatial division rules. These division rules include division based on river course, topographical zoning, or administrative grids to generate multiple flood-affected areas. Subsequently, using each flood-affected area as a unit, the hydrodynamic spatial data falling within the corresponding area is filtered and collected, and the collected hydrodynamic spatial data is bound to the corresponding area identifier to form flood-affected area status data.

[0025] Preferably, step S2 includes: In the flood-affected area status data, obtain the spatial distribution data of water depth for each area, analyze the spatial differences in water depth distribution within the area, and determine the expansion characteristics of the flood inundation range of each area. Based on the expansion characteristics of the flood inundation range in each region, abrupt changes in water depth at the regional boundaries are identified, and the changing trends of flood inundation degree in different regions are determined. In the state data of flood-affected areas, the velocity distribution at different spatial locations within the area is extracted to determine the structural differences in the flow state in each area; By combining the changing trends of flood inundation levels and structural differences in water flow patterns in different regions, the characteristics of flood state changes are distinguished, and flood state change characteristics are generated.

[0026] In one embodiment, based on the flood-affected area status data formed in step S1, corresponding water depth spatial distribution data is extracted for each flood-affected area. The water depth spatial distribution data is stored in the form of a regular grid with a grid resolution of 50m × 50m. Within a single area, the water depth values ​​of each grid cell are statistically analyzed to calculate the maximum, minimum, and standard deviation of the water depth within the area. The spatial difference in water depth distribution within the area is characterized by the water depth gradient change rate. When the water depth change rate between adjacent grid cells exceeds a preset threshold, it is determined that the flood inundation range of that area is in an expanding state.

[0027] Furthermore, the water depth change sequence of boundary grid cells is extracted along the spatial boundary of each flood-affected area. The water depth difference between adjacent grids inside and outside the boundary is compared and analyzed. When abrupt changes in water depth difference occur at multiple consecutive boundary locations, the corresponding location is identified as a region of abrupt water depth change, and the trend of flood inundation degree in that region over time is determined accordingly.

[0028] The spatial distribution data of flow velocity in each region is obtained from the state data of flood-affected areas. The flow velocity vectors at different spatial locations within the region are statistically analyzed, and the dispersion of flow velocity direction and the distribution characteristics of flow velocity amplitude are calculated to characterize the structural differences in the flow state within the region. The changing trends of regional flood inundation levels and the structural differences in flow state are jointly analyzed, and the flood state change characteristics of each region are classified and labeled to form a flood state change characteristic dataset for the corresponding region.

[0029] Preferably, in the flood-affected area status data, the spatial distribution data of water depth for each area is obtained, the spatial differences in water depth distribution within the area are analyzed, and the expansion characteristics of the flood inundation range of each area are determined, including: In the flood-affected area status data, the spatial distribution data of water depth in each area is obtained, and the area is divided into spatial units according to the preset spatial resolution; Based on the spatial distribution data of water depth within each spatial unit, the relationship between water depth changes at different spatial locations within the region is analyzed, and the spatial differences in water depth distribution within the region are quantified. Based on spatial differences, the spatial direction of continuous increase in water depth and the location of boundary changes within the region are identified to determine the expansion characteristics of the flood inundation range in each region.

[0030] In one embodiment, based on the flood-affected area status data established in step S1, corresponding water depth spatial distribution data is extracted for each flood-affected area, and the area is divided into grids according to a preset spatial resolution. The spatial resolution is set to a fixed-size two-dimensional spatial unit according to the watershed scale, so that each spatial unit corresponds to a unique water depth value or water depth statistical value.

[0031] After completing the spatial unit division, the spatial distribution data of water depth in each spatial unit within the region are compared and analyzed. The water depth difference and water depth change rate between adjacent spatial units are calculated, and a water depth change sequence is constructed along the spatial coordinate direction. This is used to quantify the relationship of water depth change between different spatial locations within the region and form a spatial difference description of water depth distribution within the region.

[0032] Furthermore, based on the spatial differences in water depth distribution, the spatial direction in which water depth increases unidirectionally in continuous spatial units is identified, and the locations where the rate of change of water depth increases significantly in this spatial direction are marked. Combined with the location of the regional boundary, the boundary location where the flood inundation range changes is determined, thereby determining the flood inundation range expansion characteristics of the corresponding flood-affected area.

[0033] Preferably, analyzing the relationship between water depth gradient changes at different spatial locations within the region, and quantifying the spatial differences in water depth distribution within the region, includes: Based on the spatial distribution data of water depth within each spatial unit, the water depth difference between adjacent spatial units is calculated. Based on the water depth difference, the direction and intensity of water depth change in different directions of each spatial unit are determined, forming the water depth gradient directional characteristics; Based on the water depth gradient direction characteristics, the set of spatial units in the region with consistent water depth change direction and similar change intensity is identified, and the water depth gradient partitioning results within the region are generated. Based on the results of water depth gradient zoning within the region, a comparative analysis is conducted on the differences in water depth variation between different zones to form the spatial differences in water depth distribution within the region.

[0034] In one embodiment, after the flood-affected area is delineated, the spatial distribution data of water depth within a single area is processed. First, the area is divided into multiple regular spatial units according to a preset spatial resolution. Each spatial unit corresponds to a water depth value, which is the average value of the water depth data within the unit or the water depth value at the center point.

[0035] Based on this, for any spatial unit, its adjacent spatial units in the east, west, south, north, and diagonal directions are selected respectively. The water depth difference between the spatial unit and its adjacent spatial units is calculated, resulting in a set of water depth difference values ​​in multiple directions, which is used to characterize the water depth variation around the spatial unit. Further, based on the set of water depth difference values, the direction of water depth variation and the corresponding intensity of variation in different directions for each spatial unit are determined. The direction of water depth variation is determined by the sign of the water depth difference value, and the intensity of water depth variation is characterized by the absolute value of the water depth difference value, thereby generating a corresponding water depth gradient direction feature for each spatial unit.

[0036] Cluster analysis is performed on the water depth gradient direction characteristics of all spatial units within the region to identify sets of spatial units with consistent water depth change directions and change in intensity within a preset difference threshold range. Each set is then divided into a water depth gradient partition, generating water depth gradient partitioning results within the region.

[0037] After obtaining the water depth gradient partitioning results, the water depth variation amplitude characteristics of spatial units in different partitions are statistically analyzed, and the water depth variation amplitudes between different partitions are compared and analyzed to determine the degree of difference between each water depth gradient partition, thereby forming a spatial difference description of the water depth distribution within the region.

[0038] Preferably, based on the expansion characteristics of the flood inundation range in each region, abrupt changes in water depth at the regional boundaries are identified, and the changing trends of flood inundation intensity in different regions are determined, including: Based on the expansion characteristics of the flood inundation range in each region, the positional changes of the flood inundation boundary in each region during adjacent evolution stages are extracted to form boundary evolution data; Based on boundary evolution data, identify areas of abrupt changes in water depth at the regional boundary; Based on the distribution of abrupt changes in water depth along the regional boundary, the temporal progression of the regional flood inundation boundary is analyzed. Based on the progress, the expansion acceleration of the flood inundation area in the region over time is calculated, thereby determining the changing trend of the flood inundation degree in each region.

[0039] In one embodiment, after obtaining the expansion characteristics of the flood inundation range of each region, the inundation range of the same region in multiple consecutive flood evolution stages is compared. For each evolution stage, the flood inundation boundary corresponding to that region is extracted, and the flood inundation boundaries of corresponding regions in adjacent evolution stages are registered. The changes in the spatial position of the boundaries are compared to obtain the displacement direction and displacement amount of the flood inundation boundary in adjacent evolution stages, thereby forming regional boundary evolution data.

[0040] Based on this, and combined with the boundary evolution data, the spatial distribution data of water depth near the regional boundary is analyzed. For boundary segments with large boundary advance, water depth data of adjacent spatial units inside and outside the boundary segment are extracted, the water depth difference inside and outside the boundary is calculated, and the water depth difference is statistically judged to identify boundary segments with significant changes in water depth difference and determine the water depth abrupt change areas at the regional boundary.

[0041] Furthermore, based on the spatial distribution of the water depth abrupt change regions on the regional flood inundation boundary, the temporal progression of the regional flood inundation boundary is analyzed. By comparing the distribution density and positional changes of the water depth abrupt change regions on the boundary in different evolution stages, the sequential relationship and continuity of the flood inundation boundary's progression in different directions are determined, thereby identifying the temporal progression status of the regional flood inundation boundary.

[0042] After determining the temporal progression, based on the changes in the flood inundation boundary displacement in adjacent evolution stages, the change in the advancement speed of the regional flood inundation range in the time dimension is calculated, and the rate of change of the advancement speed between adjacent evolution stages is further calculated as the temporal expansion acceleration of the flood inundation range. Based on the magnitude and direction of this expansion acceleration, the changing trend of the flood inundation degree in the corresponding region is determined.

[0043] Preferably, based on boundary evolution data, the regions where water depth changes abruptly at the regional boundary are determined include: Based on the boundary evolution data, the advancing displacement of the regional flood-inundated boundary in adjacent evolution stages is extracted, and the rate of change of the boundary advancing speed is calculated; Based on the rate of change of propulsion speed, boundary segments with a rate of change exceeding a preset threshold are identified, and candidate regions for discontinuous boundary propulsion are determined. By combining the spatial distribution data of water depth in the candidate regions, a local analysis of the water depth gradient at the boundary is conducted to confirm the degree of water depth abrupt change and identify the water depth abrupt change region at the boundary.

[0044] In one embodiment, based on established regional flood inundation boundary evolution data, a time-series analysis is performed on the flood inundation boundaries of the same region across multiple consecutive flood evolution stages. For two adjacent evolution stages, the flood inundation boundaries of the corresponding stages are extracted, and the boundaries of the two stages are spatially registered. The advancing displacement of the corresponding boundary points in the normal direction is calculated. The advancing displacement is removed by the time interval between adjacent evolution stages to obtain the boundary advancing velocity corresponding to each boundary point. Furthermore, the boundary advancing velocities in consecutive evolution stages are compared, and the rate of change of the boundary advancing velocity between adjacent evolution stages is calculated as the boundary advancing velocity change rate.

[0045] After obtaining the rate of change of the boundary advance velocity, the regional flood-inundated boundary is segmented along its length. For each boundary segment, the rate of change of the advance velocity corresponding to each boundary point within that segment is calculated and compared with a preset rate of change threshold. When the rate of change of the advance velocity in a certain boundary segment exceeds the preset threshold, that boundary segment is marked as a candidate region of boundary discontinuity, thereby selecting several candidate regions on the regional flood-inundated boundary.

[0046] After determining the candidate regions, the water depth variations near the candidate regions are further analyzed by combining the corresponding spatial distribution data of water depth. Specifically, adjacent spatial units are selected inside and outside the candidate regions, and the water depth data of the corresponding spatial units are extracted. The water depth difference and water depth gradient variation on both sides of the boundary are calculated. By analyzing the magnitude of the water depth gradient variation in the normal direction of the boundary, it is determined whether the water depth variation in the candidate regions exhibits abrupt changes. When the magnitude of the water depth gradient variation in the candidate regions exceeds the preset water depth gradient determination criteria, the candidate region is confirmed as a water depth abrupt change region at the region boundary.

[0047] Preferably, in the flood-affected area status data, the velocity distribution at different spatial locations within the area is extracted to determine the structural differences in the flow state in each area, including: In the flood-affected area status data, the flow velocity magnitude and flow direction information at each spatial location within the area are extracted to construct the regional water flow direction distribution; Based on the distribution of water flow direction, a continuous spatial range with consistent water flow direction within the region is identified, forming a continuous water flow direction zone; Within the continuous region of the water flow direction, the spatial distribution consistency of the flow velocity magnitude is analyzed, and local spatial locations where the flow velocity deviates significantly from the overall distribution characteristics of the continuous region of the water flow direction are identified. Based on the consistency of flow velocity distribution and local spatial location, the structural differences in water flow state in each region are determined.

[0048] In one embodiment, based on established flood-affected area status data, hydrodynamic calculation results for a single flood-affected area during the current forecast period are extracted. Specifically, using spatial grids or irregular computational units as the basic analysis objects, flow velocity magnitude data and flow direction angle data corresponding to each spatial location are obtained, and the flow direction angle is normalized using a unified coordinate system. This constructs a regional flow direction distribution at the regional scale, which characterizes the overall flow direction characteristics within the region.

[0049] After constructing the regional water flow direction distribution, connectivity analysis is performed on spatial locations within the region based on the consistency of flow direction between adjacent spatial locations. Specifically, a preset flow direction difference threshold is used as a judgment condition. When the flow direction difference between adjacent spatial locations is less than the threshold, the two spatial locations are determined to be in a state of consistent water flow direction. By connecting and aggregating spatial locations that meet the consistency condition, multiple continuous spatial ranges with consistent water flow direction within the region are identified, and each continuous spatial range is marked as a continuous water flow direction region.

[0050] After determining the continuous flow direction zones, the spatial distribution consistency of flow velocity magnitude is further analyzed for each zone. Specifically, the flow velocity magnitude at each spatial location within the continuous flow direction zone is statistically analyzed, and the mean flow velocity and velocity dispersion within the zone are calculated. This is used as the overall distribution characteristic of the continuous flow direction zone. Based on this, the flow velocity magnitude at each spatial location within the continuous zone is compared with the overall distribution characteristic. When the flow velocity magnitude at a certain spatial location deviates from the mean flow velocity of the continuous zone by more than a preset deviation threshold, that spatial location is identified as a local spatial location where the flow velocity significantly deviates from the overall distribution characteristic of the continuous flow direction zone.

[0051] After identifying the local spatial locations, the structural judgment of the water flow state within the region is made by comprehensively considering the consistency of the velocity distribution within the continuous region of water flow direction and the spatial distribution characteristics of the local spatial locations. Specifically, when there are multiple local spatial locations with significantly deviated velocities within the continuous region of water flow direction, and these local spatial locations are not randomly distributed in space, it is determined that the water flow state in this region has obvious structural differences; conversely, when the velocity distribution within the continuous region of water flow direction is generally consistent and the local deviations are small, it is determined that the water flow state structure in this region is relatively stable, thereby completing the determination of the structural differences in the water flow state in each region.

[0052] Preferably, within the continuous region of the water flow direction, the spatial distribution consistency of the flow velocity magnitude is analyzed, and local spatial locations where the flow velocity significantly deviates from the overall distribution characteristics of the continuous region of the water flow direction are identified, including: Within the continuous region of water flow direction, a benchmark for water flow performance is constructed based on the magnitude of flow velocity and consistency of flow direction at each spatial location. Based on the water flow performance benchmark, the degree of deviation of the flow velocity at each spatial location from the water flow performance benchmark is analyzed, the local spatial location of the flow velocity deviation from the overall distribution characteristics of the continuous area of ​​the water flow direction is determined, and the corresponding velocity parameters are marked. Based on the relationship between velocity parameters and flow direction changes, determine the water surface disturbance parameters corresponding to local spatial locations; By combining the continuous spatial distribution of water surface disturbance parameters, the structural discrimination of water flow state in the continuous region of water flow direction is carried out to determine the local spatial location where the flow velocity deviates significantly from the overall distribution characteristics of the continuous region of water flow direction.

[0053] In one embodiment, within the identified continuous region of water flow direction, the spatial locations contained within this continuous region are used as the analysis objects to obtain the flow velocity magnitude data and flow direction data corresponding to each spatial location. First, the consistency of flow direction within the continuous region of water flow direction is confirmed, and spatial locations whose flow direction deviates from the mainstream direction of the continuous region by more than a preset angle threshold are filtered out. In the remaining spatial locations, the flow velocity magnitude is statistically analyzed to calculate the representative value range of flow velocity within the continuous region. Combined with the flow direction consistency results, a water flow performance benchmark within the continuous region of water flow direction is constructed to characterize the overall water flow state of the continuous region at the current moment.

[0054] After establishing a flow performance benchmark, this benchmark is used as a comparison reference to analyze the flow velocity at each spatial location within the continuous flow direction. Specifically, the deviation between the actual flow velocity at each spatial location and the flow performance benchmark is calculated. When the deviation exceeds a preset deviation threshold, the flow velocity at that spatial location is determined to deviate from the overall distribution characteristics of the continuous flow direction, and the spatial location is marked as a local spatial location. Simultaneously, the corresponding flow velocity deviation magnitude or deviation level is recorded as the velocity parameter corresponding to that spatial location.

[0055] After obtaining the velocity parameters, a joint analysis is further performed in conjunction with the flow direction changes at that local spatial location. Specifically, the velocity parameters are correlated with the flow direction offset at the corresponding spatial location. When both the velocity parameters and the flow direction offset simultaneously meet the disturbance determination conditions, it is determined that there are obvious water surface disturbance characteristics at that local spatial location. Based on this, corresponding water surface disturbance performance parameters are generated for that spatial location to characterize the degree of abnormality in the water flow state at that location.

[0056] After generating the surface disturbance performance parameters, the spatial distribution of all surface disturbance performance parameters within the continuous region of the flow direction is analyzed as a whole. Specifically, it is determined whether the surface disturbance performance parameters exhibit spatial clustering or continuous distribution characteristics within the continuous region. When the surface disturbance performance parameters form a continuous distribution area in space, it is determined that there is a structural change in the flow state within the continuous region of the flow direction. Based on this, the structural discrimination of the flow state within the continuous region of the flow direction is completed, and finally, the local spatial location where the flow velocity deviates significantly from the overall distribution characteristics of the continuous region of the flow direction is determined.

[0057] Of particular importance, step S3 includes: Based on the flood state change characteristics of each region, the spatial sequence distribution relationship of flood state changes between regions is extracted, and the propagation direction of flood between adjacent regions is identified. Based on the direction of flood propagation between regions, the continuous transmission of flood state changes between regions is analyzed to determine the continuity characteristics of regional state changes during flood propagation. Based on the characteristics of propagation direction and continuity, the evolution path of floods in different regions is extrapolated, and the evolution process of floods in different regions is predicted; Based on the evolution of floods across regions, the sequence of relationships between regions in the flood evolution is determined, thus forming the evolutionary relationship of flood risk across regions.

[0058] In one embodiment, after obtaining the flood state change characteristics corresponding to each flood-affected area, the flood state change characteristics of each area within the same forecast period are used as the basis for analysis to compare and analyze the spatial distribution relationship of flood state changes between areas. Specifically, for any adjacent area, the temporal order of occurrence and spatial distribution of its flood state change characteristics are compared. When the flood state change characteristics of a certain area appear earlier than those of the adjacent area in time and are located upstream or outward of the adjacent area in spatial location, it is determined that the flood state change propagates from the former area to the latter area, and the spatial sequence of flood state changes between areas is extracted accordingly to identify the direction of flood propagation between adjacent areas.

[0059] After identifying the direction of flood propagation between regions, the propagation direction is used as a constraint to perform a continuity analysis on the transmission of flood state changes between regions. Specifically, along the identified propagation direction, the flood state change characteristics of multiple adjacent regions are sequentially correlated. When the flood state change characteristics appear sequentially in adjacent regions and the change trends are consistent or progressive, it is determined that the flood state changes in the regional sequence have continuous transmission characteristics, and the continuity characteristics of regional state changes during flood propagation are determined accordingly.

[0060] After obtaining the direction and continuity characteristics of flood propagation, the evolution path of the flood across different regions is deduced based on these characteristics. Specifically, each region is considered as a node, and the direction of flood propagation between regions is used as the node connection relationship. Effective propagation paths are then selected by combining continuity characteristics, thereby constructing an evolution path model of flood propagation across regions within the basin. Based on this evolution path model, the evolution process of the flood between regions is predicted.

[0061] After forecasting the flood evolution process, the positional relationships of each region along the flood propagation path are analyzed and ranked based on the flood's evolution across regions. Specifically, the order of association between regions in the flood evolution is determined according to their sequential positions along the path, and this order is compiled into a regional-level sequence relationship, thus forming the evolutionary relationship of flood risk across regions, which can be used in subsequent steps to determine regional-level flood control strategies.

[0062] Preferably, step S4 includes: Based on the evolution of flood risk across regions, the order of risk changes in each region during the flood evolution process is ranked to construct a regional flood risk evolution sequence; Determine regional flood control strategies based on the regional flood risk evolution sequence; Based on the regional flood control strategy and combined with the characteristics of flood status changes in each region, the visualization order of each region in the flood forecasting and control process is determined, and the display weight parameters corresponding to each region are determined based on the visualization order. Based on the visualization display order and display weight parameters, the preset digital twin visualization platform is driven to present each area in three-dimensional dynamic form.

[0063] In one embodiment, after obtaining the evolution relationship of flood risk among regions, the order of risk changes in each region during the flood evolution process is first sorted. Specifically, based on the evolution relationship of flood risk among regions, each region is arranged according to its position in the flood propagation path. When a region is upstream or in a leading position in the evolution relationship, it is placed at the beginning of the evolution sequence, thereby constructing a regional flood risk evolution sequence that reflects the order of flood risk transmission among regions over time.

[0064] After obtaining the regional flood risk evolution sequence, this sequence is used as input into the flood forecasting and dispatching model. Specifically, based on the region's position in the evolution sequence and in conjunction with preset dispatching rules, the relationship between each region and the need for priority dispatching, coordinated dispatching, or delayed dispatching during the flood evolution process is determined, thereby identifying the corresponding regional flood dispatching strategy. This ensures that the dispatching strategy at the regional level is consistent with the evolution sequence of flood risk.

[0065] After determining the regional flood control strategy, and based on this strategy and the flood state change characteristics of each region, the visualization order of each region during the flood forecasting and control process is determined. Specifically, regions prioritized or heavily controlled in the control strategy are designated as priority display regions. The visualization order is further refined based on the magnitude of changes in the flood state change characteristics of each region, and corresponding display weight parameters are assigned to each region based on this visualization order.

[0066] After determining the visualization display order and display weight parameters, the display order and display weight parameters are synchronously transmitted to a preset digital twin visualization platform. The digital twin visualization platform controls the 3D presentation sequence of each region according to the display order, and adjusts the display level, update frequency, and performance intensity of each region in the 3D scene according to the display weight parameters, thereby providing a 3D dynamic presentation of the flood evolution status of each region.

[0067] Preferably, based on the evolution relationship of flood risk across regions, the order of risk changes in each region during the flood evolution process is ranked to construct a regional flood risk evolution sequence, including: Based on the evolution of flood risk across regions, the direction of risk transmission between regions is determined, and the risk evolution correlation between regions is formed. Based on the correlation of risk evolution, the chronological relationship of risk changes in each region during the flood evolution process is determined, and the regional risk evolution sequence is generated; According to the order of regional risk evolution, the regions are arranged sequentially to construct a regional flood risk evolution sequence.

[0068] In one embodiment, after obtaining the evolution relationship of flood risk between regions, the direction of risk transmission between regions is first determined. Specifically, using the direction of flood propagation and the continuity of change between regions as input, for any pair of adjacent regions, it is determined whether the change in flood state is transmitted from the previous region to the next region. When the change in flood state in the next region lags behind the previous region in time and the trend of change remains continuous, it is determined that the risk is transmitted from the previous region to the next region, thereby forming a risk evolution relationship between regions.

[0069] After establishing risk evolution correlations between regions, the chronological order of risk changes in each region during the flood evolution process is determined based on these correlations. Specifically, based on the regional correlation network formed by the risk evolution correlations, regions without risk inputs are identified as the starting points for risk changes and used as the starting point for sorting. For regions with a single risk input, their relative order is determined based on the source of that risk input. For regions with multiple risk inputs, the order is determined based on the temporal consistency of the risk transmission direction in their corresponding regions, thereby generating the regional risk evolution sequence.

[0070] After generating the regional risk evolution sequence, the regions are arranged sequentially according to this sequence. Specifically, each region in the regional risk evolution sequence is mapped to an ordered sequence index in chronological order, and these are arranged sequentially to form a complete regional flood risk evolution sequence. This regional flood risk evolution sequence serves as the input basis for determining subsequent regional-level flood control strategies.

[0071] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A flood forecasting scheduling model visualization method based on digital twinning, characterized in that, Includes the following steps: Step S1: Obtain hydrodynamic spatial data from flood forecasts based on the preset flood forecasting and scheduling model, and determine the spatial distribution range of floods within the basin based on the hydrodynamic spatial data; perform flood impact area division according to the spatial distribution range, and establish flood impact area status data; Step S2: Based on the flood-affected area status data, determine the trend of flood inundation degree and the temporal differences of water flow status in each area, and generate flood status change characteristics. Step S3: Combining the flood state change characteristics of each region, analyze the propagation direction and continuity of the flood between different regions within the basin, predict the evolution process of the flood between regions based on the propagation direction and continuity of the flood, and determine the evolution relationship of flood risk between regions based on the evolution process; Step S4: Determine the regional flood control strategy based on the evolution of flood risk between regions; determine the visualization order and display weight of each region in the flood forecasting and control process based on the regional flood control strategy, and drive the preset digital twin visualization platform to perform three-dimensional dynamic presentation.

2. The digital-twin-based flood forecast scheduling model visualization method according to claim 1, characterized in that, Step S2 includes: In the flood-affected area status data, obtain the spatial distribution data of water depth for each area, analyze the spatial differences in water depth distribution within the area, and determine the expansion characteristics of the flood inundation range of each area. Based on the expansion characteristics of the flood inundation range in each region, abrupt changes in water depth at the regional boundaries are identified, and the changing trends of flood inundation degree in different regions are determined. In the state data of flood-affected areas, the velocity distribution at different spatial locations within the area is extracted to determine the structural differences in the flow state in each area; By combining the changing trends of flood inundation levels and structural differences in water flow patterns in different regions, the characteristics of flood state changes are distinguished, and flood state change characteristics are generated.

3. The digital-twin-based flood forecast scheduling model visualization method according to claim 2, characterized in that, In the flood-affected area status data, the spatial distribution data of water depth for each area is obtained, the spatial differences in water depth distribution within the area are analyzed, and the expansion characteristics of the flood inundation range of each area are determined, including: In the flood-affected area status data, the spatial distribution data of water depth in each area is obtained, and the area is divided into spatial units according to the preset spatial resolution; Based on the spatial distribution data of water depth within each spatial unit, the relationship between water depth changes at different spatial locations within the region is analyzed, and the spatial differences in water depth distribution within the region are quantified. Based on spatial differences, the spatial direction of continuous increase in water depth and the location of boundary changes within the region are identified to determine the expansion characteristics of the flood inundation range in each region.

4. The visualization method for flood forecasting and scheduling models based on digital twins according to claim 3, characterized in that, Analyzing the relationship between water depth gradient variations at different spatial locations within the region, and quantifying the spatial differences in water depth distribution within the region includes: Based on the spatial distribution data of water depth within each spatial unit, the water depth difference between adjacent spatial units is calculated. Based on the water depth difference, the direction and intensity of water depth change in different directions of each spatial unit are determined, forming the water depth gradient directional characteristics; Based on the water depth gradient direction characteristics, the set of spatial units in the region with consistent water depth change direction and similar change intensity is identified, and the water depth gradient partitioning results within the region are generated. Based on the results of water depth gradient zoning within the region, a comparative analysis is conducted on the differences in water depth variation between different zones to form the spatial differences in water depth distribution within the region.

5. The visualization method for flood forecasting and scheduling models based on digital twins according to claim 2, characterized in that, Based on the expansion characteristics of flood inundation range in each region, abrupt changes in water depth at regional boundaries are identified, and the changing trends of flood inundation intensity in different regions are determined, including: Based on the expansion characteristics of the flood inundation range in each region, the positional changes of the flood inundation boundary in each region during adjacent evolution stages are extracted to form boundary evolution data; Based on boundary evolution data, identify areas of abrupt changes in water depth at the regional boundary; Based on the distribution of abrupt changes in water depth along the regional boundary, the temporal progression of the regional flood inundation boundary is analyzed. Based on the progress, the expansion acceleration of the flood inundation area in the region over time is calculated, thereby determining the changing trend of the flood inundation degree in each region.

6. The visualization method for flood forecasting and scheduling models based on digital twins according to claim 5, characterized in that, Based on boundary evolution data, the regions where water depth changes abruptly at the regional boundary are identified as including: Based on the boundary evolution data, the advancing displacement of the regional flood-inundated boundary in adjacent evolution stages is extracted, and the rate of change of the boundary advancing speed is calculated; Based on the rate of change of propulsion speed, boundary segments with a rate of change exceeding a preset threshold are identified, and candidate regions for discontinuous boundary propulsion are determined. By combining the spatial distribution data of water depth in the candidate regions, a local analysis of the water depth gradient at the boundary is conducted to confirm the degree of water depth abrupt change and identify the water depth abrupt change region at the boundary.

7. The visualization method for flood forecasting and scheduling models based on digital twins according to claim 2, characterized in that, In the flood-affected area status data, the velocity distribution at different spatial locations within the area is extracted to determine the structural differences in the flow state in each area, including: In the flood-affected area status data, the flow velocity magnitude and flow direction information at each spatial location within the area are extracted to construct the regional water flow direction distribution; Based on the distribution of water flow direction, a continuous spatial range with consistent water flow direction within the region is identified, forming a continuous water flow direction zone; Within the continuous region of the water flow direction, the spatial distribution consistency of the flow velocity magnitude is analyzed, and local spatial locations where the flow velocity deviates significantly from the overall distribution characteristics of the continuous region of the water flow direction are identified. Based on the consistency of flow velocity distribution and local spatial location, the structural differences in water flow state in each region are determined.

8. The visualization method for flood forecasting and scheduling models based on digital twins according to claim 7, characterized in that, Within the continuous region of water flow direction, the spatial distribution consistency of flow velocity magnitude is analyzed, and local spatial locations where flow velocity significantly deviates from the overall distribution characteristics of the continuous region of water flow direction are identified, including: Within the continuous region of water flow direction, a benchmark for water flow performance is constructed based on the magnitude of flow velocity and consistency of flow direction at each spatial location. Based on the water flow performance benchmark, the degree of deviation of the flow velocity at each spatial location from the water flow performance benchmark is analyzed, the local spatial location of the flow velocity deviation from the overall distribution characteristics of the continuous area of ​​the water flow direction is determined, and the corresponding velocity parameters are marked. Based on the relationship between velocity parameters and flow direction changes, determine the water surface disturbance parameters corresponding to local spatial locations; By combining the continuous spatial distribution of water surface disturbance parameters, the structural discrimination of water flow state in the continuous region of water flow direction is carried out to determine the local spatial location where the flow velocity deviates significantly from the overall distribution characteristics of the continuous region of water flow direction.

9. The visualization method for flood forecasting and scheduling models based on digital twins according to claim 1, characterized in that, Step S4 includes: Based on the evolution of flood risk across regions, the order of risk changes in each region during the flood evolution process is ranked to construct a regional flood risk evolution sequence; Determine regional flood control strategies based on the regional flood risk evolution sequence; Based on the regional flood control strategy and combined with the characteristics of flood status changes in each region, the visualization order of each region in the flood forecasting and control process is determined, and the display weight parameters corresponding to each region are determined based on the visualization order. Based on the visualization display order and display weight parameters, the preset digital twin visualization platform is driven to present each area in three-dimensional dynamic form.

10. The visualization method for flood forecasting and scheduling models based on digital twins according to claim 9, characterized in that, Based on the evolution of flood risk across regions, the order of risk changes in each region during the flood evolution process is ranked to construct a regional flood risk evolution sequence, including: Based on the evolution of flood risk across regions, the direction of risk transmission between regions is determined, and the risk evolution correlation between regions is formed. Based on the correlation of risk evolution, the chronological relationship of risk changes in each region during the flood evolution process is determined, and the regional risk evolution sequence is generated; According to the order of regional risk evolution, the regions are arranged sequentially to construct a regional flood risk evolution sequence.

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