Drainage basin risk dynamic assessment-based household early warning method and decision-making platform

Abstract

The invention discloses a user arrival early warning method based on basin risk dynamic assessment and a decision-making platform, and belongs to the technical field of flood early warning. According to an existing flood risk early warning scheme, a unified response strategy is arranged by taking a village as a unit, and differential instruction issuing and accurate scheduling cannot be achieved according to the actual risk level of each user. According to the method, time-space association and directional extraction are carried out on risk assessment data, and drainage basin section monitoring data are generated; according to the method and the system provided by the invention, the monitoring data of the drainage basin section is subjected to user-level matching to obtain user-level risk data, so that user-level pushing and risk-level personalized presentation can be realized, an early-warning plan is enabled to be matched with an actual risk-avoiding demand, differential instruction issuing and accurate scheduling are realized, and the problems of mismatching of resource allocation, missed response windows and the like can be effectively avoided.

Description

TECHNICAL FIELD

[0001] The application relates to a to-house early warning method and decision platform based on dynamic watershed risk assessment, and belongs to the technical field of flood early warning. BACKGROUND

[0002] At present, most small watershed flood risk early warning schemes are based on risk division of villages, trend prediction is carried out by means of historical hydrological data and experience models, risk levels are mapped to administrative villages or natural villages, and a rough "one village, one strategy" emergency disposal mode is formed. In this scheme, risk early warning mainly guides resource allocation and personnel transfer by delimiting village inundation areas and marking key villages, but it is difficult to refine the specific risk differences of each house in the village.

[0003] In the disposal decision link, the traditional method relies on manual experience and average assumption to develop evacuation schemes, and often arranges a unified response strategy for villages as a unit, and cannot realize differentiated instruction and accurate scheduling according to the actual risk level of each house, resulting in that the emergency response accuracy is lower than the actual demand.

[0004] The information disclosed in this background section is only for the purpose of understanding the background of the inventive concept, so it can include information that does not constitute the prior art. SUMMARY

[0005] In view of the above technical problems, one of the application purposes of the present application is to provide a to-house early warning method and decision platform based on dynamic watershed risk assessment, which is used for performing spatio-temporal correlation and directional extraction on risk assessment data, generating watershed section monitoring data, then performing house-level matching on the watershed section monitoring data and associating house attributes to determine house-level water depth and risk value, obtaining house-level risk data, and then simulating a house-level flood evolution process according to the house-level risk data to generate house-level risk early warning results, so that house-level pushing and risk level individualization can be realized, the early warning plan can be matched with the actual risk avoidance demand, and thus the problems of resource allocation mismatch and response window loss can be effectively avoided.

[0006] In view of the above technical problems, another application purpose of the present application is to provide a to-house early warning method and decision platform based on dynamic watershed risk assessment, which is used for changing from traditional "village-level risk division" to "house-level risk identification and disposal" with "house" as the minimum granularity, constructing a house-level risk assessment model based on water depth simulation, obtaining the maximum water depth and inundation evolution process of each house in real time, extracting the maximum water depth of each house in different time periods, and generating a targeted response strategy and scheduling strategy in combination with house attributes and other parameters, so that more accurate emergency response can be realized.

[0007] In view of the above technical problems, another object of the present application is to provide a household early warning method and decision platform based on dynamic watershed risk assessment, which breaks through the village-level extensive assessment by constructing a household early warning mechanism, realizes household-level centimeter-level risk positioning, effectively improves the transfer efficiency, effectively reduces the disaster loss, and optimizes the resource allocation.

[0008] To achieve one of the above-mentioned objects, the first technical solution of the present application is: A household early warning method based on dynamic watershed risk assessment, comprising the following steps: Step one, collecting the spatial attribute information and risk dynamic assessment data of a certain watershed to obtain the watershed simulation results; Step two, analyzing the watershed simulation results to obtain risk assessment data; Step three, performing spatio-temporal correlation and directional extraction on the risk assessment data to generate watershed cross-section monitoring data; Step four, matching the watershed cross-section monitoring data at the household level, and associating the house attributes to determine the household-level water depth and risk value, and obtaining household-level risk data; Step five, simulating the household flood evolution process according to the household-level risk data to generate household-level risk warning results, and realizing the household early warning based on dynamic watershed risk assessment.

[0009] The present application performs spatio-temporal correlation and directional extraction on the risk assessment data to generate watershed cross-section monitoring data, then matches the watershed cross-section monitoring data at the household level, and associates the house attributes to determine the household-level water depth and risk value, and obtains household-level risk data, and then simulates the household flood evolution process according to the household-level risk data to generate household-level risk warning results, so that household-level push and risk grade personalized presentation can be realized, and the early warning plan can be matched with the actual risk avoidance demand, so that the problems of resource allocation mismatch and response window missing can be effectively avoided, and the scheme is scientific, practical and feasible.

[0010] Further, the present application takes "household" as the minimum granularity, changes from traditional "village-level risk division" to "household-level risk identification and disposal", constructs a household-level risk assessment model based on water depth simulation, obtains the maximum water depth and flooding evolution process of each household in real time, extracts the maximum water depth of each household in different time periods, and generates targeted response strategies and scheduling strategies in combination with house attributes and other parameters, and realizes more accurate emergency response.

[0011] As a preferred technical measure: Step one, the method for collecting the spatial attribute information and risk dynamic assessment data of a certain watershed to obtain the watershed simulation results is as follows: First, relying on unmanned aerial vehicle survey information and real estate registration data, household-level spatial attribute information is collected and integrated, and a house attribute data table is constructed. The household-level spatial attribute data at least includes house door number, household latitude and longitude, ground height, building type, structure type and household population; Then, the contact information of department heads, village heads and household heads is collected, and a personnel information data table is constructed; Then, at a certain interval period, the water level and flow data of the hydrological station in the region and the rainfall data of the rainfall station are collected, and a monitoring information data table is constructed according to the station code, collection time and hydrological quantity; The boundary file, geometry file, source item surface source positioning file and cross section positioning file of a certain watershed are collected to construct a simulation input file; the surface rainfall is calculated through the rainfall station data; Based on the model calculation input file and the surface rainfall, risk dynamic assessment data is generated, which at least includes watershed simulation result file and cross section flow file; the watershed simulation result file includes point physical quantity information at different time points, and the cross section flow file includes flow process of the control cross section; The risk dynamic assessment data, household-level spatial attribute data, personnel information data table and monitoring information data table are summarized to obtain the watershed simulation result.

[0012] As a preferred technical measure: Step two, the method for analyzing the watershed simulation result to obtain risk assessment data is as follows: First, extract the total time step information in the watershed simulation result to obtain the time step sequence; According to the time step sequence, each frame of data is traversed, and each single frame of data is analyzed to obtain grid topology information, which includes grid cell type identifier, cell vertex index sequence and grid boundary condition marker; Then, the point cell data is extracted from the grid topology information, which includes the coordinate values of each spatial point, the physical field attribute parameter values and the data validity identifier; the standard point cell data is obtained by performing coordinate conversion and data type standardization processing on the point cell data; The standard point cell data is stored in an unstructured grid container in order; the unstructured grid container includes a plurality of container instances, and each container instance corresponds to the complete spatial topology and physical quantity information of a single frame of data; After analyzing all time steps, a multi-frame unstructured grid container is constructed, which can associate each frame of data with the corresponding time stamp through an index mapping mechanism to form a spatio-temporal data set including complete space-time evolution information, which includes water depth and flow velocity at multiple time points; According to the watershed simulation result, the monitoring points of the cross section and the geographic vector information of each household in the village are obtained and stored in a vector data file; With the help of an open source cross-platform library for processing raster and vector geospatial data, the vector data file is parsed to obtain a cross-section vector data file, which is organized in the form of layers, and each element in the layer includes geometric information and attribute information. The cross-section vector data file and the spatio-temporal data set are combined to obtain risk assessment data.

[0013] As a preferred technical measure: Step three, the method for spatio-temporal correlation and directional extraction of risk assessment data to generate basin cross-section monitoring data is as follows: According to the cross-section vector data file, a cross-section point grid is established; According to the range of a certain village and the latitude and longitude, the cross-section point grid is cropped to obtain a plurality of village cross-section points; According to the simulation mapping coordinates of the village cross-section points, the risk assessment data is directionally extracted to determine the water depth and flow rate values of each village cross-section point at several time points; Based on the water depth and flow rate values of the plurality of village cross-section points, water level time series and flow time series about the basin cross-section are established; The spatial information, water level time series and flow time series of the same time point and the same basin cross-section are correlated and integrated to obtain spatio-temporal correlation data, which includes the village identification of the cross-section, the spatial coordinate array, the water level time array and the flow array.

[0014] As a preferred technical measure: Step four, the method for household-level matching of basin cross-section monitoring data and associating with house attributes to determine household-level water depth and risk value to obtain household-level risk data is as follows: According to the vector data file, a household-level grid about each house door number in the village is established, which includes each household coordinate value and village identification; According to each household coordinate value in the household-level grid, the corresponding point in the basin cross-section monitoring data is found to obtain the corresponding house location information of each household; According to the required time period, the data frames in the basin cross-section monitoring data are divided into a plurality of data blocks as units by multi-thread parallel processing tasks; The water depth time series is extracted from each data block; According to a plurality of water depth time series, all house location information is traversed to extract the water depth value of each household in all frames in the current time segment to form a household-level water depth sequence, and the maximum water depth value in the household-level water depth sequence is determined; Based on the maximum water depth value and the association with the house attributes, the risk level of each household is determined; The multi-thread results are summarized to form a household-level water depth and risk data set including a plurality of time segments; From the household-level water depth and risk data set, the maximum water depth value and the maximum risk level of each household are determined to generate household-level risk data; at the same time, the maximum risk level of all house risks in the village is selected as the overall risk level of the village in the time period.

[0015] As a preferred technical measure: Based on the maximum water depth value and in association with the house attributes, the risk level of each household is determined: Obtain house attributes, including house number, latitude and longitude, and foundation height; Based on the foundation height of each household and the maximum water depth value, the maximum inundation water depth value of each household is determined; According to the preset water depth threshold, the risk level is divided, and the risk level determination rule is set, and the specific rule is as follows: When the maximum inundation water depth value is greater than or equal to N meters, it is determined as high risk and needs to be immediately relocated; When the maximum inundation water depth value is less than N meters and greater than or equal to N / 2, it is determined as medium risk and needs to be prepared for relocation; When the maximum inundation water depth value is less than N / 2 meters and greater than or equal to N / 10, it is determined as low risk and needs to be vigilant; When the maximum inundation water depth value is less than N / 10 meters, it is determined as no risk; According to the risk level determination rule and the maximum inundation water depth value of each household, the risk level of each household is determined.

[0016] Further, N can be 0.8 or 1 or 1.5, and the specific value can be determined according to the foundation height of the houses in the village.

[0017] As a preferred technical measure: Step five, according to the household-level risk data, simulate the flood evolution process to the household, and the method for generating household-level risk warning results is as follows: Based on the household-level risk data, according to one or more preset water depth thresholds, the maximum inundation water depth value and the maximum risk level of each household in the time period are traversed, the risk of each time period of the house is judged, the affected houses corresponding to the risk level are screened, and the household-level risk distribution data is generated, which is used to reflect the household risk changes of different time periods, different house structures and different foundation heights in the flood evolution process; The household-level risk distribution data is stored in a structured manner, the risk level time sequence and the house detailed location information are associated and stored, and the household-level risk mapping relationship of time-risk level-space position-maximum water depth is established; At the same time, based on the household-level risk mapping relationship, the inundation area data of the village, the cross-section hydrological data and the village-level water power parameters are determined to form multi-dimensional risk assessment information from macro to micro; Cross-verify the household-level risk according to the multi-dimensional risk assessment information; If the verification result is that the two risk assessment results are contradictory, that is, the overall grade of the village is high risk, but there is no high-risk household in the village, return to step four to recalculate the household-level risk data to make the early warning result consistent; If the verification result is that the two risk assessment results are consistent, store the multi-dimensional risk assessment information in the database, and then render the multi-dimensional risk assessment information through a three-dimensional digital engine to obtain a household-level risk early warning result with a dynamic cloud chart effect.

[0018] As a preferred technical measure: It also includes step six, generating risk prediction information in different time periods according to the household-level risk early warning result, and notifying the defense objects within the early warning range to form a disposal closed loop, which specifically includes the following contents: First, according to the household-level risk early warning result and the basin section monitoring data, the risk data, the number of households to be transferred, and the early warning indicators in different time periods are summarized according to the household dimension, and the mapping relationship in the house attribute data table is associated to form risk prediction information and a household early warning list; Then, according to the risk prediction information, customized notification content is generated, and a push template is matched, which includes a high-risk household push template and a medium-risk household push template, which is used to push to the defense objects within the early warning range; Finally, according to the owner contact information in the household early warning list, the notification is sent piece by piece, and is pushed to the village head and the departments of the basin at the same time, and the sending time and the receiving state are recorded; for the undelivered numbers, the village cadres are triggered to notify the linkage mechanism, and the notification record is stored in the database together with the household-level risk early warning result to provide a traceability basis for subsequent review, and a complete disposal closed loop is formed.

[0019] To achieve one of the above purposes, the second technical solution of the present application is: A household-level early warning method based on basin risk dynamic assessment, including the following contents: Collecting the risk dynamic assessment data of a certain basin to obtain basin simulation results; Analyzing the basin simulation results to obtain risk assessment data; Performing spatiotemporal correlation and directional extraction on the risk assessment data to generate basin section monitoring data; Matching the basin section monitoring data at the household level and associating the house attributes to determine the household-level water depth and risk value, and obtain household-level risk data; Simulating the household-level flood evolution process according to the household-level risk data to generate household-level risk early warning results; Generating risk prediction information in different time periods according to the household-level risk early warning result, and notifying the defense objects within the early warning range to form a disposal closed loop.

[0020] The application breaks through the village-level extensive evaluation, effectively improves the transfer efficiency, forms a "prediction-warning-disposal" closed loop, can realize the household-level centimeter-level risk positioning, and can adapt to different basins and disaster scenes, effectively reduces the disaster loss, and optimizes the resource allocation.

[0021] To achieve one of the above purposes, the third technical solution of the application is: A decision platform based on basin risk dynamic evaluation, comprising: One or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned one household warning method based on basin risk dynamic evaluation.

[0022] Compared with the prior art, the application has the following beneficial effects: The application performs spatio-temporal correlation and directional extraction on risk assessment data, generates basin section monitoring data; then matches the household level, and associates the house attributes to determine the household water depth and risk value, and obtains the household risk data; then according to the household risk data, simulates the household flood evolution process, generates the household risk warning result, so that the household push and risk grade personalized presentation can be realized, the warning plan and the actual risk avoidance demand are matched, and therefore the problems such as resource allocation mismatch and response window loss can be effectively avoided, the scheme is scientific, practical and feasible.

[0023] Further, the application takes "household" as the minimum granularity, changes from the traditional "village-level risk division" to "household-level risk identification and disposal", constructs a household-level risk assessment model based on water depth simulation, obtains the maximum water depth and the submergence evolution process of each household in real time, extracts the maximum water depth of each household in different time periods, and generates targeted response strategies and scheduling strategies combined with house attributes and other parameters, to realize more accurate emergency response.

[0024] Further, the application breaks through the village-level extensive evaluation, effectively improves the transfer efficiency, forms a "prediction-warning-disposal" closed loop, can realize the household-level centimeter-level risk positioning, and can adapt to different basins and disaster scenes, effectively reduces the disaster loss, and optimizes the resource allocation. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 The first flowchart of the household warning method based on basin risk dynamic evaluation of the application; Figure 2 A household risk detail diagram generated by applying the application (the risk level is distinguished by color). Figure 3 A cross-section broken line schematic diagram generated by applying the present application. DETAILED DESCRIPTION

[0026] In order for those skilled in the art to better understand the scheme of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application. The present application covers any substitution, modification, equivalent method and scheme made within the essence and scope of the present application defined by the claims.

[0027] As shown in Figure 1 The first specific embodiment of the household early warning method based on the dynamic assessment of the basin risk of the present application is as follows: A household early warning method based on the dynamic assessment of the basin risk, comprising the following steps: Step one, collecting the spatial attribute information and the risk dynamic assessment data of a certain basin to obtain the basin simulation results; Step two, analyzing the basin simulation results to obtain the risk assessment data; Step three, performing the spatio-temporal correlation and directional extraction on the risk assessment data to generate the basin cross-section monitoring data; Step four, performing the household-level matching on the basin cross-section monitoring data, and associating the house attributes to determine the household-level water depth and risk value, and obtaining the household-level risk data; Step five, simulating the household-level flood evolution process according to the household-level risk data to generate the household-level risk early warning results, and realizing the household early warning based on the dynamic assessment of the basin risk.

[0028] The second specific embodiment of the household early warning method based on the dynamic assessment of the basin risk of the present application is as follows: A household early warning method based on the dynamic assessment of the basin risk, comprising the following contents: Collecting the risk dynamic assessment data of a certain basin to obtain the basin simulation results; Analyzing the basin simulation results to obtain the risk assessment data; Performing the spatio-temporal correlation and directional extraction on the risk assessment data to generate the basin cross-section monitoring data; Performing the household-level matching on the basin cross-section monitoring data, and associating the house attributes to determine the household-level water depth and risk value, and obtaining the household-level risk data; Simulating the household-level flood evolution process according to the household-level risk data to generate the household-level risk early warning results; According to the household-level risk early warning result, risk prediction information in different time periods is generated, and the responsible person and the defense object in the early warning range are notified through the form of short message, so that a disposal closed loop is formed.

[0029] The third specific embodiment of the household early warning method based on the watershed risk dynamic assessment is as follows: A household early warning method based on watershed risk dynamic assessment, comprising the following steps: S1: collecting data to obtain watershed simulation results.

[0030] S2: reading the watershed simulation results and converting the watershed simulation results to obtain risk assessment data.

[0031] S3: processing the risk assessment data to generate watershed section monitoring data.

[0032] S4: calculating the household water depth and risk according to the watershed section monitoring data to obtain household risk data.

[0033] S5: platform processing the household risk data to obtain household risk early warning results.

[0034] S6: generating risk prediction information in different time periods according to the household-level risk early warning results, and notifying the responsible person and the defense object in the early warning range through the form of short message, so that a disposal closed loop is formed.

[0035] In S1 of this embodiment, data collection and information collection are not collected in the form of village-house, but rely on high-precision unmanned aerial vehicle surveying and mapping and real estate registration data to collect and integrate household-level spatial attribute data (including: house number, household longitude and latitude, ground height, building type, structure type, number of household members), and construct a house attribute data table house_table; collect the information and contact information of department heads, village heads and householders, and construct a personnel information data table person_table.

[0036] Collect water level and flow data of hydrological stations and rainfall data of rainfall stations at intervals of 5 minutes, and construct a monitoring information data table monitor_table through station code-collection time-physical quantity.

[0037] Collect the boundary file bc.cli, the geometry file geo.slf, and the source item surface source positioning file source_region.txt to construct a model calculation input file; at the same time, collect section information (longitude, latitude) and store it in the section positioning file ctrl_secs_YKX.txt. Calculate the surface rainfall , surface rainfall from the rainfall data of each station with the weighted sum of its Thiessen polygon area weights which is calculated as follows:

[0038] where the Thiessen polygon area weights cumulatively sum to 1.

[0039] According to the input file described above, a calculation operation file run.cas is constructed, and a simulation result file result.slf and a cross-section flow file flux.text are generated by using an existing simulation model calculation. The simulation result file is used to extract physical quantity information of points at different times, and the cross-section flow file flux.text is used to extract flow processes of control cross sections.

[0040] In S2 of the embodiment, a method for parsing the watershed simulation result file result.slf is as follows: First, total time step information is extracted, each frame of data is traversed in time sequence, grid topology information (including grid cell type identification, cell vertex index sequence, and grid boundary condition marker) is parsed for single frame data, point cell data (covering coordinate values, physical field attribute parameter values, and data validity identification of each spatial point) is extracted, and coordinate conversion and data type standardization processing are performed, that is, original binary data is converted into floating point type data compatible with a Visualization Toolkit (VTK), and the above information is sequentially stored into an unstructured grid container vtkUnstructuredGrid, wherein each container instance corresponds to complete spatial topology and physical quantity information of single frame data.

[0041] After parsing of all time steps is completed, a multi-frame unstructured grid container vector <vtkunstructuredgrid>The container associates each frame data with corresponding timestamp through index mapping mechanism, and finally forms a structured data set including complete space-time evolution information, providing a standardized and directly callable data source for subsequent data processing.

[0042] The cross-section monitoring points and the geographic vector information of each household in the village are stored in a vector data file Shapefile. The vector data file Shapefile is parsed by means of the vector data reading interface (OpenGIS Simple Features Reference Implementation, OGR) of the open source cross-platform library (Geospatial Data Abstraction Library, GDAL) for processing raster and vector geospatial data. First, the initialization interface (such as GDALAllRegister()) of the open source cross-platform library GDAL library is called to register all supported vector data drivers (including the vector data file Shapefile driver). The vector data file Shapefile is opened by means of the GDALOpenEx() function of the open source cross-platform library GDAL (GDAL will automatically associate the.shx,.dbf and other auxiliary files under the same path), and a object GDALDataset (vector data container) is returned. If the file does not exist or the format is not supported, an error message will be returned and output in the visual tool kit VTK log.

[0043] The vector data of the vector data file Shapefile is organized in the form of "layer" (a vector data file Shapefile usually corresponds to one layer), and the first (and only) layer is obtained by GetLayer(0), including the geometric and attribute information of all features. Each "feature" in the layer corresponds to a geographic entity (such as a point, a line, or a face), and all features are traversed by GetNextFeature(), each feature including geometric information OGRGeometry and attribute information OGRFeatureDefn. According to the type of geometric elements (point, line, and face) in the geometric information OGRGeometry, the corresponding visual tool kit VTK data structure is used for storage (point: vtkPoints, line: vtkPolyLine, face: vtkCellArray).

[0044] The field type of the attribute information OGRFeatureDefn (such as integer, string, float, etc.) is converted into the attribute array type of the visualization toolkit VTK (vtkIntArray, vtkStringArray, vtkDoubleArray, etc.). If the attribute is associated with a "point" (such as the ID of a point feature), the attribute array is stored in the point data vtkPointData; if the attribute is associated with the "entire feature" (such as the area of a surface feature), the attribute array is stored in the cell data vtkCellData. The point vtkPoints, the cell vtkCellArray, the point data vtkPointData, and the cell data vtkCellData obtained above are combined to form the unstructured grid vtkUnstructuredGrid.

[0045] In S3, based on S2, the cross-section point grid vtkUnstructuredGrid is obtained from the cross-section vector data file Shapefile. In the cross-section point grid vtkUnstructuredGrid, the longitude and latitude values of the cross-section points and the village numbers where the cross-section points are located are stored in the form of cell data, which can be read one by one using the cell data vtkCellData. According to the simulation mapping coordinates med-x and med-y values of the cross-section points, the point locator function vtkPointLocator is used to find the corresponding points in the basin simulation result grid vtkUnstructuredGrid, to obtain the point number PointID corresponding to each cross-section point. Since the basin simulation result is a multi-frame unstructured grid container vector <vtkunstructuredgrid>The physical quantity data of water depth, flow rate and the like at multiple time points are stored, and for each unstructured grid vtkUnstructuredGrid at a time point, the water level value FREE SURFACE of the point with point number PointID is read, and multi-threading is used for acceleration.

[0046] The monitoring flow file flut.txt is read, and in the file, the data is stored in the format of time point and flow value of each section point at the time point. The read flow value is reordered according to the section point number to obtain the flow value of each section point at different time points.

[0047] Subsequently, the spatial information of the section (such as longitude and latitude, section village ID, etc.), water level time series and flow time series are associated and integrated, and in the integration process, the space-time correspondence (i.e. the spatial information, water level and flow data of the same time point and the same section are matched one by one) is realized, and the integrated data is stored in the watershed section monitoring data storage file cadata.json in the preset format (such as JSON format, including section village ID, spatial coordinate array, water level time array and flow array), which provides support for subsequent risk assessment based on flow and water level change warning.

[0048] In S4 of the embodiment, based on S2, the household-level grid vtkUnstructuredGrid accurate to each house door number of a village is obtained from the vector data file Shapefile. According to the physical quantity village identifier vid in the household-level grid vtkUnstructuredGrid, the valid positive integer village identifier is screened to form a village identifier set villageIDs, and the data in the set is unique. According to the coordinate values med-x and med-y of each household in the household-level grid vtkUnstructuredGrid, the corresponding point in the watershed simulation result grid vtkUnstructuredGrid is found by using the point locator function vtkPointLocator to obtain the corresponding position information houseIDs of each household.

[0049] According to the required time period (for example, 30 minutes), the multiple frames of unstructured grids vector <vtkunstructuredgrid>The data frames in the data frame are divided into a plurality of data blocks in units of a certain number (for example, 15 frames), and the tasks are processed in multi-thread parallel.

[0050] For each data block, the water depth data sequence waterDepthVec in the range is extracted from the water depth data sequence. All house positions houseIDs are traversed, and the water depth values of each household in all frames in the current time segment are extracted to form a household-level water depth sequence houseDepthVec, and the maximum water depth value maxWaterDepth in the sequence.

[0051] Based on the maximum water depth value, the risk level Hr is determined, and the maximum water depth is h and the risk level is R. Wherein risk level 0 represents no risk, and the smaller the risk level value is, the higher the risk level is.

[0052]

[0053] When , the risk level is high risk and immediate relocation is required; When , the risk level is medium risk and preparation for relocation is required; When , the risk level is low risk and vigilance is required; When , the risk level is no risk.

[0054] The multi-threading results are summarized to form a household-level water depth and risk data set houseDepthHrs in each time segment.

[0055] According to the matching relationship between the village identifier set villageIDs and the physical quantity vid in the unstructured grid vtkUnstructuredGrid, the grid is divided into village grids villageGrids corresponding to each village, and the village grid set villageGrids is formed, and the village-level risk data in each time segment is processed in multi-thread.

[0056] For each time segment, all village grids villageGrids are traversed, and the original village identifier vid and position information of each village are extracted. Initialize the village household data structure householdData to store the household name, water depth and overall risk level corresponding to each risk level in the village, and the data is derived from the household-level water depth and risk data set houseDepthHrs.

[0057] The maximum risk value of all house risks in the village in the time segment is taken as the overall risk level of the village in the time segment. The data of each household at each time of each village is written into the household-level risk data house_risk.json.

[0058] In S5 of the embodiment, the post-processing result file in S4 is divided into three parts, including a single-frame data file json (for cloud map rendering), a catchment cross-section monitoring data storage file cadata.json (for water depth, flow, flow rate, and submerged area data extraction record), and a household-level risk data house_risk.json (household-level risk details).

[0059] For the risk-to-house processing link, the building data of each household is analyzed in depth. By traversing the village data VillageData in the risk data file risk.json, the household risk level is quantitatively evaluated at each 30-minute time node. The affected house location information and specific submerged water depth values under three different water depth thresholds (0.1 meters, 0.5 meters, and 1.0 meters) are recorded. These thresholds correspond to different risk level division standards.

[0060] For risk judgment of each time period, the maximum submerged water depth and maximum risk of each household in the time period are traversed as the maximum risk of the household in the time period. For each time system, detailed risk distribution data is generated, including risk level numerical value and specific house location list affected by corresponding water depth level. This processing method ensures the spatio-temporal refinement of risk assessment, which can accurately reflect the household risk changes of different time periods, different house structures, and different foundation heights in the flood evolution process.

[0061] Structured storage of risk information is adopted to associate and store the time series of risk levels and detailed house location information. Through JSON serialization processing, the affected house location information of the three water depth levels is encapsulated into a structured string for storage, establishing a complete mapping relationship of time-risk level-space location-maximum water depth.

[0062] At the same time, based on the catchment cross-section monitoring data storage file cadata.json, the global inundation area data, cross-section hydrological data (water level, flow), and village-level hydraulic parameters (maximum flow rate, average flow rate, maximum water depth, average water depth, etc.) are processed to form a multi-dimensional risk assessment system from macro to micro. Finally, all processing results are stored in batches in the database to provide comprehensive data support for disaster early warning, emergency response, and post-disaster assessment. Based on the single-frame data file json, according to the demand, the jump frame data is intercepted, and then the dynamic cloud map is rendered through the three-dimensional digital engine.

[0063] In S6 of the embodiment, based on the household-level risk early warning results output by dynamic rendering, a complete disposal closed loop of "risk research and judgment-notification push-response tracking" is constructed to realize the accurate reach of early warning information from the model to the end defense object. The specific process is as follows: First, the risk information is classified and extracted, and the method is as follows: From the post-processing generated house-level risk data house_risk.json and basin section monitoring data storage file cadata.json, the risk data of different time periods (such as 1 hour, 3 hours, 6 hours) is summarized according to the house dimension, including one / two / three house-level risks, the number of houses that need to be transferred (water depth ≥ 0.5m or risk index ≥ 0.7), and the key warning indicators (maximum submerged water depth, evacuation window period), and the mapping relationship in the house attribute data table house_table in S1 is associated to form a household early warning list.

[0064] Then generate customized notification content, automatically match notification templates according to risk level, and the method is as follows: High-risk household push: "

Emergency transfer notice

Precautionary notice

[0065] Finally, it is pushed in batches through the SMS platform, and the method is as follows: Call the SMS interface, send the notice one by one according to the owner's contact information in the household list, and push it to the village head (village cadre) and the departments (water conservancy bureau / transportation bureau / defense office) of the basin at the same time, and record the sending time and receiving state. At the same time, the notification delivery rate is monitored in real time, and the village cadre notification linkage mechanism is triggered for the undelivered numbers (such as signal problems), and the notification record and risk warning result are stored in the database at the same time, providing traceable basis for subsequent review, forming a closed-loop management of "prediction-early warning-disposal".

[0066] The present application breaks through the traditional evaluation mode of taking village as the unit, and seamlessly couples unmanned aerial vehicle aerial survey, real estate data and hydraulic model grid through high-precision geographic information fusion algorithm, realizes accurate spatial mapping of basin simulation results to each household. Based on the attributes such as house-level ground height and building structure, the submerged water depth is dynamically calculated, the household-level risk quantification with centimeter-level precision is realized, and the core problems of "low evaluation precision and difficult household positioning" are solved.

[0067] At the same time, the present application establishes a three-level responsibility chain automatic triggering model, automatically matches the plan based on the household-level risk level, and synchronously pushes the differentiated early warning information to the household, the village cadre and the department head. The integrated communication gateway realizes real-time tracking of information delivery state, and automatically starts the standby linkage mechanism for undelivered early warning, forming a whole-process closed-loop management of "risk research and judgment-accurate push-response feedback", and completely solving the problem of "low risk decision-making efficiency".

[0068] A specific embodiment of the method of the present application for household early warning in a small watershed is as follows: A small watershed (an area of 2.7 km2, containing 7 administrative villages, more than 2000 households) is taken as an example to specifically illustrate the small watershed flood risk prediction method of the present application. The implementation cycle is from October 10 to 12, 2024, the simulation scenario is the flood process caused by historical monitoring rainfall, and the whole process is executed according to the five steps of "data collection model calculation-data post-processing-watershed section monitoring data-house water depth and risk calculation-disposal closed loop". Specifically, the following steps are included: Step one: collect multi-source data and integrate and calculate (S1), which includes the following contents: Call the unmanned aerial vehicle survey interface (precision ± 0.1 m) and real estate data, automatically extract the ground height value (accurate to 0.01 m) inside the household polygon through spatial overlay analysis, construct the house attribute data table house_table, and generate more than 2000 records.

[0069] Collect real-time data of 10 rain stations and 3 water level stations in the watershed through interface API at regular intervals (every 5 minutes). Perform data preprocessing: unify the timestamp to UTC+8 time zone, use linear interpolation to fill in the missing data of single station ≤2 hours, and use 3σ criterion to remove abnormal values (such as hourly rainfall > 50 mm), output the monitoring information data table monitor_table.

[0070] Based on the rainfall data in the monitoring information data table monitor_table, the Thiessen polygon method is used to automatically calculate the area average rainfall and write it into the boundary condition file bc.cli. Joint fixed grid file geo.slf, section coordinate file ctrl_secs_YKX.txt to automatically generate calculation control file run.cas.

[0071] Call the solver TELEMAC-MASCARET to perform two-dimensional hydrodynamic calculation, and use the finite element method to solve Saint-Venant equation set to obtain the watershed simulation result file result.slf. Monitor the convergence state in real time during model running, and when the residual error of 10 consecutive time steps is less than , it is determined to be converged. After calculation, output binary result file result.slf (containing 48h water level, water depth, flow velocity field data) and ASCII format section flow file flux.txt.

[0072] Step two: read the file and convert the data (S2), which includes the following contents: The basin simulation result file result.slf is parsed, the total time step is obtained as 1440 frames, each frame data is traversed in time sequence, the grid topology information is parsed for single frame data, the point cell data (including water level value FREESURFACE, water depth value WATER DEPTH and village identifier VILLAGE) is extracted, and the above information is sequentially stored in the unstructured grid container vtkUnstructuredGrid through coordinate conversion and data type standardization processing, wherein each container instance corresponds to the complete spatial topology and physical quantity information of single frame data. Finally, a multi-frame unstructured grid container vector <vtkunstructuredgrid>.

[0073] For the analysis of the cross-section point vector data file Shapefile, the initialization interface GDALAllRegister() of the geographic data abstraction library GDAL is called first to register all supported vector data drivers (including the vector data file Shapefile driver). The vector data file Shapefile is opened through the GDALOpenEx() function of GDAL to return a vector data container GDALDataset object.

[0074] The first (and only) layer including the geometry and attribute information of all features is obtained through GetLayer(0). All features are traversed through GetNextFeature(), and each feature includes geometry information OGRGeometry and attribute information OGRFeatureDefn. Only the point OGRPoint in the geometry information OGRGeometry in the cross-section vector data file Shapefile is stored in the corresponding visualization toolkit VTK data structure point vtkPoints, and the field types of the attribute information OGRFeatureDefn are strings and floating-point numbers, which are converted into the attribute array types vtkStringArray and vtkDoubleArray of the visualization toolkit VTK. The attributes in the cross-section vector data file Shapefile are only associated with the "overall feature", and the attribute array coordinate values med-x, med-y and village identifier vid are all stored in the cell data vtkCellData. Finally, the data structure point vtkPoints and the cell data vtkCellData are summarized to form the cross-section unstructured grid vtkUnstructuredGrid.

[0075] The analysis of the household-level vector data file Shapefile is similar to that of the cross-section point vector data file Shapefile, and finally the village household-level unstructured grid vtkUnstructuredGrid is obtained, which contains the physical quantity village identifier vid, the simulation mapping coordinate values med-x and med-y of the household, the longitude and latitude values, and the house number location.

[0076] Step three: create the cross-section data structure as shown below, i.e. the basin cross-section monitoring data (S3), which is the basis for subsequent early warning decision-making data, which includes the following contents: In the cross-section point grid vtkUnstructuredGrid, there are 7 cross-section points, which belong to 7 different villages respectively. The longitude and latitude values of the cross-section points and the village numbers where the cross-section points are located are stored in the form of cell data. The longitude and latitude values can be read one by one using the cell data vtkCellData, and the data type is double. The longitude array vtkDoubleArray, the latitude array vtkDoubleArray and the village ID vtkDoubleArray are obtained. Each array includes the data of the cross-section points of the 7 villages, and the parsed data is written into the cross-section data structure crossSectionData in turn.

[0077] According to the simulation mapping coordinates med-x and med-y values of the cross-section points, the point locator function vtkPointLocator is used to find the corresponding points in the watershed simulation result grid vtkUnstructuredGrid, and the point number PointID corresponding to each cross-section point is obtained.

[0078] Since the watershed simulation result is a multi-frame unstructured grid container vector <vtkunstructuredgrid>The physical quantity data of water depth, flow rate and the like at multiple time points are stored, and each frame of data is processed using multi-thread parallel acceleration. For each time point of unstructured grid vtkUnstructuredGrid, the memory pointer directly accesses the visualization toolkit VTK data structure to obtain the water level array, and then reads the water level value FREE SURFACE of the corresponding point according to the point number PointID. The finally summarized water level data is grouped according to the village ID, and the water level time series array of each village cross section is stored in the water level variable waterlevel of the cross section data structure crossSectionData.

[0079] The monitoring flow file flut.txt is read, and the data is stored in the file in the format of time and flow value of each cross section point at the time. Since each row of data is 4, 2 rows of data form a complete set of data. The read flow value is reordered according to the cross section point number to obtain the flow value flutArray of each cross section point at different times, which is stored in the discharge variable of the corresponding cross section data structure crossSectionData, and finally written into the basin cross section monitoring data storage file cadata.json.

[0080] Step four: calculating the household-level water depth and risk (S4), which includes the following contents: According to the village household-level vector data file Shapefile, the household-level grid vtkUnstructuredGrid accurate to the house number of each village is obtained. According to the physical quantity village identifier vid in the household-level grid vtkUnstructuredGrid, the valid positive integer village identifier is screened to form a village identifier set villageIDs, and the data in the set are unique, such as "601, 602, 603, 604, 605, 606, 607". According to the household coordinate values med-x and med-y in the household-level grid vtkUnstructuredGrid, the corresponding point in the basin simulation result grid vtkUnstructuredGrid is found by using the point locator function vtkPointLocator, to obtain the corresponding position information houseIDs of each household, for example, the household with the simulation mapping coordinate values med-x=401412.068 and med-y=3357437.699 corresponds to the house identifier point houseID 9889 in the basin simulation result grid vtkUnstructuredGrid.

[0081] Because it needs to judge the submerged water depth and risk level of each household once every 30 minutes, multiple frames of unstructured grid vector <vtkunstructuredgrid>The data in the water depth data sequence waterDepthVec is divided into 96 time segments with 15 frames as a unit. Multi-thread parallel processing is performed on the data in each time segment. For each data block, the water depth data sequence waterDepthVec in the range is extracted from the water depth data sequence. All house positions houseIDs are traversed to extract the water depth values of all frames in the current time segment for each household to form a household-level water depth sequence houseDepthVec, and the maximum water depth value maxWaterDepth in the sequence is screened out.

[0082] Based on the maximum water depth value maxWaterDepth, the maximum risk value of the household in this time segment can be obtained. Let the maximum water depth be h and the risk level be R. Risk level 0 represents no risk, and the smaller the risk level value, the higher the risk level.

[0083]

[0084] For example, house No. 216 in the village identified as 602 has a maximum water depth of 0.13 m and a risk level of 3, i.e., a low-level risk, between 4:30 and 5:00.

[0085] The multi-thread results are summarized to form a household-level water depth and risk data set houseDepthHrs in each time segment.

[0086] According to the matching relationship between the village identifier set villageIDs and the physical quantity vid in the unstructured grid vtkUnstructuredGrid, the grid is divided into village grids villageGrid corresponding to each village to form a village grid set villageGrids. According to the household-level water depth and risk data set houseDepthHrs in each time segment, a multi-thread processing parallel task is constructed to calculate the village-level risk data.

[0087] For each time segment, the timestamp is the timestamp of the last time, such as the timestamp of the 15th frame is 2025-06-29, 00:30:00. Traverse all village grids villageGrids, extract the original village identifier vid and location information for each village. Each point on the village grid villageGrid, that is, each household belonging to the village, traverses the data of each household. According to the physical quantity location of the village grid villageGrid, the house number of the household is known, and the submerged water depth value water depth and risk value hr are found in the household-level water depth and risk data set houseDepthHrs. If the risk value hr is 0, the household-level data structure householdData is not stored, if the risk value is 1, that is, a high-risk area, the house number is recorded in the level1 array, the water depth value is recorded in the depth1 array, and the risk value is 2 or 3, and the same applies. Write the results to the household-level risk data house_risk.json for platform use.

[0088] For example, in the village identified as 604, the household with house number 216 has a submerged water depth exceeding 0.1 m at 4:00-4:30, with a maximum of 0.188 m.

[0089] Step five: dynamically render the post-processing risk data (S5), which includes the following: For single-frame data files json, the background timing task is performed once every 30 minutes, and the single post-processing result predicts 1440 data files json for the next 48 hours. Through frame skipping processing, the frame data json in the specified time period is obtained, and the cloud chart is rendered.

[0090] For example, the warning period is from 12:00 on August 10 to 15:00 on August 10, and there is a frame of data every 2 minutes. The current time is 12:00 on August 10, the household risk details for the next 3 hours are predicted, and the risk levels are distinguished by color, such as high risk represented by red, medium risk represented by orange, low risk represented by yellow, and no risk represented by green. See Figure 2 .

[0091] From Figure 2 It can be seen that the house with house number 70 has a maximum submerged water depth of 1.22 meters (m), and its foundation height is 500.706 millimeters (mm), so the house with this house number is in a high-risk state; the house with house number 68 has a maximum submerged water depth of 1.19 meters, and its foundation height is 500.558 millimeters, so the house with this house number is in a high-risk state; the house with house number 216 has a maximum submerged water depth of 1.01 meters, and its foundation height is 502.496 millimeters, so the house with this house number is in a high-risk state.

[0092] The maximum inundation water depth of the house with house number 221 is 0.86 meters, and the foundation height thereof is 502.838 millimeters, so the house with the house number is in a medium risk state; the maximum inundation water depth of the house with house number 268 is 0.61 meters, and the foundation height thereof is 503.246 millimeters, so the house with the house number is in a medium risk state.

[0093] The maximum inundation water depth of the house with house number 120 is 0.45 meters, and the foundation height thereof is 500.29 millimeters, so the house with the house number is in a low risk state; the maximum inundation water depth of the house with house number 231 is 0.21 meters, and the foundation height thereof is 503.487 millimeters, so the house with the house number is in a low risk state; the maximum inundation water depth of the house with house number 305 is 0.11 meters, and the foundation height thereof is 505.129 millimeters, so the house with the house number is in a low risk state.

[0094] The maximum inundation water depth of the house with house number 73 is 0.05 meters, and the foundation height thereof is 500.215 millimeters, so the house with the house number is in a no-risk state. The risk levels of different households can be intuitively displayed by color differentiation.

[0095] As shown in Figure 3 , according to the predicted flow, rainfall, river embankment elevation, predicted water level, prepared transfer water level, immediate transfer water level, etc., the hydrological change trend of the cross section can be judged to assist in assessing the flood risk of the basin.

[0096] The predicted flow is obtained through the simulation results of the basin, and is used to represent the change trend of the flow in the prediction period, with the unit of cubic meters per second ; the rainfall is the rainfall at a certain time point in a certain place predicted by the meteorological bureau, with the unit of millimeters mm; the river embankment elevation is the foundation height value of the river embankment, with the unit of meters m; the predicted water level is obtained according to the simulation results of the basin, and is used to represent the change trend of the water level value in the prediction period, with the unit of meters m; the prepared transfer water level is the water level value that needs to be transferred by residents within a period of time, with the unit of meters m; the immediate transfer water level is the water level value that must be immediately transferred by residents, with the unit of meters m.

[0097] For the basin cross section monitoring data storage file cadata.json, in the cross section data list crossSection, the cross section water depth and cross section flow data at a certain time are split according to the village identifier vid and stored in the result table result. In the village data list vilageData, the maximum flow rate, average flow rate, maximum water depth, average water depth, and village inundation area data are split according to the village identifier vid and stored in the result table result.

[0098] For the house risk data file house_risk.josn and the house data file householdData, based on time slices, the risk households with different risk levels at the same time are mapped to the maximum flood depth of the households at that time and stored in the risk detail table risk_detail.

[0099] Step Six: Establish a closed-loop response mechanism and deliver precise early warnings (S6), which includes the following: Based on the dynamic risk data generated in step five, a three-level early warning system of "household-village-department" is constructed, which is combined with a risk recalculation mechanism every 3 hours to dynamically adjust decisions and form a closed-loop management of the entire process.

[0100] Customized notification generation, matching templates according to risk level, includes the following: SMS notifications for households at risk: "[Emergency Evacuation Notice] Dear Head of Household XX, XX Village, XX District, your house is expected to be affected by flooding between 15:00 and 18:00 on August 20, 2025. The maximum predicted water depth is 1.2m, and the risk level is Level 1. Please evacuate as soon as possible to XX Shelter [XX Department]." SMS notifications for village flood control personnel in villages at risk: "[Emergency Evacuation Notice] Dear Flood Control Personnel of XX Village, XX District, XX Village is expected to enter Level 1 risk between 15:00 and 18:00 on August 20, 2025. Households at Level 1 risk are numbered 216 and 225, and households at Level 2 risk are numbered 466. Please notify and lead everyone to evacuate to XX Shelter [XX Department] as needed."

[0101] This embodiment verifies the effectiveness of the method of the present invention: the household-level water depth simulation error is ≤0.1m, the risk level accuracy rate is 92%, and high-risk households are accurately located (e.g., the high-risk household identification rate is improved by 40%). It provides emergency departments with a "targeted evacuation" list, avoiding indiscriminate evacuation. The entire process takes 40 minutes, achieving the prevention and control goal of "precise to the household and dynamically controllable" flood risk in small watersheds. Simultaneously, it dynamically visualizes the risk evolution over the next 6 hours, assisting in optimizing evacuation routes (e.g., improving evacuation efficiency by 45% in a certain watershed).

[0102] Furthermore, this invention can be adapted to different small watersheds (it can be reused by changing geographical / model parameters), supports simulation of multiple scenarios (different rainfall, reservoir scheduling), and can be extended to disaster prevention and control such as flash floods and urban waterlogging.

[0103] A server embodiment applying the method of the present invention: A server comprising: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned watershed risk dynamic assessment-based household early warning method.

[0104] Finally, it should be noted that: the above-described embodiments, only for the specific embodiments of the present application, in order to illustrate the technical solutions of the present application, rather than limit it, the protection scope of the present application is not limited to this, although the foregoing detailed description of the present application, the person skilled in the art should understand: any familiar with the technical field of the technical person within the scope of the present application disclosed by the technology, it still can be modified to the technical solution recorded in the foregoing examples, or part of the technical features of the equivalent replacement; and these modifications or replacement, without the corresponding technical solutions of the spirit and scope of the present application, deviate from the technical solutions of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application. Should be covered within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.< / vtkunstructuredgrid> < / vtkunstructuredgrid> < / vtkunstructuredgrid> < / vtkunstructuredgrid> < / vtkunstructuredgrid> < / vtkunstructuredgrid>

Claims

1. A household-level early warning method based on dynamic watershed risk assessment, characterized in that: Includes the following steps: Step 1: Collect spatial attribute information and dynamic risk assessment data of a watershed to obtain watershed simulation results; Step two: Analyze the watershed simulation results to obtain risk assessment data; Step 3: Perform spatiotemporal correlation and targeted extraction on the risk assessment data to generate watershed cross-section monitoring data; Step 4: Perform household-level matching on the watershed cross-section monitoring data and associate it with house attributes to determine the household-level water depth and risk value, thereby obtaining household-level risk data. Step 5: Based on household-level risk data, simulate the flood evolution process down to the household level, generate household-level risk early warning results, and realize household-level early warning based on dynamic watershed risk assessment.

2. The household-level early warning method based on dynamic watershed risk assessment as described in claim 1, characterized in that: Step one involves collecting spatial attribute information and dynamic risk assessment data for a specific watershed. The method for obtaining watershed simulation results is as follows: First, relying on UAV aerial survey information and real estate registration data, household-level spatial attribute information is collected and integrated to construct a housing attribute data table. Household-level spatial attribute data includes at least the household address, household latitude and longitude, foundation height, building type, structural form, and number of people in the household. Then, collect the contact information of department heads, village leaders, and household heads to construct a personnel information data table; Then, at certain intervals, water level and flow data from hydrological stations in the area and rainfall data from rain gauge stations are collected, and a monitoring information data table is constructed based on station codes, collection time, and hydrological data. Collect boundary files, geometry files, source term surface location files, and cross-section location files of a certain watershed, and construct simulation input files; The areal rainfall was calculated using data from rain gauge stations. Based on the model calculation input file and areal rainfall, dynamic risk assessment data is generated, which includes at least a watershed simulation result file and a cross-sectional flow file; the watershed simulation result file includes point physical quantity information at different times, and the cross-sectional flow file includes the flow process of the control section; By summarizing the dynamic risk assessment data, household-level spatial attribute data, personnel information data table, and monitoring information data table, the watershed simulation results are obtained.

3. The household-level early warning method based on dynamic watershed risk assessment as described in claim 1, characterized in that: Step two involves analyzing the watershed simulation results to obtain risk assessment data, using the following method: First, extract the total time step information from the watershed simulation results to obtain the time step sequence; According to the time step sequence, each frame of data is traversed, and each single frame of data is parsed to obtain the grid topology information; Next, extract point cell data from the grid topology information; perform coordinate transformation and data type standardization on the point cell data to obtain standard point cell data; Standard point cell data are stored in an ordered manner into an unstructured mesh container; the unstructured mesh container includes several container instances, each container instance corresponding to the complete spatial topology and physical quantity information of a single frame of data; After parsing all time steps, a multi-frame unstructured grid container is constructed. This multi-frame unstructured grid container can associate the data of each frame with the corresponding timestamp through an index mapping mechanism, forming a spatiotemporal data set that includes complete spatiotemporal evolution information, including water depth and flow velocity at multiple times. Based on the watershed simulation results, the geographic vector information of the monitoring points at the cross section and each household in the village is obtained and stored in a vector data file; Using an open-source, cross-platform library for processing raster and vector geospatial data, vector data files are parsed to obtain cross-sectional vector data files; By combining cross-sectional vector data files and spatiotemporal datasets, risk assessment data is obtained.

4. The household-level early warning method based on dynamic watershed risk assessment as described in claim 3, characterized in that: Step three involves performing spatiotemporal correlation and targeted extraction on the risk assessment data to generate watershed cross-section monitoring data. The method is as follows: Based on the cross-section vector data file, create a cross-section point grid; Based on the scope and latitude and longitude of a village, the cross-sectional point grid is clipped to obtain multiple village cross-sectional points; Based on the simulated mapping coordinates of village cross-sections, risk assessment data is extracted in a targeted manner to determine the water depth and flow velocity values ​​of each village cross-section at several times. Based on the water depth and flow velocity values ​​at multiple village cross-sections, water level time series and flow time series for watershed cross-sections were established. Spatial information, water level time series, and flow time series of the same cross section in the same watershed at the same time are correlated and integrated to obtain spatiotemporal correlated data, which includes village identifiers of the cross section, spatial coordinate arrays, water level time arrays, and flow arrays.

5. The household-level early warning method based on dynamic watershed risk assessment as described in claim 1, characterized in that: Step four involves performing household-level matching on the watershed cross-section monitoring data and associating it with building attributes to determine the household-level water depth and risk value. The method for obtaining household-level risk data is as follows: Based on the vector data file, a household-level grid is established for each household's address in the village, which includes the coordinates of each household and the village identifier. Based on the coordinates of each household in the household-level grid, the corresponding point is found in the watershed cross-section monitoring data to obtain the house location information corresponding to each household; Based on the required time period, the data frames in the watershed cross-section monitoring data are divided into several data blocks in units of a certain quantity, and the task is processed in parallel by multi-threading. Extract water depth time series from each data block; Based on several water depth time series, all house location information is traversed, and the water depth value of each household in all frames within the current time segment is extracted to form a household-level water depth sequence. At the same time, the maximum water depth value in the household-level water depth sequence is determined. Based on the maximum water depth and in conjunction with the house attributes, the risk level of each household is determined; The results from multiple threads are aggregated to form a set of household-level water depth and risk data that includes multiple time segments; From the household-level water depth and risk data set, determine the maximum water depth value and maximum risk level for each household, and generate household-level risk data; At the same time, the highest risk level among all houses in the village is selected as the overall risk level of the village during the specified time period.

6. The household-level early warning method based on dynamic watershed risk assessment as described in claim 5, characterized in that: A method for determining the risk level of each household based on the maximum water depth and in association with house attributes: Obtain the property's attributes, including address, latitude and longitude, and foundation height; The maximum inundation depth for each household is determined based on the foundation height and maximum water depth of each household. Risk levels are classified according to preset water depth thresholds, and risk level determination rules are set as follows: When the maximum flood depth is greater than or equal to N meters, it is considered a high-risk situation and requires immediate evacuation. When the maximum flood depth is less than N meters but greater than or equal to N / 2, it is judged as medium risk and evacuation preparations are required. When the maximum flood depth is less than N / 2 meters and greater than or equal to N / 10, it is considered low risk but requires vigilance. When the maximum flood depth is less than N / 10 meters, it is considered to be without risk; The risk level of each household is determined based on the risk level assessment rules and the maximum inundation depth for each household.

7. The household-level early warning method based on dynamic watershed risk assessment as described in claim 1, characterized in that: Step 5: Based on household-level risk data, simulate the flood evolution process down to the household level and generate household-level risk early warning results using the following method: Based on household-level risk data, and according to one or more preset water depth thresholds, the maximum inundation water depth and maximum risk level of each household are traversed over time periods. The risk of each house in each time period is judged, and the affected houses with the corresponding risk levels are screened to generate household-level risk distribution data, which is used to reflect the changes in household risk at different times, different house structures, and different foundation heights during the flood evolution process. Household-level risk distribution data is stored in a structured manner, and the risk level time series is associated with the detailed location information of the house, establishing a household-level risk mapping relationship of time-risk level-spatial location-maximum water depth; At the same time, based on the household-level risk mapping relationship, the flooding area data, cross-sectional hydrological data and village-level hydraulic parameters of the entire village are determined, forming a multi-dimensional risk assessment information from macro to micro. Based on multi-dimensional risk assessment information, cross-validation of household-level risks is conducted. If the verification results show a contradiction between the two risk assessment results, i.e. the village as a whole is at high risk but there are no high-risk households in the village, then return to step four to recalculate the household-level risk data to make the early warning results consistent. If the verification results show that the two risk assessment results are consistent, the multi-dimensional risk assessment information will be stored in the database, and then the multi-dimensional risk assessment information will be rendered by a three-dimensional digital engine to obtain a household-level risk warning result with dynamic cloud map effect.

8. The household-level early warning method based on dynamic watershed risk assessment as described in claim 7, characterized in that: It also includes step six, which generates risk prediction information for different time periods based on the household-level risk warning results and notifies the defense targets within the warning range to form a closed-loop response. This step specifically includes the following: First, risk data, the number of households requiring relocation, and warning indicators for different time periods are summarized from household-level risk warning results and watershed section monitoring data, and then linked with the mapping relationship in the housing attribute data table to form risk prediction information and household-specific warning lists. Next, based on the risk prediction information, a customized notification content is generated and a push template is matched, including a high-risk user push template and a medium-risk user push template, which are used to push to the defense targets within the warning range; Finally, notifications were sent to each homeowner in the household early warning list using their contact information, and simultaneously pushed to the heads of natural villages and various departments in the watershed, with the sending time and receiving status recorded. For numbers that have not been delivered, a joint mechanism involving village officials visiting households to notify them is triggered. At the same time, the notification record and the household-level risk warning results are stored in the database to provide a basis for subsequent review and form a complete closed loop for handling.

9. A household-level early warning method based on dynamic watershed risk assessment, characterized in that: Includes the following: Data on dynamic risk assessment of a certain watershed are collected to obtain watershed simulation results; The watershed simulation results were analyzed to obtain risk assessment data; Spatiotemporal correlation and targeted extraction of risk assessment data are performed to generate watershed cross-section monitoring data; Household-level matching was performed on the watershed cross-section monitoring data, and the house attributes were associated to determine the water depth and risk value at the household level, thus obtaining household-level risk data. Based on household-level risk data, the evolution of floods to households is simulated, and household-level risk early warning results are generated. Based on the household-level risk warning results, risk prediction information for different time periods is generated and notified to the defense targets within the warning range, forming a closed-loop response.

10. A decision-making platform based on dynamic watershed risk assessment, characterized in that: It includes: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a household early warning method based on dynamic watershed risk assessment as described in any one of claims 1-9.

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