A flood emergency risk research and judgment method and system based on multi-source surveying and mapping data

Abstract

The application discloses a flood emergency risk research and judgment method and system based on multi-source surveying and mapping data, comprising: in response to an emergency research and judgment request input by a user, a structured task object containing a target area, a time window, a disaster type, a research and judgment target, a target result type and a unique task identifier is analyzed and generated; the pre-registered surveying and mapping analysis tools and their dependency relationships are determined to determine the tools to be called and their dependency relationship diagram; initial data is obtained and arranged into a data package; a structured result state associated with the task identifier is established; each tool is called in sequence according to the dependency relationship, data is read from the data package or the previous structured result for surveying and mapping analysis processing; structured indexes output by each tool are read from the structured result state, and an emergency research and judgment result is generated according to a preset template. The automatic arrangement and calling of multi-source surveying and mapping data are realized, manual repeated switching between different tools is avoided, and the automation degree of flood emergency research and judgment is improved.

Description

Technical Field

[0001] This application belongs to the field of data processing technology, specifically relating to a method and system for assessing flood emergency risks based on multi-source mapping data. Background Technology

[0002] Rainstorms and floods are among the most frequent and widespread natural disasters in my country. According to statistics from the Ministry of Emergency Management, the direct economic losses caused by floods in recent years have exceeded 200 billion yuan annually, posing a serious threat to people's lives and property and urban infrastructure. In flood emergency response, quickly and accurately identifying the flood area, assessing the exposure risk of affected areas, and formulating rescue routes are crucial steps in reducing disaster losses. With the development of synthetic aperture radar (SAR) remote sensing, geographic information systems (GIS), and artificial intelligence technologies, utilizing multi-source mapping data for flood emergency assessment has become the mainstream approach in the industry.

[0003] Currently, the analysis of surveying and mapping data for emergency response to rainstorms and floods typically employs a distributed technology approach. This involves remote sensing processing software, GIS software, statistical tools, or independent algorithms each performing their respective analysis stages. Data transfer usually relies on methods such as exporting intermediate files, manual transfer, or re-importing. Figure 1 As shown, the process begins with using remote sensing processing software or independent algorithms to read pre- and post-disaster synthetic aperture radar (SAR) images. Flood raster or vector patterns are obtained through low-echo identification, pre- and post-disaster difference analysis, threshold segmentation, image classification, or deep learning, and exported as intermediate files. Subsequently, analysts use digital elevation models (DEMs), permanent water body data, or precipitation data in other software or scripts to assist in interpreting the remote sensing results. For example, permanent water bodies are used as masks to exclude perennial rivers and lakes, DEMs are converted into slope or low-lying areas, and precipitation raster data is statistically analyzed as cumulative precipitation. The verification results are then exported as intermediate files or manually re-imported. Next, analysts overlay the flooded area with spatial elements such as roads, hospitals, schools, and stations in GIS software, calculating the affected road lengths, the number of facilities, and the distance from facilities to the flooded area. Finally, risk statistics are generated based on the flooded area, road impact, and the number of facilities. Analysts then manually summarize the layers, statistical tables, and textual conclusions output from different software into an emergency report.

[0004] Although the aforementioned technologies can respectively complete remote sensing interpretation, spatial overlay, risk statistics, or report generation, the inventors have discovered through long-term research and practice that the existing technologies still have the following objective defects:

[0005] First, there is a lack of unified organization among multi-source data and tools. Existing technologies require analysts to repeatedly switch between remote sensing processing, GIS spatial analysis, statistical calculation, and report generation tools. Each step requires manual data selection, parameter setting, result export, and starting the next step. Because there is no unified task object and tool dependency relationship between the steps, analysts are prone to missing a step or incorrectly combining results from different batches when the target area, time window, or data version changes. For example, low-echo areas in SAR imagery are not necessarily all post-disaster flooding; permanent water bodies, building shadows, smooth surfaces, farmland, and other dark targets can all produce similar appearances. Although existing technologies can use permanent water bodies, digital elevation models, slope, or precipitation data separately for auxiliary judgment, these verification steps often rely on manual operation, and the verification results exist in the form of independent layers, without forming an automated data dependency and result constraint with the flood identification results. For example, the flood raster or vector map output by remote sensing algorithms mainly answers "where there may be water accumulation", but emergency personnel also need to know "which roads and facilities are affected". Existing technologies usually require manual reloading of road and facility data, and then performing intersection, buffering, distance and statistical analysis separately. There is a lack of unified data transmission and result constraints between flood patches and roads, facilities and risk grids.

[0006] Second, existing technologies generally only retain the final map or report, making it difficult to trace which tools were used, which input data were used, which parameters were adopted, and whether manual review is currently required for a certain conclusion. Summary of the Invention

[0007] This application provides a flood emergency risk assessment method and system based on multi-source mapping data, which solves the problems in the prior art such as the decentralized execution of multi-source mapping data processing tools, reliance on manual data transfer, lack of unified task identification and structured association of results in each stage leading to data mismatch, untraceable results, and low efficiency of emergency assessment.

[0008] Firstly, a method for assessing flood emergency risks based on multi-source mapping data, the method comprising:

[0009] In response to an emergency assessment request input by a user, the emergency assessment request is parsed to generate a structured task object containing the target area, time window, disaster type, assessment target, target result type, and unique task identifier;

[0010] Based on the assessment objectives and target result types in the structured task object, the pre-registered surveying and analysis tool information, and the associated prerequisite tools of each tool, the tools to be invoked for this assessment request are determined, and the dependencies of the tools to be invoked are determined, forming a complete working tool dependency diagram; the surveying and analysis tool information includes at least the tool identifier, input data type and fields, output data type and fields, prerequisite tools, and prerequisite results;

[0011] Based on the target area, the time window, and the input data type of the tool to be invoked, determine the initial data type and the time period of the initial data required for this analysis request, obtain the corresponding data, and organize it into a data packet for this analysis request;

[0012] Establish a structured result status associated with the task identifier to record the execution results of each tool; the structured result status includes at least the task identifier, tool identifier, execution status, input data reference, actual parameters, structured indicators, and layer or result file reference;

[0013] According to the tool dependency diagram, the tools to be called are called in sequence, and the corresponding data is called from the data packet of this analysis request according to the input data type and field in the information of each tool to be called, or the corresponding data is read from the structured result status output by the previously executed tools, and the corresponding mapping analysis processing is performed.

[0014] Read the structured indicators output by each tool from the structured result status, and organize and generate emergency assessment results according to the preset template.

[0015] Optionally, in the above scheme, the pre-registered mapping and analysis tool information includes remote sensing identification tools, spatial verification tools, exposure analysis tools, risk assessment tools, route risk tools, or result generation tools.

[0016] In the above scheme, optionally, the remote sensing identification tool is used to acquire SAR polarization data of the target area in the pre-disaster and post-disaster phases, perform spatial registration and pre-disaster and post-disaster change detection on the SAR polarization data, generate candidate flood masks, form permanent water body masks using permanent water body data, remove pixels corresponding to permanent water bodies from the candidate flood masks, perform connected component analysis on the candidate flood masks after removing permanent water bodies, remove connected components with fewer than a preset pixel threshold, vectorize the remaining connected components, and obtain the candidate flood range.

[0017] The spatial verification tool is used to acquire the digital elevation model (DEM) of the target area and the cumulative precipitation raster data covering the target area within the target time window; generate slope maps and low-lying area maps based on the DEM; statistically analyze the average precipitation, maximum precipitation, and the proportion of pixels with precipitation values ​​exceeding a preset precipitation threshold within the candidate flood range covered by the cumulative precipitation raster data; and generate spatial verification marker data by superimposing the slope map, low-lying area map, average precipitation, maximum precipitation, and pixel proportion onto the candidate flood range. The spatial verification marker data is used to identify reliable areas within the candidate flood range that meet the topographic and precipitation conditions, as well as areas that require manual verification.

[0018] The exposure analysis tool is used to uniformly project the candidate flood range, road data, and key facility data onto the same metric projection coordinate system; to perform validity processing and merging of flood patches in the candidate flood range to form a flood surface for spatial calculation; to calculate the length of the intersection between the road data and the flood surface to obtain the set of affected roads; to generate a flood buffer zone with a preset distance based on the flood surface; to calculate the shortest spatial distance between the key facilities and the flood surface; and to identify key facilities that intersect with the flood surface as directly exposed facilities, and key facilities located within the flood buffer zone and not intersecting with the flood surface as adjacent exposed facilities, thus obtaining a set of facility exposures; the key facilities include at least one or more of hospitals, schools, and stations;

[0019] The risk assessment tool is used to overlay the flooded area, the set of affected roads, and the set of exposed facilities onto the metric projection coordinate system to generate a regular square risk grid covering the target area with a side length of a preset grid size. It then calculates the flooded area, affected road length, total number of key facilities, and number of various key facilities within each risk grid. The flooded area, affected road length, and total number of key facilities in all risk grids are normalized within the region to obtain normalized flood indicators, normalized road indicators, and normalized facility indicators. A comprehensive risk score is calculated according to preset weighting coefficients, and the risk level of each risk grid is determined based on a preset risk level threshold, generating a risk grid result. The risk grid result includes the risk score, risk level, and risk cause field for each grid. The generation rules for the risk cause field are as follows: when the normalized flood indicator exceeds a preset contribution threshold, the risk cause field includes a flood inundation contribution identifier; when the normalized road indicator exceeds a preset contribution threshold, the risk cause field includes a road exposure contribution identifier; and when the normalized facility indicator exceeds a preset contribution threshold, the risk cause field includes a facility exposure contribution identifier.

[0020] The route risk tool is used to respond to the selection instruction of target key facilities, obtain the starting coordinates of the target key facilities, the overall spatial envelope of the flood surface, the risk grid results, and the ending coordinates of the candidate target points; generate a regular route from the starting coordinates to the ending coordinates based on road network data, or generate a spatial auxiliary judgment route based on the flood spatial envelope; outside the overall spatial envelope of the flood surface, set multiple bypass control points according to preset bypass distances, and generate multiple flood avoidance candidate routes passing through different bypass control points; calculate the route length of the regular route and each flood avoidance candidate route, the number of flood patches intersecting with the flood surface, the shortest distance from the route to the flood surface, and the number of high-risk grids hit; calculate the traffic risk score of each route according to preset route risk scoring rules, determine the route with the lowest traffic risk score among the multiple flood avoidance candidate routes as the flood avoidance backup route, and generate an activation or review suggestion for the flood avoidance backup route based on the comparison result of the traffic risk score of the flood avoidance backup route and the traffic risk score of the regular route;

[0021] The results generation tool is used to read the total flood inundation area, total number of flood patches, spatial verification results, cumulative precipitation statistics, total length of affected roads, number of directly exposed facilities, number of adjacent exposed facilities, and number of grids for each risk level in the risk grid results from the output results of the remote sensing identification tool, spatial verification tool, exposure analysis tool, and risk assessment tool; and to fill the above data into the corresponding fields according to the preset emergency assessment report template to generate an emergency assessment results file.

[0022] Optionally, in the above scheme, obtaining the corresponding data includes:

[0023] Based on the target area, the time window, and the input data type of the tool to be invoked, determine the initial data type and the time period of the initial data required for this analysis request;

[0024] Based on the initial data type and the time period of the initial data, determine whether the corresponding data exists in the local cache. If it exists, extract the corresponding data and perform unified processing of coordinate reference, spatial range and raster grid, as the data packet for this assessment request. The data in the local cache is multi-source mapping data obtained in advance through different interfaces, including at least one of SAR image data, DEM data, permanent water body data, precipitation data, road data or key facility data.

[0025] In the above scheme, optionally, if the corresponding data does not exist in the local cache, a list of missing data is output for user input.

[0026] Optionally, in the above scheme, the surveying and analysis tool information may also include the tool's applicable boundaries; the tool execution status in the structured result status includes pending invocation, invocation, completed, or abnormal.

[0027] Optionally, the above scheme may further include: recording the execution status, input source, actual parameters, output indicators, result path and applicable boundaries of each tool to form a traceable analysis chain.

[0028] Secondly, a flood emergency risk assessment system based on multi-source mapping data, the system comprising:

[0029] Task parsing module: In response to user-inputted emergency assessment requests, it parses the emergency assessment requests and generates a structured task object containing the target area, time window, disaster type, assessment target, target result type, and unique task identifier;

[0030] The module for determining the tools to be invoked and their dependencies is used to determine the tools to be invoked for this assessment request based on the assessment objectives and target result types in the structured task object, the pre-registered surveying and analysis tool information, and the associated prerequisite tools of each tool. It also determines the dependencies of the tools to be invoked, forming a complete tool dependency graph. The surveying and analysis tool information includes at least the tool identifier, input data type and fields, output data type and fields, prerequisite tools, and prerequisite results.

[0031] Data management module: used to determine the initial data type and time period of the initial data required for this analysis request based on the target area, the time window and the input data type of the tool to be invoked, obtain the corresponding data and organize it into a data packet for this analysis request;

[0032] Structured Result Status Establishment Module: Used to establish a structured result status associated with the task identifier to record the execution results of each tool; the structured result status includes at least the task identifier, tool identifier, execution status, input data reference, actual parameters, structured indicators, and layer or result file reference;

[0033] Tool execution module: According to the tool dependency graph, the tools to be called are called in sequence, and the corresponding data is called from the data packet of this analysis request according to the input data type and field in the information of each tool to be called, or the corresponding data is read from the structured result status output by the previously executed tools, and the corresponding mapping analysis processing is performed.

[0034] The assessment results generation module is used to read the structured indicators output by each tool from the structured result status and organize and generate emergency assessment results according to a preset template.

[0035] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the flood emergency risk assessment method based on multi-source mapping data described in the first aspect above.

[0036] Fourthly, a computer program product includes a computer program / instructions that, when executed by a processor, implement the steps of the flood emergency risk assessment method based on multi-source mapping data described in the first aspect above.

[0037] Compared with the prior art, this application has at least the following beneficial effects:

[0038] This application unifies target areas, time windows, disaster types, and assessment objectives through structured task objects. Based on tool dependencies, it automatically calls and executes mapping and analysis tools, writing the outputs of each tool into a structured result state associated with the same task identifier. This allows subsequent tools to directly obtain input from the structured results of preceding tools, achieving continuous closed-loop processing of flood identification, spatial verification, exposure analysis, risk assessment, route assessment, and result generation. This reduces data mismatch and version inconsistencies caused by switching between multiple tools, improves the spatial rationality and interpretability of SAR flood identification results, and enables automatic conversion and continuous driving from flood patches to roads, facilities, risk grids, and routes. This enhances the interpretability of risk scoring results and the coherence of subsequent assessments. Furthermore, by recording the input sources, parameters, output indicators, and applicable boundaries of each tool through structured result states, a complete and traceable assessment chain is formed, improving the reliability and auditability of emergency assessment results. Attached Figure Description

[0039] Figure 1 A flowchart of an existing method for assessing flood emergency risks based on multi-source mapping data;

[0040] Figure 2 A flowchart of a flood emergency risk assessment method based on multi-source mapping data provided in this application;

[0041] Figure 3 Another flowchart of a flood emergency risk assessment method based on multi-source mapping data provided in this application;

[0042] Figure 4 The execution flowchart of the route risk tool provided in this application. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments.

[0044] The data flow in existing technologies generally follows this pattern: raw data – single-algorithm processing – exporting intermediate files – manual transfer or re-import – GIS spatial overlay – manual statistics – manual report writing. Intermediate outputs may include GeoTIFF, GeoJSON, Shapefile, CSV, images, and text documents. Different outputs are typically linked by filenames, manual memorization, or temporary tables, lacking unified task identifiers, tool status, input sources, output fields, and applicable boundary records.

[0045] Existing technologies require manual data selection, parameter setting, result export, and initiation of each step. For example, some existing technologies can generate risk maps or risk levels, but the risk score may only exist as the final display result and cannot be used as input for high-risk facility screening and route risk calculation. Furthermore, if the risk level does not simultaneously output cause fields such as flood area, road exposure, and key facility exposure, users find it difficult to determine the basis for the score. This problem can be improved through the grid results with risk cause fields in this invention, as well as the subsequent linkage between the risk grid and facilities and routes.

[0046] Existing reports are often generated manually by copying and pasting or by directly organizing text using generic generation models. Manual compilation is prone to version misuse and numerical omissions; if the results generation process lacks structured data constraints, problems such as unclear indicator sources or insufficient consistency of results may arise. This problem can be improved by the structured results library, field source records, and template constraint mechanism in this invention, ensuring that indicators such as flood area, road length, number of facilities, risk level, and route are all derived from the output of preceding tools.

[0047] This application focuses on incorporating multi-source mapping data processing, spatial verification, exposure analysis, risk grids, and results generation into a continuous closed loop under the same task identifier, so that subsequent assessment steps are subject to the structured constraints of the preceding mapping tool outputs.

[0048] In one embodiment, such as Figure 2 and Figure 3 As shown, a method for assessing flood emergency risks based on multi-source mapping data is provided, including:

[0049] Step S1: In response to the emergency assessment request input by the user, parse the emergency assessment request and generate a structured task object containing the target area, time window, disaster type, assessment target, target result type and unique task identifier.

[0050] In step S1, the structured task object includes at least the following contents as shown in Table 1:

[0051] Table 1

[0052]

[0053] Compared to existing approaches that simply send natural language to generative models, structured task objects provide deterministic parameters for data selection, tool invocation, and result archiving.

[0054] Step S2: Based on the assessment objectives and target result types in the structured task object, the pre-registered surveying and analysis tool information, and the associated prerequisite tools of each tool, determine the tools to be invoked for this assessment request, and determine the dependencies of the tools to be invoked to form a complete working tool dependency diagram; the surveying and analysis tool information includes at least the tool identifier, input data type and fields, output data type and fields, prerequisite tools, and prerequisite results;

[0055] In step S2, each mapping analysis tool includes at least the descriptive information from Table 2:

[0056] Table 2

[0057]

[0058] The dependencies are used to determine whether the preceding results meet the calling conditions. If the preceding results are not generated, the corresponding subsequent tools are not executed; once the preceding results are generated, their structured output is used as input for the subsequent tools. For example, the risk grid scoring tool depends on flood extent, road exposure results, and key facility exposure results; the route risk tool depends on target facilities, flood extent, and risk grid results; and the report generation tool depends on the structured results of each analysis tool.

[0059] Step S3: Determine the initial data type and time period of the initial data required for this analysis request based on the target area, the time window, and the input data type of the tool to be invoked; obtain the corresponding data; and organize it into a data packet for this analysis request.

[0060] In step S3, the initial data type and time period of the initial data required for this analysis request are determined based on the target area, the time window, and the input data type of the tool to be invoked. It is then determined whether corresponding data exists in the local cache. If it exists, the corresponding manifest file and data path are read. If no data packet exists, a list of missing data is output, or the data access tool is invoked to obtain data for the corresponding area and time window.

[0061] Multi-source mapping data packages can include: pre- and post-disaster SAR VV and VH polarization data, cumulative or hourly precipitation data, DEM and slope data, permanent water body data, road data, data on key facilities such as hospitals, schools, and stations, analysis area boundaries, validation samples, or reference flood products. Data requiring joint calculations should be unified to the same coordinate reference, spatial extent, and raster grid.

[0062] Step S4: Establish a structured result status associated with the task identifier to record the execution results of each tool; the structured result status includes at least the task identifier, tool identifier, execution status, input data reference, actual parameters, structured indicators, and layer or result file reference.

[0063] In step S4, each tool generates a structured result record upon completion, the record including at least the elements shown in Table 3:

[0064] Table 3

[0065]

[0066] Subsequent tools and results generation modules reference preceding results through task_id, tool_id, and result fields, thereby establishing a link between conclusions and data sources.

[0067] "Actual parameters" and "structured indicators" represent an "input configuration - output result" relationship during tool execution. "Actual parameters" record the actual parameter values ​​used by a tool in a given task, such as analysis time window, threshold, buffer distance, raster resolution, coordinate system, and weights. "Structured indicators" record the results obtained by the tool after running according to the aforementioned actual parameters, which can be used by subsequent tools or for comprehensive analysis, such as flood area, affected road length, number of key facilities, nearest waterlogged distance, and number of route crossings. The system links the two through task identifiers and tool identifiers, enabling it to trace which tool and which set of parameters generated each indicator.

[0068] Step S5: According to the tool dependency graph, call the tools to be called in sequence, and call the corresponding data from the data packet of this analysis request according to the input data type and field in the information of each tool to be called, or read the corresponding data from the structured result status output by the previously executed tools, and perform the corresponding mapping analysis processing.

[0069] Step S6: Read the structured indicators output by each tool from the structured result status, and organize and generate emergency assessment results according to the preset template.

[0070] In step S6, the flood area, number of patches, cumulative precipitation, road exposure length, number of key facilities, risk grid level, and route risk indicators are read from the structured results. The preset template organization includes: event overview, core indicators, precipitation background, SAR flood identification results, topography and permanent water body verification, road and key facility exposure, high-risk grid list and risk causes, traffic risk comparison, disposal suggestions, applicable boundaries, and manual review status.

[0071] Natural language models can organize structured fields into language, but they must not modify or create key indicators not generated by preceding tools. Therefore, the values ​​in the report can be traced back to the corresponding tools and result files. Rule templates, natural language models, or a combination of both can also be used; output can be in HTML, Markdown, PDF, Word, JSON, map services, or push notifications; reports can be generated segment by segment as the tool completes; and sections on missing data, outliers, manual review, and accuracy evaluation can be added.

[0072] In this embodiment, data dependencies and result constraints between processes are established through the following technical means:

[0073] 1. Convert natural language tasks into structured task objects;

[0074] 2. Encapsulate each surveying and analysis capability into a tool with inputs, outputs, dependencies, and applicable boundaries;

[0075] 3. Write the output of each tool into the structured state corresponding to the same task;

[0076] 4. Subsequent tools obtain input from the structured output of preceding tools;

[0077] 5. Key metrics in the report may only cite structured results from completed tools;

[0078] 6. Risk grid results continue to serve as input for facility screening and route risk assessment.

[0079] The aforementioned flood emergency risk assessment method based on multi-source mapping data unifies the target area, time window, disaster type, and assessment objective through structured task objects. It automatically calls and executes mapping analysis tools based on tool dependencies, writing the outputs of each tool into a structured result state associated with the same task identifier. This allows subsequent tools to directly obtain input from the structured results of preceding tools, achieving continuous closed-loop processing of flood identification, spatial verification, exposure analysis, risk assessment, route assessment, and result generation. This reduces data mismatch and version inconsistency caused by switching between multiple tools, improves the spatial rationality and interpretability of SAR flood identification results, and realizes automatic conversion and continuous driving from flood patches to roads, facilities, risk grids, and routes. This enhances the interpretability of risk scoring results and the coherence of subsequent assessments. Simultaneously, by recording the input sources, parameters, output indicators, and applicable boundaries of each tool through structured result states, a complete and traceable assessment chain is formed, improving the reliability and auditability of emergency assessment results.

[0080] In one embodiment, the pre-registered mapping and analysis tool information includes remote sensing identification tools, spatial verification tools, exposure analysis tools, risk assessment tools, route risk tools, or results generation tools.

[0081] In one embodiment, the remote sensing identification tool is used to acquire SAR polarization data of the target area in the pre-disaster and post-disaster phases, perform spatial registration and pre-disaster and post-disaster change detection on the SAR polarization data, generate candidate flood masks, form permanent water body masks using permanent water body data, remove pixels corresponding to permanent water bodies from the candidate flood masks, perform connected component analysis on the candidate flood masks after removing permanent water bodies, remove connected components with fewer than a preset pixel threshold, vectorize the remaining connected components, and obtain the candidate flood range.

[0082] The spatial verification tool is used to acquire the digital elevation model (DEM) of the target area and the cumulative precipitation raster data covering the target area within the target time window; it generates slope maps and low-lying area maps based on the DEM, and statistically analyzes the average precipitation, maximum precipitation, and the proportion of pixels with precipitation values ​​exceeding a preset precipitation threshold within the candidate flood range coverage area of ​​the cumulative precipitation raster data. It then overlays the slope map, low-lying area map, average precipitation, maximum precipitation, and pixel proportion onto the candidate flood range to generate spatial verification marker data. The spatial verification marker data is used to identify reliable areas within the candidate flood range that meet the topographic and precipitation conditions, as well as areas that require manual verification.

[0083] The exposure analysis tool is used to uniformly project the candidate flood range, road data, and key facility data onto the same metric projection coordinate system. It performs validity processing and merging of flood patches within the candidate flood range to form a flood surface for spatial calculation. It calculates the length of the intersection between road data and the flood surface to obtain the set of affected roads. Based on the flood surface, it generates a flood buffer zone with a preset distance, calculates the shortest spatial distance between the key facilities and the flood surface, and identifies key facilities intersecting the flood surface as directly exposed facilities, and key facilities located within the flood buffer zone but not intersecting the flood surface as adjacent exposed facilities, thus obtaining a facility exposure set. Key facilities include at least one or more of hospitals, schools, and stations.

[0084] The risk assessment tool overlays the flooded area, affected road set, and facility exposure set onto a metric projection coordinate system to generate a regular square risk grid covering the target area with a preset grid size. It then calculates the flooded area, affected road length, total number of key facilities, and number of various key facilities within each risk grid. The flooded area, affected road length, and total number of key facilities in all risk grids are normalized within the region to obtain normalized flood indices, normalized road indices, and normalized facility indices. A comprehensive risk score is calculated according to preset weighting coefficients, and the risk level of each risk grid is determined based on preset risk level thresholds, generating a risk grid result. The risk grid result includes the risk score, risk level, and risk cause field for each grid. The generation rules for the risk cause field are as follows: when the normalized flood indices exceed a preset contribution threshold, the risk cause field includes a flood inundation contribution identifier; when the normalized road indices exceed a preset contribution threshold, the risk cause field includes a road exposure contribution identifier; and when the normalized facility indices exceed a preset contribution threshold, the risk cause field includes a facility exposure contribution identifier.

[0085] The route risk tool is used in response to the selection instruction of target key facilities to obtain the starting coordinates of the target key facilities, the overall spatial envelope of the flood surface, the risk grid results, and the ending coordinates of the candidate target points; it generates a regular route from the starting coordinates to the ending coordinates based on road network data, or generates a spatial auxiliary judgment route based on the flood spatial envelope; outside the overall spatial envelope of the flood surface, multiple bypass control points are set according to preset bypass distances to generate multiple flood-avoidance candidate routes passing through different bypass control points; the route length, the number of flood patches intersecting with the flood surface, the shortest distance from the route to the flood surface, and the number of high-risk grids hit are calculated for each regular route and each flood-avoidance candidate route; the traffic risk score of each route is calculated according to preset route risk scoring rules, and the route with the lowest traffic risk score among the multiple flood-avoidance candidate routes is determined as the flood-avoidance backup route; based on the comparison result of the traffic risk score of the flood-avoidance backup route and the traffic risk score of the regular route, a suggestion for activation or review of the flood-avoidance backup route is generated;

[0086] The results generation tool is used to read the total flood inundation area, total number of flood patches, spatial verification results, cumulative precipitation statistics, total length of affected roads, number of directly exposed facilities, number of adjacent exposed facilities, and number of grids for each risk level in the risk grid results from the output results of remote sensing identification tools, spatial verification tools, exposure analysis tools, and risk assessment tools. According to the preset emergency assessment report template, the above data are filled into the corresponding fields to generate an emergency assessment results file.

[0087] In this embodiment, the remote sensing identification tool reads pre-disaster and post-disaster SAR images, performs raster alignment based on the reference image, and calculates the backscattering difference between the post-disaster and pre-disaster images. For any polarization p, the backscattering change of pixel x is expressed as:

[0088]

[0089] in, and These represent the p-polarized backscattering values ​​of pixel x in the post-disaster and pre-disaster images, respectively; This represents the pre- and post-disaster changes in the corresponding polarization mode.

[0090] The low echo threshold Tlow is determined based on post-disaster VH imagery, and the descent threshold Tdec is determined based on VH polarization changes. The result of identifying suspected flood candidate pixels can be expressed as follows:

[0091]

[0092] in, Represents candidate flood markers for pixel x; Indicates pixel validity; This indicates a permanent water body marker. Therefore, only pixels that are valid, exhibit low echoes after a disaster, or show a significant decrease in echoes before and after a disaster, and do not belong to permanent water bodies, are included in the candidate flood range. Low echo threshold. The descent threshold is determined using the Otsu method. The preset quantile of the VH polarization change is used to determine the threshold; when the automatic threshold does not meet the preset conditions, a backup threshold is used.

[0093] Spatial verification tools utilize permanent water body masks to mark perennial water surfaces such as rivers and lakes, preventing them from being identified as newly added flooding. The spatial verification tool generates slope and low-lying areas based on the DEM. The low-lying area determination result for pixel x can be expressed as:

[0094]

[0095] in, The marker indicates a low-lying area; z(x) and s(x) represent the elevation and slope values ​​of pixel x, respectively; Tz and Ts represent the elevation threshold and slope threshold, respectively. This is a conditional indicator function. The elevation threshold can be set using the preset quantiles of effective DEM pixels, and the slope threshold can be set according to the terrain features of the target area. The system statistically analyzes the cumulative average and maximum precipitation and the proportion of high precipitation areas within the target time window, forming a precipitation background field corresponding to the flood identification results.

[0096] The above-mentioned permanent water bodies, topography, and precipitation results can be used for:

[0097] 1. Clearly Mark Unreasonable Areas: First, unify the permanent water body data and the candidate flood range obtained in the previous step to the same coordinate system, spatial resolution, and spatial range; then convert the permanent water body vector or raster data into a permanent water body mask, convert the candidate flood range into a binary mask, and compare the two masks pixel by pixel. Pixels identified as candidate floods but not belonging to the permanent water body range are retained; candidate pixels within the permanent water body range are marked as non-newly added flood areas, thus eliminating the interference of perennial rivers and lakes on flood identification results. For areas located at the edge of permanent water bodies or where the water body range has changed significantly before and after a disaster, a buffer verification zone can be set, and the corresponding candidate pixels are marked as pending verification instead of being directly eliminated. Furthermore, calculate the slope and low-lying areas based on the DEM, and reduce the confidence of candidate pixels with high slopes or elevations that do not meet the preset terrain conditions; count the cumulative precipitation within the task time window, and when the cumulative precipitation does not meet the preset precipitation conditions, mark the corresponding candidate area as pending verification. The verification results of permanent water bodies, topography, and precipitation are all written into the structured results status as intermediate indicators.

[0098] 2. Spatial rationality of marking flood results;

[0099] 3. Generate areas that require manual review;

[0100] 4. Define the interpretation and applicable boundaries of the results in the report.

[0101] The exposure analysis tool projects flood extent and road data onto a metric coordinate system, merges flood patches to form flood surfaces, and uses spatial indexing to filter candidate roads. Let Ri be the linear geometric object of the i-th candidate road, and F be the merged flood surface. Then, the spatial exposure length of this road within the flood extent is:

[0102]

[0103] in, This represents the spatial intersection of the road geometry and the flood surface. This indicates that the length of a linear geometric object is being measured. The total spatial exposed length of all candidate roads is:

[0104]

[0105] according to Filter the affected roads, summarize the number of affected roads and the total exposed length of space, and output the corresponding road geometry objects and their exposed length attributes.

[0106] The exposure analysis tool generates a preset distance buffer based on the flood surface and performs intersection judgment, buffer inclusion judgment and shortest distance calculation for key facilities.

[0107] Exposure types at key facilities can include at least:

[0108] 1. Direct exposure: Key facilities are directly exposed to floodwaters;

[0109] 2. Proximity Exposure: Key facilities do not directly intersect with the flood surface, but are located within the flood buffer zone;

[0110] 3. Non-exposed: Key facilities are located outside the flood buffer zone.

[0111] Let Pj be the geometric object of the j-th key facility, and let its shortest geometric distance to the flood surface F be... ;

[0112] Where dj represents the spatial proximity of key facilities to the flooded area; when Pj intersects with F, In one specific embodiment, the buffer distance is 200 meters. This value can be adjusted according to the type of disaster, data accuracy, and operational needs.

[0113] The risk assessment tool generates a grid of preset size in a metric projected coordinate system. For each grid related to the flood area, the following statistics are collected: flooded area within the grid, length of affected roads within the grid, number of key facilities within the grid, number of various key facility sub-items, optional precipitation, low-lying areas, or population indicators.

[0114] Each indicator is normalized within the region, and a weighted combination method is used to calculate the grid's comprehensive risk score. For the g-th grid, its comprehensive risk score can be expressed as:

[0115]

[0116] in, Let wk represent the k-th normalized risk indicator of the g-th grid, and wk represent the corresponding indicator weight. In a specific embodiment, three indicators are selected: flood area, affected road length, and number of key facilities, with weights set to 0.5, 0.3, and 0.2 respectively, i.e.:

[0117]

[0118] in, These represent the normalized flooded area, affected road length, and number of key facilities, respectively. Each indicator and its weight can be adjusted based on disaster type, data availability, or business objectives. In addition to outputting risk scores and risk levels, the risk grid also outputs a risk cause field. The risk cause field should at least include a description of the major contributors to flooded area, road exposure, and key facility exposure. The risk grid results are not the end of the process but continue to serve as input for ranking high-risk facilities and calculating route risk.

[0119] In response to the selection of key target facilities, the route risk tool obtains: the coordinates of the key target facilities, the flood range, the flood buffer zone or flood spatial envelope, the risk grid, and the temporary safe assembly direction point or candidate target point. The route risk tool generates a regular route A, and determines the detour direction based on the flood spatial envelope, the risk grid, and the location of the target facilities, generating a backup route B to avoid flooding. Figure 4 As shown.

[0120] Alternative routes can be generated as follows: First, the shortest path obtained without considering flooding obstacles or only based on conventional road costs is taken as the conventional route A. Then, road edges intersecting with flooded areas are deleted or assigned higher costs, and road edges hitting high-risk grids and close to flooded areas are given additional risk costs. Candidate control points or safe target points are set in the north, south, east, and west detour directions of the flood spatial envelope, and multiple candidate paths are generated using Dijkstra's algorithm or A* algorithm. The number of flood crossings, the number of high-risk grid hits, the nearest water accumulation distance, and the route length of each candidate path are calculated. Paths that have too much overlap with the conventional route or still cross flooded areas are eliminated, and the one with the lowest overall risk is selected as the alternative route B from the remaining candidate paths. When road topology data is unavailable, spatially assisted analysis routes can also be generated using directional control points outside the flood spatial envelope.

[0121] The route risk tool calculates the risks for both routes separately, as shown in Table 4.

[0122] Table 4

[0123]

[0124] The comprehensive traffic risk level of the regular and backup routes is determined based on the above indicators. The four indicators include route length L, number of times the route crosses flood-prone areas Nf, number of high-risk grid points Nh, and nearest waterlogged distance d. An implementable comprehensive score is... A larger R value indicates a higher risk; the weights and distance thresholds can be configured based on regional experience. The current embodiment's risk determination rule is as follows: If the rice is deemed high-risk, then... Route length is classified as medium risk if it is within a certain range; otherwise, it is classified as low risk. Route length serves as a ranking and cost indicator within the same risk level. If the alternative route reduces flood crossings or high-risk grid hits compared to the conventional route, and the overall score is significantly lower, then a recommendation to prioritize or review the alternative route is output.

[0125] When an alternative route reduces flood contact, increases the nearest waterlogged distance, or reduces high-risk grid hits compared to the conventional route, a conclusion is generated suggesting the activation or review of the alternative route. The route in the current implementation is for spatial auxiliary analysis and is not equivalent to real-time navigation; in alternative implementations, routes can be generated based on road topology networks, dynamic obstacle costs, and shortest path algorithms.

[0126] The results generation tool extracts data from structured results, including flood area, number of patches, cumulative precipitation, road exposure length, number of key facilities, risk grid level, and route risk indicators. The results generation module is organized according to a preset template: event overview, core indicators, precipitation background, SAR flood identification results, topographic and permanent water body verification, road and key facility exposure, high-risk grid list and risk causes, traffic risk comparison, remediation recommendations, applicable boundaries, and manual review status.

[0127] Natural language models can organize structured fields into language, but they must not modify or create key metrics not generated by preceding tools. Therefore, the values ​​in the report can be traced back to the corresponding tools and results files.

[0128] In one embodiment, obtaining the corresponding data includes:

[0129] Based on the target area, the time window, and the input data type of the tool to be invoked, determine the initial data type and the time period of the initial data required for this analysis request;

[0130] Based on the initial data type and the time period of the initial data, determine whether the corresponding data exists in the local cache. If it exists, extract the corresponding data and perform unified processing of coordinate reference, spatial range and raster grid, as the data packet for this assessment request. The data in the local cache is multi-source mapping data obtained in advance through different interfaces, including at least one of SAR image data, DEM data, permanent water body data, precipitation data, road data or key facility data.

[0131] In this embodiment, the present application may use pre-cached offline data packets; data may be obtained from online remote sensing, meteorological, or basic geographic data services; data may be provided by UAVs, ground sensors, or field reporting systems; and a hybrid approach of prioritizing local caching and supplementing online when data is missing may be adopted. In the offline data packet implementation, the system pre-caches SAR, DEM, permanent water bodies, roads, facilities, and administrative boundary data of the target area. During task execution, the system prioritizes reading the data path from the local inventory and writes the tool status, parameters, and result files into a local structured status file. In the online access implementation, the system calls the remote sensing, meteorological, or basic geographic data service interface according to the structured task object to obtain the data corresponding to the target area and time window, and triggers subsequent mapping and analysis tools after the data access is completed.

[0132] In one embodiment, if the corresponding data does not exist in the local cache, a list of missing data is output for user input.

[0133] In one embodiment, the mapping analysis tool information also includes the tool's applicable boundaries and the tool's execution status; the tool's execution status includes pending invocation, invocation, completed, or abnormal.

[0134] In one embodiment, the method further includes recording the execution status, input source, actual parameters, output indicators, result path, and applicable boundaries of each tool to form a traceable analysis chain.

[0135] In one embodiment, a flood emergency risk assessment system based on multi-source mapping data is provided, comprising:

[0136] Task parsing module: In response to user-inputted emergency assessment requests, it parses the emergency assessment requests and generates a structured task object containing the target area, time window, disaster type, assessment target, target result type, and unique task identifier;

[0137] The module for determining the tools to be invoked and their dependencies is used to determine the tools to be invoked for this assessment request based on the assessment objectives and target result types in the structured task object, the pre-registered surveying and analysis tool information, and the associated prerequisite tools of each tool. It also determines the dependencies of the tools to be invoked, forming a complete tool dependency graph. The surveying and analysis tool information includes at least the tool identifier, input data type and fields, output data type and fields, prerequisite tools, and prerequisite results.

[0138] Data management module: used to determine the initial data type and time period of the initial data required for this analysis request based on the target area, the time window and the input data type of the tool to be invoked, obtain the corresponding data and organize it into a data packet for this analysis request;

[0139] Structured Result Status Establishment Module: Used to establish a structured result status associated with the task identifier to record the execution results of each tool; the structured result status includes at least the task identifier, tool identifier, execution status, input data reference, actual parameters, structured indicators, and layer or result file reference;

[0140] Tool execution module: According to the tool dependency graph, the tools to be called are called in sequence, and the corresponding data is called from the data packet of this analysis request according to the input data type and field in the information of each tool to be called, or the corresponding data is read from the structured result status output by the previously executed tools, and the corresponding mapping analysis processing is performed.

[0141] The assessment results generation module is used to read the structured indicators output by each tool from the structured result status and organize and generate emergency assessment results according to a preset template.

[0142] Taking a case of a rainstorm and flood in Zhengzhou as an example, the system loads pre-disaster and post-disaster SAR VV / VH data, cumulative precipitation data, DEM, permanent water bodies, and data on roads, hospitals, schools, and stations.

[0143] SAR tools identify suspected flood pixels based on low VH echoes after a disaster and significant decreases in VH before and after a disaster. They are then validated using permanent water body data and small patches are filtered out to obtain a suspected flood area of ​​approximately 26.2 square kilometers. General generative models, on the other hand, may generate numerical values ​​or conclusions without structured data support.

[0144] The road exposure tool calculated the intersections of roads with flooded areas, identifying approximately 103.0 kilometers of potentially affected roads. The facility screening tool used a 200-meter buffer zone to identify 73 key facilities that were either adjacent to or directly exposed.

[0145] The risk grid tool generates a 1-kilometer grid, calculates the flooded area, affected road length, and number of key facilities within each grid, and forms high, medium, and low risk grids and the causes of the risks.

[0146] When a high-risk school, hospital, or station is selected, the route risk tool generates a regular route A and a backup route B to avoid flooding. It calculates the route length, flood contact, nearest flood distance, and high-risk grid hit, and outputs a comparison of the passage risk.

[0147] The results generation tool generates map results, risk lists, and emergency assessment briefings from the above structured results.

[0148] The technical effects of this application include:

[0149] 1. Improve the automatic arrangement capability of multi-source mapping analysis workflow and reduce data mismatch: This patent unifies the target area, time window, disaster type and result target through structured task objects, and allows each tool to read and write results under the same task_id. Since subsequent tools obtain the output of preceding tools through dependencies, rather than relying on analysts to manually import files, it can reduce the situation where results from different regions, different time windows or different versions are incorrectly combined.

[0150] 2. Improve data consistency in the assessment results generation process: This application saves the output indicators of each tool through structured result status and limits the result generation module to generate result content based on the structured indicators generated by the completed tools, so that key indicators such as flood area, road length, number of facilities, and risk level all have corresponding data sources, reducing the data inconsistency problems caused by manual sorting or text generation.

[0151] 3. Improved auditability of the analysis process: This patent records the inputs, parameters, outputs, status, applicable boundaries, and completion time of each tool. When a risk conclusion needs to be reviewed, the corresponding tool and result file can be traced back. Compared to solutions that only retain the final map or report, this patent preserves the complete analysis chain, which is beneficial for review, repeated execution, and manual verification.

[0152] In one embodiment, a computer device is also provided, the processor of which provides computing and control capabilities, and the computer device loads and runs a computer program to implement the above-described flood emergency risk assessment method based on multi-source mapping data.

[0153] In one embodiment, a computer program product is also provided, including a computer program / instructions that, when executed by a processor, implement the steps of the methods described in the embodiments above.

[0154] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for assessing flood emergency risks based on multi-source mapping data, characterized in that, The method includes: In response to an emergency assessment request input by a user, the emergency assessment request is parsed to generate a structured task object containing the target area, time window, disaster type, assessment target, target result type, and unique task identifier; Based on the assessment objectives and target result types in the structured task object, the pre-registered surveying and analysis tool information, and the associated prerequisite tools of each tool, the tools to be invoked for this assessment request are determined, and the dependencies of the tools to be invoked are determined, forming a complete working tool dependency diagram; the surveying and analysis tool information includes at least the tool identifier, input data type and fields, output data type and fields, prerequisite tools, and prerequisite results; Based on the target area, the time window, and the input data type of the tool to be invoked, determine the initial data type and the time period of the initial data required for this analysis request, obtain the corresponding data, and organize it into a data packet for this analysis request; Establish a structured result status associated with the task identifier to record the execution results of each tool; the structured result status includes at least the task identifier, tool identifier, execution status, input data reference, actual parameters, structured indicators, and layer or result file reference; According to the tool dependency diagram, the tools to be called are called in sequence, and the corresponding data is called from the data packet of this analysis request according to the input data type and field in the information of each tool to be called, or the corresponding data is read from the structured result status output by the previously executed tools, and the corresponding mapping analysis processing is performed. Read the structured indicators output by each tool from the structured result status, and organize and generate emergency assessment results according to the preset template.

2. The flood emergency risk assessment method based on multi-source mapping data according to claim 1, characterized in that, The pre-registered mapping and analysis tool information includes remote sensing identification tools, spatial verification tools, exposure analysis tools, risk assessment tools, route risk tools, or results generation tools.

3. The flood emergency risk assessment method based on multi-source mapping data according to claim 2, characterized in that, The remote sensing identification tool is used to acquire SAR polarization data of the target area in the pre-disaster and post-disaster phases, perform spatial registration and pre-disaster and post-disaster change detection on the SAR polarization data, generate candidate flood masks, form permanent water body masks using permanent water body data, and remove pixels corresponding to permanent water bodies from the candidate flood masks. Connectivity analysis is performed on the candidate flood masks after removing permanent water bodies. Connectivity regions with fewer than a preset pixel threshold are removed. The remaining connected regions are vectorized to obtain the candidate flood range. The spatial verification tool is used to acquire the digital elevation model (DEM) of the target area and the cumulative precipitation raster data covering the target area within the target time window; generate slope maps and low-lying area maps based on the DEM; statistically analyze the average precipitation, maximum precipitation, and the proportion of pixels with precipitation values ​​exceeding a preset precipitation threshold within the candidate flood range covered by the cumulative precipitation raster data; and generate spatial verification marker data by superimposing the slope map, low-lying area map, average precipitation, maximum precipitation, and pixel proportion onto the candidate flood range. The spatial verification marker data is used to identify reliable areas within the candidate flood range that meet the topographic and precipitation conditions, as well as areas that require manual verification. The exposure analysis tool is used to uniformly project the candidate flood range, road data, and key facility data onto the same metric projection coordinate system; to perform validity processing and merging of flood patches in the candidate flood range to form a flood surface for spatial calculation; to calculate the length of the intersection between the road data and the flood surface to obtain the set of affected roads; to generate a flood buffer zone with a preset distance based on the flood surface; to calculate the shortest spatial distance between the key facilities and the flood surface; and to identify key facilities that intersect with the flood surface as directly exposed facilities, and key facilities located within the flood buffer zone and not intersecting with the flood surface as adjacent exposed facilities, thus obtaining a set of facility exposures; the key facilities include at least one or more of hospitals, schools, and stations; The risk assessment tool is used to overlay the flooded area, the set of affected roads, and the set of exposed facilities onto the metric projection coordinate system to generate a regular square risk grid that covers the target area and has a side length of a preset grid size. The flood-inundated area, affected road length, total number of key facilities, and quantity of various key facilities within each risk grid were statistically analyzed. The flood inundation area, affected road length, and total number of key facilities in all risk grids are normalized within the region to obtain normalized flood indicators, normalized road indicators, and normalized facility indicators; the comprehensive risk score is calculated according to the preset weight coefficients, and the risk level of each risk grid is determined according to the preset risk level threshold to generate the risk grid results. The risk grid results include the risk score, risk level, and risk cause fields for each grid. The generation rules for the risk cause field are as follows: when the normalized flood index exceeds the preset contribution threshold, the risk cause field includes a flood inundation contribution identifier; when the normalized road index exceeds the preset contribution threshold, the risk cause field includes a road exposure contribution identifier; when the normalized facility index exceeds the preset contribution threshold, the risk cause field includes a facility exposure contribution identifier. The route risk tool is used to respond to the selection instruction of the target key facility, obtain the starting coordinates of the target key facility, the overall spatial envelope of the flood surface, the risk grid result, and the ending coordinates of the candidate target point; generate a regular route from the starting coordinates to the ending coordinates based on the road network data, or generate a spatial auxiliary judgment route based on the flood spatial envelope; Outside the overall spatial envelope of the flooded surface, multiple bypass control points are set according to a preset bypass distance to generate multiple candidate routes for avoiding water accumulation that pass through different bypass control points; the route length of the conventional route and each candidate route for avoiding water accumulation, the number of flood patches intersecting with the flooded surface, the shortest distance from the route to the flooded surface, and the number of high-risk grids hit are calculated respectively. The traffic risk score of each route is calculated according to the preset route risk scoring rules. The route with the lowest traffic risk score among the multiple candidate routes to avoid water accumulation is determined as the backup route to avoid water accumulation. Based on the comparison result between the traffic risk score of the backup route to avoid water accumulation and the traffic risk score of the regular route, a suggestion to activate or review the backup route to avoid water accumulation is generated. The results generation tool is used to read the total flood inundation area, total number of flood patches, spatial verification results, cumulative precipitation statistics, total length of affected roads, number of directly exposed facilities, number of adjacent exposed facilities, and number of grids for each risk level in the risk grid results from the output results of the remote sensing identification tool, spatial verification tool, exposure analysis tool, and risk assessment tool; and to fill the above data into the corresponding fields according to the preset emergency assessment report template to generate an emergency assessment results file.

4. The flood emergency risk assessment method based on multi-source mapping data according to claim 1, characterized in that, The acquisition of the corresponding data includes: Based on the target area, the time window, and the input data type of the tool to be invoked, determine the initial data type and the time period of the initial data required for this analysis request; Based on the initial data type and the time period of the initial data, determine whether the corresponding data exists in the local cache. If it exists, extract the corresponding data and perform unified processing of coordinate reference, spatial range and raster grid, as the data packet for this assessment request. The data in the local cache is multi-source mapping data obtained in advance through different interfaces, including at least one of SAR image data, DEM data, permanent water body data, precipitation data, road data or key facility data.

5. The flood emergency risk assessment method based on multi-source mapping data according to claim 4, characterized in that, If the corresponding data is not found in the local cache, a list of missing data will be output for the user to input.

6. The flood emergency risk assessment method based on multi-source mapping data according to claim 1, characterized in that, The mapping and analysis tool information also includes the tool's applicable boundaries; the tool execution status in the structured result status includes pending invocation, invocation, completed, or abnormal.

7. The flood emergency risk assessment method based on multi-source mapping data according to claim 1, characterized in that, The method also includes recording the execution status, input source, actual parameters, output indicators, result path and applicable boundaries of each tool to form a traceable analysis chain.

8. A flood emergency risk assessment system based on multi-source mapping data, characterized in that, The system includes: Task parsing module: In response to user-inputted emergency assessment requests, it parses the emergency assessment requests and generates a structured task object containing the target area, time window, disaster type, assessment target, target result type, and unique task identifier; The module for determining the tools to be invoked and their dependencies is used to determine the tools to be invoked for this assessment request based on the assessment objectives and target result types in the structured task object, the pre-registered surveying and analysis tool information, and the associated prerequisite tools of each tool. It also determines the dependencies of the tools to be invoked, forming a complete tool dependency graph. The surveying and analysis tool information includes at least the tool identifier, input data type and fields, output data type and fields, prerequisite tools, and prerequisite results. Data management module: used to determine the initial data type and time period of the initial data required for this analysis request based on the target area, the time window and the input data type of the tool to be invoked, obtain the corresponding data and organize it into a data packet for this analysis request; Structured Result Status Establishment Module: Used to establish a structured result status associated with the task identifier to record the execution results of each tool; the structured result status includes at least the task identifier, tool identifier, execution status, input data reference, actual parameters, structured indicators, and layer or result file reference; Tool execution module: According to the tool dependency graph, the tools to be called are called in sequence, and the corresponding data is called from the data packet of this analysis request according to the input data type and field in the information of each tool to be called, or the corresponding data is read from the structured result status output by the previously executed tools, and the corresponding mapping analysis processing is performed. The assessment results generation module is used to read the structured indicators output by each tool from the structured result status and organize and generate emergency assessment results according to a preset template.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to claims 1-7.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method described in claims 1-7.

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