A method and device for predicting and analyzing a flooding range of urban street drainage facilities

Abstract

The application relates to the technical field of urban flood control and drainage, and discloses a method and device for predicting and analyzing a water gushing submerged range of urban street drainage facilities, which comprises six steps of data preprocessing, memory data initialization, initial catchment range searching, submerged analysis and incremental searching, water gushing point merging processing and submerged range extraction. In the application, the DEM grid is marked with invalid values through building and drainage pipeline data, the real street terrain constraint is restored, the D8 seed spreading model and the elevation statistical mapping are adopted to realize staged incremental searching, the total water gushing amount is taken as a control amount to gradually submerge in a filling pit mode, the water gushing points are automatically merged and connected, joint submerged analysis is completed, and finally, the accurate submerged range polygon and boundary water level information are directly output. The application greatly reduces the grid traversal times, improves the calculation efficiency and simulation authenticity, and can meet the requirements of urban waterlogging emergency rapid judgment, drainage system operation and maintenance and flood control and disaster reduction decision-making.

Description

Technical Field

[0001] This invention belongs to the field of urban flood control and drainage technology, specifically referring to a method and device for predicting and analyzing the inundation range of urban street drainage facilities. Background Technology

[0002] As a core infrastructure for urban flood control and drainage, drainage pipeline systems are frequently plagued by disasters such as water inrush and backflow due to structural defects such as aging pipes, insufficient pipe diameter, and unreasonable network layout, as well as the impact of exceeding design flow rates caused by short-term heavy rainfall. These disasters not only lead to road flooding and traffic paralysis but may also trigger a chain of risks, including underground water ingress, power outages, and environmental pollution. Therefore, conducting research on the analysis and calculation of the inundation range of drainage pipeline water inrush, and quantifying the impact boundary, spread path, and potential risk level of water inrush accidents, can provide a scientific basis for the optimized design of urban drainage systems, the formulation of emergency response plans, and the rational allocation of flood control and disaster reduction resources.

[0003] Current analysis of the inundation range of urban drainage pipes mostly employs multi-point active inundation analysis methods, and the commonly used techniques mainly fall into two categories: An improved seed spread algorithm based on a stack structure: This algorithm utilizes a stack data structure to manage flooded grid cells and employs... China Unicom or The method employs a connectivity rule-based neighborhood search, initializes a buffer stack for each flood seed point, and iteratively checks whether the grid elevation is lower than the preset flood level. Finally, it merges the flooded areas of each point. While the logic of this method is simple, it requires repeated iterations, and the search range is large and computationally time-consuming when the flooded area is far from the flood point.

[0004] Hydrodynamic model coupled with GIS analysis: The process of water flow diffusion and confluence is simulated by hydrodynamic models such as MIKE21 and EFDC, and then imported into GIS for overlay correction. This method can take into account the physical laws of water flow and has high accuracy. However, the models are complex, have many parameters, and have long calculation cycles, making it difficult to meet the needs of online rapid analysis.

[0005] Existing technologies generally suffer from the following drawbacks: Multiple iterations are required to adjust the floodwater level, resulting in a large amount of repetitive searches and low computational efficiency. Flood simulation requires a complete calculation of the water flow evolution process and cannot directly output the final inundation range; The simulation results deviated significantly from the actual flooding situation because it did not fully take into account the constraints of the actual street topography, such as buildings and drainage facilities. Multiple water inflow points are calculated independently, and automatic merging of connected regions is not implemented, resulting in poor overall analysis performance.

[0006] To address the aforementioned issues, this invention proposes a method and apparatus for predicting and analyzing the inundation range of urban street drainage facilities. Under the premise of ensuring terrain connectivity constraints, the method significantly reduces the number of grid traversals through phased search and incremental search, directly outputting accurate inundation range and water level, thus meeting the needs for rapid assessment in urban flooding emergencies. Summary of the Invention

[0007] In response to the above situation, and to address the problems of excessive iterations, low computational efficiency, insufficient simulation realism, and inability to quickly output results in existing urban drainage flood inundation analysis methods, this invention provides a method and device for predicting and analyzing the flood inundation range of urban street drainage facilities. This method achieves high-precision, high-efficiency, and highly realistic rapid calculation of flood inundation range and water level, supporting emergency response to urban flooding and pipeline operation and maintenance decisions.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: A method for predicting and analyzing the inundation range of urban street drainage facilities includes the following steps: S1, Data Preprocessing: Acquire high-precision DEM raster data of the urban area, building vector data, drainage pipeline and pipe point data, and uniformly correct them to the CGCS2000 rectangular coordinate system; Overlay buildings with drainage pipelines and pipe points and adjust the building range; set the area occupied by buildings in the DEM as invalid data; Determine the water inflow points and the water inflow volume of each water inflow point.

[0009] S2, Memory data initialization: Load the DEM elevation data into a one-dimensional elevation array, construct a one-dimensional equivalent inrush depth array and a one-dimensional submerged marker array; convert the inrush point coordinates into a raster index, and sort them according to the corresponding elevation values ​​from smallest to largest to form an inrush point queue.

[0010] S3, Initial catchment area search: Starting from the first inrush point, the search is performed based on the D8 seed spread model, distinguishing between the grid to be flooded and the boundary grid, and storing them in the elevation statistical mapping by grouping by elevation.

[0011] S4, Inundation Analysis and Incremental Search: Using the total inflow as the control variable, a depression-filling mode is adopted to inundate in stages from low to high elevation. Based on the relationship between the remaining inflow and the current group's inundation water demand, water deduction, elevation update, inundation marking, and incremental search are performed until the current inflow point is inundated. S5, Inflow Point Merging Processing: When other inflow points are encountered during the search, the inflow volume is accumulated and the water volume of that inflow point is cleared, merging them into a joint inundation analysis; when traversing the remaining inflow points, already processed inflow points are skipped. S6, Inundation Range Extraction: Based on the inundation mark array, the elevation of non-inundated grids is set to invalid values, the connected boundaries of the inundated grids are extracted, and the inundation range polygon and boundary water level information are output.

[0012] Furthermore, in S1, the DEM raster data is in tif format, and the building vector data is in shp format; invalid data is assigned a value of 0, and the effective elevation range is 1400–1500.

[0013] Furthermore, in S2, the area of ​​a single grid cell is xinv×yinv, and the inflow volume is expressed as the equivalent water depth, which is equal to the inflow volume / the area of ​​a single grid cell.

[0014] Furthermore, in step S2, the water inflow points are sorted by elevation from smallest to largest, so that similar water inflow points are automatically included in the search, reducing the number of initial search attempts.

[0015] Further, S3 specifically includes: S31, initializing the search queue, elevation statistical mapping, access marker queue, and statistical marker queue; S32, adding the first water inrush point to the search queue and marking its access status; S33, using the D8 model to search in eight surrounding directions, skipping grids with invalid elevations, grids exceeding the boundary, and grids already visited; S34, adding grids with elevations less than or equal to the main grid to the search queue, and storing all grids in the elevation statistical mapping grouped by elevation until no new grids are added to be searched, thus obtaining the flooded area and boundary grids.

[0016] Furthermore, the step-by-step flooding in the depression-filling mode in S4 includes: S41, sorting the elevation statistical mapping by elevation from smallest to largest; S42, performing flooding on the lowest elevation group, with the flooding water level being the second lowest elevation value, and calculating the flooding water demand for the current group; S43, comparing the remaining inflow with the flooding water demand, performing water deduction, grid elevation modification, setting the flooding flag to true, adding to the search queue and updating the elevation statistical mapping, and cyclically incrementally searching until the inflow is exhausted.

[0017] Furthermore, in step S5, the elevation of the later-integrated water inflow point is higher than that of the initial water inflow point, and the water volume of the water inflow point is cleared after merging.

[0018] Furthermore, in step S6, the elevation of the non-submerged grid is set to an invalid value of 1, the submerged grid boundary is extracted through connected component search, and the final elevation of the boundary grid is used as the water level value to output the submerged range polygon.

[0019] A device for predicting and analyzing the flooding range of urban street drainage facilities, comprising: The data preprocessing module acquires high-precision DEM raster data of the urban area, building vector data, drainage pipeline and pipe point data, and uniformly corrects them to the CGCS2000 rectangular coordinate system; it overlays buildings with drainage pipelines and pipe points and adjusts the building range, sets the area occupied by buildings in the DEM as invalid data; and determines the water inflow points and the water inflow volume of each water inflow point.

[0020] The memory initialization module loads the DEM elevation data into a one-dimensional elevation array, constructs a one-dimensional equivalent inrush depth array and a one-dimensional submerged marker array; converts the inrush point coordinates into a raster index, and sorts them according to the corresponding elevation values ​​from smallest to largest to form an inrush point queue.

[0021] The range search module starts from the first water inrush point and searches based on the D8 seed spread model, distinguishing between the grid to be flooded and the boundary grid, and storing them in the elevation statistical mapping by grouping them by elevation.

[0022] The flooding calculation module uses the total inflow as the control quantity and adopts a depression-filling mode to flood the area step by step from low to high elevation. Based on the relationship between the remaining inflow and the current group's flooding water demand, it performs water deduction, elevation update, flooding marking, and incremental search until the current inflow point is flooded.

[0023] The inrush point merging module accumulates the inrush volume and clears the volume of other inrush points when they are encountered during the search process, and merges them into a joint flooding analysis. When traversing the remaining inrush points, it skips the already processed inrush points.

[0024] The results output module sets the elevation of the non-submerged grid to invalid values ​​based on the submerged marker array, extracts the connected boundaries of the submerged grid, and outputs the submerged range polygon and boundary water level information.

[0025] Furthermore, the inundation calculation module uses elevation statistical mapping to achieve incremental search, which greatly reduces the number of grid traversals and directly outputs the final inundation range and water level.

[0026] The beneficial effects achieved by the present invention using the above structure are as follows: (1) In this invention, a three-stage process of “flooded area search - flooding - incremental search” is adopted, and incremental search is achieved by combining elevation statistical mapping, which avoids repeated traversal, greatly reduces the amount of grid search, and supports online fast analysis.

[0027] (2) In this invention, invalid values ​​are marked on the DEM by buildings and drainage pipelines / pipe points to restore the real street terrain, eliminate the influence of building obstruction on water flow, and make the flooding results more realistic.

[0028] (3) Sort by elevation and automatically merge connected water inflow points to reduce the number of independent calculations and improve the overall analysis stability and efficiency. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the structure of a method for predicting and analyzing the flood inundation range of urban street drainage facilities proposed in this invention.

[0030] Figure 2 This is the processed DEM data.

[0031] Figure 3 This is a search and statistical analysis of the initial catchment area.

[0032] Figure 4 This is the result of a local flooding analysis.

[0033] Figure 5 This is the result of a global flooding analysis. Detailed Implementation

[0034] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0035] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0036] To address the issues of numerous iterations, low computational efficiency, insufficient simulation realism, and inability to quickly output results in the analysis of urban drainage pipe flood inundation range, this application proposes a method for predicting and analyzing the flood inundation range of urban street drainage facilities. Figure 1 This includes the following steps: S1, Data Preparation: Acquire high-precision DEM raster data of the urban area, building vector data, drainage pipeline and pipe point data, and uniformly correct them to the CGCS2000 rectangular coordinate system; Overlay buildings with drainage pipelines and pipe points and adjust the building range, and set the area occupied by buildings in the DEM as invalid data; Determine the water inflow points and the water inflow volume of each water inflow point; S2, Memory data initialization: Load the DEM elevation data into a one-dimensional elevation array, construct a one-dimensional equivalent inrush water depth array and a one-dimensional submerged marker array; convert the inrush point coordinates into a raster index, and sort them according to the corresponding elevation values ​​from smallest to largest to form an inrush point queue. S3, Initial catchment area search: Starting from the first water inrush point, the search is based on the D8 seed spread model, distinguishing between the grid to be flooded and the boundary grid, and storing them in the elevation statistical mapping by grouping by elevation; S4, Inundation Analysis and Incremental Search: Using the total inflow as the control quantity, the depression filling mode is adopted to inundate the water level from low to high elevation. Based on the relationship between the remaining inflow and the water demand of the current group, water deduction, elevation update, inundation marking and incremental search are performed until the current inflow point is inundated. S5, Inrush Point Connection Processing: When other inrush points are encountered during the search, the inrush volume is accumulated and the water volume of that inrush point is cleared, and they are merged into a joint flooding analysis. When traversing the remaining inrush points, the processed inrush points are skipped. S6, Inundation Range Extraction: Based on the inundation marker array, the elevation of the non-inundated grid is set to an invalid value, the connected boundary of the inundated grid is extracted, and the inundation range polygon and boundary water level information are output.

[0037] To better understand the working process of the urban street drainage facility flood inundation range prediction and analysis method and device according to an embodiment of this application, refer to... Figures 1-5 The following is a specific embodiment: Step 1, Data Preparation The data required for this method includes high-precision DEM data of the urban area, data on the number of residential buildings, and data on collected drainage pipelines and viewpoints, and should be processed according to the following requirements.

[0038] We need to collect high-precision DEM data of the urban area. The data is raster data in TIFF format. We need to use GIS software to correct the raster data to a rectangular coordinate system (CGCS2000).

[0039] Collect vector data of urban residential buildings (data format is shp) and correct it to the same coordinate system as the raster data.

[0040] Collect data on drainage pipelines and pipe points (drainage wells) and calibrate them to a unified coordinate system; Overlay drainage pipeline and pipe point data with the building area, adjust the building scope, and avoid the building scope covering pipe points and pipelines; like Figure 2 As shown, by overlaying the processed building data with the DEM data, the entire area occupied by buildings in the DEM file is set to invalid data. Figure 2 The black area represents the building area, the gray area represents the normal DEM, the white area represents invalid values, and the brown dots represent drainage well points.

[0041] Step 2: Initialize elevation data and water inrush point data in memory. Elevation information is loaded from the TIFF file. The elevation data is represented as raster data with ysize rows and xsize columns, where each pixel is xinv wide and yinv high, and the area of ​​a single raster is xinv * yinv. A one-dimensional array, denoted as `elevation`, is used to organize and manage the elevation data, with a length of ysize * xsize. A one-dimensional data structure `wd[ysize * xsize]` is used to record the water inflow of each raster (for ease of comparison, the water inflow volume corresponding to the water depth of a single raster is used as the water inflow volume; water inflow volume (water depth) = water inflow volume / unit raster area).

[0042] All water inflow point coordinates are converted into row and column values ​​x0, y0 using the coordinate information on the raster data. Then, the corresponding array index is obtained by index = y0 * xsize + x0, and wd[index] is recorded as the water inflow volume of that water inflow point.

[0043] All inflow points are arranged in ascending order of their corresponding elevation values, as this will be necessary for subsequent inundation analysis. This approach has the advantage that when multiple inflow points are close together, the search from low to high elevations can incorporate other inflow points along the path into the incremental search process, thus reducing the number of inflow points requiring initial inundation searching and improving performance. After initialization, the following data is cached in memory: Step 2.1 Elevation array elevation[ysize * xsize], where invalid values ​​and values ​​for building areas are all 0 (the valid elevation for this area is between [1400, 1500], so setting invalid values ​​to 0 can distinguish them); Step 2.2 Equivalent inrush water depth wd[ysize * xsize], where the value at the inrush point is the equivalent water depth data converted from the water volume at that inrush point; Step 2.3 Flooded marker data [ysize * xsize], the default value is false for each point.

[0044] Step 2.4 The water inflow point queue gpts[n] is arranged in ascending order of elevation, and the index value of the elevation array elevation corresponding to each water inflow point is recorded.

[0045] Step 3. Initial catchment area search The search for the initial flooded area begins from the cell containing the first water inrush point, and is based on the D8 seed spread model. The initial grid is set as the main grid, and the search proceeds to the grids in the eight surrounding directions, following these principles: (1) Grids with elevations lower than the main grid will be the first to be flooded, requiring iterative searching until no grid with a lower elevation is found; these are the grids to be flooded. (2) When the water level in the surrounding cells is similar to that in the main grid, the water will spread; these also require iterative searching and are the grids to be flooded. (3) Grids with elevations higher than the main grid are the boundary grids of the flooded area. When the water level in the flooded area exceeds the elevation of the initial grid, the boundary grids will be flooded. These grids do not require iterative searching but need to be registered; they are recorded as boundary grids.

[0046] The specific search process mainly includes the following steps: Step 3.1 Clear the search queue (seachqueue) and the elevation statistics map (statMap).<float,queue[int]> First, create an access marker queue (visited) and a statistics marker queue (statistics). The search queue records the grids to be searched; the access marker queue records nodes already added to the search sequence to prevent duplicate searches; the elevation statistics map (statMap) groups all elevation data entering the elevation statistics map according to elevation, and the statistics marker queue records the status of being added to the elevation statistics map to avoid duplicate statistics. Add the first water inflow point (gpt0) to the search sequence and mark its access status. Add the elevation information of gpt0 to the statistics map (statMap) and mark its statistical status. Iterate through the search queue starting from 0, performing the search process from step 3.2.

[0047] Step 3.2 Use the D8 seed propagation method to search the surrounding 8 grids. The surrounding 8 grids are distinguished according to the following logic: a) Grids with invalid elevations need to be skipped; b) Grids that exceed the boundary need to be skipped; c) Grids that have been visited need to be skipped; d) Grids with elevations less than or equal to the main grid need to be added to the search queue and marked as visited. At the same time, these two types of grids are added to the statistics queue (if they are not in the statistics mapping) and marked as statistics; e) Grids with elevations greater than the main grid are added to the statistics queue (if they are not in the statistics mapping) and marked as statistics.

[0048] Step 3.3 If new grids to be searched were added in Step 3.2, the search queue will be expanded, and the loop will continue. Repeat Step 3.2 until the last item in the search queue is reached and no new nodes are found. Then proceed to Step 4 for flooding analysis and incremental search. When the search index var is the last item in the search queue, and no new nodes were added in the last search process, the flooding range (grids in the search queue) and boundary range (stored in the statistical map statmap, but not in the search queue) starting from the initial inrush point gpt0 can be obtained.

[0049] At this point, the initial search process ends. The data status in the elevation statistical mapping statMap can be referenced by the elevation value elevation0 of gpt0. Grids with elevation less than or equal to elevation0 are all connected to gpt0 and will accumulate water; grids with elevation greater than elevation0 are the boundary ranges of the grids to be submerged previously. See the figure below for the specific effect. In the figure, brown (43) represents the location of the water inrush point, green represents the search area to be submerged, and yellow represents the boundary range. These points are all grouped and stored in statMap according to their elevation values. When performing submersion analysis, the submersion calculation can be continued upwards in a step-like manner based on the lowest elevation group in statMap (see step 4).

[0050] Step 4, flooding analysis and incremental search.

[0051] The flooding phase requires the total inflow volume watervol calculated in this operation (recorded as equivalent water depth in a single grid, in meters) as a global reference. Each time a flooding calculation is performed, the water volume floodvol required for flooding is calculated. The water volume required for flooding is deducted from the total water volume until watervol is completely deducted, at which point the entire flooding process is complete.

[0052] The flooding simulation process uses a depression-filling model, and the calculation steps are as follows: Step 4.1 Sort the flooded mapping group data in ascending order by key (elevation); Step 4.2 Submerge the group with the lowest elevation, with the submersion level being the second lowest elevation value, and calculate the submersion volume. Let the smallest group be queue1, the group length be n, the elevation be elev1, and the submersion level be elev2. Calculate the water volume floodvol (in meters, expressed as the equivalent water depth per grid) required to submerge these grids up to elev2, where floodvol = (elev2 - elev1) * n. Step 4.3 Compare the values ​​of watervol and floodvol to determine whether to perform an incremental search or complete the flooding analysis: a) If watervol is greater than floodvol, it means that subsequent analysis processes still need to be continued. At this point, floodvol is deducted from watervol; meanwhile, the elevations of the units in queue1 are modified to elev2; the inundation flags (recorded in the flood array) corresponding to the grids in queue1 are set to true; the values in queue1 are added to the searchqueueuu search queue; all entries of queue1 in statMap are added to the group corresponding to elev2, and elev1 and its group are removed from statMap. The searchqueue loop continues, and the process proceeds to step 4.4.

[0053] b) If watervol is equal to floodvol, it means that queue1 is just completely inundated, that is, the inundation analysis process is completed. At this point, the elevations of the units in queue1 are modified to elev2, the inundation flags (recorded in the flood array) corresponding to the grids in queue1 are set to true, and the inundation analysis of the gpt0 node is completed. The process returns to step 3 to carry out the analysis of the next water inrush point; c) If watervol is less than floodvol, it means that the remaining volume is insufficient to inundate queue1 to the water level of elev2, and some adjustments are required. The actual inundation water level elev = elev1 + watervol / n; the elevations of the units in queue1 are modified to elev2, the inundation flags (recorded in the flood array) corresponding to the grids in queue1 are set to true, and the inundation analysis of the gpt0 node is completed. The process returns to step 3 to carry out the analysis of the next water inrush point; Step 4.4: If a single inundation in step 4.3 is insufficient to complete the entire inundation process, it is necessary to perform a search from the inundated position. By adding the grids in the inundation queue queue1 to the tail of searchqueue again, the search can be continued from step 3.2. The processes from 3.2 to 4.3 are looped until the entire inundation process is completed (the search process is completed from states b and c in step 4.3). After the inundation is completed, the display effect is as follows Figure 4 shown.

[0054] Step 5: Connection processing of water inrush points.

[0055] During the search in step 3.2, other water inflow points may be encountered (the corresponding water inflow volume is recorded in the wd array with a value greater than 0). In this case, two or more connected points can be merged for searching by superposition, reducing the need for separate calculations of water inflow points. This requires that the elevation of later-added water inflow points be higher than that of the initial water inflow points to maintain correct connectivity and the grid flooding order determined based on this connectivity. The connectivity processing procedure is as follows: (1) In step 3.1, all water inflow points are arranged from smallest to largest; (2) When searching in step 3.2, when a new water inflow point is encountered (the water inflow value recorded in wd is greater than 0), the water inflow of the water inflow point is accumulated in the watervol control variable; at the same time, the water inflow value recorded in the wd array of the water inflow point is modified to 0 to avoid secondary search; (3) Continue the cycle of 3.2-4.3 until the entire analysis is completed; at this point, the joint inundation analysis of multiple water inflow points can be completed in one analysis, and their requirements for topographic connectivity can be met. (4) When continuing to traverse unanalyzed water inflow points, skip those water inflow points where the water volume (the value recorded in the wd array) is 0. Repeat steps 3-5 until all water inflow points have been analyzed.

[0056] 6. Extraction of the flooding range.

[0057] During steps 3-5, the values ​​in the elevation array are modified while performing flooding analysis. After the analysis is complete, the flooding range is extracted based on the flooding marker array (flood array), as follows: 6.1 Iterate through the flood array, modify the elevation (in the elevation array) of the cells in the flood array that have a value of false to an invalid value (in this article, the invalid value is 1), and leave the elevations in the flood array that have a value of true unchanged; 6.2 Starting from any submerged grid, search for other adjacent submerged grids and extract their boundaries to obtain the boundary points and elevations of the region.

[0058] 6.3 Traverse the other unvisited flooded grids until all boundary ranges are calculated, and use the final elevation of the cell where each point on the boundary is located as the z-value of each point.

[0059] 6.4 Record and output the polygon information, which is the final flooded area, and the z-value is the final water level information. The flooding effect is as follows. Figure 5 As shown.

[0060] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0061] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

[0062] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for predicting and analyzing the inundation range of urban street drainage facilities, characterized in that, Includes the following steps: S1. Acquire high-precision DEM raster data of the urban area, building vector data, drainage pipeline and pipe point data, and uniformly correct them to the CGCS2000 rectangular coordinate system; overlay buildings with drainage pipelines and pipe points and adjust the building range, and set the area occupied by buildings in the DEM as invalid data; determine the water inflow points and the water inflow volume of each water inflow point. S2, load the DEM elevation data into a one-dimensional elevation array, construct a one-dimensional equivalent inrush water depth array and a one-dimensional submerged marker array; convert the inrush point coordinates into a raster index, and sort them according to the corresponding elevation values ​​from smallest to largest to form an inrush point queue; S3 starts from the first water inrush point and searches based on the D8 seed spread model, distinguishing between the grid to be flooded and the boundary grid, and storing them in elevation statistical mapping by grouping according to elevation. S4 uses the total inflow as the control quantity and adopts the depression filling mode to gradually flood from low to high elevation. Based on the relationship between the remaining inflow and the current group's flooding water demand, it performs water deduction, elevation update, flooding mark and incremental search until the current inflow point is flooded. S5. When other water inflow points are encountered during the search, the water inflow volume is accumulated and the water volume of the water inflow point is cleared. The data is then merged into a joint flooding analysis. When traversing the remaining water inflow points, the processed water inflow points are skipped. S6: Based on the flooding marker array, set the elevation of the non-flooded grid to an invalid value, extract the connected boundary of the flooded grid, and output the flooded range polygon and boundary water level information.

2. The method for predicting and analyzing the flood inundation range of urban street drainage facilities according to claim 1, characterized in that, In step S1, the DEM raster data is in TIFF format, and the building vector data is in SHHP format; invalid data is assigned a value of 0, and the effective elevation range is 1400–1500.

3. The method for predicting and analyzing the flood inundation range of urban street drainage facilities according to claim 2, characterized in that, In S2, the area of ​​a single grid cell is xinv×yinv, and the inflow volume is expressed as the equivalent water depth, which is equal to the inflow volume / the area of ​​a single grid cell.

4. The method for predicting and analyzing the flood inundation range of urban street drainage facilities according to claim 3, characterized in that, In step S2, the water inflow points are sorted by elevation from smallest to largest, so that similar water inflow points are automatically included in the search, reducing the number of initial search attempts.

5. The method for predicting and analyzing the flood inundation range of urban street drainage facilities according to claim 4, characterized in that, S3 specifically includes: S31, Initialize the search queue, elevation statistics mapping, access marker queue and statistics marker queue; S32, add the first water inflow point to the search queue and mark its access status; S33 uses the D8 model to search in eight directions around it, skipping invalid elevations, grids outside the boundary, and grids that have already been visited; S34 adds grids with elevations less than or equal to the main grid to the search queue. All grids are grouped by elevation and stored in the elevation statistical mapping until no new grids are added to be searched, thus obtaining the flooded area and boundary grid.

6. The method for predicting and analyzing the flood inundation range of urban street drainage facilities according to claim 5, characterized in that, The sinkhole-filling submersion mode in S4 includes: S41, sort the elevation statistical mappings by elevation from smallest to largest; S42, perform flooding on the minimum elevation group, with the flooding water level being the second minimum elevation value, and calculate the flooding water demand for the current group; S43, compare the remaining inflow with the flooding demand, perform water deduction, grid elevation modification, flooding flag set to true, add to search queue and update elevation statistics mapping, and perform incremental search in a loop until the inflow is exhausted.

7. The method for predicting and analyzing the flood inundation range of urban street drainage facilities according to claim 6, characterized in that, In step S5, the elevation of the later-integrated water inflow point is higher than that of the initial water inflow point, and the water volume of the water inflow point is cleared after merging.

8. The method for predicting and analyzing the flood inundation range of urban street drainage facilities according to claim 7, characterized in that, In step S6, the elevation of the non-submerged grid is set to an invalid value of 1. The submerged grid boundary is extracted by connected component search. The final elevation of the boundary grid is used as the water level value, and the submerged range polygon is output.

9. A device for predicting and analyzing the flood inundation range of urban street drainage facilities, characterized in that, include: The data preprocessing module acquires high-precision DEM raster data of the urban area, building vector data, drainage pipeline and pipe point data, and uniformly corrects them to the CGCS2000 rectangular coordinate system; it overlays buildings with drainage pipelines and pipe points and adjusts the building range, sets the area occupied by buildings in the DEM as invalid data; and determines the water inflow points and the water inflow volume of each water inflow point. The memory initialization module loads the DEM elevation data into a one-dimensional elevation array, constructs a one-dimensional equivalent inrush water depth array and a one-dimensional submerged marker array; converts the inrush point coordinates into a raster index, and sorts them according to the corresponding elevation values ​​from smallest to largest to form an inrush point queue. The range search module starts from the first water inrush point and searches based on the D8 seed spread model, distinguishing between the grid to be flooded and the boundary grid, and storing them in the elevation statistical mapping by grouping by elevation. The flooding calculation module uses the total inflow as the control quantity and adopts a depression-filling mode to flood the area step by step from low to high elevation. Based on the relationship between the remaining inflow and the current group's flooding water demand, it performs water deduction, elevation update, flooding marking, and incremental search until the current inflow point is flooded. The inrush point merging module accumulates the inrush volume and clears the water volume of the inrush point when other inrush points are encountered during the search process, and merges them into a joint flooding analysis. When traversing the remaining inrush points, it skips the processed inrush points. The results output module sets the elevation of the non-submerged grid to invalid values ​​based on the submerged marker array, extracts the connected boundaries of the submerged grid, and outputs the submerged range polygon and boundary water level information.

10. The device for predicting and analyzing the flood inundation range of urban street drainage facilities according to claim 9, characterized in that, The inundation calculation module uses elevation statistical mapping to achieve incremental search, which greatly reduces the number of grid traversals and directly outputs the final inundation range and water level.

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