Drainage partition intelligent planning calculation method and system based on multi-source data fusion

Abstract

The application discloses a multi-source data fusion drainage partition intelligent planning calculation method and system, relates to the technical field of urban drainage engineering, and comprises the following steps: acquiring geographic information data, drainage pipeline data and remote sensing image data of a target region; performing identification processing on the remote sensing image to extract building features; spatially registering the feature identification result and the geographic information data; calculating the roof inclination degree of the building to determine a catchment area; constructing a pipe network topology relationship to calculate catchwater capacity; generating a drainage path map based on an elevation difference and the topology relationship; and calculating the flow coupling degree between regions to divide independent catchment partitions. The application realizes building three-dimensional feature extraction and accurate runoff feature calculation through multi-source data fusion, and improves the accuracy and rationality of drainage partition planning.

Description

Technical Field

[0001] This invention relates to the field of urban drainage engineering technology, specifically to a method and system for intelligent planning and calculation of drainage zones based on multi-source data fusion. Background Technology

[0002] Traditional drainage zoning planning methods rely primarily on manual experience, obtaining topographical information through site surveys and mapping, determining catchment area and runoff coefficient using simplified calculation methods, and then delineating drainage zone boundaries based on engineering experience. This method suffers from problems such as low data acquisition efficiency, limited calculation accuracy, and strong subjectivity in zoning.

[0003] Existing technologies extract building outlines and land cover information from remote sensing images and combine them with geographic information data to calculate the catchment area. However, in practical applications, there are still problems such as insufficient data fusion accuracy, inaccurate runoff characteristic calculation, and lack of scientific basis for the delineation of drainage zone boundaries.

[0004] Current drainage zoning planning methods lack consideration for three-dimensional features such as building height and roof slope, and cannot accurately reflect the drainage characteristics of a region; they do not make full use of the drainage network topology and pipe segment bearing capacity for zoning optimization, resulting in zoning results that are difficult to meet actual drainage needs. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for intelligent planning and calculation of drainage zones based on multi-source data fusion, aiming to solve at least one of the technical problems existing in the prior art.

[0006] The technical solution of this invention is: a method for intelligent planning and calculation of drainage zones based on multi-source data fusion, comprising the following steps:

[0007] Acquire geographic information data, drainage pipeline data, and remote sensing image data of the target area;

[0008] Image recognition processing is performed on remote sensing image data to extract the roof outline features, building height features, and land cover type features of buildings, and generate image feature recognition results;

[0009] In a geographic information system, image feature recognition results are spatially registered with geographic information data to generate a fused geographic dataset.

[0010] The roof tilt is determined by calculating the ratio of the building's projected area to the actual roof area based on the fused geographic dataset. The roof catchment area and the ground catchment area are distinguished by combining the characteristics of the land cover type. The roof runoff characteristics and the ground runoff characteristics are calculated separately.

[0011] The network topology is constructed based on drainage pipeline data, and the water catchment capacity of each pipe segment is calculated.

[0012] The vertical elevation difference is calculated based on drainage pipeline data and building height characteristics. Drainage priority is determined based on the vertical elevation difference and pipeline topology. Roof runoff characteristics and ground runoff characteristics are allocated to the pipeline network according to the drainage priority level matrix to generate a drainage path map.

[0013] Using the water catchment capacity as a constraint, the flow coupling degree between regions is calculated based on the drainage path map, and regions with flow coupling degree exceeding the set boundary value are divided into independent water catchment zones.

[0014] Image recognition processing is performed on remote sensing image data to extract building roof outline features, building height features, and land cover type features, generating image feature recognition results including:

[0015] The remote sensing image data is segmented at multiple scales according to the difference in gray values ​​of adjacent pixels to obtain image segmentation blocks;

[0016] Based on image segmentation blocks, gray value distribution, block texture, and block boundary features are calculated to extract building area boundaries;

[0017] Obtain the coordinates of the building boundary points based on the building area boundary;

[0018] The building projection area is extracted using the coordinates of the building boundary points, and the building height feature is obtained by calculating the ratio of the building projection length to the actual length. At the same time, the building roof outline feature is extracted based on the coordinates of the building boundary points.

[0019] Based on the spectral distribution and morphological features of the image segmentation blocks, land cover type features are extracted to generate image feature recognition results.

[0020] In a geographic information system, spatial registration is performed between image feature recognition results and geographic information data to generate a fused geographic dataset, including:

[0021] Obtain geographic information data from the geographic information system, extract road intersections and building corners from the geographic information data as spatial control points, and obtain the coordinates of the spatial control points;

[0022] Extract the image feature points corresponding to the spatial control points from the image feature recognition results, calculate the correspondence between the image feature points and the spatial control points, and generate spatial registration parameters.

[0023] The coordinate transformation of the roof outline features in the image feature recognition results is performed using spatial registration parameters to obtain the transformed outline coordinates.

[0024] Calculate the spatial deviation between the transformed contour coordinates and the building contours in the geographic information data, construct a spatial registration optimization function, and optimize and update the spatial registration parameters according to the spatial registration optimization function until the spatial deviation of the transformed contour coordinates meets the registration accuracy requirements.

[0025] The optimized spatial registration parameters are used to perform spatial transformation on building height characteristics and land cover type characteristics, and then overlaid with geographic information data to generate a fused geographic dataset.

[0026] The roof slope is determined by calculating the ratio of the building's projected area to the actual roof area using a fused geographic dataset. Furthermore, roof runoff and surface runoff characteristics are differentiated based on land cover type features. The roof runoff characteristics and surface runoff characteristics are calculated separately, including:

[0027] The building boundary points are extracted from the fused geographic dataset to construct the projection surface, and the projected area of ​​the building is calculated. At the same time, the roof outline of the building is extracted to construct the roof surface, and the actual area of ​​the roof is calculated.

[0028] Calculate the ratio of the building's projected area to the actual roof area to determine the degree of roof slope;

[0029] Determine the roof water flow direction based on the roof slope and building boundary points, and construct a roof water flow path network;

[0030] Based on the roof water catchment path network, the cumulative water catchment volume is calculated, and areas where the cumulative water catchment volume meets the preset zoning threshold are divided into roof water catchment areas. Land cover type features are extracted from the fused geographic dataset, and ground water catchment areas are divided according to surface permeability.

[0031] The roof runoff coefficient is obtained by multiplying the area of ​​the roof catchment area by the roof slope. The roof runoff volume is then calculated by combining the roof runoff coefficient to generate roof runoff characteristics.

[0032] The surface runoff coefficient is obtained by multiplying the surface catchment area by the permeability coefficient corresponding to the surface cover type characteristics. The surface runoff is then calculated by combining the surface runoff coefficient to generate surface runoff characteristics.

[0033] Based on the drainage pipeline data, the pipeline network topology is constructed, and the water catchment capacity of each pipe segment is calculated, including:

[0034] Extract the starting point coordinates, ending point coordinates, and pipe diameter of the drainage pipeline segment from the drainage pipeline data to construct a drainage pipeline segment parameter matrix;

[0035] Calculate the burial depth difference and plane distance of the pipe segment based on the coordinates of the starting and ending points of the pipe segment, obtain the slope of the drainage pipe segment, identify the connection points of the drainage pipe segment using the drainage pipe segment parameter matrix, and construct the drainage pipe segment connection matrix; determine the flow direction of the drainage pipe segment based on the slope of the drainage pipe segment, and establish the pipe network topology;

[0036] Calculate the hydraulic parameters of the drainage pipe section based on the slope and pipe diameter of the drainage pipe section;

[0037] The design flow rate of the drainage pipe section is calculated based on the hydraulic parameters of the drainage pipe section. The design flow rate of the upstream drainage pipe section is transferred to the connection point of the downstream drainage pipe section according to the pipe network topology, and the runoff of the drainage pipe section is calculated.

[0038] The drainage pipe section's catchment capacity is determined by comparing its flow rate with the design flow rate.

[0039] Vertical elevation differences are calculated based on drainage pipeline data and building height characteristics. Drainage priorities are determined based on these vertical elevation differences and pipeline topology. Roof runoff characteristics and ground runoff characteristics are allocated to the pipeline network according to the drainage priority matrix, generating a drainage path map including:

[0040] Building elevation values ​​are extracted from building height features, and pipe segment elevation values ​​are extracted from drainage pipeline data to generate building elevation matrices and pipe segment elevation matrices.

[0041] The vertical elevation difference matrix is ​​obtained by calculating the difference between the building elevation matrix and the pipe segment elevation matrix. The gravity flow direction is then calculated based on the vertical elevation difference matrix to generate the gravity flow direction matrix.

[0042] The drainage transmission hierarchy is calculated by combining the gravity flow direction matrix with the pipeline topology, a drainage priority matrix is ​​constructed, the flow range that the pipe segment can accept is calculated based on the drainage priority matrix, and a pipe segment flow allocation matrix is ​​generated.

[0043] The roof runoff characteristics are matched with the drainage priority matrix, and the roof runoff is allocated to the corresponding pipe section according to the flow range that the pipe section can accept, thus generating a roof runoff configuration scheme.

[0044] The surface runoff characteristics are matched with the drainage priority matrix, and the surface runoff is allocated to the corresponding pipe segment according to the flow range that the pipe segment can accept, thus generating a surface runoff configuration scheme.

[0045] The roof runoff configuration scheme and the ground runoff configuration scheme are merged into a pipe segment water collection scheme, and the water collection volume of the pipe segment is verified according to the pipe segment flow distribution matrix. A drainage path map is constructed based on the pipe segment water collection scheme.

[0046] Using catchment capacity as a constraint, the flow coupling degree between regions is calculated based on the drainage path map. Regions with flow coupling degrees exceeding a set boundary value are divided into independent catchment zones, including:

[0047] Extract water catchment schemes for pipe segments from the drainage path map, identify upstream and downstream buildings connected to each pipe segment, and construct a water catchment correlation matrix between buildings;

[0048] The runoff exchange between buildings is calculated based on the water catchment correlation matrix between buildings, and the ratio of the runoff exchange to the total runoff of the building itself is determined as the flow coupling degree.

[0049] Using the remaining carrying capacity of each pipe segment in the water catchment capacity as a constraint, the flow coupling degree is corrected to generate a corrected flow coupling degree matrix.

[0050] Identify target areas whose flow coupling exceeds a set boundary value from the modified flow coupling matrix, determine the area independence based on the connection status of the boundary pipe segments of the target area in the pipe network topology, and divide areas without shared boundary pipe segments into independent catchment zones.

[0051] This invention provides a drainage zoning intelligent planning and calculation system based on multi-source data fusion, the system comprising:

[0052] The data acquisition module is used to acquire geographic information data, drainage pipeline data, and remote sensing image data of the target area;

[0053] The feature extraction module is used to perform image recognition processing on remote sensing image data, extract the roof outline features, building height features, and land cover type features of buildings, and generate image feature recognition results;

[0054] The spatial registration module is used to spatially register image feature recognition results with geographic information data in a geographic information system to generate a fused geographic dataset.

[0055] The runoff calculation module is used to calculate the ratio of the building's projected area to the actual roof area based on the fused geographic dataset to determine the roof's tilt, and to distinguish between roof catchment areas and ground catchment areas by combining the characteristics of land cover type, and to calculate the roof runoff characteristics and ground runoff characteristics respectively.

[0056] The pipeline analysis module is used to construct the pipeline topology based on drainage pipeline data and calculate the water catchment capacity of each pipe segment;

[0057] The drainage path generation module is used to calculate the vertical elevation difference based on drainage pipeline data and building height characteristics, determine drainage priority based on the vertical elevation difference and pipeline topology, and allocate roof runoff characteristics and ground runoff characteristics to the pipeline according to the drainage priority level matrix to generate a drainage path map.

[0058] The zoning module is used to calculate the flow coupling degree between regions based on the drainage path map, with the water catchment capacity as a constraint, and to divide the regions whose flow coupling degree exceeds the set boundary value into independent water catchment zones.

[0059] One technical solution provided in this embodiment of the invention is an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0060] One technical solution provided in this embodiment of the invention is a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the steps in any of the aforementioned methods.

[0061] This invention achieves intelligent extraction and precise positioning of building three-dimensional features through multi-source data fusion, improving the accuracy of feature recognition; it constructs a drainage priority matrix by combining vertical elevation difference and pipeline topology, making runoff allocation more consistent with actual drainage patterns; it introduces a flow coupling degree evaluation mechanism to achieve quantitative calculation of the degree of drainage correlation between regions, improving the scientific nature of zoning boundary division; and it optimizes zoning by using the water catchment capacity of pipe sections as a constraint, ensuring the engineering feasibility of drainage zoning schemes, and comprehensively improving the accuracy and practicality of drainage zoning planning, providing more reliable technical support for urban drainage system planning and design. Attached Figure Description

[0062] Figure 1 A flowchart of a multi-source data fusion-based intelligent planning and calculation method for drainage zones provided in an embodiment of the present invention;

[0063] Figure 2 This is a flowchart illustrating the drainage path planning and flow allocation process according to an embodiment of the present invention. Detailed Implementation

[0064] like Figure 1 As shown, Figure 1 This is a flowchart of a drainage zoning intelligent planning and calculation method based on multi-source data fusion provided in an embodiment of the present invention. The method includes the following steps:

[0065] Acquire geographic information data, drainage pipeline data, and remote sensing image data of the target area;

[0066] Image recognition processing is performed on remote sensing image data to extract the roof outline features, building height features, and land cover type features of buildings, and generate image feature recognition results;

[0067] In a geographic information system, image feature recognition results are spatially registered with geographic information data to generate a fused geographic dataset.

[0068] The roof tilt is determined by calculating the ratio of the building's projected area to the actual roof area based on the fused geographic dataset. The roof catchment area and the ground catchment area are distinguished by combining the characteristics of the land cover type. The roof runoff characteristics and the ground runoff characteristics are calculated separately.

[0069] The network topology is constructed based on drainage pipeline data, and the water catchment capacity of each pipe segment is calculated.

[0070] The vertical elevation difference is calculated based on drainage pipeline data and building height characteristics. Drainage priority is determined based on the vertical elevation difference and pipeline topology. Roof runoff characteristics and ground runoff characteristics are allocated to the pipeline network according to the drainage priority level matrix to generate a drainage path map.

[0071] Using the water catchment capacity as a constraint, the flow coupling degree between regions is calculated based on the drainage path map, and regions with flow coupling degree exceeding the set boundary value are divided into independent water catchment zones.

[0072] Image recognition processing is performed on remote sensing image data to extract building roof outline features, building height features, and land cover type features, generating image feature recognition results including:

[0073] The remote sensing image data is segmented at multiple scales according to the difference in gray values ​​of adjacent pixels to obtain image segmentation blocks;

[0074] Based on image segmentation blocks, gray value distribution, block texture, and block boundary features are calculated to extract building area boundaries;

[0075] Obtain the coordinates of the building boundary points based on the building area boundary;

[0076] The building projection area is extracted using the coordinates of the building boundary points, and the building height feature is obtained by calculating the ratio of the building projection length to the actual length. At the same time, the building roof outline feature is extracted based on the coordinates of the building boundary points.

[0077] Based on the spectral distribution and morphological features of the image segmentation blocks, land cover type features are extracted to generate image feature recognition results.

[0078] Remote sensing imagery data containing building information is acquired; this data can be high-resolution images obtained through aerial photography or satellite remote sensing. After acquisition, the remote sensing imagery data undergoes multi-scale segmentation. Multi-scale segmentation is an image segmentation method based on the differences in grayscale values ​​of adjacent pixels. The segmentation scale parameter is set to 50, the shape factor to 0.3, and the compactness factor to 0.7. By adjusting these parameters, the size and shape of the segmented blocks can be effectively controlled, ensuring that the segmentation results preserve the complete structure of the building while distinguishing different land cover types. During the segmentation process, adjacent pixels with similar grayscale values ​​are grouped into the same block; when the difference in grayscale values ​​between adjacent pixels exceeds a set threshold of 15, they are divided into different blocks. This process is iterated until all pixels are classified, ultimately generating a set of image segmentation blocks.

[0079] Based on the obtained image segmentation blocks, grayscale distribution, block texture, and block boundary features are calculated to extract building region boundaries. Grayscale distribution features are obtained by calculating the mean and standard deviation of pixels within each block. Building regions typically exhibit a high and uniform mean grayscale value and a low standard deviation. Block texture features are extracted using the gray-level co-occurrence matrix, calculating the homogeneity, contrast, and correlation of pixels within the block. Building regions typically exhibit high homogeneity and low contrast. Block boundary features are extracted by calculating the regularity and linearity of the block boundaries. Building boundaries typically appear as straight line segments or regular curves. Utilizing these features comprehensively, a block is identified as a building region when its mean grayscale value is greater than 120, standard deviation is less than 25, homogeneity is greater than 0.65, contrast is less than 0.3, and boundary regularity is greater than 0.8.

[0080] After the building area boundaries are determined, the coordinates of the building boundary points are extracted using an edge detection algorithm. Edge detection employs a combination of Gaussian blurring and gradient calculation. A Gaussian filter with a kernel size of 3×3 is applied to the building area image for smoothing to reduce noise interference. The image gradient is calculated, and points exceeding a set threshold of 60 are marked as edge points. The marked edge points are then connected and filtered to remove isolated points and short edges, retaining continuous edges longer than 10 pixels. The resulting sequence of edge points constitutes the set of building boundary point coordinates, with each boundary point represented by pixel coordinates.

[0081] After obtaining the coordinates of the building's boundary points, the building's projection area and roof outline features are extracted. The building's projection area refers to the building's projection on the ground, extracted by analyzing the positional relationships of the building's boundary points under different viewpoints. Using known imaging geometry relationships from remote sensing images, the building's projection range on the ground is calculated. The ratio of the building's projected length to its actual length is related to the building's height and the solar altitude angle. When the solar altitude angle is 45 degrees, the building's projected length equals its height. By measuring the building's projected length and combining it with the solar altitude angle information, the building's height features are calculated. For example, when a building's projected length is measured to be 20 meters and the solar altitude angle is 60 degrees, the calculated building height is approximately 11.5 meters. Based on the building's boundary point coordinates, the building's roof outline features are extracted, including the roof shape, area, and slope. Polygon fitting is performed on the boundary points; when the fitting error is less than 2 pixels, the polygon is determined to be the building's roof outline.

[0082] Based on the spectral distribution and morphological features of image segmentation blocks, land cover type features are extracted. Spectral distribution features are obtained by analyzing the reflectance characteristics of different bands. For example, vegetation areas have high reflectance in the near-infrared band and low reflectance in the visible red band; water bodies have strong absorption in the near-infrared band and low reflectance; building areas show relatively small differences in reflectance across bands. Block morphological features include block area, perimeter, compactness, and shape index. When a block's near-infrared reflectance is greater than 0.6 and its red band reflectance is less than 0.2, it is identified as a vegetation area; when its near-infrared reflectance is less than 0.1 and its blue band reflectance is between 0.05 and 0.15, it is identified as a water body area; when its shape index is greater than 0.8 and its average grayscale value is greater than 150, it is identified as a road area. Using these rules, a land cover classification result is finally generated, including types such as buildings, vegetation, water bodies, and roads.

[0083] The remote sensing image data image recognition processing method provided by this invention can automatically and efficiently extract building roof outline features, height features, and land cover type features from remote sensing images, effectively improving the accuracy and efficiency of remote sensing image interpretation. It can process remote sensing image data over large areas. Through the combination of multi-scale segmentation and feature extraction techniques, it achieves accurate extraction of building information, providing important data support for urban management and decision-making.

[0084] In a geographic information system, spatial registration is performed between image feature recognition results and geographic information data to generate a fused geographic dataset, including:

[0085] Obtain geographic information data from the geographic information system, extract road intersections and building corners from the geographic information data as spatial control points, and obtain the coordinates of the spatial control points;

[0086] Extract the image feature points corresponding to the spatial control points from the image feature recognition results, calculate the correspondence between the image feature points and the spatial control points, and generate spatial registration parameters.

[0087] The coordinate transformation of the roof outline features in the image feature recognition results is performed using spatial registration parameters to obtain the transformed outline coordinates.

[0088] Calculate the spatial deviation between the transformed contour coordinates and the building contours in the geographic information data, construct a spatial registration optimization function, and optimize and update the spatial registration parameters according to the spatial registration optimization function until the spatial deviation of the transformed contour coordinates meets the registration accuracy requirements.

[0089] The optimized spatial registration parameters are used to perform spatial transformation on building height characteristics and land cover type characteristics, and then overlaid with geographic information data to generate a fused geographic dataset.

[0090] Geographic information data acquired from a Geographic Information System (GIS) typically includes vector and raster data such as road networks, building outlines, and terrain elevations. This data possesses precise geographic coordinate information and can serve as a reference for spatial registration. Points with distinct geographic features are extracted from this geographic information data as spatial control points, primarily road intersections and building corners. Road intersections are usually located where multiple roads intersect, possessing a clear spatial location; building corners are the corners of building outlines. These points have accurate coordinates and are easily identifiable in the geographic information data. More than 20 evenly distributed spatial control points are selected from the geographic information data. Each control point's coordinates in the geographic coordinate system are recorded; for example, the geographic coordinates of a road intersection might be 116°25′30″E, 39°55′45″N.

[0091] Image feature points corresponding to the aforementioned spatial control points are extracted from the image feature recognition results. The coordinates of these feature points are typically represented in pixel coordinates. It is necessary to find the corresponding feature points in the image feature data. Corresponding points are found in the image feature data through pattern matching or feature descriptor matching. For road intersections, the intersection location is found in the road network of the image feature data; for building corners, the corner point is found in the identified building outlines. Feature point matching can employ a template matching algorithm, setting the search window size to 15×15 pixels. Gray-level similarity is calculated within the window, with a similarity threshold of 0.85. A match is considered successful if the similarity exceeds this threshold. During the matching process, the coordinates of each pair of corresponding points are recorded, forming a correspondence table between spatial control points and image feature points.

[0092] Based on the established point correspondence, spatial registration parameters are calculated. These parameters describe the transformation relationship between the image coordinate system and the geographic coordinate system, including translation, rotation, and scaling parameters. Affine transformation is used for coordinate transformation, a method that can handle translation, rotation, scaling, and tilting transformations. The least squares method is used to calculate the transformation matrix based on the coordinate differences between matching point pairs. Using more than six pairs of corresponding points for transformation matrix calculation improves transformation accuracy. The calculated transformation matrix is ​​denoted as the spatial registration parameters and used for subsequent coordinate transformations.

[0093] The calculated spatial registration parameters are used to perform coordinate transformation on the roof outline features in the image feature recognition results. The coordinates of each point in the roof outline feature are substituted into the transformation matrix to calculate its position in the geographic coordinate system, obtaining the transformed outline coordinates. The topological relationship of the outline is preserved during the transformation process to ensure its integrity.

[0094] The spatial deviation between the transformed contour coordinates and the building contours in the geographic information data is calculated, and a spatial registration optimization function is constructed. The spatial deviation is quantified by calculating the overlap and boundary distance between the transformed contour and the corresponding building contours in the geographic information. The overlap is calculated using the area overlap ratio, and the boundary distance is calculated using Hausdorff distance. A comprehensive evaluation index is constructed as the spatial registration optimization function; the smaller the function value, the higher the registration accuracy. The gradient descent method is used to optimize and adjust the spatial registration parameters. The parameters are adjusted in each iteration, and the transformed contour coordinates and spatial deviation are recalculated. An upper limit of 100 iterations is set. The optimization process is considered convergent when the spatial deviation is less than a set threshold of 0.5 meters or the improvement margin is less than 0.05 meters for three consecutive iterations, yielding the final optimized spatial registration parameters.

[0095] Spatial transformations of building height and land cover type features are performed using optimized spatial registration parameters. The coordinates of both features are transformed using the same transformation matrix, preserving the attribute values ​​while adjusting only their spatial location. The transformed feature data is then overlaid with road network, administrative boundary, and topographic data from a geographic information system to generate a fused geographic dataset. This fused dataset contains complete 3D building information and land cover classification information; each building includes its accurate geographic location, outline shape, height, and corresponding land cover type.

[0096] The spatial registration method for image feature recognition results and geographic information data provided by this invention achieves accurate fusion of spatial data from different sources. By extracting feature control points, calculating spatial transformation parameters, and performing optimization, the matching problem between data from different coordinate systems and with varying precision is effectively solved. This not only improves the accuracy of data fusion but also ensures the spatial consistency and integrity of the fused data, providing a reliable data foundation for subsequent spatial analysis and decision-making. The optimized registration algorithm can adapt to the data characteristics of different regions, and the fused geographic dataset integrates the advantages of multi-source data, providing more comprehensive and accurate geospatial information.

[0097] The roof slope is determined by calculating the ratio of the building's projected area to the actual roof area using a fused geographic dataset. Furthermore, roof runoff and surface runoff characteristics are differentiated based on land cover type features. The roof runoff characteristics and surface runoff characteristics are calculated separately, including:

[0098] The building boundary points are extracted from the fused geographic dataset to construct the projection surface, and the projected area of ​​the building is calculated. At the same time, the roof outline of the building is extracted to construct the roof surface, and the actual area of ​​the roof is calculated.

[0099] Calculate the ratio of the building's projected area to the actual roof area to determine the degree of roof slope;

[0100] Determine the roof water flow direction based on the roof slope and building boundary points, and construct a roof water flow path network;

[0101] Based on the roof water catchment path network, the cumulative water catchment volume is calculated, and areas where the cumulative water catchment volume meets the preset zoning threshold are divided into roof water catchment areas. Land cover type features are extracted from the fused geographic dataset, and ground water catchment areas are divided according to surface permeability.

[0102] The roof runoff coefficient is obtained by multiplying the area of ​​the roof catchment area by the roof slope. The roof runoff volume is then calculated by combining the roof runoff coefficient to generate roof runoff characteristics.

[0103] The surface runoff coefficient is obtained by multiplying the surface catchment area by the permeability coefficient corresponding to the surface cover type characteristics. The surface runoff is then calculated by combining the surface runoff coefficient to generate surface runoff characteristics.

[0104] Building boundary points are extracted from a fused geographic dataset to construct a projection surface. These boundary points are represented by closed polygons, each with explicit geographic coordinates. Using these boundary points, a polygonal projection surface is constructed through planar geometric calculations, and its area represents the building's projected area. Triangulation is used to decompose the polygon into several triangles, and the total area is obtained by summing the areas of each triangle. The roof outline is extracted to construct the roof surface. Roof outline points are typically located at the top edge of the building, and their elevation values ​​differ significantly from the bottom. The roof surface is constructed based on these outline points, considering the roof's geometry, including different types such as flat and pitched roofs. For flat roofs, the actual area is approximately equal to the projected area; for pitched roofs, the geometric characteristics of the sloping surface must be considered. The actual roof area is determined using three-dimensional geometric calculations, for example, a building with a projected area of ​​200m². 2 The actual area of ​​the roof is 220m². 2 .

[0105] Calculate the ratio of the building's projected area to the actual roof area to determine the roof's slope. This ratio reflects the roof's slope; the closer the ratio is to 1, the flatter the roof; the smaller the ratio, the greater the slope. A slope grading standard is established: a ratio greater than 0.95 is considered a flat roof; a ratio between 0.8 and 0.95 is considered a low-slope roof; a ratio between 0.6 and 0.8 is considered a medium-slope roof; and a ratio less than 0.6 is considered a high-slope roof. Taking the aforementioned building as an example, the ratio of projected area to actual area is 200 / 220 = 0.91, classifying it as a low-slope roof.

[0106] The roof's water flow direction is determined based on the roof's slope and building boundary points, constructing a roof water flow path network. For flat roofs, it is assumed that rainwater is evenly distributed and flows in all directions; for sloping roofs, the main water flow direction is determined based on the roof's slope. By analyzing the roof's elevation change trend, the ridge line and eaves line are identified, and the flow direction perpendicular to the ridge line is determined as the main water flow direction. A gridded roof model is established, with a grid size of 0.5 meters. The flow direction is calculated for each grid; the elevation difference between adjacent grids determines the water flow direction, with water always flowing towards the adjacent grid with the lower elevation. Connecting the flow directions of adjacent grids forms a complete roof water flow path network, represented by a directed graph structure, where nodes are grid centers and edges represent water flow directions.

[0107] The cumulative water catchment volume is calculated based on the roof's water flow path network, and areas where the cumulative water catchment volume meets a preset zoning threshold are designated as roof catchment zones. The cumulative water catchment volume calculation employs a flow accumulation algorithm, tracing the water flow path from the highest point of the roof and accumulating the upstream catchment area of ​​each grid cell. When the cumulative water catchment volume of a grid cell exceeds a set threshold of 10m... 2When the cumulative length of the channel formed by consecutive grids exceeds 50m, the grid is marked as a water collection channel; 2 At that time, the main roof catchment area was designated. Land cover type features were extracted from the fused geographic dataset, and surface catchment areas were divided according to surface permeability. Land cover types include buildings, roads, green spaces, and water bodies, each with different permeability. Buildings and hard-surface roads have extremely low permeability and are marked as impermeable areas; green spaces have high permeability and are marked as permeable areas; water bodies directly receive rainwater and are not included in the catchment area. Surface catchment areas were divided according to the spatial distribution of different cover types. For example, if a building occupies 1000m² in a certain area... 2 Hard surface road area 500m² 2 Together, they form a surface catchment area with a total area of ​​1500m². 2 .

[0108] The roof runoff coefficient is obtained by multiplying the roof catchment area by the roof slope. This coefficient is then used to calculate the roof runoff volume, generating roof runoff characteristics. The roof runoff coefficient reflects the efficiency with which rainwater is converted into runoff from the roof; the greater the slope, the higher the runoff efficiency. For the aforementioned low-slope roof, the slope is 0.91, and the roof catchment area is 220m². 2 The calculated roof runoff coefficient is 220 × 0.91 = 200.2. Considering the local design rainfall and assuming a design rainfall intensity of 50 mm / h, the roof runoff of this building is 200.2 × 50 ÷ 1000 = 10.01 m³. 3 / h. Roof runoff characteristics include information such as total runoff, runoff coefficient, and catchment area distribution. These characteristics are used for subsequent drainage facility planning.

[0109] The surface runoff coefficient is obtained by multiplying the area of ​​the surface catchment area by the permeability coefficient corresponding to the surface cover type. This permeability coefficient is then used to calculate surface runoff, generating surface runoff characteristics. Different surface cover types have different permeability coefficients: 0.9 for hard surfaces, 1.0 for building bases, and 0.15 for green spaces. For the aforementioned surface catchment area, with a building area of ​​1000 m²... 2 The runoff coefficient is 1000 × 1.0 = 1000; the area of ​​the paved road is 500 m². 2 The runoff generation coefficient is 500 × 0.9 = 450; the total surface runoff generation coefficient is 1450. Similarly, considering the design rainfall intensity of 50 mm / h, the calculated surface runoff is 1450 × 50 ÷ 1000 = 72.5 m³. 3 / h. Surface runoff characteristics include total runoff, the contribution ratio of different cover types, and spatial distribution, providing a basis for drainage network layout.

[0110] This invention achieves refined management of drainage zones by accurately distinguishing between roof catchment areas and ground catchment areas and calculating their runoff characteristics separately. It fully considers the influence of building three-dimensional structural features and land cover types, improving the accuracy of runoff calculation. Through roof tilt and surface permeability analysis, it can accurately assess the runoff capacity and characteristics of different areas, providing a scientific basis for drainage facility planning. It overcomes the shortcomings of traditional runoff calculation methods that ignore building three-dimensional structures, achieving more accurate runoff assessment and improving the rationality and effectiveness of drainage system design. It has significant application value for flood control and disaster reduction, water resource management, and urban drainage system optimization.

[0111] Based on the drainage pipeline data, the pipeline network topology is constructed, and the water catchment capacity of each pipe segment is calculated, including:

[0112] Extract the starting point coordinates, ending point coordinates, and pipe diameter of the drainage pipeline segment from the drainage pipeline data to construct a drainage pipeline segment parameter matrix;

[0113] Calculate the burial depth difference and plane distance of the pipe segment based on the coordinates of the starting and ending points of the pipe segment, obtain the slope of the drainage pipe segment, identify the connection points of the drainage pipe segment using the drainage pipe segment parameter matrix, and construct the drainage pipe segment connection matrix; determine the flow direction of the drainage pipe segment based on the slope of the drainage pipe segment, and establish the pipe network topology;

[0114] Calculate the hydraulic parameters of the drainage pipe section based on the slope and pipe diameter of the drainage pipe section;

[0115] The design flow rate of the drainage pipe section is calculated based on the hydraulic parameters of the drainage pipe section. The design flow rate of the upstream drainage pipe section is transferred to the connection point of the downstream drainage pipe section according to the pipe network topology, and the runoff of the drainage pipe section is calculated.

[0116] The drainage pipe section's catchment capacity is determined by comparing its flow rate with the design flow rate.

[0117] The starting and ending coordinates of pipe segments, as well as their diameters, are extracted from drainage pipeline data to construct a drainage pipe segment parameter matrix. This matrix is ​​stored in a tabular structure, with each row representing a pipe segment and columns including segment number, starting and ending coordinates, and pipe diameter. During extraction, the raw data is cleaned to remove invalid data and outliers, ensuring data quality. Both the starting and ending coordinates of the pipe segments contain three-dimensional information—planar coordinates and elevation information—for subsequent slope calculations. Pipe diameter data is expressed as an inner diameter value in millimeters, such as a 400 mm diameter segment. If a drainage area contains 458 pipe segments, the constructed drainage pipe segment parameter matrix will contain 458 rows of data, with the starting and ending coordinates and pipe diameter of each segment fully recorded in the matrix.

[0118] The slope of the drainage pipe segment is obtained by calculating the difference in burial depth and the horizontal distance between the starting and ending coordinates of the pipe segment. The difference in burial depth is calculated as the difference in elevation between the starting and ending points, and the horizontal distance is calculated as the straight-line distance between the starting and ending points on the horizontal plane. The slope is expressed as the ratio of the difference in burial depth to the horizontal distance. For example, if the starting elevation of a pipe segment is 25.6 meters, the ending elevation is 24.8 meters, and the horizontal distance is 100 meters, the calculated slope is 0.008. For slope calculation, considering data accuracy, a minimum effective slope of 0.002 is set. Slope data below this value are reviewed or corrected. The connection points of the drainage pipe segments are identified using a drainage pipe segment parameter matrix, and a drainage pipe segment connection matrix is ​​constructed. Connection point identification is based on the principle of spatial location matching. When the coordinates of the ending and starting points of two pipe segments are the same or the distance is less than a preset threshold (such as 0.5 meters), they are determined to be connected. The drainage pipe segment connection matrix is ​​represented by an adjacency list structure, recording the upstream and downstream pipe segment numbers of each pipe segment. The flow direction of the drainage pipe section is determined based on its slope, establishing the pipe network topology. The flow direction points from the higher elevation end to the lower elevation end, forming a directed graph structure. The pipe network topology is represented as a connected path from the source point (the starting point without upstream pipe sections) to the sink point (the ending point without downstream pipe sections), reflecting the transmission relationship of water flow in the pipe network.

[0119] The hydraulic parameters of the drainage pipe section are calculated based on its slope and diameter. These parameters mainly include the roughness coefficient, wetted perimeter, hydraulic radius, and Manning's coefficient. The roughness coefficient is determined based on the pipe material: 0.013 for concrete pipes and 0.01 for plastic pipes. Using Manning's formula, combined with the slope, hydraulic radius, and roughness coefficient, the full-flow velocity is calculated. For the aforementioned pipe section with a diameter of 400 mm, a slope of 0.008, and a roughness coefficient of 0.013, the calculated full-flow velocity is 2.24 m / s.

[0120] The design flow rate of the drainage pipe section is calculated based on its hydraulic parameters. The design flow rate is taken as the maximum design flow rate of the pipe section under full-flow conditions, and is calculated as the full-flow velocity multiplied by the cross-sectional area of ​​the pipe section. For the aforementioned pipe section, the full-flow velocity is 2.24 m / s, and the pipe diameter is 400 mm, resulting in a calculated design flow rate of 0.281 m³ / s. 3 / s. Based on the pipeline topology, the design flow rate of the upstream drainage pipe segment is transferred to the connection point of the downstream drainage pipe segment, and the runoff of the drainage pipe segment is calculated. The runoff calculation uses an accumulation method, meaning the runoff of the downstream pipe segment equals the sum of the design flows of all directly connected upstream pipe segments. For nodes with multiple upstream pipe segments flowing into them, such as three-way or four-way connection points, the design flow rate of each upstream pipe segment is calculated separately, and these are summed to obtain the total runoff of the connection point. This accumulation process starts from the source point and proceeds down the topology level by level until the sink point.

[0121] The drainage pipe section's catchment capacity is determined by comparing its runoff volume with its design flow rate. The catchment capacity is expressed as the ratio of the runoff volume to the design flow rate. A ratio less than 1 indicates sufficient capacity, while a ratio greater than 1 suggests a potential risk of overflow. For example, if a pipe section has a design flow rate of 0.281 m³ / h... 3 / s, the flow rate is 0.215m 3 The water catchment capacity ratio is 0.765, indicating that this pipe section has sufficient drainage capacity. Risk levels are assigned based on the catchment capacity ratio: a ratio less than 0.7 is low risk, a ratio between 0.7 and 0.9 is medium risk, a ratio between 0.9 and 1.0 is high risk, and a ratio greater than 1.0 indicates overload risk. For pipe sections with overload risk, renovation or expansion should be prioritized in the drainage zoning plan.

[0122] The drainage network topology construction and catchment capacity calculation method provided by this invention enables a comprehensive assessment of the drainage system's function. This method constructs a complete network topology by accurately calculating pipe segment slope, flow direction, and hydraulic parameters, providing a scientific basis for drainage zoning planning. The catchment flow transfer calculation based on the topology reflects the actual movement of water within the network, improving the accuracy of the catchment capacity assessment. By comparing the catchment flow with the design flow, potential overflow risk points in the network are identified, providing targeted suggestions for drainage facility renovation and expansion.

[0123] like Figure 2 As shown, the vertical elevation difference is calculated based on drainage pipeline data and building height characteristics. Drainage priority is determined based on the vertical elevation difference and pipeline topology. Roof runoff characteristics and ground runoff characteristics are allocated to the pipeline network according to the drainage priority level matrix, generating a drainage path map including:

[0124] Building elevation values ​​are extracted from building height features, and pipe segment elevation values ​​are extracted from drainage pipeline data to generate building elevation matrices and pipe segment elevation matrices.

[0125] The vertical elevation difference matrix is ​​obtained by calculating the difference between the building elevation matrix and the pipe segment elevation matrix. The gravity flow direction is then calculated based on the vertical elevation difference matrix to generate the gravity flow direction matrix.

[0126] The drainage transmission hierarchy is calculated by combining the gravity flow direction matrix with the pipeline topology, a drainage priority matrix is ​​constructed, the flow range that the pipe segment can accept is calculated based on the drainage priority matrix, and a pipe segment flow allocation matrix is ​​generated.

[0127] The roof runoff characteristics are matched with the drainage priority matrix, and the roof runoff is allocated to the corresponding pipe section according to the flow range that the pipe section can accept, thus generating a roof runoff configuration scheme.

[0128] The surface runoff characteristics are matched with the drainage priority matrix, and the surface runoff is allocated to the corresponding pipe segment according to the flow range that the pipe segment can accept, thus generating a surface runoff configuration scheme.

[0129] The roof runoff configuration scheme and the ground runoff configuration scheme are merged into a pipe segment water collection scheme, and the water collection volume of the pipe segment is verified according to the pipe segment flow distribution matrix. A drainage path map is constructed based on the pipe segment water collection scheme.

[0130] Building elevation values ​​are extracted from building height features, and pipe segment elevation values ​​are extracted from drainage pipeline data to generate building elevation matrices and pipe segment elevation matrices. Building elevation values ​​mainly include the building's base elevation and the surrounding ground elevation. Spatial interpolation is used during extraction to ensure the consistency and accuracy of the elevation data. For drainage pipeline data, the starting elevation, ending elevation, and burial depth of the pipe segment are extracted, and the actual elevation value of the pipe segment is calculated. The building elevation matrix uses a two-dimensional array structure, with rows representing different buildings and columns including building number, base elevation, and surrounding ground elevation. The pipe segment elevation matrix also uses a two-dimensional array structure, with rows representing different pipe segments and columns including segment number, starting elevation, ending elevation, and average elevation. In a certain area, there are 326 buildings and 458 pipe segments, resulting in a 326-row, 3-column building elevation matrix and a 458-row, 4-column pipe segment elevation matrix.

[0131] The vertical elevation difference matrix is ​​obtained by calculating the difference between the building elevation matrix and the pipe segment elevation matrix. The gravity flow direction is then calculated based on this matrix, generating a gravity flow direction matrix. The vertical elevation difference calculation uses the ground elevation around the building as a benchmark and compares it with the average elevation of adjacent pipe segments. For each building, all pipe segments within a certain radius of its spatial location are searched, and the difference between the building elevation and the elevation of each pipe segment is calculated, forming the vertical elevation difference matrix. In this matrix, rows represent buildings, columns represent pipe segments, and matrix elements are the corresponding elevation differences. Positive values ​​indicate that the building elevation is higher than the pipe segment, and water can flow naturally to the pipe segment by gravity; negative values ​​indicate that the building elevation is lower than the pipe segment, requiring auxiliary drainage facilities such as pumping stations. The gravity flow direction is determined based on the magnitude and sign of the vertical elevation difference. The gravity flow direction matrix uses the same structure, but the element values ​​are replaced with values ​​representing flow direction priority; larger values ​​indicate higher priority. Priority calculation considers the absolute value of the elevation difference and distance factors; the larger the elevation difference and the closer the distance, the higher the priority. For the aforementioned region, the generated vertical elevation difference matrix and gravity flow direction matrix are both 326 rows and 458 columns.

[0132] The drainage transmission level represents the shortest path length from the source point to the current pipe segment. A lower level indicates proximity to the drainage source, while a higher level indicates proximity to the drainage destination. The pipe network topology is represented as a directed graph, and the transmission level of each pipe segment is calculated using a breadth-first search algorithm. The drainage priority matrix combines gravity flow direction and transmission level, employing a weighted method to calculate the overall priority. The weight allocation is adjusted according to actual needs; typically, gravity flow direction has a weight of 0.7, and transmission level has a weight of 0.3. The acceptable flow range for a pipe segment is calculated based on the segment's design flow rate and allocated flow rate, represented as an upper and lower limit. The upper limit equals the design flow rate, and the lower limit is determined according to the drainage priority level; higher priority segments have lower lower limits, ensuring that high-priority segments receive drainage first. The pipe segment flow allocation matrix records the design flow rate, allocated flow rate, upper acceptable flow rate limit, and lower acceptable flow rate limit for each pipe segment.

[0133] The roof runoff characteristics are matched with the drainage priority matrix, and the roof runoff is allocated to the corresponding pipe segments according to their acceptable flow range, generating a roof runoff configuration scheme. Roof runoff characteristics include runoff volume, runoff coefficient, and roof area. The matching process uses a priority ranking method, sorting the pipe segments related to the current building in the drainage priority matrix from highest to lowest priority, and attempting to allocate roof runoff to the ranked pipe segments in turn. The allocation follows these principles: prioritizing higher priority pipe segments; the flow received by a pipe segment does not exceed its acceptable flow limit; roof runoff of a building can be allocated to multiple pipe segments, but the total allocation equals the total roof runoff volume. For a certain building, its roof runoff volume is 0.025 m³ / h. 3 / s, by querying the drainage priority matrix, the highest priority pipe segment number is found to be M102, which can accept a flow rate range of 0.01 to 0.18 m³ / s. 3 / s, therefore 0.025m 3 All roof runoff per second is allocated to this pipe segment. The roof runoff allocation scheme records the allocation of roof runoff for each building, including the building number, the allocated pipe segment number, and the allocated flow rate.

[0134] Surface runoff characteristics include runoff volume, runoff coefficient, catchment area, and land cover type. The surface runoff allocation process is similar to roof runoff allocation, but the spatial relationship between the surface catchment area and the pipe segment must be considered. Surface catchment areas are typically large and may span multiple pipe segments; therefore, spatial distance is taken into account during allocation, prioritizing allocation to closer and higher-priority pipe segments. For a given surface catchment area, its surface runoff volume is 0.08 m³ / s. 3 / s, through spatial analysis and priority sorting, is allocated to three pipe segments: M102 receives 0.03m. 3 / s, M105 receives 0.03m 3 / s, M108 receives 0.02m3 / s. The surface runoff allocation scheme records the distribution of each surface catchment area, including the area number, the allocation pipe segment number, and the allocated flow rate.

[0135] The roof runoff configuration scheme and the ground runoff configuration scheme are merged into a pipe segment catchment scheme. The catchment volume of the pipe segment is verified based on the pipe segment flow distribution matrix, and a drainage path map is constructed based on the pipe segment catchment scheme. During the merging process, the roof runoff and ground runoff received by the same pipe segment are added together to form the total catchment volume of the pipe segment. The verification process compares the total catchment volume of the pipe segment with the design flow rate of the pipe segment to ensure that it does not exceed the design capacity. For example, if a certain pipe segment M102 receives 0.025m³ of roof runoff... 3 / s and surface runoff 0.03m 3 / s, total water volume is 0.055m³. 3 / s, its design flow rate is 0.18m³ / s. 3 / s, verification passed. The drainage path diagram is represented by a directed graph structure, where nodes are pipe segment connection points and edges are pipe segments. The attributes of the edges include information such as flow rate, flow direction, and pipe diameter. The drainage path diagram visually displays the complete path of runoff from buildings and surface catchment areas to the pipe network, and then from the pipe network to the drainage outlet, providing an intuitive reference for drainage planning.

[0136] This invention achieves scientific allocation of runoff and rational planning of drainage paths by calculating vertical elevation differences and determining drainage priorities. It fully considers key factors such as topographic elevation, pipe network topology, and gravity flow direction, overcoming the shortcomings of traditional drainage planning that neglects vertical relationships. By establishing a drainage priority matrix, it achieves optimized allocation of drainage resources, avoiding problems such as local overload of the pipe network and poor drainage. Based on a multi-source data fusion analysis method, it improves the accuracy and scientific nature of drainage path planning, providing a reliable basis for the construction and renovation of drainage facilities.

[0137] Using catchment capacity as a constraint, the flow coupling degree between regions is calculated based on the drainage path map. Regions with flow coupling degrees exceeding a set boundary value are divided into independent catchment zones, including:

[0138] Extract water catchment schemes for pipe segments from the drainage path map, identify upstream and downstream buildings connected to each pipe segment, and construct a water catchment correlation matrix between buildings;

[0139] The runoff exchange between buildings is calculated based on the water catchment correlation matrix between buildings, and the ratio of the runoff exchange to the total runoff of the building itself is determined as the flow coupling degree.

[0140] Using the remaining carrying capacity of each pipe segment in the water catchment capacity as a constraint, the flow coupling degree is corrected to generate a corrected flow coupling degree matrix.

[0141] Identify target areas whose flow coupling exceeds a set boundary value from the modified flow coupling matrix, determine the area independence based on the connection status of the boundary pipe segments of the target area in the pipe network topology, and divide areas without shared boundary pipe segments into independent catchment zones.

[0142] A drainage path map is a directed graph representing the complete path of water flow from its source to its sink, including information such as pipe segments, connection points, and flow direction. During extraction, the upstream inflow point and downstream outflow point of each pipe segment are considered to identify the structures associated with each segment. The identification process uses a spatial location matching method; when a structure is located within a certain range of a pipe segment, it is considered associated. Upstream structures are those that input runoff into the pipe segment, while downstream structures are those that receive runoff from the pipe segment. The water catchment association matrix between structures uses a two-dimensional array structure, where rows and columns represent buildings. The matrix element value indicates whether there is a water catchment association between two buildings; a value of 1 indicates an association, and a value of 0 indicates no association. For example, if a region has 326 buildings, the constructed water catchment association matrix would be 326 rows and 326 columns. By analyzing pipe segment M102 in the drainage path diagram, it was found that the upstream structures connected to this pipe segment are B078 and B079, and the downstream structure is B085. Therefore, in the water catchment association matrix, the elements (B078, B085) and (B079, B085) are both set to 1, indicating that B078 and B079 have a water catchment association with B085.

[0143] The runoff exchange between buildings is calculated based on the inter-building water catchment correlation matrix. The ratio of the runoff exchange to the total runoff of each building is determined as the flow coupling degree. The runoff exchange refers to the amount of water transferred from one building to another through the drainage network, and the unit is m³. 3 / s. The calculation requires combining the flow distribution information in the pipe segment catchment scheme to determine the runoff exchange between each pair of buildings with catchment associations. The total runoff of a building itself includes the sum of the roof runoff and the surrounding ground runoff. The flow coupling degree is expressed as a dimensionless ratio, reflecting the intensity of runoff exchange between buildings. The flow coupling degree matrix has the same structure as the catchment association matrix, but the element values ​​change from Boolean values ​​to real values, representing the flow coupling degree between the corresponding buildings. For the aforementioned buildings B078 and B085, the total runoff of B078 is 0.025m. 3 / s, of which the runoff delivered to B085 is 0.018m³. 3 The calculated flow coupling degree is 0.72, calculated at / s. The flow coupling degree matrix, through global analysis, reflects the hydraulic connection strength between structures throughout the region, providing a basis for subsequent zoning.

[0144] The catchment capacity refers to the ratio of the maximum flow rate a pipe section can handle to the actual flow rate it handles, reflecting the load status of the pipe section. The remaining capacity represents the flow rate that the pipe section can still accept, calculated by subtracting the actual flow rate from the design flow rate. The correction process considers the impact of the pipe section's load status on the flow coupling degree. When the load on a pipe section connecting two buildings is high, its flow coupling degree should be increased; conversely, it should be decreased. The correction uses a weighted adjustment method, setting a weight coefficient based on the remaining capacity of the pipe section; the smaller the remaining capacity, the larger the weight coefficient. The corrected flow coupling degree is obtained by multiplying the original flow coupling degree by the weight coefficient. For pipe section M102 connecting buildings B078 and B085, its design flow rate is 0.18 m³ / s. 3 / s, actual flow rate is 0.155m³ / s. 3 / s, remaining carrying capacity is 0.025m 3 / s, corresponding to a weighting coefficient of 1.5, an original flow coupling degree of 0.72, and a corrected flow coupling degree of 1.08. The corrected flow coupling degree matrix retains the original matrix structure but updates the element values, reflecting the strength of the hydraulic connection between structures after considering the pipeline network load conditions.

[0145] Target areas with flow coupling exceeding a set boundary value are identified from the modified flow coupling matrix. Regional independence is determined based on the connection status of the boundary pipe segments within the target area in the pipe network topology. Areas without shared boundary pipe segments are classified as independent catchment areas. The set boundary value is a flow coupling threshold determined based on actual needs and empirical values, typically set between 0.8 and 1.2. When the modified flow coupling between two buildings exceeds the set boundary value, they are determined to belong to the same target area. A target area refers to a set of highly coupled buildings, extracted from the modified flow coupling matrix using a clustering algorithm. Regional independence is determined based on the connection status of the boundary pipe segments of the target area, which are pipe segments connecting buildings within and outside the target area. When there is no shared boundary pipe segment between two target areas, they are classified as independent catchment areas. An independent catchment area is a relatively independent regional unit of the drainage system, with highly coupled internal buildings and low coupling with external areas. Based on the aforementioned modified flow coupling matrix, with a set boundary value of 0.9, three target areas were identified, containing 58, 75, and 93 buildings respectively. Analysis of the boundary pipe connection status of the three target areas revealed that there are two shared boundary pipe segments between area 1 and area 2, but no shared boundary pipe segments between area 2 and area 3, and no shared boundary pipe segments between area 1 and area 3. Therefore, area 2 and area 3 are divided into two independent catchment areas, while area 1 is merged into area 2, forming a larger catchment area.

[0146] This invention achieves accurate identification of highly correlated areas and scientific division of independent catchment zones in drainage systems by constructing a catchment correlation matrix between buildings and calculating corrected flow coupling. It fully considers the connectivity of drainage paths and the carrying capacity of the pipe network, overcoming the limitations of traditional zoning methods that rely solely on topography or administrative boundaries. By introducing catchment carrying capacity as a constraint, the zoning results better reflect the actual operating characteristics of drainage systems, effectively avoiding localized overload problems in the pipe network caused by unreasonable zoning. Analyzing the connection status of boundary pipe sections ensures the relative independence between independent catchment zones, facilitating subsequent zoning-level drainage planning and management.

[0147] A schematic diagram of the structure of the intelligent planning and calculation system for drainage zoning based on multi-source data fusion provided in this embodiment of the invention, the system comprising:

[0148] The data acquisition module is used to acquire geographic information data, drainage pipeline data, and remote sensing image data of the target area;

[0149] The feature extraction module is used to perform image recognition processing on remote sensing image data, extract the roof outline features, building height features, and land cover type features of buildings, and generate image feature recognition results;

[0150] The spatial registration module is used to spatially register image feature recognition results with geographic information data in a geographic information system to generate a fused geographic dataset.

[0151] The runoff calculation module is used to calculate the ratio of the building's projected area to the actual roof area based on the fused geographic dataset to determine the roof's tilt, and to distinguish between roof catchment areas and ground catchment areas by combining the characteristics of land cover type, and to calculate the roof runoff characteristics and ground runoff characteristics respectively.

[0152] The pipeline analysis module is used to construct the pipeline topology based on drainage pipeline data and calculate the water catchment capacity of each pipe segment;

[0153] The drainage path generation module is used to calculate the vertical elevation difference based on drainage pipeline data and building height characteristics, determine drainage priority based on the vertical elevation difference and pipeline topology, and allocate roof runoff characteristics and ground runoff characteristics to the pipeline according to the drainage priority level matrix to generate a drainage path map.

[0154] The zoning module is used to calculate the flow coupling degree between regions based on the drainage path map, with the water catchment capacity as a constraint, and to divide the regions whose flow coupling degree exceeds the set boundary value into independent water catchment zones.

[0155] One technical solution provided in this embodiment of the invention is an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0156] One technical solution provided in this embodiment of the invention is a computer-readable storage medium storing a computer program, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0157] The specific embodiments described above are preferred embodiments of the present invention and are not intended to limit the specific scope of the present invention. The scope of the present invention includes, but is not limited to, these specific embodiments. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A multi-source data fusion drainage partition intelligent planning calculation method, characterized in that, Includes the following steps: Acquire geographic information data, drainage pipeline data, and remote sensing image data of the target area; Image recognition processing is performed on remote sensing image data to extract the roof outline features, building height features, and land cover type features of buildings, and generate image feature recognition results; In a geographic information system, image feature recognition results are spatially registered with geographic information data to generate a fused geographic dataset. The roof tilt is determined by calculating the ratio of the building's projected area to the actual roof area based on the fused geographic dataset. The roof catchment area and the ground catchment area are distinguished by combining the characteristics of the land cover type. The roof runoff characteristics and the ground runoff characteristics are calculated separately. The network topology is constructed based on drainage pipeline data, and the water catchment capacity of each pipe segment is calculated. The vertical elevation difference is calculated based on drainage pipeline data and building height characteristics. Drainage priority is determined based on the vertical elevation difference and pipeline topology. Roof runoff characteristics and ground runoff characteristics are allocated to the pipeline network according to the drainage priority level matrix to generate a drainage path map. Using the water catchment capacity as a constraint, the flow coupling degree between regions is calculated based on the drainage path map, and regions with flow coupling degree exceeding the set boundary value are divided into independent water catchment zones. Vertical elevation differences are calculated based on drainage pipeline data and building height characteristics. Drainage priorities are determined based on these vertical elevation differences and pipeline topology. Roof runoff characteristics and ground runoff characteristics are allocated to the pipeline network according to the drainage priority matrix, generating a drainage path map including: Building elevation values ​​are extracted from building height features, and pipe segment elevation values ​​are extracted from drainage pipeline data to generate building elevation matrices and pipe segment elevation matrices. The vertical elevation difference matrix is ​​obtained by calculating the difference between the building elevation matrix and the pipe segment elevation matrix. The gravity flow direction is then calculated based on the vertical elevation difference matrix to generate the gravity flow direction matrix. The drainage transmission hierarchy is calculated by combining the gravity flow direction matrix with the pipeline topology, a drainage priority matrix is ​​constructed, the flow range that the pipe segment can accept is calculated based on the drainage priority matrix, and a pipe segment flow allocation matrix is ​​generated. The roof runoff characteristics are matched with the drainage priority matrix, and the roof runoff is allocated to the corresponding pipe section according to the flow range that the pipe section can accept, thus generating a roof runoff configuration scheme. The surface runoff characteristics are matched with the drainage priority matrix, and the surface runoff is allocated to the corresponding pipe segment according to the flow range that the pipe segment can accept, thus generating a surface runoff configuration scheme. The roof runoff configuration scheme and the ground runoff configuration scheme are merged into a pipe segment water collection scheme, and the water collection volume of the pipe segment is verified according to the pipe segment flow distribution matrix. A drainage path map is constructed based on the pipe segment water collection scheme.

2. The method of claim 1, wherein, Image recognition processing is performed on remote sensing image data to extract building roof outline features, building height features, and land cover type features, generating image feature recognition results including: The remote sensing image data is segmented at multiple scales according to the difference in gray values ​​of adjacent pixels to obtain image segmentation blocks; Based on image segmentation blocks, gray value distribution, block texture, and block boundary features are calculated to extract building area boundaries; Obtain the coordinates of the building boundary points based on the building area boundary; The building projection area is extracted using the coordinates of the building boundary points, and the building height feature is obtained by calculating the ratio of the building projection length to the actual length. At the same time, the building roof outline feature is extracted based on the coordinates of the building boundary points. Based on the spectral distribution and morphological features of the image segmentation blocks, land cover type features are extracted to generate image feature recognition results.

3. The method according to claim 1, characterized in that, In a geographic information system, spatial registration is performed between image feature recognition results and geographic information data to generate a fused geographic dataset, including: Obtain geographic information data from the geographic information system, extract road intersections and building corners from the geographic information data as spatial control points, and obtain the coordinates of the spatial control points; Extract the image feature points corresponding to the spatial control points from the image feature recognition results, calculate the correspondence between the image feature points and the spatial control points, and generate spatial registration parameters. The coordinate transformation of the roof outline features in the image feature recognition results is performed using spatial registration parameters to obtain the transformed outline coordinates. Calculate the spatial deviation between the transformed contour coordinates and the building contours in the geographic information data, construct a spatial registration optimization function, and optimize and update the spatial registration parameters according to the spatial registration optimization function until the spatial deviation of the transformed contour coordinates meets the registration accuracy requirements. The optimized spatial registration parameters are used to perform spatial transformation on building height characteristics and land cover type characteristics, and then overlaid with geographic information data to generate a fused geographic dataset.

4. The method according to claim 1, characterized in that, The roof slope is determined by calculating the ratio of the building's projected area to the actual roof area using a fused geographic dataset. Furthermore, roof runoff and surface runoff characteristics are differentiated based on land cover type features. The roof runoff characteristics and surface runoff characteristics are calculated separately, including: The building boundary points are extracted from the fused geographic dataset to construct the projection surface, and the projected area of ​​the building is calculated. At the same time, the roof outline of the building is extracted to construct the roof surface, and the actual area of ​​the roof is calculated. Calculate the ratio of the building's projected area to the actual roof area to determine the degree of roof slope; Determine the roof water flow direction based on the roof slope and building boundary points, and construct a roof water flow path network; Based on the roof water catchment path network, the cumulative water catchment volume is calculated, and areas where the cumulative water catchment volume meets the preset zoning threshold are divided into roof water catchment areas. Land cover type features are extracted from the fused geographic dataset, and ground water catchment areas are divided according to surface permeability. The roof runoff coefficient is obtained by multiplying the area of ​​the roof catchment area by the roof slope. The roof runoff volume is then calculated by combining the roof runoff coefficient to generate roof runoff characteristics. The surface runoff coefficient is obtained by multiplying the surface catchment area by the permeability coefficient corresponding to the surface cover type characteristics. The surface runoff is then calculated by combining the surface runoff coefficient to generate surface runoff characteristics.

5. The method according to claim 1, characterized in that, Based on the drainage pipeline data, the pipeline network topology is constructed, and the water catchment capacity of each pipe segment is calculated, including: Extract the starting point coordinates, ending point coordinates, and pipe diameter of the drainage pipeline segment from the drainage pipeline data to construct a drainage pipeline segment parameter matrix; Calculate the burial depth difference and plane distance of the pipe segment based on the coordinates of the starting and ending points of the pipe segment, obtain the slope of the drainage pipe segment, identify the connection points of the drainage pipe segment using the drainage pipe segment parameter matrix, and construct the drainage pipe segment connection matrix; determine the flow direction of the drainage pipe segment based on the slope of the drainage pipe segment, and establish the pipe network topology; Calculate the hydraulic parameters of the drainage pipe section based on the slope and pipe diameter of the drainage pipe section; The design flow rate of the drainage pipe section is calculated based on the hydraulic parameters of the drainage pipe section. The design flow rate of the upstream drainage pipe section is transferred to the connection point of the downstream drainage pipe section according to the pipe network topology, and the runoff of the drainage pipe section is calculated. The drainage pipe section's catchment capacity is determined by comparing its flow rate with the design flow rate.

6. The method according to claim 1, characterized in that, Using catchment capacity as a constraint, the flow coupling degree between regions is calculated based on the drainage path map. Regions with flow coupling degrees exceeding a set boundary value are divided into independent catchment zones, including: Extract water catchment schemes for pipe segments from the drainage path map, identify upstream and downstream buildings connected to each pipe segment, and construct a water catchment correlation matrix between buildings; The runoff exchange between buildings is calculated based on the water catchment correlation matrix between buildings, and the ratio of the runoff exchange to the total runoff of the building itself is determined as the flow coupling degree. Using the remaining carrying capacity of each pipe segment in the water catchment capacity as a constraint, the flow coupling degree is corrected to generate a corrected flow coupling degree matrix. Identify target areas whose flow coupling exceeds a set boundary value from the modified flow coupling matrix, determine the area independence based on the connection status of the boundary pipe segments of the target area in the pipe network topology, and divide areas without shared boundary pipe segments into independent catchment zones.

7. A multi-source data fusion-based intelligent planning and calculation system for drainage zoning, used to implement the method described in any one of claims 1-6, characterized in that, The system includes: The data acquisition module is used to acquire geographic information data, drainage pipeline data, and remote sensing image data of the target area; The feature extraction module is used to perform image recognition processing on remote sensing image data, extract the roof outline features, building height features, and land cover type features of buildings, and generate image feature recognition results; The spatial registration module is used to spatially register image feature recognition results with geographic information data in a geographic information system to generate a fused geographic dataset. The runoff calculation module is used to calculate the ratio of the building's projected area to the actual roof area based on the fused geographic dataset to determine the roof's tilt, and to distinguish between roof catchment areas and ground catchment areas by combining the characteristics of land cover type, and to calculate the roof runoff characteristics and ground runoff characteristics respectively. The pipeline analysis module is used to construct the pipeline topology based on drainage pipeline data and calculate the water catchment capacity of each pipe segment; The drainage path generation module is used to calculate the vertical elevation difference based on drainage pipeline data and building height characteristics, determine drainage priority based on the vertical elevation difference and pipeline topology, and allocate roof runoff characteristics and ground runoff characteristics to the pipeline according to the drainage priority level matrix to generate a drainage path map. The zoning module is used to calculate the flow coupling degree between regions based on the drainage path map, with the water catchment capacity as a constraint, and to divide the regions whose flow coupling degree exceeds the set boundary value into independent water catchment zones.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 6.

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