Drainage pipe network catchment area division method and device based on geographic information unit data

Abstract

The invention relates to the technical field of drainage pipe networks, and discloses a drainage pipe network catchment area division method and device based on geographic information unit data. The drainage pipe network catchment area division method based on geographic information unit data comprises the following steps: obtaining drainage pipe network data, geographic information unit data and auxiliary data of a target area, wherein the geographic information unit data comprises interest points and interest areas of a geographic information system; dividing the target area into a plurality of catchment areas based on the points of interest and boundary lines of the areas of interest; for each catchment area, attribute data of the catchment area are determined based on the geographic information unit data and the auxiliary data corresponding to the catchment area, the attribute data are marked in the catchment area, and the attribute data are used for representing building information, population information, water volume information, land information and function information of the catchment area; and associating the drainage pipe network nodes with the catchment area based on the drainage pipe network data to generate the drainage pipe network catchment area. According to the method, the catchment area division rate and the pipe network model calculation precision can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of drainage pipe network, and particularly relates to a drainage pipe network catchment area division method and device based on geographic information unit data. BACKGROUND

[0002] The urban drainage pipe network model plays an important role in solving the simulation of urban sewage and waterlogging, and its construction includes the establishment of pipe network topology structure, attribute data conversion, catchment division, rainfall boundary input and parameter setting processes. The catchment division and parameter setting have a significant influence on the accuracy of the simulation results, and are the key link of the model construction.

[0003] The current drainage pipe network catchment division method mainly includes manual drawing based on urban administrative division map, topographic map and other data and automatic division based on the Thiessen polygon rule. The manual drawing method can meet the simulation accuracy requirement, but is time-consuming and inefficient, and is difficult to adapt to the needs of large-scale urban drainage pipe network model construction. The division method based on the Thiessen polygon rule does not consider the actual terrain and land use in the mechanical division process, so that there is a large difference between the division result and the actual flow mode, and the division accuracy is low. SUMMARY

[0004] Therefore, the present application provides a drainage pipe network catchment division method and device based on geographic information unit data, which can improve the catchment division rate and the pipe network model calculation accuracy.

[0005] In a first aspect, the present application provides a drainage pipe network catchment division method based on geographic information unit data, which includes: obtaining drainage pipe network data, geographic information unit data and auxiliary data of a target area, the geographic information unit data including interest points and interest regions of a geographic information system, and the auxiliary data including population data and land data of the target area; dividing the target area into a plurality of catchment areas based on the boundary lines of the interest points and the interest regions; for each catchment area, determining attribute data of the catchment area based on the geographic information unit data and the auxiliary data corresponding to the catchment area, and marking the attribute data in the catchment area, the attribute data being used to represent building information, population information, water quantity information, land information and function information of the catchment area; associating drainage pipe network nodes with the catchment areas based on the drainage pipe network data, and generating drainage pipe network catchment areas.

[0006] In the implementation mode, the geographical information unit data of the target area is taken as the division basis, and the catchment area is precisely divided and element labeled in combination with population data, land data and other factors, which can overcome the defects of ignoring the actual terrain and land use in the traditional method, make the catchment area boundary more in line with the characteristics of urban drainage, and improve the accuracy and rationality of the division. By using the interest points and interest areas to automatically divide the catchment area, the workload of manual drawing and adjustment is reduced, and the modeling efficiency is greatly improved. At the same time, by labeling the attribute data of the catchment area, the information of the catchment area is further enriched, and on this basis, the drainage pipe network data and the catchment area are combined, which can effectively improve the precision of the pipe network model.

[0007] In an optional implementation mode, the target area is divided into multiple catchment areas based on the boundary lines of the interest points and the interest areas, including: performing spatial matching on the interest points and the interest areas to obtain interest points corresponding to each interest area; determining the functional types of the interest areas based on the interest points; when there are at least two different functional types in the interest area, dividing the interest area into multiple catchment areas according to the functional types; otherwise, taking the interest area as a catchment area.

[0008] In the implementation mode, the matching of the interest points and the interest areas can improve the data integrity and consistency, and provide an accurate data basis for subsequent catchment area division.

[0009] In an optional implementation mode, for each catchment area, the attribute data of the catchment area is determined based on the geographical information unit data and the auxiliary data corresponding to the catchment area, including: extracting first attribute data of the catchment area from the geographical information unit data and the auxiliary data corresponding to the catchment area; performing data processing and analysis on the first attribute data to determine second attribute data of the catchment area.

[0010] In an optional implementation mode, the first attribute data of the catchment area is extracted from the geographical information unit data and the auxiliary data corresponding to the catchment area, including: generating a corresponding catchment area ID for each catchment area; extracting the functional types by extracting the interest points, the interest areas and the auxiliary data corresponding to the catchment area; generating the green rate, the catchment area area and the total building area by extracting and calculating the interest areas corresponding to the catchment area; generating the total population by extracting the auxiliary data corresponding to the catchment area.

[0011] In an optional implementation mode, the first attribute data is processed and analyzed to determine the second attribute data of the catchment area, including: calculating the impervious rate of the catchment area based on the green rate; calculating the building density of the catchment area based on the catchment area area and the total building area; calculating the population density of the catchment area based on the total population and the total building area; calculating the water consumption and the sewage discharge based on the population density or the building density, the catchment area area.

[0012] In this implementation, the annotation of the catchment area attributes is strengthened, including the impermeable rate, population density, building density and other key parameters, to provide rich data support for subsequent drainage system analysis.

[0013] In an optional implementation, based on the drainage pipe network data, the drainage pipe network nodes are associated with the catchment areas to generate drainage pipe network catchment areas, including: determining the number of drainage pipe network nodes contained in the catchment area based on the drainage pipe network data; if the number of drainage pipe network nodes contained in the catchment area is 1, associating the drainage pipe network node with the catchment area to generate the drainage pipe network catchment area; if the number of drainage pipe network nodes contained in the catchment area is 0, selecting the drainage pipe network node closest to the catchment area from the drainage pipe network data based on the interest point of the catchment area, associating the drainage pipe network node with the catchment area to generate the drainage pipe network catchment area; and if the number of drainage pipe network nodes contained in the catchment area is greater than 1, dividing the catchment area into multiple drainage pipe network catchment areas based on the coordinates of the drainage pipe network nodes, each drainage pipe network catchment area corresponding to a drainage pipe network node.

[0014] In an optional implementation, the auxiliary data includes road network data of the target area, based on the drainage pipe network data, the drainage pipe network nodes are associated with the catchment areas to generate drainage pipe network catchment areas, which further includes: determining a blank area based on the target area and the multiple catchment areas, and dividing the catchment areas based on the blank area; determining the functional type of the catchment area of the blank area based on the road network data and the land data of the target area; determining the target catchment area based on the functional type, the functional type of the target catchment area being the same as that of the catchment area of the blank area; determining the attribute data of the catchment area of the blank area based on the attribute data of the target catchment area, and marking the attribute data in the catchment area of the blank area.

[0015] In an optional implementation, the drainage pipe network catchment area division method based on geographic information unit data further includes: when there is a new interest area in the target area, determining whether the new interest area overlaps with the current catchment area; if the new interest area does not overlap with the current catchment area, dividing a new catchment area based on the new interest area; and if the new interest area overlaps with the current catchment area, re-dividing the catchment area of the overlapping area based on the interest point and the boundary line of the interest area of the overlapping area.

[0016] In an optional implementation, the drainage pipe network catchment area division method based on geographic information unit data further includes: when there is a new interest point in the target area, determining whether the new interest point is in the current interest area; if the new interest point is not in the current interest area, constructing a new catchment area based on the new interest point; and if the new interest point is in the current interest area, adjusting the functional type of the current catchment area corresponding to the current interest area based on the new interest point.

[0017] In this implementation, an automatic update mechanism is set up, which can flexibly respond to the changes in rapid urban development, reduce the need for manual intervention, and improve the real-time performance and flexibility of the drainage network model.

[0018] Secondly, the present invention provides a drainage network catchment area division device based on geographic information unit (GIS) data. This device includes: an acquisition module for acquiring drainage network data, GIS data, and auxiliary data for a target area; the GIS data includes points of interest (POIs) and regions of interest (ROIs) from a geographic information system (GIS); the auxiliary data includes population data and land data for the target area; a division module for dividing the target area into multiple catchment areas based on the boundary lines of the POIs and ROIs; a labeling module for determining attribute data for each catchment area based on the corresponding GIS data and auxiliary data, and labeling the attribute data within the catchment area; the attribute data characterizing the building information, population information, water volume information, land information, and functional information of the catchment area; and an association module for associating drainage network nodes with catchment areas based on the drainage network data to generate drainage network catchment areas.

[0019] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the drainage network catchment area division method based on geographic information unit data as described in the first aspect or any corresponding embodiment.

[0020] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the drainage network catchment area division method based on geographic information unit data according to the first aspect or any corresponding embodiment described above.

[0021] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the drainage network catchment area division method based on geographic information unit data as described in the first aspect or any corresponding embodiment. Attached Figure Description

[0022] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1This is a flowchart of a drainage network catchment area division method based on geographic information unit data according to an embodiment of the present invention;

[0024] Figure 2 This is a flowchart of another method for dividing the catchment area of ​​a drainage network based on geographic information unit data according to an embodiment of the present invention;

[0025] Figure 3 This is a flowchart of a blank area supplementation method according to an embodiment of the present invention;

[0026] Figure 4 This is a structural diagram of the data update mechanism according to an embodiment of the present invention;

[0027] Figure 5 This is a structural diagram of a drainage network catchment area division system based on geographic information unit data according to an embodiment of the present invention;

[0028] Figure 6 This is a schematic diagram of a catchment area division according to an embodiment of the present invention;

[0029] Figure 7 This is a structural block diagram of a drainage network catchment area division device based on geographic information unit data according to an embodiment of the present invention;

[0030] Figure 8 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] There are three main methods for traditionally delineating catchment areas: First, manual hand-drawing based on urban administrative maps and topographic maps, which offers high accuracy but is time-consuming and inefficient. Second, automatic delineation based on the Thiessen polygon method, generating irregular polygonal catchment areas centered on storm drain inspection wells, which is fast but less accurate. Third, combining GIS (Geographic Information System) and DEM (Digital Elevation Model) data, utilizing GIS hydrological analysis and spatial analysis tools to complete the catchment area delineation. Among these methods, while manual hand-drawing can meet simulation accuracy requirements, it requires significant manual intervention, is time-consuming and inefficient, and is unsuitable for large-scale urban drainage network model construction. The Thiessen polygon method, on the other hand, does not consider actual terrain and land use during the mechanical delineation process, resulting in significant discrepancies between the delineated results and actual runoff patterns. Furthermore, due to limitations in the accuracy of pipeline surveys and the generalized model, this method often results in unreasonable or inaccurate catchment area delineation, requiring significant manual adjustments and impacting modeling efficiency. While the GIS method can combine high-precision DEMs and land use data for delineation, data acquisition costs are high, and it cannot fully consider factors such as the impermeability of urban landmarks and building drainage paths. This method is primarily suitable for areas with significant topographic relief, but its applicability is limited in plains or areas with gentle slopes due to larger delineation errors.

[0033] Therefore, this application proposes a method for dividing the catchment area of ​​drainage pipe network based on geographic information unit data. This method fully considers the role of geographic information data such as points of interest and regions of interest, makes full use of urban functional unit information to optimize the catchment area, improves the accuracy of the catchment area parameter assignment, and further improves the reliability of drainage simulation results.

[0034] According to an embodiment of the present invention, a method for dividing the catchment area of ​​a drainage network based on geographic information unit data is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] This embodiment provides a method for dividing drainage network catchment areas based on geographic information unit data. Figure 1 This is a flowchart of a drainage network catchment area division method based on geographic information unit data according to an embodiment of the present invention. It should be noted that if substantially the same result is obtained, this embodiment does not necessarily reflect that result. Figure 1 The illustrated process sequence is limited. For example... Figure 1 As shown, the process includes the following steps:

[0036] Step S101: Obtain drainage network data, geographic information unit data, and auxiliary data for the target area.

[0037] The target area includes the target city, target district, etc.

[0038] Among them, drainage network data refers to a set of information related to various pipes, ancillary facilities and their operating status in the drainage system of the target area.

[0039] In one implementation, the drainage network data includes attributes such as the coordinates, ID, elevation, pipe segment topology, and drainage capacity of the drainage network nodes.

[0040] Geographic information unit data refers to the most basic and indivisible geographic data unit that constitutes a geographic information system. Geographic information unit data can be a point, a line, a surface, or a volume, and has clear spatial location and attribute information.

[0041] In one implementation, geographic information unit data includes points of interest and regions of interest from the geographic information system.

[0042] Points of Interest (POIs) are markers that record functional units within a target area, including information such as coordinates, name, and function type. These functional units include, for example, residential communities, parks, and schools.

[0043] For example, the point of interest record contains the name, coordinates, and function type of a certain community.

[0044] An Area of ​​Interest (AOI) records the actual boundary outline corresponding to the functional unit of the target area, including attributes such as geographic coordinates, name, greening rate, functional classification, and building area.

[0045] For example, the region of interest records the geographical boundaries, polygon coordinates, greening rate, area, and functional type of a park.

[0046] The auxiliary data includes population data and land data for the target area.

[0047] In one implementation, the auxiliary data includes population size statistics and land use types.

[0048] Population size statistics are used to label information such as the resident population and population density within the catchment area, supporting the consideration of population factors in model calculations.

[0049] Land use types provide information on the land use within a catchment area, used to differentiate the hydrological response characteristics of different functional zones. Land uses include residential, commercial, industrial, and green space, and land use type data also includes building data.

[0050] In another implementation, auxiliary data also includes road network data.

[0051] Road network data is used to supplement functional information in areas not covered by POI or AOI, and to assist in the delineation of catchment areas.

[0052] In another implementation, the auxiliary data also includes a description of the data source.

[0053] POI and AOI regional data generally come from urban GIS platforms or third-party map APIs (Application Programming Interfaces), or can be provided by planning and design units; population and building data can be accessed through public statistical information from street offices or housing and construction departments, or obtained through real estate platforms.

[0054] Furthermore, data preprocessing is performed on drainage network data, geographic information unit data, and auxiliary data.

[0055] Data preprocessing includes coordinate unification. Specifically, coordinate unification is performed on all data to ensure spatial consistency for subsequent calculations.

[0056] Data preprocessing also includes data cleaning. Data cleaning includes data deduplication, outlier removal, and missing value imputation.

[0057] Specifically, duplicate data records in the drainage network data and geographic information unit data are deleted; invalid coordinate points in the drainage network data and geographic information unit data are removed. For example, invalid coordinate points include incorrect POI data coordinates, network coordinate offsets, etc. Furthermore, for missing attribute information in the POI data or AOI area, it is filled by interpolation from adjacent areas or by default values.

[0058] Step S102: Divide the target area into multiple catchment areas based on the boundary lines of the points of interest and regions of interest.

[0059] The target area is first divided according to the boundary line of the AOI area. The AOI area is then divided according to the functional attributes of each POI data within the AOI area, resulting in multiple catchment areas.

[0060] Specifically, the boundary line of the AOI region is used as the boundary line of the catchment area. Each AOI region corresponds to at least one catchment area. For each AOI region, the catchment area is further divided into multiple sub-units based on the POI data and the functional attributes contained in the AOI region. Each sub-unit is an independent catchment area.

[0061] Step S103: For each catchment area, determine the attribute data of the catchment area based on the geographic information unit data and auxiliary data corresponding to the catchment area, and mark the attribute data within the catchment area.

[0062] Among them, attribute data is used to characterize the building information, population information, water volume information, land information and functional information of the catchment area.

[0063] In one possible implementation, the attribute data includes data directly extracted from geographic information unit data and auxiliary data. For example, the attribute data includes catchment area ID, function type, greening rate, catchment area, total building area, total population, water consumption per unit area, and wastewater generation coefficient.

[0064] In another possible implementation, the attribute data also includes data obtained by analyzing and processing geographic information unit data and auxiliary data. For example, attribute data may include impermeability, population density, building density, daily water consumption, and wastewater discharge.

[0065] The attribute data corresponding to each catchment area is filled into the catchment area to form a catchment area layer.

[0066] Step S104: Based on the drainage network data, associate the drainage network nodes with the catchment area to generate the drainage network catchment area.

[0067] Based on the coordinate data of drainage network nodes in the drainage network data, the location of drainage network nodes and catchment areas is determined, and the drainage network nodes and catchment areas are associated to form a catchment area layer.

[0068] The drainage network catchment area delineation method based on geographic information unit (GIS) data provided in this embodiment uses GIS data of the target area as the basis for delineation, combined with population data, land data, and other factors, to accurately delineate and label the catchment areas. This overcomes the shortcomings of traditional methods that ignore actual terrain and land use, making the catchment area boundaries more consistent with urban drainage characteristics and improving the accuracy and rationality of the delineation. By using points of interest (POIs) and regions of interest (ROIs) for fully automated catchment area delineation, the workload of manual hand-drawing and adjustments is reduced, significantly improving modeling efficiency. Furthermore, by labeling the attribute data of the catchment areas, the information of the catchment areas is further enriched. Based on this, combining the drainage network data with the catchment areas can effectively improve the accuracy of the network model.

[0069] This embodiment provides a method for dividing drainage network catchment areas based on geographic information unit data. Figure 2 This is a flowchart of another method for dividing drainage network catchment areas based on geographic information unit data according to an embodiment of the present invention. It should be noted that if substantially the same result is obtained, this embodiment does not necessarily reflect that result. Figure 2 The illustrated process sequence is limited. For example... Figure 2 As shown, the process includes the following steps:

[0070] Step S201: Obtain drainage network data, geographic information unit data and auxiliary data for the target area.

[0071] Please see details Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0072] Step S202: Divide the target area into multiple catchment areas based on the boundary lines of the points of interest and the regions of interest.

[0073] Specifically, step S202 includes:

[0074] Step S2021: Spatial matching of interest points and interest regions is performed to obtain the interest points corresponding to each interest region.

[0075] Spatial matching is performed between POI data and AOI regions to determine whether the POI data is within the AOI region. If the POI data is within the AOI region, it is assigned to the corresponding AOI region.

[0076] In one possible implementation, when no POI data exists within the AOI area, a virtual POI point is generated within the AOI area, and the information of the AOI area is assigned to the virtual POI point to ensure that all AOI areas have representative POI points, thereby improving the integrity and consistency of the data.

[0077] In one possible implementation, when no corresponding AOI area exists for POI data, the functional type of the POI data is identified, and a reasonable buffer AOI area is generated by combining this information with the boundary lines of surrounding AOI areas. The functional types of the POI data include commercial areas, residential areas, roads, etc.

[0078] In one possible implementation, when the POI data is of the road type, a reasonable buffer AOI area is generated by combining the road grid data and the surrounding AOI area.

[0079] Based on the above method, a one-to-one or one-to-many spatial and attribute association relationship between AOI regions and POI data is finally generated.

[0080] Step S2022: Determine the functional type of the region of interest based on the points of interest.

[0081] The functional attributes of the AOI region determine the overall functional type of the region, and then the specific functional types contained in the AOI region are determined based on the functional attributes of the POI data within the AOI region.

[0082] In this context, it can be understood that the functional attributes of the AOI region represent the functional information of the entire region, while the functional attributes of the POI data represent the functional information of a smaller region. When the AOI region is large, it may contain multiple smaller functional types simultaneously.

[0083] Step S2023: When there is one function type in the region of interest, the region of interest is treated as a catchment area; when there are at least two different function types in the region of interest, the region of interest is divided into multiple catchment areas according to the function type.

[0084] In one possible implementation, when a functional type exists in an AOI region, the spatial boundary of the AOI region is used as the boundary of the catchment area. Each AOI region corresponds to a catchment area unit, and the catchment area inherits the functional classification, spatial attributes, and other information of the AOI region.

[0085] In another possible implementation, when there are at least two different functional types in the AOI region, the AOI region is further subdivided according to the POI data with different functional heterogeneity, and the AOI region is divided into multiple sub-units. Each sub-unit is an independent catchment area, and the catchment area inherits the functional classification of the POI data and the spatial attributes of the AOI region.

[0086] For example, when the AOI area is a university campus, a large industrial park, etc., there are multiple functional types in the AOI area, corresponding to multiple functional sub-areas of the POI data, such as teaching area, dormitory area, commercial area, etc. The AOI area is divided into multiple sub-units according to the functional sub-areas, and each sub-unit is an independent catchment area.

[0087] In this implementation, the application uses POI data and AOI areas as the basis for division. Therefore, the resulting catchment area naturally has the characteristics of urban functional units, such as residential areas, parks, schools, roads, commercial areas, and industrial areas. Compared with traditional division methods, it has a stronger real-world semantic relevance.

[0088] Step S203: For each catchment area, determine the attribute data of the catchment area based on the geographic information unit data and auxiliary data corresponding to the catchment area, and mark the attribute data within the catchment area.

[0089] Specifically, step S203 includes:

[0090] Step S2031: Extract the first attribute data of the catchment area from the geographic information unit data and auxiliary data corresponding to the catchment area, and mark the first attribute data within the catchment area.

[0091] In one possible implementation, the first attribute data includes the catchment area ID, function type, greening rate, catchment area, total building area, and total population.

[0092] In another possible implementation, the first attribute data also includes the proportion of other permeable areas.

[0093] Specifically, a corresponding catchment area ID is generated for each catchment area; the points of interest, regions of interest, and auxiliary data corresponding to the catchment area are extracted to generate the function type; the regions of interest corresponding to the catchment area are extracted and calculated to generate the greening rate, the proportion of other permeable areas, the catchment area, and the total building area; and the auxiliary data corresponding to the catchment area are extracted to generate the total population.

[0094] In another possible implementation, the first attribute data also includes unit water consumption and wastewater generation coefficient.

[0095] Furthermore, when there are missing values ​​or abnormal fields in the first attribute data, the distribution of the first attribute data of the same type in the catchment area is used to fill the gaps, and the first attribute data is marked in the catchment area.

[0096] For example, please refer to Table 1, which is a data description of the first attribute data.

[0097] Table 1. Data Description of the First Attribute Data

[0098]

[0099]

[0100] Step S2032: Process and analyze the first attribute data to determine the second attribute data of the catchment area, and mark the second attribute data within the catchment area.

[0101] In one implementation, the first attribute data includes impermeability. The impermeability of the catchment area is calculated based on the greening rate.

[0102] Impermeability R imp It is an important indicator reflecting a region's runoff generation capacity and rainfall response. Impermeability R imp The estimation method is as follows:

[0103] R imp =1-R green .

[0104] Among them, R greenThis refers to the greening rate.

[0105] In other implementations, the impermeability of the catchment area is calculated based on the greening rate and the proportion of other permeable areas. Impermeability R imp The estimation method is as follows:

[0106] R imp =1-R green -R perm .

[0107] Among them, R perm This represents the proportion of other permeable areas, such as water surfaces and bare land.

[0108] If the AOI area encompasses multiple functional types, the catchment area of ​​a large AOI area can be further subdivided into various functional sub-zones, such as the teaching area and sports area of ​​a campus. The impermeability is estimated using a weighted average method, as follows:

[0109]

[0110] Among them, R imp_i A represents the individual impermeability of the sub-region. i Let A be the area of ​​the subregion. total This represents the total area of ​​the catchment area.

[0111] Understandably, the greening rate and other permeable area ratios can be provided by the AOI area field. If missing, default values ​​can be set according to the function type, and these default values ​​are stored in the memory module. Please refer to Table 2, which shows the greening rate and other permeable area ratios under normal circumstances for each function type.

[0112] Table 2 shows the greening rate and proportion of other permeable areas under normal circumstances for each functional type.

[0113]

[0114]

[0115] Furthermore, when an AOI region field is missing or the estimated value deviates significantly from the experience range of the memory module, the system will activate a feedback mechanism to mark the value and suggest that the user manually confirm it or use the default value.

[0116] In one implementation, the first attribute data includes population density. The population density of the catchment area is calculated based on the total population and total building area.

[0117] Population density D pop The calculation formula is:

[0118]

[0119] Among them, P total For the total population, A AOI This refers to the catchment area.

[0120] For large AOI regions containing multiple POI data, estimation and weighted merging can be performed based on POI data type partitioning. The calculation formula is as follows:

[0121]

[0122] Among them, D pop_i For each sub-region, population density, A i This represents the area of ​​the sub-region.

[0123] In one implementation, the first attribute data includes building density. The building density of the catchment area is calculated based on the catchment area and the total building area.

[0124] Building density D build Calculation formula:

[0125]

[0126] Among them, A build This refers to the total building area.

[0127] In one implementation, the first attribute data includes water consumption and wastewater discharge. Water consumption and wastewater discharge are calculated based on population density or building density and catchment area.

[0128] To achieve quantitative analysis of the drainage system load, the daily water consumption and sewage discharge of each catchment area are estimated based on the population density or building density of the catchment area, combined with functional type, hydrological empirical parameters and dynamic adjustment factors.

[0129] Specifically, based on industry experience and standards, the unit water consumption U for different functional types is first given. unit With the wastewater generation coefficient α.

[0130] Understandably, both unit water consumption and wastewater generation coefficient can be set according to industry standard tables. If these are missing, default values ​​can be set based on the function type, and these default values ​​are stored in the memory module. Please refer to Table 3, which shows the unit water consumption and wastewater generation coefficient under normal circumstances for each function type.

[0131] Table 3 shows the unit water consumption and wastewater generation coefficient under normal circumstances for each functional type.

[0132]

[0133]

[0134] The settings for unit water consumption and wastewater generation coefficient will prioritize referencing industry experience ranges based on historical data from the memory module; if the estimation results deviate significantly from the experience average, the system will automatically record and trigger a feedback mechanism.

[0135] Furthermore, to more realistically reflect water usage changes under different scenarios, a dynamic adjustment factor is introduced. This dynamic adjustment factor includes the weather factor W. factor Seasonal factor S factor User behavior factor U factor .

[0136] Weather factor W factor The globally defined parameters represent the overall regulatory impact of regional weather conditions on water use behavior and are uniformly applied to the water consumption calculation of all catchment areas during the simulation.

[0137] For example, please refer to Table 4, which lists the weather factors for different weather scenarios.

[0138] Table 4 Weather factors corresponding to different weather scenarios

[0139] Weather Context Description Weather factor W factor ]]> Normal Weather Normal Weather Conditions 1.00 Dry Period High Temperature and Low Rainfall, Increased Water Use 1.05 Rainy Period Reduced Outdoor Activities, Decreased Water Use 0.90 Extreme Cold Weather Increased Water Use for Commercial and Industrial Heating 1.10

[0140] Seasonal factor S factor It takes into account the differences in water use under seasonal changes for different functional types, such as increased irrigation demand in summer and increased water supply for heating in winter.

[0141] For example, please refer to Table 5, which shows the seasonal factors corresponding to different functional types.

[0142] Table 5. Seasonal factors corresponding to different functional types

[0143] Function Type Summer S factor ]] Winter S factor ]]> Residential Area 1.15 1.10 School 1.10 1.05 Commercial Area 1.20 1.05 Industrial Area 1.05 1.15 Park 1.10 1.05 Road 1.10 1.05

[0144] User behavior factor U factor This refers to adjustments made to water use through human intervention (such as water conservation policies, water restriction policies, etc.), which can be dynamically set according to the actual situation.

[0145] For example, please refer to Table 6, which shows the user behavior factors corresponding to different function types.

[0146] Table 6 User behavior factors corresponding to different function types

[0147] Function Type User behavior factor U factor <!-- 10 -->]]> Residential Area 10% reduction when implementing water-saving policies School Approximately 5% reduction through water-saving education and facility upgrades Commercial Area Approximately 10% reduction through installation of water-saving devices and reduced flushing frequency Industrial Area 10% to 15% reduction through optimization of water use processes and production limits Park Approximately 5% reduction through reduced irrigation duration and use of rainwater recycling systems Road Approximately 5% reduction through reduced sprinkling frequency or use of reclaimed water for cleaning

[0148] Furthermore, the daily water consumption Q in the catchment area is calculated by combining the unit water consumption and dynamic adjustment factors. water The estimation formula is:

[0149] Q water =Dpop ×A AOI ×U unit ×W factor ×S factor ×U factor .

[0150] For industrial, commercial, and road areas, the building area or ground area can be used to replace D. pop ×A AOI item.

[0151] Furthermore, the wastewater discharge Q in the catchment area is calculated by combining the daily water consumption and wastewater generation coefficient α. sewage The estimation formula is:

[0152] Q sewage =Q water ×α.

[0153] The second attribute data is labeled within the catchment area to generate a complete vector map layer of the catchment area.

[0154] For example, please refer to Table 7, which is a data description of the second attribute data.

[0155] Table 7 Data Description for the Second Attribute Data

[0156]

[0157] Furthermore, this second attribute data can provide boundary condition inputs for subsequent drainage models such as SWMM and water quality models, supporting multi-objective optimization scheduling and policy simulation analysis.

[0158] In one possible implementation, after dividing the target area into multiple catchment areas using the above-mentioned division method, there may be blank areas that have not been divided. Therefore, this application proposes a method for filling blank areas. Figure 3 This is a flowchart of a blank area filling method according to an embodiment of the present invention. It should be noted that if substantially the same result is obtained, this embodiment does not necessarily reflect that result. Figure 3 The illustrated process sequence is limited. For example... Figure 3 As shown, the process includes the following steps:

[0159] Step S301: Based on the target area and multiple catchment areas, determine the blank area, and divide the catchment area based on the blank area.

[0160] Specifically, the difference between the target area and multiple catchment areas is calculated to obtain blank areas, and each independent blank area is treated as an independent catchment area.

[0161] Furthermore, based on the road network data and land data of the blank area, the blank area is divided into multiple catchment areas.

[0162] Step S302: Determine the functional type of the catchment area in the blank area based on the road network data and land data of the target area.

[0163] Step S303: Determine the target catchment area based on the function type.

[0164] Based on the functional type of the catchment area in the blank area, select a target catchment area with the same functional type from the historically determined catchment areas.

[0165] Step S304: Determine the attribute data of the watershed in the blank area based on the attribute data of the target watershed, and mark the attribute data in the watershed of the blank area.

[0166] Specifically, the attribute data of the target catchment area is used as the reference value for the catchment area of ​​the blank area to determine the attribute data of the catchment area of ​​the blank area, and the attribute data is marked in the catchment area of ​​the blank area.

[0167] Furthermore, all catchment areas within the target area are integrated to form a complete, seamless, and non-repeating catchment area layer.

[0168] In one possible implementation, where multiple catchment areas overlap, the attribution is adjusted according to a preset priority of "AOI-led > POI-led > other data".

[0169] In one possible implementation, an area threshold is set, and catchment areas smaller than the area threshold are merged into adjacent areas with similar functional attributes and the largest adjacent area. The area threshold can be assumed to be 500 square meters.

[0170] Step S204: Based on the drainage network data, associate the drainage network nodes with the catchment area to generate the drainage network catchment area.

[0171] Specifically, the number of drainage network nodes in the catchment area is determined based on drainage network data, and corresponding strategies are adopted according to the different numbers of drainage network nodes.

[0172] When the catchment area contains only one drainage network node, the drainage network node is directly associated with the catchment area to generate the drainage network catchment area.

[0173] When the number of drainage network nodes contained in the catchment area is 0, the drainage network node closest to the catchment area is selected from the drainage network data based on the POI data points of the catchment area, and the drainage network node is associated with the catchment area to generate the drainage network catchment area.

[0174] In one specific implementation, the Euclidean distance from the POI data points of the catchment area to each drainage network node is calculated, and the drainage network node with the closest Euclidean distance is associated with the catchment area to generate the drainage network catchment area.

[0175] In another specific implementation, the Euclidean distance from the centroid of the catchment area to each drainage network node is calculated, and the drainage network node with the closest Euclidean distance is associated with the catchment area to generate the drainage network catchment area.

[0176] In another specific implementation, based on the DEM (Digital Elevation Model), the downstream direction of the water flow in the catchment area is determined using the D8 algorithm, and the catchment area is associated with the actual downstream drainage network nodes to generate the drainage network catchment area.

[0177] When the catchment area contains more than one drainage network node, the catchment area is divided into multiple drainage network catchment areas based on the coordinates of the drainage network node, and each drainage network catchment area corresponds to one drainage network node.

[0178] Specifically, if a catchment area corresponds to multiple drainage network nodes (such as a large park or industrial area), the catchment area needs to be further subdivided. Within the original catchment area, Thiessen polygon sub-regions are generated based on the coordinates of the drainage network nodes, and a corresponding catchment area is assigned to each network node to ensure reasonable drainage zoning.

[0179] In practical applications, urban spatial patterns and drainage system structures evolve over time. Therefore, this system incorporates an automatic update mechanism to respond to the addition and modification of AOI and POI data, as well as changes in the drainage network topology. Thus, this embodiment provides a data update mechanism. Figure 4 This is a structural diagram of the data update mechanism according to an embodiment of the present invention, wherein data update includes adding or modifying AOI areas, adding or modifying POI data, and adjusting the pipeline network topology.

[0180] (1) Adding or modifying AOI areas.

[0181] When a new AOI area is added to the target area, such as a new residential area or industrial park, it is determined whether the new AOI area overlaps with the current catchment area. If the new AOI area does not overlap with the current catchment area, a new catchment area is divided based on the new AOI area, and its attribute values ​​are initialized by calling the default function type and memory module. If the new AOI area overlaps with the current catchment area, the catchment area is redivided based on the POI data points of the overlapping area and the boundary line of the AOI area, and the attribute data is recalculated using the steps in step S203.

[0182] When there are AOI area boundary adjustments in the target area, such as road widening or land parcel merging, the catchment area is redefined and the attribute data is recalculated using the steps in step S203.

[0183] (2) Adding or modifying POI data.

[0184] When a new Point of Interest (POI) is added to the target area, such as a newly built school, commercial site, or industrial facility, it is determined whether the new POI is within the current Area of ​​Interest (AOI). If the new POI is not within the current AOI, a new catchment area is constructed based on the new POI, and the initial template of the corresponding function type is retrieved from the memory module for attribute inference. If the new POI is within the current AOI, it is determined whether the new POI affects the function type of the AOI. If it does, the function type of the current catchment area corresponding to the current AOI is adjusted based on the new POI.

[0185] When there is a change in the attribute of the POI in the target area, such as "school" being updated to "hospital", the attribute data is recalculated using the steps in step S203, and the relevant parameters such as water intensity and wastewater generation coefficient are recalculated.

[0186] (3) Adjustment of pipeline network topology.

[0187] When the pipeline network topology changes, such as adding new pipelines or modifying node positions, the connection between the catchment area and the pipeline network is re-matched using the method in step S204.

[0188] The data update mechanism of this application is triggered by either periodic execution or event-driven methods.

[0189] In one implementation, the system periodically checks for the addition or modification of AOI areas, POI data, and adjustments to the pipeline topology at preset time points. For example, automatic synchronization can be performed at midnight daily.

[0190] In one implementation, the database is automatically triggered when it receives AOI area data, or POI data is updated, or when the pipeline topology is adjusted.

[0191] In practical applications, to further enhance the intelligence and dynamic adaptability of catchment area delineation, this embodiment provides a drainage network catchment area delineation system based on geographic information unit data. Figure 5 This is a structural diagram of a drainage network catchment area division system based on geographic information unit data according to an embodiment of the present invention. It introduces two major subsystems: a "memory module" and a "feedback mechanism". In the processes of data updating, attribute inference, and parameter estimation, an experience backtracking and correction closed loop is formed to ensure that the data and model remain efficient and accurate in the ever-changing urban environment.

[0192] The memory module records experiential data on attribute annotations and also has learning and improvement functions, enabling the system to continuously optimize based on historical operations and data corrections.

[0193] The memory module includes a historical attribute recording unit, a manual correction recording unit, and an experience inference and automatic initialization unit.

[0194] In the historical attribute recording unit, the system records typical attribute annotation results corresponding to each functional type, including key parameters such as building density, impermeability, population density, greening rate, and water consumption per unit area. When processing similar spatial units, the system will prioritize using these historical attribute data as initial reference values ​​to reduce reliance on new data. If attribute data is missing, it will be supplemented using historical attribute data.

[0195] In the manual correction recording unit, the system records all manually modified attribute fields and saves the context of the modification and the before-and-after comparison values. When processing regions with similar spatial structures, the system will prioritize using the modified discrimination logic to reduce redundant corrections caused by similar errors.

[0196] In the experience-based inference and automatic initialization unit, the system can provide initialization recommendations for the functional type and attribute values ​​of newly added AOI or POI regions based on the experience accumulated in the memory module. For example, when the system discovers a new POI, it can quickly infer the functional type and related attributes of the region by referring to data of similar POIs through the memory module.

[0197] The feedback mechanism continuously improves the accuracy of attribute labeling and promotes the system's self-iteration and optimization by forming a closed loop of "result correction - error analysis - rule evolution".

[0198] The feedback mechanism includes a manual correction learning unit, a high-risk area marking and verification unit, and a dynamic write-back and automatic recalculation unit.

[0199] In the manual correction learning unit, the system monitors user modifications to attribute fields (such as function type, water usage parameters, and greening rate) in real time, recording the values ​​before and after the modification, time, user ID, and spatial location, forming a correction log. Statistical analysis of the correction data can extract relevant patterns, and machine learning methods (such as decision trees and random forests) can be used to gradually optimize models for function type discrimination and attributes. When attribute deviations for a certain function type occur frequently, the system automatically adjusts relevant parameters or models to improve the processing accuracy for similar areas in the future.

[0200] In the high-risk area marking and verification unit, when the system detects that a catchment area has undergone multiple manual modifications or has a low model confidence value, that area will be marked as a "high-risk area," and the user will be prompted to perform manual verification. High-risk areas will be prioritized and may be added to the subsequent model optimization sample pool to improve future annotation accuracy.

[0201] In the dynamic write-back and automatic recalculation unit, when the system detects a manually confirmed field update (such as population density, impermeability, etc.), it will automatically call the relevant calculation module to recalculate the results and ensure that all updated data is reflected in the final output layer in real time.

[0202] The drainage network catchment area delineation method based on geographic information unit (GIS) data provided in this embodiment combines POI data, AOI regional data, and other geographic information data, comprehensively considering factors such as urban functional zoning and land use types. This makes the delineation results more consistent with urban drainage characteristics and improves model accuracy. Simultaneously, this application achieves automatic catchment area delineation, reducing manual intervention and significantly improving modeling efficiency. By combining relevant data, this application strengthens the attribute labeling of catchment areas, providing richer data support for drainage system analysis and improving simulation accuracy. Furthermore, this application achieves automatic catchment area updating, reducing manual adjustments and improving the model's timeliness and flexibility.

[0203] In a specific example, taking a small square area of ​​a city as an example, the above method is used to divide the catchment area. Ultimately, a total of 18 catchment areas are divided in this area. Specifically, the method includes the following steps:

[0204] Step S1, Data Preparation.

[0205] To obtain the drainage network data for a small square area in a certain city, please refer to Table 8, which is a specific drainage network data table.

[0206] Table 8. Data table for a specific drainage pipe network

[0207] Drainage Pipe Network Node (Junction) Longitude Latitude J121 115.9994 29.69741 J122 116.0044 29.69806 J123 116.0026 29.69994 J124 116.0036 29.70068 J125 116.0061 29.70252 J127 116.0136 29.70015 J129 116.0101 29.69839 J130 116.0122 29.69907 J131 116.0107 29.6978 J133 116.0073 29.69514 J137 116.0041 29.6925 J141 116.0095 29.70503 J143 116.005 29.69746

[0208] Geographic information unit (GIS) data for a small, square area in a certain city was obtained. Specifically, nine GIS units were collected, including seven residential areas, one office area, and one school. The seven residential areas and one office area have both Point of Interest (POI) and Area of ​​Interest (AOI) data, while the school only has POI data. Please refer to Table 9, which is a table of specific GIS unit data.

[0209] Table 9 Data table for a specific geographic information unit

[0210]

[0211]

[0212] Step S2, water catchment area delineation.

[0213] Based on the aforementioned geographic information units, nine preliminary catchment areas were drawn in this area, numbered S1–S9. Among them, catchment area S9 is a separate catchment area generated based on the school's POI.

[0214] Step S3: Data preparation and attribute labeling.

[0215] For each catchment area, the relevant elements are labeled using the formulas described above, for example, using formula R. imp =1-R green -R perm The impermeability of the catchment area S1 is calculated to be 50%.

[0216] Similarly, other elements can be added as needed to support subsequent drainage network modeling and hydrological analysis.

[0217] Step S4: The drainage network is linked to the catchment area.

[0218] Based on the drainage network data obtained in step S1, it is associated with the designated catchment areas. Since there are multiple drainage network nodes in catchment areas S1 and S2, the area is further subdivided, and the blank area is designated as catchment area S10, ultimately forming 18 catchment areas.

[0219] For example, please refer to Figure 6 , Figure 6 This is a schematic diagram of a catchment area division according to an embodiment of the present invention. Figure 6 As shown, residential building 1 is divided into water catchment areas S102-S107, residential building 2 is divided into water catchment areas S201-S204, residential buildings 2-7 are divided into water catchment areas S3-S7 respectively, office building 1 is divided into water catchment area S8, and primary school 1 is divided into water catchment area S9.

[0220] Specifically, please refer to Table 10, which is a table showing the correspondence between drainage network nodes and catchment areas.

[0221] Table 10 Correspondence between Drainage Pipeline Network Nodes and Catchment Areas

[0222]

[0223]

[0224] Step S5: Region merging and automatic update.

[0225] Even after linking the drainage network to the catchment area, some uncovered areas (mainly roads) were still found. These areas were addressed by merging them into adjacent catchment areas to ensure complete overall coverage.

[0226] This embodiment also provides a drainage network catchment area delineation device based on geographic information unit data. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0227] This embodiment provides a drainage network catchment area division device based on geographic information unit data. Figure 7 This is a structural block diagram of a drainage network catchment area division device based on geographic information unit data according to an embodiment of the present invention, as shown below. Figure 7 As shown, the device includes:

[0228] The acquisition module 701 is used to acquire drainage network data, geographic information unit data and auxiliary data of the target area. The geographic information unit data includes points of interest and regions of interest of the geographic information system, and the auxiliary data includes population data and land data of the target area.

[0229] The partitioning module 702 is used to divide the target area into multiple catchment areas based on the points of interest and the boundary lines of the regions of interest.

[0230] The annotation module 703 is used to determine the attribute data of each catchment area based on the geographic information unit data and auxiliary data corresponding to the catchment area, and to annotate the attribute data within the catchment area. The attribute data is used to characterize the building information, population information, water volume information, land information and functional information of the catchment area.

[0231] The association module 704 is used to associate drainage network nodes with catchment areas based on drainage network data to generate drainage network catchment areas.

[0232] In some alternative implementations, the partitioning module 702 includes:

[0233] The matching unit is used to spatially match points of interest with regions of interest to obtain the points of interest corresponding to each region of interest.

[0234] The determination unit is a functional type used to determine the region of interest based on points of interest.

[0235] The division unit is used to divide the region of interest into multiple catchment areas according to the function type when there are at least two different function types in the region of interest; otherwise, the region of interest is treated as a single catchment area.

[0236] In some alternative implementations, the annotation module 703 includes:

[0237] The extraction unit is used to extract the first attribute data of the catchment area from the geographic information unit data and auxiliary data corresponding to the catchment area.

[0238] The analysis unit is used to process and analyze the first attribute data to determine the second attribute data of the catchment area.

[0239] In some optional implementations, the extraction unit includes:

[0240] The first extraction subunit is used to generate a corresponding catchment area ID for each catchment area.

[0241] The second extraction subunit is used to extract the points of interest, regions of interest, and auxiliary data corresponding to the catchment area to generate the function type.

[0242] The third extraction subunit is used to extract and calculate the area of ​​interest corresponding to the catchment area, and generate the greening rate, catchment area area, and total building area.

[0243] The fourth extraction subunit is used to extract the auxiliary data corresponding to the catchment area and generate the total population.

[0244] In some optional implementations, the analysis unit includes:

[0245] The first analysis sub-unit is used to calculate the impermeability of the catchment area based on the greening rate.

[0246] The second analysis sub-unit is used to calculate the building density of the catchment area based on the catchment area and the total building area.

[0247] The third analysis sub-unit is used to calculate the population density of the catchment area based on the total population and total building area.

[0248] The fourth analysis sub-unit is used to calculate water consumption and wastewater discharge based on population density or building density and catchment area.

[0249] In some alternative implementations, the association module 704 includes:

[0250] The judgment unit is used to determine the number of drainage network nodes contained in the catchment area based on drainage network data.

[0251] The first generation unit is used to associate the drainage network node with the drainage network if the number of drainage network nodes contained in the catchment area is 1, and generate the drainage network catchment area.

[0252] The second generation unit is used to select the drainage network node closest to the drainage network from the drainage network data based on the interest point of the drainage network if the number of drainage network nodes contained in the catchment area is 0, and associate the drainage network node with the drainage network to generate the drainage network catchment area.

[0253] The third generation unit is used to divide the catchment area into multiple drainage network catchment areas based on the coordinates of the drainage network nodes if the number of drainage network nodes in the catchment area is greater than 1. Each drainage network catchment area corresponds to a drainage network node.

[0254] In some alternative implementations, the partitioning module 702 further includes:

[0255] The first determining unit is used to determine the blank area based on the target area and multiple catchment areas, and to divide the catchment areas based on the blank area.

[0256] The second determining unit is used to determine the functional type of the catchment area in the blank area based on the road network data and land data of the target area.

[0257] The third step is to determine the target catchment area based on its functional type. The target catchment area and the catchment area of ​​the blank area have the same functional type.

[0258] Annotation module 703 also includes:

[0259] The annotation unit is used to determine the attribute data of the watershed in the blank area based on the attribute data of the target watershed, and to annotate the attribute data in the watershed of the blank area.

[0260] In some alternative embodiments, the device further includes an update module, which includes:

[0261] The region of interest update unit is used to determine whether the newly added region of interest overlaps with the current catchment area when a new region of interest exists in the target area. If the newly added region of interest does not overlap with the current catchment area, a new catchment area is divided based on the newly added region of interest. If the newly added region of interest overlaps with the current catchment area, the catchment area is redivided based on the interest points and the boundary lines of the overlapping areas.

[0262] The Point of Interest (POI) update unit is used to determine whether a new POI is within the current POI region when a new POI is added to the target region. If the new POI is not within the current POI region, a new catchment area is constructed based on the new POI. If the new POI is within the current POI region, the function type of the current catchment area corresponding to the current POI region is adjusted based on the new POI.

[0263] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0264] In this embodiment, the drainage network catchment area division device based on geographic information unit data is presented in the form of functional units. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0265] This invention also provides a computer device having the above-described features. Figure 7 The device shown is a drainage network catchment area division device based on geographic information unit data.

[0266] Please see Figure 8 , Figure 8 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 8 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 8 Take a processor 10 as an example.

[0267] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0268] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0269] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0270] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0271] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 8 Taking the example of a connection between China and Israel via a bus.

[0272] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0273] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0274] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0275] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for delineating drainage network catchment areas based on geographic information unit data, characterized in that, The method includes: The system acquires drainage network data, geographic information unit data, and auxiliary data for the target area. The geographic information unit data includes points of interest and regions of interest from a geographic information system, and the auxiliary data includes population data and land data for the target area. The target area is divided into multiple catchment areas based on the points of interest and the boundary lines of the regions of interest; For each catchment area, attribute data of the catchment area is determined based on the geographic information unit data and the auxiliary data corresponding to the catchment area, and the attribute data is marked in the catchment area. The attribute data is used to characterize the building information, population information, water volume information, land information and functional information of the catchment area. Based on the drainage network data, the drainage network nodes are associated with the catchment area to generate the drainage network catchment area.

2. The drainage network catchment area division method based on geographic information unit data according to claim 1, characterized in that, The target region is divided into multiple catchment areas based on the boundary lines of the points of interest and the regions of interest, including: Spatial matching is performed between the points of interest and the regions of interest to obtain the points of interest corresponding to each region of interest. The functional type of the interest region is determined based on the points of interest. When there are at least two different functional types in the region of interest, the region of interest is divided into multiple catchment areas according to the functional types; otherwise, the region of interest is treated as a single catchment area.

3. The method for dividing drainage network catchment areas based on geographic information unit data according to claim 1, characterized in that, For each catchment area, determining the attribute data of the catchment area based on the corresponding geographic information unit data and the auxiliary data includes: Extract the first attribute data of the catchment area from the geographic information unit data and the auxiliary data corresponding to the catchment area; The first attribute data is processed and analyzed to determine the second attribute data of the catchment area.

4. The method for dividing drainage network catchment areas based on geographic information unit data according to claim 3, characterized in that, The step of extracting the first attribute data of the catchment area from the geographic information unit data and the auxiliary data corresponding to the catchment area includes: Generate a corresponding catchment area ID for each of the aforementioned catchment areas; Extract the points of interest, regions of interest, and auxiliary data corresponding to the catchment area to generate a function type; Extract and calculate the region of interest corresponding to the catchment area to generate the greening rate, catchment area, and total building area; The auxiliary data corresponding to the catchment area is extracted to generate the total population.

5. The drainage network catchment area division method based on geographic information unit data according to claim 4, characterized in that, The step of processing and analyzing the first attribute data to determine the second attribute data of the catchment area includes: Calculate the impermeability of the catchment area based on the greening rate; Calculate the building density of the catchment area based on the catchment area and the total building area; Calculate the population density of the catchment area based on the total population and the total building area; Water consumption and wastewater discharge are calculated based on the population density or building density and the catchment area.

6. The method for dividing drainage network catchment areas based on geographic information unit data according to claim 1, characterized in that, The step of associating drainage network nodes with the catchment areas based on the drainage network data to generate drainage network catchment areas includes: The number of drainage network nodes contained in the catchment area is determined based on the drainage network data. If the catchment area contains only one drainage network node, then the drainage network node is associated with the catchment area to generate the drainage network catchment area. If the number of drainage network nodes contained in the catchment area is 0, then based on the point of interest of the catchment area, the drainage network node closest to the catchment area is selected from the drainage network data, and the drainage network node is associated with the catchment area to generate the drainage network catchment area. If the catchment area contains more than one drainage network node, the catchment area is divided into multiple drainage network catchment areas based on the coordinates of the drainage network node, and each drainage network catchment area corresponds to one drainage network node.

7. The method for dividing drainage network catchment areas based on geographic information unit data according to claim 4, characterized in that, The auxiliary data includes road network data of the target area. The step of associating drainage network nodes with the catchment area based on the drainage network data to generate a drainage network catchment area also includes: Based on the target area and the multiple catchment areas, a blank area is determined, and the catchment areas are divided based on the blank area; The functional type of the catchment area in the blank area is determined based on the road network data and land data of the target area. The target catchment area is determined based on the functional type, and the functional type of the target catchment area and the catchment area of ​​the blank area are the same. Based on the attribute data of the target catchment area, the attribute data of the catchment area of ​​the blank area is determined, and the attribute data is marked within the catchment area of ​​the blank area.

8. The method for dividing drainage network catchment areas based on geographic information unit data according to claim 1, characterized in that, The method further includes: When a new region of interest is added to the target area, it is determined whether the new region of interest overlaps with the current catchment area; If the newly added region of interest does not overlap with the current catchment area, then a new catchment area is defined based on the newly added region of interest; If the newly added region of interest overlaps with the current catchment area, the catchment area is redefined based on the points of interest in the overlapping region and the boundary line of the region of interest.

9. The method for dividing drainage network catchment areas based on geographic information unit data according to claim 1, characterized in that, The method further includes: When a new point of interest is added to the target area, it is determined whether the new point of interest is within the current area of ​​interest. If the newly added point of interest is not within the current area of ​​interest, a new catchment area is constructed based on the newly added point of interest; If the newly added point of interest is located within the current area of ​​interest, then the function type of the current catchment area corresponding to the current area of ​​interest is adjusted based on the newly added point of interest.

10. A drainage network catchment area delineation device based on geographic information unit data, characterized in that, The device includes: The acquisition module is used to acquire drainage network data, geographic information unit data, and auxiliary data of the target area. The geographic information unit data includes points of interest and regions of interest of the geographic information system, and the auxiliary data includes population data and land data of the target area. A segmentation module is used to divide the target region into multiple catchment areas based on the points of interest and the boundary lines of the regions of interest; The annotation module is used to determine the attribute data of each catchment area based on the geographic information unit data and the auxiliary data corresponding to the catchment area, and to annotate the attribute data within the catchment area. The attribute data is used to characterize the building information, population information, water quantity information, land information and functional information of the catchment area. The association module is used to associate drainage network nodes with the catchment area based on the drainage network data, thereby generating the drainage network catchment area.

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