Urban greening planning method fusing sponge city technology

Abstract

This invention relates to the technical field of urban greening planning and discloses an urban greening planning method integrating sponge city technology. The method includes: constructing a three-dimensional probability distribution model of underground pipelines corresponding to two-dimensional planning units within the urban planning area, generating an underground pipeline corridor model; constructing a root growth volume model of candidate tree planting units, generating a conflict index describing planting conflicts between the candidate tree planting units and the underground pipeline corridor model; screening candidate tree planting units according to vegetation selection rules to form a set of plantable space areas, and comprehensively optimizing them using sponge city technology to form an urban greening planning scheme. This invention, by constructing a complete data flow closed loop from underground pipeline probability modeling and planting conflict calculation to sponge city efficiency optimization, achieves unified quantification and collaborative decision-making regarding underground space safety, vegetation growth space, and urban hydrological regulation capacity, thereby improving the systematicness and computability of urban greening planning.

Description

Technical Field

[0001] This invention relates to the field of urban greening planning using big data processing, and more particularly to an urban greening planning method that integrates sponge city technology. Background Technology

[0002] With the increasing intensity of urban underground space development and the promotion of the sponge city concept, urban greening planning is no longer limited to landscape optimization but needs to simultaneously meet multiple objectives such as underground pipeline safety, surface ecological restoration, and stormwater regulation. However, existing greening planning methods lack consideration for the spatial distribution of underground pipelines, especially in high-density built-up areas where underground pipelines are often difficult to accurately locate. In these areas, tree planting often intertwines with underground pipeline corridors, and without systematic analysis, this can easily lead to problems such as root damage to pipelines, obstruction of drainage systems, or insufficient green space regulation capacity. Therefore, how to achieve synergistic optimization of greening layout and sponge city functions under the constraints of underground pipeline space has become an important technical issue in refined urban planning.

[0003] Existing technologies have attempted to optimize urban greening schemes from the perspective of ecological health or environmental risk. For example, patent CN115935855B proposes an urban greening method and device based on optimizing tree pollen concentration indicators. This scheme sets a comprehensive risk threshold for tree pollen, constructs an urban meteorological characteristic spectrum, establishes a computational fluid dynamics model library and a pollen concentration distribution simulation scenario library, predicts the comprehensive pollen risk value at spatial points at pedestrian height, and iteratively adjusts the greening scheme to ensure that the predicted risk value is below the threshold, thereby optimizing the urban living environment. This technology achieves a quantitative assessment of the impact of greening layout on air quality and has strong significance for environmental risk control. However, it mainly focuses on simulating the surface airflow field and pollen diffusion risk, and cannot solve the problem of coordinated planning between greening planting and underground pipeline space.

[0004] To address this issue, this invention proposes an urban greening planning method that integrates sponge city technology. It establishes a complete process link from underground space modeling and conflict risk identification to sponge efficiency optimization, realizing coordinated planning for the protection of underground pipeline facilities and the enhancement of ecological functions, thereby improving the scientific nature, computability, and reliability of urban greening planning and engineering implementation. Summary of the Invention

[0005] This invention proposes an urban greening planning method integrating sponge city technology. Traditional urban greening planning typically relies on existing underground pipeline maps or empirical buffer distances for avoidance, which is difficult to address issues such as missing data, delayed updates, or spatial discontinuities. Steps S1-S2 construct a three-dimensional probability distribution model of underground pipelines at the scale of a two-dimensional planning unit, and further perform connected domain analysis and spatial aggregation to generate an underground pipeline corridor model. This transforms the spatial representation of underground pipelines from a static linear expression to a continuous probability field expression, solving the problem of difficulty in quantifying and modeling the spatial distribution of underground pipelines and improving the spatial accuracy of underground pipeline identification. Step S3 constructs a root... The system uses a growth volume model and combines it with the spatial distribution characteristics of the underground pipeline corridor model to calculate the three-dimensional spatial overlapping volume and directional coupling relationship, forming a conflict index to achieve a three-dimensional quantitative analysis of planting risks. This breaks through the limitations of two-dimensional buffer zone avoidance and improves the scientific nature and engineering reliability of planting safety assessment. Step S4, after forming a set of plantable space areas, introduces sponge city technology parameters to construct sponge efficiency indicators. It then performs secondary screening and comprehensive optimization of the set of plantable space areas to achieve a synergistic improvement between greening layout and surface infiltration capacity and stormwater storage capacity. This solves the problem that greening planning only focuses on landscape or safety while ignoring hydrological functions.

[0006] To achieve the above objectives, this invention provides an urban greening planning method integrating sponge city technology, comprising the following steps: S1: Collect underground pipeline management data and road information data in the urban planning area, divide the urban planning area into multiple two-dimensional planning units of equal size, and calculate the three-dimensional probability distribution model of underground pipelines corresponding to the two-dimensional planning unit based on the underground pipeline management data and road information data. S2: Based on the three-dimensional probability distribution model of underground pipelines corresponding to the two-dimensional planning unit, perform connectivity analysis and spatial aggregation processing on the two-dimensional planning unit in the urban planning area to generate an underground pipeline corridor model of the urban planning area. S3: Based on the urban green space planning text indicators and land use control conditions, divide multiple candidate tree planting units in the urban planning area, construct a root growth volume model of the candidate tree planting units, calculate the spatial distribution characteristics of the underground pipeline corridor model, and generate a conflict index describing the planting conflict between the candidate tree planting units and the underground pipeline corridor model based on the spatial distribution characteristics and the root growth volume model. S4: Based on the conflict index of the candidate tree planting units, candidate tree planting units are selected according to the vegetation screening rules to form a set of plantable space areas. The set of plantable space areas is then comprehensively optimized in conjunction with sponge city technology, and urban greening is carried out based on the comprehensively optimized set of plantable space areas.

[0007] As a further improvement of the present invention: Further, in step S1, underground pipeline management data and road information data of the urban planning area are collected, and the urban planning area is divided into multiple equal-sized two-dimensional planning units, including: The underground pipeline management data includes the location of manhole covers and the pipeline routing vector between any two manhole covers; the road information data includes the center location of all roads within the urban planning area and the coordinates of the two ends of the roads. Two-dimensional planning units with fixed side lengths are generated using the center position of the road as the seed point, thereby dividing the urban planning area into multiple two-dimensional planning units of equal size.

[0008] Furthermore, the calculation of the three-dimensional probability distribution model of underground pipelines corresponding to the two-dimensional planning unit in step S1 also includes: S11: Based on the underground pipeline management data and road information data of the urban planning area, calculate the density of manhole cover locations and slope angles of all manhole covers in the two-dimensional planning unit, the average road direction angle of all roads, and the average pipeline direction vector of underground pipelines between any two manhole covers. S12: Based on the manhole cover density and the average road direction angle of the two-dimensional planning unit, construct a coupling strength feature that characterizes the consistency between manhole cover density and road direction. S13: Convert the mean value of the pipeline direction vector into the vertical extension ratio of the vertical component and the horizontal component of the underground pipeline, and calculate the correlation characteristics between the underground pipeline direction and the terrain slope of the two-dimensional planning unit based on the vertical extension ratio, the mean value of the road direction angle and the slope angle. S14: Weight the coupling strength feature and correlation feature, and use the weighting result as the underground pipeline spatial semantic feature of the two-dimensional planning unit. Divide the underground area of ​​the two-dimensional planning unit into several voxel units of the same size, obtain the vertical distance from the center point of the voxel unit to the ground surface, and calculate the probability of the existence of underground pipelines in the voxel unit based on the underground pipeline spatial semantic feature and vertical distance of the two-dimensional planning unit. Specifically, the formula for calculating the probability of the existence of underground pipelines in the voxel unit is as follows: ; in, This represents the probability of the existence of underground pipelines in voxel element s in the nth two-dimensional programming unit. This represents the set of voxel units of the nth two-dimensional programming unit. This represents the vertical distance from the center point of voxel unit s to the Earth's surface. This represents an exponential function with the natural constant as its base. The spatial semantic features of underground pipelines in the nth two-dimensional planning unit are obtained by weighted calculation of the coupling strength features and correlation features of the nth two-dimensional planning unit. , Represents the spatial semantic influence coefficient. Indicates the influence coefficient of depth attenuation; S15: The center point coordinates of all voxel units in the two-dimensional planning unit and the probability of the existence of underground pipelines are used as the three-dimensional probability distribution model of underground pipelines corresponding to the two-dimensional planning unit.

[0009] Furthermore, step S2 involves performing connectivity analysis and spatial aggregation processing on the two-dimensional planning units within the urban planning area, including: S21: Based on the three-dimensional probability distribution model of underground pipelines corresponding to the two-dimensional planning unit, a global probability threshold is set, and voxel units with a probability of existence of underground pipelines higher than the global probability threshold are regarded as high-probability voxel units. S22: Calculate the distance between the center point coordinates of high-probability voxel units, merge high-probability voxel units with a distance less than a preset distance threshold into the same three-dimensional connected body, and obtain multiple three-dimensional connected bodies; S23: Calculate the connection direction and connection distance between any two three-dimensional connected bodies. If the connection direction and connection distance between the two three-dimensional connected bodies both satisfy the preset connection rules, then aggregate the two three-dimensional connected bodies to obtain the underground pipeline corridor model of the urban planning area.

[0010] Furthermore, the underground pipeline corridor model is composed of multiple aggregated three-dimensional connected bodies.

[0011] Further, step S3 involves constructing a root growth volume model for candidate tree planting units and calculating the spatial distribution characteristics of the underground pipeline corridor model, including: S31: Obtain the two-dimensional center coordinates of the candidate tree planting unit, and construct the horizontal expansion radius of the tree root system at the root growth depth d: ; in, This represents the horizontal radius of the tree's root system at a root growth depth d. , This indicates the minimum root growth depth of the trees planted in the candidate tree planting unit. This indicates the maximum root growth depth of the trees planted in the candidate tree planting unit. This indicates the maximum horizontal radius of a tree's root system on the earth's surface. Indicates the root morphology adjustment coefficient; S32: Generate the root system generation volume model of the candidate tree planting unit based on the surface expansion radius and the two-dimensional center coordinates of the candidate tree planting unit; S33: Extract the three-dimensional connected body from the underground pipeline corridor model and calculate the connectivity features of the three-dimensional connected body, wherein the connectivity features include the center position coordinates, average depth and extension direction of the three-dimensional connected body; S34: Segment the connectivity features of all three-dimensional connected bodies to form the spatial distribution features of the underground pipeline corridor model.

[0012] Furthermore, step S3, which generates a conflict index describing the planting conflict between the candidate tree planting units and the underground pipeline corridor model, also includes: S35: Based on the underground pipeline corridor model and the root system generation volume model of the candidate tree planting unit, calculate the three-dimensional spatial overlap volume between the root system generation volume model of the candidate tree planting unit and the three-dimensional connected body in the underground pipeline corridor model. S36: Calculate the main extension direction of the root system generation volume model, and based on the spatial distribution characteristics of the underground pipeline corridor model, couple and modulate to obtain the conflict index of planting conflict between the candidate tree planting units and the underground pipeline corridor model. The formula for calculating the conflict index is: ; ; in, This represents the conflict index between the m-th candidate tree planting unit and the underground pipeline corridor model, indicating the planting conflict. This indicates the number of candidate tree planting units obtained through screening. Let B represent the conflict coefficient between the m-th candidate tree planting unit and the b-th 3D connected element in the underground pipeline corridor model, where B represents the number of 3D connected elements in the underground pipeline corridor model. Indicates the selection of a set The maximum value in, This represents the three-dimensional spatial overlap volume between the root system generation volume model of the m-th candidate tree planting unit and the b-th three-dimensional connected volume in the underground pipeline corridor model. This represents the volume of the root system generation volume model for the m-th candidate tree planting unit. This indicates the main extension direction of the root system generation volume model for the m-th candidate tree planting unit. This indicates the extension direction of the b-th three-dimensional connected element in the underground pipeline corridor model. Represents the L2 norm. This represents the average depth of the b-th three-dimensional connected volume in the underground pipeline corridor model. Let represent the Euclidean distance between the two-dimensional center coordinates of the m-th candidate tree planting unit and the center coordinates of the b-th three-dimensional connected element. Represents the distance scale parameter. All represent conflict weighting coefficients, and the conflict index The larger the value, the higher the risk of planting conflict when planting trees at the candidate tree planting unit.

[0013] Furthermore, in step S4, the set of plantable space areas is comprehensively optimized in conjunction with sponge city technology, including: S41: Based on sponge city technology, obtain the sponge performance parameters of the two-dimensional planning unit where any candidate tree planting unit in the set of plantable space areas is located, and calculate the sponge efficiency index of the two-dimensional planning unit where the candidate tree planting unit is located based on the sponge performance parameters. S42: Based on the sponge efficiency index of the two-dimensional planning unit where the candidate tree planting unit is located, a second screening is performed on the candidate tree planting units in the set of plantable space areas to obtain a set of plantable space areas after comprehensive optimization that maximizes sponge efficiency.

[0014] Compared with existing technologies, this invention proposes an urban greening planning method that integrates sponge city technology, which has the following beneficial effects: First, this invention constructs spatial semantic features of underground pipelines by structurally fusing multi-source spatial information such as manhole cover density, road direction angle, slope angle, and underground pipeline orientation vector. It further introduces voxel modeling and a logistic function probability mapping mechanism to achieve a continuous expression of the probability of underground pipeline existence. Compared to traditional methods based on empirical inference or simple buffer analysis, this invention can quantify the consistency strength between manhole cover distribution and road orientation at the two-dimensional planning unit scale. Simultaneously, it characterizes the three-dimensional orientation features of underground pipelines through the vertical extension ratio and establishes a direction correlation model based on terrain slope aspect to calculate correlation features, giving the inference of underground pipeline spatial distribution clear physical meaning and spatial constraint logic. Furthermore, by adjusting the probability of underground pipeline existence in voxel units at different depths through vertical distance, it achieves a mapping transformation from planar statistical features to a three-dimensional spatial probability field, thereby forming a continuously computable three-dimensional probability distribution model of underground pipelines. This improves the accuracy and stability of underground pipeline spatial prediction and provides a reliable data foundation for subsequent conflict analysis and underground space optimization planning.

[0015] Meanwhile, this invention, starting from a three-dimensional spatial scale, comprehensively considers the root system volume ratio, consistency of extension direction, underground pipeline burial depth, and spatial distance attenuation effect to establish a multi-factor coupled conflict index model. Among these factors, the volume overlap term... Characterizing the degree of encroachment in the direct physics model, directional coupling terms The depth and distance index modulation mechanism reflects the potential invasion probability of root growth trends and pipeline orientation. This reflects the spatial attenuation effect of underground structure distribution on risk intensity. Furthermore, by taking the maximum conflict coefficient as the conflict index output, this invention can effectively identify the constraint relationship of the most unfavorable underground interconnection on planting safety, and realize the engineering conservative control of risk assessment. This method integrates geometric relationships with spatial semantic features, improves the accuracy and stability of underground space conflict identification, and provides a calculable and interpretable decision-making basis for urban greening planning and underground pipeline protection. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating an urban greening planning method that integrates sponge city technology, as provided in an embodiment of the present invention.

[0017] Figure 2 This is an example diagram showing the overlap between the root system generation volume model of a tree and the volume model of an underground pipeline corridor, provided in an embodiment of the present invention. Detailed Implementation

[0018] The realization of the objectives, functional characteristics, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This invention provides an urban greening planning method integrating sponge city technology. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices configured to execute the method provided in this invention: a server, a terminal, etc. In other words, the urban greening planning method integrating sponge city technology can be executed by software or hardware installed on a terminal device or a server device, wherein the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0020] Reference Figure 1 as well as Figure 2 Embodiment 1 of the present invention is as follows: A method for urban greening planning that integrates sponge city technology, the method comprising: S1: Collect underground pipeline management data and road information data of the urban planning area, divide the urban planning area into multiple two-dimensional planning units of equal size, and calculate the three-dimensional probability distribution model of underground pipelines corresponding to the two-dimensional planning unit based on the underground pipeline management data and road information data.

[0021] Specifically, step S1 involves collecting underground pipeline management data and road information data for the urban planning area, and dividing the urban planning area into multiple equal-sized two-dimensional planning units, including: The underground pipeline management data includes the location of manhole covers and the pipeline routing vector between any two manhole covers; the road information data includes the center location of all roads within the urban planning area and the coordinates of the two ends of the roads. Specifically, the underground pipeline management data and road information data of the urban planning area are derived from the urban basic geographic information system or the surveying and mapping results of the natural resources department. The method for collecting and calculating the pipeline routing vector between any two manhole covers is as follows: ; in, This represents the pipeline routing vector. This represents the Euclidean distance between any two manhole covers. The three-dimensional coordinate differences of the two ends of the underground pipeline between the two manhole covers on the X-axis, Y-axis, and Z-axis are represented respectively. Optionally, when calculating the coordinate difference between the endpoints of underground pipelines corresponding to manhole covers or the coordinate difference between the two ends of a road, a coordinate difference rule based on orientation priority is adopted. First, the east-west direction is used as the primary discrimination axis to determine the relative positional relationship of the two endpoints in the plane coordinate system. When one endpoint is located east of the other endpoint and the other endpoint is located west, the eastern endpoint and the western endpoint are used as corresponding endpoints, and the three-dimensional coordinate difference between the endpoint in the east direction and the endpoint in the west direction is calculated. If the absolute value of the coordinate difference between the two endpoints in the direction of the primary discrimination axis is less than a preset threshold (e.g., 5 meters), they are considered to be in the same east-west horizontal zone. Then, the north-south direction is used as the secondary discrimination axis, and the southern endpoint and the northern endpoint are determined according to the relative positional relationship of the secondary discrimination axis direction. The three-dimensional coordinate difference between the endpoint in the south direction and the endpoint in the north direction is calculated. Through the above orientation-priority difference calculation method, the consistency and uniqueness of the difference direction determination are ensured, and the instability of vector expression caused by directional ambiguity is avoided.

[0022] Two-dimensional planning units with fixed side lengths are generated using the center position of the road as the seed point, thereby dividing the urban planning area into multiple two-dimensional planning units of equal size.

[0023] Step S1, which calculates the three-dimensional probability distribution model of underground pipelines corresponding to the two-dimensional planning unit, also includes: S11: Based on the underground pipeline management data and road information data of the urban planning area, calculate the density of manhole cover locations and slope angles of all manhole covers in the two-dimensional planning unit, the average road direction angle of all roads, and the average pipeline direction vector of underground pipelines between any two manhole covers. Specifically, the formula for calculating the density of manhole cover locations within the two-dimensional planning unit is as follows: ; in, This represents the density of manhole cover locations within the nth two-dimensional programming unit. This represents the number of manhole covers within the nth two-dimensional programming unit. This represents the area of ​​a two-dimensional programming unit. This represents the unit area, used for normalizing the area of ​​two-dimensional planning units. The unit area is set to 10 square meters. N represents the number of two-dimensional programming units; The formula for calculating the road direction angle of the road is: ; in, Indicates the road direction angle. These represent the coordinates of the two road endpoints on the X-axis. These represent the Y-coordinates of the two road endpoints. It is the arctangent function; The formula for calculating the slope angle of all manhole covers within the two-dimensional planning unit is as follows: ; in, This represents the slope angle of all manhole covers within the nth two-dimensional programming unit. This represents the rate of change of the terrain elevation of all manhole covers within the nth two-dimensional programming unit along the Y-axis. This represents the rate of change of terrain elevation of all manhole covers within the nth two-dimensional programming unit along the X-axis, where terrain elevation is the coordinate of the manhole cover on the Z-axis. The X-axis is set to run from east to west, and the Y-axis is set to run from south to north. It is the arctangent function; S12: Based on the manhole cover density and the average road direction angle of the two-dimensional planning unit, construct a coupling strength feature that characterizes the consistency between manhole cover density and road direction. Specifically, the formula for calculating the coupling strength characteristic is as follows: ; in, This represents the coupling strength characteristic of the nth two-dimensional programming unit. This represents the mean road direction angle of all roads within the nth two-dimensional programming unit; S13: Convert the mean value of the pipeline direction vector into the vertical extension ratio of the vertical component and the horizontal component of the underground pipeline, and calculate the correlation characteristics between the underground pipeline direction and the terrain slope of the two-dimensional planning unit based on the vertical extension ratio, the mean value of the road direction angle and the slope angle. Specifically, the formula for calculating the correlation feature is as follows: ; in, This represents the correlation characteristics of the nth two-dimensional programming unit. This represents the vertical extension ratio of the nth two-dimensional programming unit. These are the scalar values ​​of the pipeline routing vector of the nth two-dimensional planning unit on the X, Y, and Z axes, respectively. S14: Weight the coupling strength feature and correlation feature, and use the weighting result as the underground pipeline spatial semantic feature of the two-dimensional planning unit. Divide the underground area of ​​the two-dimensional planning unit into several voxel units of the same size, obtain the vertical distance from the center point of the voxel unit to the ground surface, and calculate the probability of the existence of underground pipelines in the voxel unit based on the underground pipeline spatial semantic feature and vertical distance of the two-dimensional planning unit. Specifically, the formula for calculating the probability of the existence of underground pipelines in the voxel unit is as follows: ; in, This represents the probability of the existence of underground pipelines in voxel element s in the nth two-dimensional programming unit. This represents the set of voxel units of the nth two-dimensional programming unit. This represents the vertical distance from the center point of voxel unit s to the Earth's surface. This represents an exponential function with the natural constant as its base. The spatial semantic features of underground pipelines in the nth two-dimensional planning unit are obtained by weighted calculation of the coupling strength features and correlation features of the nth two-dimensional planning unit. Based on experience, the weighting weights for the coupling strength feature and the correlation feature were set to 0.6 and 0.4 respectively. Represents the spatial semantic influence coefficient. Indicates the depth attenuation influence coefficient, set Set to 0.2. It is 0.4; S15: The center point coordinates of all voxel units in the two-dimensional planning unit and the probability of the existence of underground pipelines are used as the three-dimensional probability distribution model of underground pipelines corresponding to the two-dimensional planning unit.

[0024] Specifically, the coupling strength feature characterizes the degree of consistency between the spatial distribution of manhole covers and the road direction. When the density of manhole covers is high and the angle with the road direction is small, it indicates that the underground pipelines are highly dependent on the road layout and have a typical roadside laying structure, reflecting the degree of support of the distribution of ground facilities for the existence of underground pipelines. The correlation feature describes the directional matching relationship between the underground pipeline route and the terrain slope. If the underground pipeline route is consistent with the slope, it indicates that the underground pipeline layout conforms to the natural drainage or gravity flow direction, reflecting the degree of adaptation between the underground structure and the natural terrain. The spatial semantic features of underground pipelines are a comprehensive representation of the trend intensity of underground pipelines within a two-dimensional planning unit, reflecting the overall strength of the conditions for forming or laying underground pipelines in the two-dimensional planning unit at the spatial structure level.

[0025] S2: Based on the three-dimensional probability distribution model of underground pipelines corresponding to the two-dimensional planning unit, perform connectivity analysis and spatial aggregation processing on the two-dimensional planning unit in the urban planning area to generate an underground pipeline corridor model of the urban planning area.

[0026] Step S2 involves performing connectivity analysis and spatial aggregation processing on the two-dimensional planning units within the urban planning area, including: S21: Based on the three-dimensional probability distribution model of underground pipelines corresponding to the two-dimensional planning unit, a global probability threshold is set, and voxel units with a probability of existence of underground pipelines higher than the global probability threshold are regarded as high-probability voxel units. Optionally, the global probability threshold can be set to 0.5 or 0.6; S22: Calculate the distance between the center point coordinates of high-probability voxel units, merge high-probability voxel units with a distance less than a preset distance threshold into the same three-dimensional connected body, and obtain multiple three-dimensional connected bodies; Optionally, a preset distance threshold can be set to 3 times the radius of the voxel unit; Optionally, a 26-neighborhood connectivity rule can be used to merge high-probability voxel units into the same three-dimensional connected body; S23: Calculate the connection direction and connection distance between any two three-dimensional connected bodies. If the connection direction and connection distance between the two three-dimensional connected bodies both satisfy the preset connection rules, then aggregate the two three-dimensional connected bodies to obtain the underground pipeline corridor model of the urban planning area.

[0027] Specifically, the calculation process for the connection direction and connection distance between any two three-dimensional connected volumes is as follows: S231: Calculate the mean coordinates of the center points of all high-probability voxel units in the three-dimensional connected body, and use them as the center position coordinates of the three-dimensional connected body. Calculate the Euclidean distance between the center position coordinates of any two three-dimensional connected bodies, and use it as the connection distance between any two three-dimensional connected bodies. S232: Perform three-dimensional discretization sampling on the three-dimensional connected volume to obtain a set of discrete sampled coordinate points, and calculate the geometric center of the set of discrete sampled coordinate points; S233: Construct the three-dimensional covariance matrix of the discrete sampling coordinate point set based on the geometric center of the discrete sampling coordinate point set; Specifically, the three-dimensional covariance matrix of the discrete sampling coordinate point set is constructed in the following form: ; Where C represents the three-dimensional covariance matrix of the discrete sampling coordinate point set, and the three-dimensional covariance matrix C contains... as well as For example, the calculation formula is as follows: ; in, This represents the number of sampling coordinate points in the discrete sampling coordinate point set. Represents the set of discrete sampling coordinate points. The X-coordinate values ​​of each sampling point. Represents the set of discrete sampling coordinate points. The Y-coordinate values ​​of each sampling point. This represents the mean Y-axis coordinate value of all sampled coordinate points in the discrete sampled coordinate point set. This represents the mean of the X-axis coordinates of all sampled coordinate points in the discrete sampled coordinate point set. ; S234: Perform eigenvalue decomposition on the three-dimensional covariance matrix, extract the eigenvector corresponding to the largest eigenvalue, and normalize the extracted eigenvector to obtain a normalized eigenvector. Use the normalized eigenvector as the extension direction of the three-dimensional connected body. S235: Calculate the similarity of the extension directions of any two three-dimensional connected bodies, and use it as the connection direction of the two three-dimensional connected bodies. The closer the connection direction is to 1, the more consistent the extension directions of the two three-dimensional connected bodies are, and they belong to the same underground pipeline corridor direction.

[0028] Specifically, if the connection direction between two three-dimensional connected bodies is higher than the empirical value of 0.6 and the connection distance is lower than the empirical value of 20 meters, it means that the connection direction and connection distance between the two three-dimensional connected bodies both meet the preset connection rules.

[0029] The underground pipeline corridor model is composed of multiple aggregated three-dimensional connected bodies.

[0030] S3: Based on the urban green space planning text indicators and land use control conditions, divide the urban planning area into multiple candidate tree planting units, construct the root growth volume model of the candidate tree planting units, and calculate the spatial distribution characteristics of the underground pipeline corridor model. Based on the spatial distribution characteristics and the root growth volume model, generate a conflict index describing the planting conflict between the candidate tree planting units and the underground pipeline corridor model.

[0031] Specifically, step S3 involves constructing a root growth volume model for candidate tree planting units and calculating the spatial distribution characteristics of the underground pipeline corridor model, including: Specifically, by reading the control indicators such as green space ratio, tree configuration ratio, minimum tree spacing and minimum planting area of ​​a single tree in the urban green space planning text and land use control conditions, and screening out plots that meet the green space nature, the screened plots are trimmed, including removing areas that overlap with building structures and road structures, to obtain the green space plot area of ​​the urban planning area. Furthermore, based on the minimum tree spacing and the minimum planting area of ​​a single tree, the green space area is discretized according to the minimum tree spacing requirement. Candidate points are preferentially arranged along the main axis of the plot, and the distance between adjacent candidate points is not less than the minimum tree spacing. A single-tree control range is constructed with each candidate point as the center, which is consistent with the minimum planting area of ​​a single tree. When the control range is completely located within the green space area, the candidate point and the single-tree control range of the candidate point are determined as a tree candidate planting unit. Each tree candidate planting unit can be planted with one tree.

[0032] S31: Obtain the two-dimensional center coordinates of the candidate tree planting unit, and construct the horizontal expansion radius of the tree root system at the root growth depth d: ; in, This represents the horizontal radius of the tree's root system at a root growth depth d. , This indicates the minimum root growth depth of the trees planted in the candidate tree planting unit. This indicates the maximum root growth depth of the trees planted in the candidate tree planting unit. This indicates the maximum horizontal radius of the tree's root system on the ground surface, and is set as follows: It is 0.5 meters. This represents the root morphology adjustment coefficient, set to... Set to 0.2. =0; S32: Generate the root system generation volume model of the candidate tree planting unit based on the surface expansion radius and the two-dimensional center coordinates of the candidate tree planting unit; Specifically, the root system generation volume model of the candidate tree planting unit is as follows: ; in, This represents the root system generation volume model of the m-th candidate tree planting unit. Let M represent the two-dimensional center coordinates of the m-th candidate tree planting unit, and M represent the number of candidate tree planting units selected. This represents the three-dimensional discrete coordinate point in the root system generation volume model of the m-th candidate tree planting unit; S33: Extract the three-dimensional connected body from the underground pipeline corridor model and calculate the connectivity features of the three-dimensional connected body, wherein the connectivity features include the center position coordinates, average depth and extension direction of the three-dimensional connected body; Specifically, the calculation method for the center position coordinates and extension direction of the three-dimensional connected body is as shown in steps S231 to S235. The average depth of the three-dimensional connected body is calculated as follows: the average coordinate of the center point of all high-probability voxel units in the three-dimensional connected body on the Z-axis is calculated as the average depth of the three-dimensional connected body. S34: Segment the connectivity features of all three-dimensional connected bodies to form the spatial distribution features of the underground pipeline corridor model.

[0033] It should be noted that this invention achieves a quantitative expression of the spatial morphology of tree roots by constructing a three-dimensional volumetric model of root system that decays with depth. At the same time, it extracts the structural features of the three-dimensional interconnected body of underground pipeline corridors and performs spatial distribution modeling, providing a unified three-dimensional geometric analysis basis for subsequent conflict determination between root system and underground pipelines, thereby improving the spatial accuracy and engineering reliability of planting planning.

[0034] Step S3, which generates a conflict index describing the planting conflict between the candidate tree planting units and the underground pipeline corridor model, also includes: S35: Based on the underground pipeline corridor model and the root system generation volume model of the candidate tree planting unit, calculate the three-dimensional spatial overlap volume between the root system generation volume model of the candidate tree planting unit and the three-dimensional connected volume in the underground pipeline corridor model; refer to... Figure 2 The example diagram showing the overlap between the root system generation volume model of the tree and the underground pipeline corridor model illustrates the three-dimensional spatial overlap between the root system generation volume model of the tree candidate planting unit provided by the present invention and the three-dimensional connected body in the underground pipeline corridor model. The three-dimensional spatial overlap volume is calculated by calculating the number of coordinate points in the overlapping area. S36: Calculate the main extension direction of the root system generation volume model, and based on the spatial distribution characteristics of the underground pipeline corridor model, couple and modulate to obtain the conflict index of planting conflict between the candidate tree planting units and the underground pipeline corridor model. The formula for calculating the conflict index is: ; ; in, This represents the conflict index between the m-th candidate tree planting unit and the underground pipeline corridor model, indicating the planting conflict. This indicates the number of candidate tree planting units obtained through screening. Let B represent the conflict coefficient between the m-th candidate tree planting unit and the b-th 3D connected element in the underground pipeline corridor model, where B represents the number of 3D connected elements in the underground pipeline corridor model. Indicates the selection of a set The maximum value in, This represents the three-dimensional spatial overlap volume between the root system generation volume model of the m-th candidate tree planting unit and the b-th three-dimensional connected volume in the underground pipeline corridor model. This represents the volume of the root system generation volume model for the m-th candidate tree planting unit. This indicates the main extension direction of the root system generation volume model for the m-th candidate tree planting unit. This indicates the extension direction of the b-th three-dimensional connected element in the underground pipeline corridor model. Represents the L2 norm.

[0035] This represents the average depth of the b-th three-dimensional connected volume in the underground pipeline corridor model. Let represent the Euclidean distance between the two-dimensional center coordinates of the m-th candidate tree planting unit and the center coordinates of the b-th three-dimensional connected element. Indicates the distance scale parameter, set It is 2. All represent conflict weighting coefficients, which are set based on experience. The conflict indices are 0.6 and 0.4 respectively. The larger the value, the higher the risk of planting conflict when planting trees at the candidate tree planting unit.

[0036] Specifically, the calculation process for the main extension direction of the root system generating volume model is as follows: the root system generating volume model is three-dimensionally discretized and sampled to obtain a set of discrete sampling coordinate points of the root system generating volume model, and the geometric center of the set of discrete sampling coordinate points of the root system generating volume model is calculated to construct the three-dimensional covariance matrix of the root system generating volume model. The three-dimensional covariance matrix of the root system generating volume model is eigenvalue decomposed, and the eigenvector corresponding to the largest eigenvalue after eigenvalue decomposition is extracted. The extracted eigenvector is then normalized to obtain a normalized eigenvector, and the normalized eigenvector is used as the main extension direction of the root system generating volume model.

[0037] S4: Based on the conflict index of the candidate tree planting units, candidate tree planting units are selected according to the vegetation screening rules to form a set of plantable space areas. The set of plantable space areas is then comprehensively optimized in conjunction with sponge city technology, and urban greening is carried out based on the comprehensively optimized set of plantable space areas.

[0038] Specifically, step S4 involves comprehensive optimization of the set of plantable space areas using sponge city technology, including: Specifically, the vegetation screening rules are as follows: a hierarchical screening mechanism is constructed based on the conflict index corresponding to each candidate tree planting unit. The hierarchical screening mechanism includes a primary safety threshold and a secondary control threshold. When the conflict index is less than the primary safety threshold, the candidate tree planting unit is determined to be a priority planting unit and added to the set of plantable spatial areas. When the conflict index is between the primary safety threshold and the secondary control threshold, it is determined to be a conditionally plantable unit. When the conflict index is greater than the secondary control threshold, it is determined to be an unplantable unit and is removed. The primary safety threshold is less than the secondary control threshold. Furthermore, for conditionally plantable units, a secondary determination is made based on the minimum boundary distance between the conditionally plantable unit and the three-dimensional connected body. When the minimum boundary distance meets the minimum safety distance requirement, it is allowed to be included in the set of plantable space regions; otherwise, it is excluded. The minimum safety distance requirement can be set to 5 meters.

[0039] S41: Based on sponge city technology, obtain the sponge performance parameters of the two-dimensional planning unit where any candidate tree planting unit in the set of plantable space areas is located, and calculate the sponge efficiency index of the two-dimensional planning unit where the candidate tree planting unit is located based on the sponge performance parameters. S42: Based on the sponge efficiency index of the two-dimensional planning unit where the candidate tree planting unit is located, a second screening is performed on the candidate tree planting units in the set of plantable space areas to obtain a set of plantable space areas after comprehensive optimization that maximizes sponge efficiency.

[0040] Specifically, the sponge performance parameters include surface permeability coefficient, effective depression volume, and catchment area coefficient. The effective depression volume represents the water storage volume calculated by the topographic elevation difference of the two-dimensional planning unit and the depth of the depression green space that can be formed. The catchment area coefficient represents the proportion of the surrounding runoff area that can be received. Both the surface permeability coefficient and the catchment area coefficient are normalized to between 0 and 1. The formula for calculating the sponge performance index based on the aforementioned sponge performance parameters is as follows: ; Where E represents the sponge city performance index. In order, they are surface permeability coefficient, effective depression volume, and catchment area coefficient. Indicates the preset maximum water storage volume, set. It is 50 cubic meters. All represent the weighting coefficients for sponge performance, set The values ​​are 0.3, 0.4, and 0.3 respectively. Optionally, during the secondary screening of candidate tree planting units in the set of plantable space areas, candidate tree planting units with higher sponge efficiency index are selected and added to the optimized set of plantable space areas. During the secondary screening, the number of candidate tree planting units in the optimized set of plantable space areas is controlled to be no less than the preset minimum number of trees.

[0041] It should be noted that this invention introduces a hierarchical screening mechanism for candidate tree planting units based on underground pipeline conflict control. By setting a primary safety threshold and a secondary control threshold, it achieves tiered management of tree planting risks, avoiding the reduced planting efficiency caused by simple binary elimination. Simultaneously, by verifying the nearest safe distance for plantable units, the engineering reliability of underground pipeline protection is further improved.

[0042] Furthermore, this invention introduces sponge city technology parameters, weighting and integrating surface infiltration capacity, effective depression volume, and catchment area coefficient to construct a sponge efficiency index. Under the premise of meeting minimum tree quantity constraints, it selects candidate tree planting units with high sponge efficiency, achieving synergistic optimization of ecological security and stormwater regulation capacity. This invention organically integrates underground safety constraints with urban hydrological regulation functions, upgrading greening layout from single-objective safety control to multi-objective comprehensive optimization, improving the rationality of urban underground space utilization and the overall efficiency of the sponge system.

[0043] It should be noted that the terms "comprising," "including," or any other variations thereof used herein are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0044] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0045] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for urban greening planning that integrates sponge city technology, characterized in that, The method includes: S1: Collect underground pipeline management data and road information data in the urban planning area, divide the urban planning area into multiple two-dimensional planning units of equal size, and calculate the three-dimensional probability distribution model of underground pipelines corresponding to the two-dimensional planning unit based on the underground pipeline management data and road information data. S2: Based on the three-dimensional probability distribution model of underground pipelines corresponding to the two-dimensional planning unit, perform connectivity analysis and spatial aggregation processing on the two-dimensional planning unit in the urban planning area to generate an underground pipeline corridor model of the urban planning area. S3: Based on the urban green space planning text indicators and land use control conditions, divide multiple candidate tree planting units in the urban planning area, construct a root growth volume model of the candidate tree planting units, calculate the spatial distribution characteristics of the underground pipeline corridor model, and generate a conflict index describing the planting conflict between the candidate tree planting units and the underground pipeline corridor model based on the spatial distribution characteristics and the root growth volume model. S4: Based on the conflict index of the candidate tree planting units, candidate tree planting units are selected according to the vegetation screening rules to form a set of plantable space areas. The set of plantable space areas is then comprehensively optimized in conjunction with sponge city technology, and urban greening is carried out based on the comprehensively optimized set of plantable space areas.

2. The urban greening planning method integrating sponge city technology as described in claim 1, characterized in that, In step S1, underground pipeline management data and road information data of the urban planning area are collected, and the urban planning area is divided into multiple equal-sized two-dimensional planning units, including: The underground pipeline management data includes the location of manhole covers and the pipeline routing vector between any two manhole covers; the road information data includes the center location of all roads within the urban planning area and the coordinates of the two ends of the roads. Two-dimensional planning units with fixed side lengths are generated using the center position of the road as the seed point, thereby dividing the urban planning area into multiple two-dimensional planning units of equal size.

3. The urban greening planning method integrating sponge city technology as described in claim 2, characterized in that, Step S1, which calculates the three-dimensional probability distribution model of underground pipelines corresponding to the two-dimensional planning unit, also includes: S11: Based on the underground pipeline management data and road information data of the urban planning area, calculate the density of manhole cover locations and slope angles of all manhole covers in the two-dimensional planning unit, the average road direction angle of all roads, and the average pipeline direction vector of underground pipelines between any two manhole covers. S12: Based on the manhole cover density and the average road direction angle of the two-dimensional planning unit, construct a coupling strength feature that characterizes the consistency between manhole cover density and road direction. S13: Convert the mean value of the pipeline direction vector into the vertical extension ratio of the vertical component and the horizontal component of the underground pipeline, and calculate the correlation characteristics between the underground pipeline direction and the terrain slope of the two-dimensional planning unit based on the vertical extension ratio, the mean value of the road direction angle and the slope angle. S14: Weight the coupling strength feature and correlation feature, and use the weighting result as the underground pipeline spatial semantic feature of the two-dimensional planning unit. Divide the underground area of ​​the two-dimensional planning unit into several voxel units of the same size, obtain the vertical distance from the center point of the voxel unit to the ground surface, and calculate the probability of the existence of underground pipelines in the voxel unit based on the underground pipeline spatial semantic feature and vertical distance of the two-dimensional planning unit. S15: The center point coordinates of all voxel units in the two-dimensional planning unit and the probability of the existence of underground pipelines are used as the three-dimensional probability distribution model of underground pipelines corresponding to the two-dimensional planning unit.

4. The urban greening planning method integrating sponge city technology as described in claim 1, characterized in that, Step S2 involves performing connectivity analysis and spatial aggregation processing on the two-dimensional planning units within the urban planning area, including: S21: Based on the three-dimensional probability distribution model of underground pipelines corresponding to the two-dimensional planning unit, a global probability threshold is set, and voxel units with a probability of existence of underground pipelines higher than the global probability threshold are regarded as high-probability voxel units. S22: Calculate the distance between the center point coordinates of high-probability voxel units, merge high-probability voxel units with a distance less than a preset distance threshold into the same three-dimensional connected body, and obtain multiple three-dimensional connected bodies; S23: Calculate the connection direction and connection distance between any two three-dimensional connected bodies. If the connection direction and connection distance between the two three-dimensional connected bodies both satisfy the preset connection rules, then aggregate the two three-dimensional connected bodies to obtain the underground pipeline corridor model of the urban planning area.

5. The urban greening planning method integrating sponge city technology as described in claim 4, characterized in that, The underground pipeline corridor model is composed of multiple aggregated three-dimensional connected bodies.

6. The urban greening planning method integrating sponge city technology as described in claim 1, characterized in that, Step S3 involves constructing a root growth volume model for candidate tree planting units and calculating the spatial distribution characteristics of the underground pipeline corridor model, including: S31: Obtain the two-dimensional center coordinates of the candidate tree planting unit, and construct the horizontal expansion radius of the tree root system at the root growth depth d: ； in, This represents the horizontal radius of the tree's root system at a root growth depth d. , This indicates the minimum root growth depth of the trees planted in the candidate tree planting unit. This indicates the maximum root growth depth of the trees planted in the candidate tree planting unit. This indicates the maximum horizontal radius of a tree's root system on the earth's surface. Indicates the root morphology adjustment coefficient; S32: Generate the root system generation volume model of the candidate tree planting unit based on the surface expansion radius and the two-dimensional center coordinates of the candidate tree planting unit; S33: Extract the three-dimensional connected body from the underground pipeline corridor model and calculate the connectivity features of the three-dimensional connected body, wherein the connectivity features include the center position coordinates, average depth and extension direction of the three-dimensional connected body; S34: Segment the connectivity features of all three-dimensional connected bodies to form the spatial distribution features of the underground pipeline corridor model.

7. The urban greening planning method integrating sponge city technology as described in claim 6, characterized in that, Step S3, which generates a conflict index describing the planting conflict between the candidate tree planting units and the underground pipeline corridor model, also includes: S35: Based on the underground pipeline corridor model and the root system generation volume model of the candidate tree planting unit, calculate the three-dimensional spatial overlap volume between the root system generation volume model of the candidate tree planting unit and the three-dimensional connected body in the underground pipeline corridor model. S36: Calculate the main extension direction of the root system generation volume model, and based on the spatial distribution characteristics of the underground pipeline corridor model, couple and modulate to obtain the conflict index of planting conflict between the candidate tree planting units and the underground pipeline corridor model. The formula for calculating the conflict index is: ； ； in, This represents the conflict index between the m-th candidate tree planting unit and the underground pipeline corridor model, indicating the planting conflict. This indicates the number of candidate tree planting units obtained through screening. Let B represent the conflict coefficient between the m-th candidate tree planting unit and the b-th 3D connected element in the underground pipeline corridor model, where B represents the number of 3D connected elements in the underground pipeline corridor model. Indicates the selection of a set The maximum value in, This represents the three-dimensional spatial overlap volume between the root system generation volume model of the m-th candidate tree planting unit and the b-th three-dimensional connected volume in the underground pipeline corridor model. This represents the volume of the root system generation volume model for the m-th candidate tree planting unit. This indicates the main extension direction of the root system generation volume model for the m-th candidate tree planting unit. This indicates the extension direction of the b-th three-dimensional connected element in the underground pipeline corridor model. Represents the L2 norm. This represents the average depth of the b-th three-dimensional connected volume in the underground pipeline corridor model. Let represent the Euclidean distance between the two-dimensional center coordinates of the m-th candidate tree planting unit and the center coordinates of the b-th three-dimensional connected element. Represents the distance scale parameter. All represent conflict weighting coefficients, and the conflict index The larger the value, the higher the risk of planting conflict when planting trees at the candidate tree planting unit.

8. The urban greening planning method integrating sponge city technology as described in claim 1, characterized in that, Step S4 involves comprehensive optimization of the set of plantable space areas using sponge city technology, including: S41: Based on sponge city technology, obtain the sponge performance parameters of the two-dimensional planning unit where any candidate tree planting unit in the set of plantable space areas is located, and calculate the sponge efficiency index of the two-dimensional planning unit where the candidate tree planting unit is located based on the sponge performance parameters. S42: Based on the sponge efficiency index of the two-dimensional planning unit where the candidate tree planting unit is located, a second screening is performed on the candidate tree planting units in the set of plantable space areas to obtain a set of plantable space areas after comprehensive optimization that maximizes sponge efficiency.

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