Rapid simulation method, system and storage medium for urban lifeline waterlogging risk assessment

Abstract

The present invention relates to a rapid simulation method, system and storage medium for assessing urban lifeline waterlogging risk. The method comprises obtaining key data; constructing a multi-layer superimposed grid model including a surface layer, an underground pipe network layer and a lifeline facility layer based on the key data; using the multi-layer superimposed grid model as a basis, using a numerical simulation method to pre-calculate the water depth distribution data under different rainfall intensities, and using a pre-calculation method to interpolate the water depth value under the actual rainfall intensity; superimposing the water depth value with the lifeline facility to calculate the flooding risk degree of the lifeline facility; and visually displaying the water depth distribution data and the flooding risk degree. The present invention realizes the pre-calculation and real-time interpolation of the water depth distribution under different rainfall conditions. Under actual rainfall conditions, accurate water depth distribution can be quickly obtained without complex real-time calculations, which greatly shortens the simulation time and provides efficient technical support for emergency response to urban waterlogging.

Description

Technical Field

[0001] The present invention relates to the technical field of urban flood control and disaster management, and in particular to a rapid simulation method, system and storage medium for urban lifeline waterlogging risk assessment. Background Art

[0002] Under the general trend of global climate change, extreme rainfall events are occurring more and more frequently. This phenomenon has caused urban waterlogging to become one of the severe challenges facing urban development, especially posing an increasingly serious threat to urban lifeline systems. Urban lifeline systems cover key infrastructure such as water supply, electricity, communications, and transportation. The normal operation of these infrastructures is directly related to the life safety of urban residents and the stability of the entire city. Therefore, being able to quickly and accurately assess the risk of urban lifeline waterlogging is of great practical significance at present.

[0003] The key point of waterlogging risk assessment is to accurately simulate the spatial distribution of waterlogging depth and deeply analyze its potential impact on various key infrastructures in the city. At present, the existing waterlogging simulation methods can be divided into the following two categories: the first is the mathematical and physical model, which is based on mature hydrological and hydrodynamic theories. Its advantage is that the simulation results have high accuracy and can accurately reflect the relevant waterlogging situation. However, it also has obvious disadvantages, which are manifested in the high computational complexity and long computational time. When facing scenes such as urban waterlogging that require rapid emergency response, it is difficult to play an effective role and provide strong support for relevant decisions in a timely manner. The second is the data-driven model, which mainly relies on advanced technologies such as neural networks and machine learning. Its outstanding feature is that it can quickly generate simulation results and has certain advantages in timeliness. But at the same time, it has a strong dependence on data. Once the number of data samples is small or in extreme scenarios, it is likely to cause large deviations in the simulation results, making it difficult to meet the various needs in practical applications.

[0004] In summary, in view of the many shortcomings of existing waterlogging simulation methods, there is an urgent need to develop a simulation method that is both efficient and accurate, so as to achieve rapid assessment and accurate prediction of waterlogging risks. Summary of the invention

[0005] To this end, the technical problem to be solved by the present invention is to overcome the technical defects of the existing simulation methods that cannot have both high efficiency and accuracy, and to propose a rapid simulation method, system and storage medium for urban lifeline waterlogging risk assessment.

[0006] In order to solve the above technical problems, the present invention creatively provides a rapid simulation method for urban lifeline waterlogging risk assessment, comprising the following steps:

[0007] Step S1: Acquire key data of the study area, wherein the key data includes high-resolution terrain data, underground pipe network distribution data, rainfall data, and urban lifeline system distribution data, and perform interpolation processing on the high-resolution terrain data, and perform network topology optimization processing on the underground pipe network distribution data;

[0008] Step S2: Based on the key data obtained and processed, a multi-layered grid model including a surface layer, an underground pipe network layer and a lifeline facility layer is constructed, wherein each layer of the grid contains information on spatial resolution, physical properties and interaction relationships; Step S3: Based on the multi-layered grid model as a basic framework, a numerical simulation method is used according to the shallow water equation to pre-calculate the water depth distribution data under different rainfall intensities. When the actual rainfall intensity is obtained, the water depth value under the actual rainfall intensity is interpolated using the pre-calculation method;

[0009] Step S4: superimposing the water depth value with the lifeline facilities to calculate the flooding risk level of the lifeline facilities, wherein the lifeline facilities are components of the urban lifeline system;

[0010] Step S5: Visually display the water depth distribution data obtained in step S3 and the flooding risk level of the lifeline facilities obtained in step S4.

[0011] In one embodiment of the present invention, in step S1, the method for interpolating high-resolution terrain data includes calculating elevation data using a bilinear interpolation method:

[0012]

[0013] Where (x, y) represents the coordinate position of the target point in the plane rectangular coordinate system, z(x, y) represents the elevation of the target point, x1, x2, y1, y2 represent the coordinates of the four adjacent data points (x1, y1), (x2, y1), (x1, y2), (x2, y2) around the target point (x, y), z 11 , Z 21 、z 12 、z 22 Represents the known elevation values ​​corresponding to four adjacent data points (x1, y1), (x2, y1), (x1, y2), (x2, y2), where the A, B, C, and D parameters are calculated as A=x2-x, B=x-x1, C=y2-y, and D=y-y1.

[0014] In one embodiment of the present invention, in step S1, a method for performing network topology optimization processing of underground pipe network distribution data on pipe network nodes includes:

[0015] Step S11: performing node redundancy removal operation on the original underground pipe network distribution data;

[0016] Step S12: Convert the underground pipe network distribution data after the redundancy removal operation into a graph structure. Specifically, convert each node and pipeline actually existing in the pipe network into a vertex and an undirected edge in the graph structure. An undirected edge connects two vertices. The adjacency matrix A is used to represent the pipe network topology structure as follows:

[0017]

[0018] Step S13: When it is determined that at least three nodes are located on the same straight line, the at least three nodes are merged into a straight line path to optimize the pipe network topology structure.

[0019] In one embodiment of the present invention, in step S2, a method for constructing a multi-layered grid model including a surface layer, an underground pipe network layer, and a lifeline facility layer based on the acquired and processed key data includes:

[0020] Step S21: define the spatial resolution of each layer of grid as:

[0021] Δx×Δy;

[0022] Wherein, Δx represents the grid cell spacing in the horizontal direction, and Δy represents the grid cell spacing in the vertical direction;

[0023] Step S22: define the overall structure of the grid model based on the spatial resolution, and explain the functional description of the surface layer, underground pipe network layer and lifeline facility layer, wherein the overall structure of the grid model is:

[0024]

[0025] Among them, G total (i, j, k) represents a comprehensive attribute or state value of the entire multi-layered grid model at the three-dimensional space coordinate (i, j, k), i, j represent the coordinate index in the horizontal direction, and k represents the coordinate index in the vertical direction;

[0026] Step S23: according to the functional description of the surface layer in step S22, the terrain elevation data is used to assign a height value to each grid, and the bilinear interpolation method is used to perform elevation interpolation and smoothing calculation of unknown points to construct the surface layer;

[0027] Step S24: Use GIS technology to overlay the underground pipe network and lifeline facilities on the surface layer and construct a spatial index. The location of the underground pipe network and lifeline facilities in each grid unit is determined through the spatial index to construct the underground pipe network layer and the lifeline facility layer, where the spatial index is:

[0028]

[0029] Among them, G pipe (i, j) represents the positional relationship between the underground pipe network and the grid unit, G facility (i, j) represents the positional relationship between the lifeline facilities and the grid cells.

[0030] In one embodiment of the present invention, in step S3, a method for precalculating water depth distribution data under different rainfall intensities using a numerical simulation method according to a shallow water equation includes:

[0031] Step S31: pre-calculate the water depth distribution data under different rainfall intensities R, where the rainfall intensity R∈[R min , R max ], and use the grid unit to record each sampling point (x, y) in the rainfall intensity R k The depth distribution of water accumulation h k (x, y);

[0032] Step S32: according to the actual rainfall intensity R real Get the corresponding water depth distribution. When actual rainfall occurs, if the actual rainfall intensity R real The rainfall intensity R in step S31 k If they are not equal, the corresponding water depth distribution is obtained by interpolation in the pre-calculation scheme, and R k ≤R real <R k+1 , then the distribution of water depth under actual rainfall intensity is:

[0033]

[0034] Among them, h real (x, y) represents the actual rainfall intensity R real The depth distribution of water accumulation under k (x, y) represents the rainfall intensity R k The depth distribution of water accumulation, h k+1 (x, y) represents the rainfall intensity R k+1 Distribution of water depth.

[0035] In one embodiment of the present invention, in step S4, the method for calculating the flooding risk level of lifeline facilities includes:

[0036] Step S41: according to the spatial position of the lifeline facility, obtaining the water depth value corresponding to the position of the lifeline facility from the water depth distribution grid;

[0037] Step S42: Based on the acquired water depth value and empirical parameters at the location of the lifeline facility, determine the flooding level of the lifeline facility:

[0038]

[0039] Among them, h represents the water depth value.

[0040] In one embodiment of the present invention, in step S5, the method for visually displaying the water depth distribution data and the flooding risk level of lifeline facilities includes:

[0041] Step S51: Map the water depth distribution data into a two-dimensional color map, and color-code the lifeline facilities according to the lifeline risk level as follows:

[0042]

[0043] Wherein, Color(f) represents the color code assigned to the facility according to the risk level of the lifeline facility; Step S52: superimpose the water depth distribution data and the flooding risk level of the lifeline facility into the GIS system.

[0044] Based on the same inventive concept, the present invention also provides a rapid simulation system for urban lifeline waterlogging risk assessment, comprising:

[0045] A data processing module is used to obtain key data of the study area, including high-resolution terrain data, underground pipe network distribution data, rainfall data, and urban lifeline system distribution data, and to perform interpolation processing on the high-resolution terrain data and network topology optimization processing on the underground pipe network distribution data;

[0046] A grid model construction module is used to construct a multi-layered grid model including a surface layer, an underground pipe network layer and a lifeline facility layer based on the key data obtained and processed, wherein each layer of the grid contains information on spatial resolution, physical properties and interaction relationships;

[0047] The water depth calculation module is used to pre-calculate the water depth distribution data under different rainfall intensities using a numerical simulation method based on a multi-layer superimposed grid model according to the shallow water equation. When the actual rainfall intensity is obtained, the water depth value under the actual rainfall intensity is interpolated using the pre-calculation method;

[0048] A facility risk calculation module, which is used to superimpose the water depth value with the lifeline facility to calculate the flooding risk level of the lifeline facility, wherein the lifeline facility is a component of the urban lifeline system;

[0049] The visualization module is used to visualize the water depth distribution data and the flooding risk level of lifeline facilities.

[0050] Furthermore, the present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method when executing the program.

[0051] Furthermore, the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above-mentioned method are implemented.

[0052] The above technical solution of the present invention has the following advantages compared with the prior art:

[0053] 1. The numerical simulation and interpolation technology based on the shallow water equation realizes the pre-calculation and real-time interpolation of the water depth distribution under different rainfall conditions. Under actual rainfall conditions, accurate water depth distribution can be quickly obtained without complex real-time calculation, which greatly shortens the simulation time and provides efficient technical support for urban waterlogging emergency response;

[0054] 2. The present invention can dynamically evaluate the flooding risk of facilities by superimposing the depth of accumulated water and the spatial position of lifeline facilities, providing a reference for quantifying the probability of functional damage of facilities. Combined with the importance weight of facilities, the system-level comprehensive risk can be further calculated, providing a scientific basis for disaster prevention and mitigation, and ensuring the safe operation of key lifeline facilities;

[0055] 3. The present invention constructs a multi-layer grid model of surface topography, underground pipe network and lifeline facilities, comprehensively considers the multi-layer dynamic interaction characteristics such as surface water accumulation, pipe network drainage capacity and the impact of flooding on facilities, and through inter-layer coupling analysis, it can more realistically reflect the evolution process of urban waterlogging under rainfall and its impact on the lifeline system, thereby improving the scientific nature of risk assessment;

[0056] 4. The present invention can intuitively display the spatial distribution of water depth and the risk of facility flooding, clearly grasp the dynamic evolution of waterlogging and risk distribution, and improve the scientificity and effectiveness of flood control planning and emergency response. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flow chart of a rapid simulation method for urban lifeline waterlogging risk assessment proposed by the present invention.

[0058] Figure 2 It is a schematic flow chart of step S1 of the present invention.

[0059] Figure 3 It is a schematic flow chart of step S2 of the present invention.

[0060] Figure 4 It is a schematic flow chart of step S3 of the present invention.

[0061] Figure 5 It is a schematic flow chart of step S4 of the present invention.

[0062] Figure 6 It is a schematic flow chart of step S5 of the present invention.

[0063] Figure 7 It is a statistical chart of urban lifelines of different risk levels in the main urban area of ​​Nanjing City according to the present invention. DETAILED DESCRIPTION

[0064] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0065] Reference Figure 1 As shown, an embodiment of the present invention provides a rapid simulation method for urban lifeline waterlogging risk assessment, comprising the following steps:

[0066] Step S1: Acquire key data of the study area, wherein the key data includes high-resolution terrain data, underground pipe network distribution data, rainfall data, and urban lifeline system distribution data, and perform interpolation processing on the high-resolution terrain data, and perform network topology optimization processing on the underground pipe network distribution data;

[0067] Step S2: Based on the key data obtained and processed, a multi-layered grid model including a surface layer, an underground pipe network layer and a lifeline facility layer is constructed, wherein each layer of the grid contains information on spatial resolution, physical properties and interaction relationships; Step S3: Based on the multi-layered grid model as a basic framework, a numerical simulation method is used according to the shallow water equation to pre-calculate the water depth distribution data under different rainfall intensities. When the actual rainfall intensity is obtained, the water depth value under the actual rainfall intensity is interpolated using the pre-calculation method;

[0068] Step S4: superimposing the water depth value with the lifeline facilities to calculate the flooding risk level of the lifeline facilities, wherein the lifeline facilities are components of the urban lifeline system;

[0069] Step S5: Visually display the water depth distribution data obtained in step S3 and the flooding risk level of the lifeline facilities obtained in step S4.

[0070] The present invention is based on the numerical simulation and interpolation technology of shallow water equations, which realizes the pre-calculation and real-time interpolation of water depth distribution under different rainfall conditions. Under actual rainfall conditions, accurate water depth distribution can be quickly obtained without complex real-time calculations, which greatly shortens the simulation time and provides efficient technical support for urban waterlogging emergency response.

[0071] The present invention constructs a multi-layer grid model of surface topography, underground pipe networks and lifeline facilities, comprehensively considering multi-layer dynamic interaction characteristics such as surface water accumulation, pipe network drainage capacity and the impact of flooding on facilities. Through inter-layer coupling analysis, it can more realistically reflect the evolution process of urban waterlogging under the action of rainfall and its impact on the lifeline system, thereby improving the scientific nature of risk assessment.

[0072] Among them, in step S1, when processing high-resolution terrain data, since the original data often exists in the form of discrete points, in order to more accurately depict the continuous changes in the terrain surface, it is necessary to estimate the elevation values ​​of other unmeasured points (i.e., target points) through interpolation methods. Therefore, this embodiment uses a bilinear interpolation method to calculate elevation data, so that the terrain data is more continuous and smooth in space, and can better reflect the undulating characteristics of the actual terrain. Specifically, the elevation data calculated using the bilinear interpolation method is:

[0073]

[0074] Where (x, y) represents the coordinate position of the target point in the plane rectangular coordinate system, z(x, y) represents the elevation of the target point, x1, x2, y1, y2 represent the coordinates of the four adjacent data points (x1, y1), (x2, y1), (x1, y2), (x2, y2) around the target point (x, y), z 11 , Z 21 、z 12 、z 22 Represents the known elevation values ​​corresponding to four adjacent data points (x1, y1), (x2, y1), (x1, y2), (x2, y2), where the A, B, C, and D parameters are calculated as A=x2-x, B=x-x1, C=y2-y, and D=y-y1.

[0075] Further, refer to Figure 2 As shown, in step S1, the method for performing network topology optimization processing on underground pipe network distribution data of pipe network nodes comprises the following steps:

[0076] Step S11: performing node redundancy removal operation on the original underground pipe network distribution data;

[0077] Step S12: Convert the underground pipe network distribution data after the redundancy removal operation into a graph structure. Specifically, convert each node and pipeline actually existing in the pipe network into a vertex and an undirected edge in the graph structure. An undirected edge connects two vertices. The adjacency matrix is ​​used to represent the pipe network topology structure as follows:

[0078]

[0079] Step S13: When it is determined that at least three nodes are located on the same straight line, the at least three nodes are merged into a straight line path to optimize the pipe network topology structure.

[0080] Specifically, in step S11, in the original underground pipe network distribution data, there may be some nodes with very close physical locations, and the existence of these nodes may increase the complexity of data processing and may have a certain impact on the accuracy of the results in subsequent analysis. Therefore, this embodiment performs a node de-redundancy operation on the original underground pipe network distribution data, that is, if the physical locations of two nodes are very close (for example, less than a threshold), they are merged into one node.

[0081] In step S12, after completing the node de-redundancy operation, the pipeline network node data is simplified to a certain extent. At this time, the underground pipeline network distribution data after the de-redundancy operation is converted into a graph structure, the main purpose of which is to facilitate further optimization and analysis of the pipeline network. The pipeline network system itself is relatively complex, containing many nodes and pipelines connecting them. By converting it into a mathematical model such as a graph, relevant knowledge and tools such as graph theory can be used to more clearly and systematically study the topological structure of the pipeline network, the relationship between each part, and its operating characteristics, thereby facilitating subsequent network simplification and optimization operations. Specifically, each node actually existing in the pipeline network is converted into a vertex in the graph structure, which means that the position of each node in the actual pipeline network has a corresponding abstract vertex in the graph structure to represent it, and this vertex not only represents the location information of the node, but also can be accompanied by attributes related to the operation of the pipeline network, such as pressure and flow. And each pipeline actually existing in the pipeline network is converted into an undirected edge in the graph structure. The role of the pipeline is to connect two nodes so that they form a connected network in the pipeline network system. In the graph structure, this connection relationship is reflected by undirected edges. Moreover, these undirected edges can also be accompanied by pipe-related attributes such as pipe diameter, size, length, flow rate, etc., so that when analyzing the graph structure, the flow characteristics of water in the pipe network can be studied based on the attributes of these edges.

[0082] In step S13, after the node redundancy has been completed to simplify the data, and the pipe network data has been converted into a graph structure for analysis, the network simplification and optimization operation is further carried out. The purpose is to further simplify the pipe network structure, reduce unnecessary nodes and pipes, and make the pipe network system more concise and clear while maintaining the hydraulic performance of the pipe network, so as to facilitate the subsequent analysis and management based on the pipe network data and various research works related to the pipe network. If the three nodes (i, j, k) are on the same straight line and the pressure loss is negligible, they are merged into a straight line path. It can be judged specifically according to the angle of the three nodes:

[0083]

[0084] Among them, (p j -p i ) represents the vector from point i to point j, (p k -p j ) represents the vector from point j to point k.

[0085] Among them, refer to Figure 3 As shown, in step S2, a method for constructing a multi-layered grid model including a surface layer, an underground pipe network layer and a lifeline facility layer based on the key data obtained and processed includes the following steps:

[0086] Step S21: define the spatial resolution of each layer of grid as:

[0087] Δx×Δy;

[0088] Wherein, Δx represents the grid cell spacing in the horizontal direction, and Δy represents the grid cell spacing in the vertical direction;

[0089] Step S22: define the overall structure of the grid model based on the spatial resolution, and explain the functional description of the surface layer, underground pipe network layer and lifeline facility layer, wherein the overall structure of the grid model is:

[0090]

[0091] Among them, G total (i, j, k) represents a comprehensive attribute or state value of the entire multi-layered grid model at the three-dimensional space coordinate (i, j, k), i, j represent the coordinate index in the horizontal direction, and k represents the coordinate index in the vertical direction;

[0092] Step S23: according to the functional description of the surface layer in step S22, the terrain elevation data is used to assign a height value to each grid, and the bilinear interpolation method is used to perform elevation interpolation and smoothing calculation of unknown points to construct the surface layer;

[0093] Step S24: Use GIS technology to overlay the underground pipe network and lifeline facilities on the surface layer and construct a spatial index. The location of the underground pipe network and lifeline facilities in each grid unit is determined through the spatial index to construct the underground pipe network layer and the lifeline facility layer, where the spatial index is:

[0094]

[0095] Among them, G pipe (i, j) represents the positional relationship between the underground pipe network and the grid unit, G facility (i, j) represents the positional relationship between the lifeline facilities and the grid cells.

[0096] Specifically, in step S22, after determining the spatial resolution of each layer of grid, the overall structure of the grid model is further defined so as to grasp the composition mode of the multi-layer superimposed grid model and the relationship between each layer as a whole. At the same time, the functional description of each layer (surface layer, underground pipe network layer and lifeline facility layer) is clarified so that the corresponding attributes and data of each layer of grid can be accurately assigned according to its specific function when constructing each layer of grid in the subsequent construction, so that the entire model can comprehensively and accurately reflect various situations related to the surface, underground pipe network and lifeline facilities in the study area. Among them, the functional description of each layer is as follows: the surface layer bears the important function of describing the elements related to the surface in the entire model, such as rainfall, surface runoff, water accumulation depth, etc. The underground pipe network layer is mainly used to describe the relevant characteristics of the underground pipe network, including the layout of the pipe network, the connection relationship of the pipes, the pipe diameter, the drainage capacity, etc. The lifeline facility layer, its function is to indicate the location and risk of lifeline facilities. The lifeline facilities here usually include water supply facilities, power facilities, communication facilities, etc.

[0097] Specifically, in step S23, in order to more efficiently query and manage the location information of underground pipelines and lifeline facilities in the multi-layered superimposed grid model, a spatial index is constructed. Through such an index definition, the specific distribution of the pipeline in the entire multi-layered superimposed grid model can be quickly determined, that is, which grid cells have pipelines passing through them and which do not. This facilitates the subsequent analysis of the interaction between urban waterlogging and the pipeline network, so that the involved pipeline parts and their locations can be accurately found.

[0098] In this embodiment, through the above steps S21-S24, a complete multi-layer superimposed grid model including the surface layer, underground pipeline network layer and lifeline facility layer can be constructed. The model can comprehensively and accurately reflect the conditions related to the surface, underground pipeline network and lifeline facilities in the study area, and provides a solid foundation for subsequent related work such as waterlogging analysis and lifeline system risk assessment based on the model.

[0099] Among them, refer to Figure 4As shown, in step S3, a method for precalculating water depth distribution data under different rainfall intensities using a numerical simulation method according to a shallow water equation includes the following steps:

[0100] Step S31: pre-calculate the water depth distribution data under different rainfall intensities R, where the rainfall intensity R∈[R min , R max ], and use the grid unit to record each sampling point (x, y) in the rainfall intensity R k The depth distribution of water accumulation h k (x, y);

[0101] Step S32: according to the actual rainfall intensity R real Get the corresponding water depth distribution. When actual rainfall occurs, if the actual rainfall intensity R real The rainfall intensity R in step S31 k If they are not equal, the corresponding water depth distribution is obtained by interpolation in the pre-calculation scheme, and R k ≤R real <R k+1 , then the actual rainfall intensity R real The water depth distribution under is:

[0102]

[0103] Among them, h real (x, y) represents the actual rainfall intensity R real The depth distribution of water accumulation under k (x, y) represents the rainfall intensity R k The depth distribution of water accumulation, h k+1 (x, y) represents the rainfall intensity R k+1 Distribution of water depth.

[0104] Specifically, step S31 firstly performs a series of different rainfall intensities R (the value range of R is [R min , R max ]) is used to calculate the depth distribution of waterlogging in advance. Here, various possible rainfall intensity scenarios are simulated and calculated, and each grid unit is used as a unit to record the distribution of each sampling point under different rainfall intensities R k The corresponding water depth h k (x, y). This step is equivalent to constructing a k and water depth h kThe basic database of (x, y) relationship provides a pre-calculated data basis for the subsequent acquisition of the actual water depth according to the actual rainfall intensity. The distribution of water depth is recorded in the form of each grid unit, so that the water depth information can be closely combined with the previously constructed multi-layer superimposed grid model (constructed in the relevant operation of step S2). In this way, when analyzing the interaction between water accumulation and various layers of the urban lifeline system (such as the surface layer, underground pipe network layer and lifeline facility layer) in the subsequent analysis, it is convenient to conduct accurate analysis based on the position correspondence of the grid cells, because each layer is also based on the division of grid cells to describe relevant information when it is constructed.

[0105] Further, step S32 is based on step S31, according to the actual rainfall intensity R real To obtain the corresponding water depth distribution h k When real-time rainfall occurs, its rainfall intensity may not be exactly the same as a certain R pre-calculated in step S31. k The values ​​are equal, so it is necessary to obtain the corresponding water depth distribution by interpolation in the pre-calculated scheme. This step realizes the transition from the pre-calculated water depth data corresponding to the discrete rainfall intensity to the water depth obtained according to the actual arbitrary rainfall value, making the calculation of water depth more in line with the actual rainfall situation. In actual calculation, in order to improve the interpolation efficiency, the following methods can be used: (1) Binary search for adjacent points: Since R k Usually in order, adjacent R can be quickly located by binary search k and R k+1 , which can reduce the time complexity from O(n) to O(n log n). (2) The interpolation operation can be parallelized on the GPU to process the water depth interpolation of multiple grid points, further accelerating the calculation.

[0106] At the same time, step S32 also considers the situation where the actual rainfall value is in a special range and gives corresponding processing methods, such as: (1) The actual rainfall is less than the pre-calculated minimum value: When the actual rainfall is less than the pre-calculated minimum value R min When h real (x, y) = h1(x, y), that is, the water depth corresponding to the minimum rainfall intensity R1 in the pre-calculation is directly used as the actual flooding depth. This is because in this case, the actual rainfall intensity can be considered to be weak, and the water accumulation situation can be approximately handled according to the situation of the minimum rainfall intensity in the pre-calculation.

[0107] (2) Actual rainfall is greater than the pre-calculated maximum value: When the actual rainfall is greater than the pre-calculated maximum value R max When, according to the formula To calculate the actual flooding depth. This is because when the rainfall intensity exceeds the pre-calculated range, it is necessary to calculate the maximum rainfall intensity R in the pre-calculated range.n and its adjacent R n-1 The corresponding water accumulation depth is calculated through a certain proportional relationship to estimate the actual flooding depth as reasonably as possible under such extremely heavy rainfall conditions.

[0108] Among them, refer to Figure 5 As shown, in step S4, the method for calculating the flooding risk level of lifeline facilities includes the following steps:

[0109] Step S41: according to the spatial position of the lifeline facility, obtaining the water depth value corresponding to the position of the lifeline facility from the water depth distribution grid;

[0110] Step S42: Based on the acquired water depth value and empirical parameters at the location of the lifeline facility, determine the flooding level of the lifeline facility:

[0111]

[0112] Among them, h represents the water depth value.

[0113] Through steps S41-S42, the flooding risk level of lifeline facilities can be calculated more accurately and presented in the form of intuitive risk levels, for example Figure 7 As shown, it provides an important basis for risk assessment and response decision-making of lifeline facilities in urban waterlogging situations.

[0114] Among them, refer to Figure 6 As shown, in step S5, the method for visually displaying the water depth distribution data and the flooding risk level of lifeline facilities includes the following steps:

[0115] Step S51: Map the water depth distribution data into a two-dimensional color map, and color-code the lifeline facilities according to the lifeline risk level as follows:

[0116]

[0117] Wherein, Color(f) represents the color code assigned to the facility according to the risk level of the lifeline facility; Step S52: superimpose the water depth distribution data and the flooding risk level of the lifeline facility into the GIS system.

[0118] Specifically, in step S51, the abstract water depth distribution data is converted into an intuitive two-dimensional color map (usually understood as a heat map), so that people can quickly and intuitively perceive the distribution of water depth in the study area through color changes. This visualization method can present complex numerical data in a more understandable graphical form, so that relevant personnel (such as urban planners, emergency management personnel, etc.) can roughly understand the depth distribution of water without in-depth analysis of specific values. The implementation method uses specific mapping rules, such as setting different colors according to different value ranges of water depth. For example, deeper water may correspond to dark blue, shallower water corresponds to light blue, and no water accumulation area corresponds to transparent or some light background color, etc. In this way, when the water depth distribution data is processed according to this mapping rule, a two-dimensional color map that can intuitively reflect the distribution of water depth can be obtained.

[0119] Furthermore, in step S51, in order to highlight the degree of flooding risk faced by lifeline facilities in the visualization display, each lifeline facility is assigned a specific color code according to its corresponding risk level. In this way, when viewing the visualization results, the risk status of different facilities can be distinguished at a glance, which helps to quickly focus on high-risk facility areas so that corresponding countermeasures can be taken in time or further analysis can be carried out. Through this color coding method, the risk level information of the facility is presented in an intuitive visual form, which cooperates with the two-dimensional color map of the water depth distribution to more comprehensively display the overall situation under waterlogging conditions.

[0120] Specifically, in step S52, GIS (Geographic Information System) has a powerful spatial analysis and display capability. The water depth distribution data and the flooding risk level of lifeline facilities (which have been converted into a two-dimensional color map and facility color coding in the previous step) are superimposed on the GIS system, which can make full use of the advantages of GIS and display the spatial relationship between water depth and facility risk on a unified geographic space platform. This enables relevant personnel to more clearly understand which areas have deeper water accumulation and what risk level the lifeline facilities in these areas face, thereby providing more targeted and practical visualization support for urban waterlogging risk analysis, flood control planning, and emergency response. The implementation method can use the two-dimensional color map of water depth distribution as one layer, and the color-coded lifeline facilities as another layer, and then use the layer overlay function provided by the GIS software to overlay and display the two layers according to their positional relationship in the actual geographic space. In this way, the distribution of water depth and the risk status of each lifeline facility can be seen on the GIS platform at the same time, and the specific conditions of different areas can be further understood through the zoom, translation, query and other functions of the GIS system. For example, the zoom function can be used to view the details of the water depth and facility risks in the local area, the pan function can be used to browse the overall situation of the entire study area, and the query function can be used to obtain detailed information on specific facilities or areas, so as to make decisions and conduct risk assessments more accurately.

[0121] By superimposing the depth of accumulated water with the spatial position of lifeline facilities, the present invention can dynamically assess the risk of flooding of facilities, provide a reference for quantifying the probability of functional damage to facilities, and further calculate the system-level comprehensive risk in combination with the importance weight of the facilities, thus providing a scientific basis for disaster prevention and mitigation and ensuring the safe operation of key lifeline facilities.

[0122] The present invention can intuitively display the spatial distribution of water accumulation depth and the risk of facility flooding, clearly grasp the dynamic evolution of waterlogging and risk distribution, and improve the scientificity and effectiveness of flood control planning and emergency response.

[0123] Based on the same inventive concept, the embodiment of the present invention also provides a rapid simulation system for urban lifeline waterlogging risk assessment, including:

[0124] A data processing module is used to obtain key data of the study area, including high-resolution terrain data, underground pipe network distribution data, rainfall data, and urban lifeline system distribution data, and to perform interpolation processing on the high-resolution terrain data and network topology optimization processing on the underground pipe network distribution data;

[0125] A grid model construction module is used to construct a multi-layered grid model including a surface layer, an underground pipe network layer and a lifeline facility layer based on the key data obtained and processed, wherein each layer of the grid contains information on spatial resolution, physical properties and interaction relationships;

[0126] The water depth calculation module is used to pre-calculate the water depth distribution data under different rainfall intensities using a numerical simulation method based on a multi-layer superimposed grid model according to the shallow water equation. When the actual rainfall intensity is obtained, the water depth value under the actual rainfall intensity is interpolated using the pre-calculation method;

[0127] A facility risk calculation module, which is used to superimpose the water depth value with the lifeline facility to calculate the flooding risk level of the lifeline facility, wherein the lifeline facility is a component of the urban lifeline system;

[0128] The visualization module is used to visualize the water depth distribution data and the flooding risk level of lifeline facilities.

[0129] The present invention is based on the numerical simulation and interpolation technology of shallow water equations, which realizes the pre-calculation and real-time interpolation of water depth distribution under different rainfall conditions. Under actual rainfall conditions, accurate water depth distribution can be quickly obtained without complex real-time calculations, which greatly shortens the simulation time and provides efficient technical support for urban waterlogging emergency response.

[0130] The system of the above-mentioned embodiment is used to implement the corresponding rapid simulation method for urban lifeline waterlogging risk assessment in the above-mentioned embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0131] It should be noted that the drawings of the embodiments of the present disclosure only involve structures related to the embodiments of the present disclosure, and other structures may refer to general designs.

[0132] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0133] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0134] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for rapid simulation of urban lifeline waterlogging risk assessment described in any of the above embodiments is implemented.

[0135] In the embodiment of the present invention, the processor may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic devices.

[0136] The processor can call the program stored in the memory. Specifically, the processor can execute the operations in the embodiment of the above-mentioned method for detecting voids in heterogeneous space under steel plates based on the joint detection model.

[0137] The memory is used to store one or more programs, which may include program codes, and the program codes include computer operating instructions.

[0138] In addition, the memory may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0139] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned method for rapid simulation of urban lifeline waterlogging risk assessment are implemented.

[0140] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0141] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0142] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0143] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0144] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from these are still within the protection scope of the invention.

Claims

1. A rapid simulation method for urban lifeline waterlogging risk assessment, characterized by: The following steps are involved: Step S1: Acquire key data of the study area, wherein the key data includes high-resolution terrain data, underground pipe network distribution data, rainfall data, and urban lifeline system distribution data, and perform interpolation processing on the high-resolution terrain data, and perform network topology optimization processing on the underground pipe network distribution data; Step S2: constructing a multi-layered grid model including a surface layer, an underground pipe network layer and a lifeline facility layer based on the key data obtained and processed, wherein each layer of the grid contains information on spatial resolution, physical properties and interaction relationships; Step S3: Using the multi-layer superimposed grid model as the basic framework, a numerical simulation method is used according to the shallow water equation to pre-calculate the water depth distribution data under different rainfall intensities. When the actual rainfall intensity is obtained, the water depth value under the actual rainfall intensity is interpolated using the pre-calculation method; Step S4: superimposing the water depth value with the lifeline facilities to calculate the flooding risk level of the lifeline facilities, wherein the lifeline facilities are components of the urban lifeline system; Step S5: Visually display the water depth distribution data obtained in step S3 and the flooding risk level of the lifeline facilities obtained in step S4.

2. The rapid simulation method for urban lifeline waterlogging risk assessment according to claim 1 is characterized by: In step S1, the method for interpolating high-resolution terrain data includes calculating elevation data using a bilinear interpolation method: Where (x, y) represents the coordinate position of the target point in the plane rectangular coordinate system, z(x, y) represents the elevation of the target point, x1, x2, y1, y2 represent the coordinates of the four adjacent data points (x1, y1), (x2, y1), (x1, y2), (x2, y2) around the target point (x, y), z 11 、z 21 、z 12 、z 22 Represents the known elevation values ​​corresponding to four adjacent data points (x1, y1), (x2, y1), (x1, y1), (x2, y2), where the calculation method of A, B, C, and D parameters is A=x2-x, B=x-x1, C=y2-y, and D=y-y1.

3. The rapid simulation method for urban lifeline waterlogging risk assessment according to claim 1 or 2 is characterized in that: In step S1, the method for performing network topology optimization processing on underground pipe network distribution data of pipe network nodes includes: Step S11: performing node redundancy removal operation on the original underground pipe network distribution data; Step S12: converting the underground pipe network distribution data after the redundancy removal operation into a graph structure, specifically converting each node and pipeline actually existing in the pipe network into a vertex and an undirected edge in the graph structure, where an undirected edge connects two vertices, and using the adjacency matrix A to represent the pipe network topology structure as follows: Step S13: When it is determined that at least three nodes are located on the same straight line, the at least three nodes are merged into a straight line path to optimize the pipe network topology structure.

4. The rapid simulation method for urban lifeline waterlogging risk assessment according to claim 2 is characterized by: In step S2, a method for constructing a multi-layered grid model including a surface layer, an underground pipe network layer, and a lifeline facility layer based on the acquired and processed key data includes: Step S21: define the spatial resolution of each layer of grid as: Δx×Δy; Wherein, Δx represents the grid cell spacing in the horizontal direction, and Δy represents the grid cell spacing in the vertical direction; Step S22: define the overall structure of the grid model based on the spatial resolution, and explain the functional description of the surface layer, underground pipe network layer and lifeline facility layer, wherein the overall structure of the grid model is: Among them, G total (i, j, k) represents a comprehensive attribute or state value of the entire multi-layered grid model at the three-dimensional space coordinate (i, j, k), i, j represent the coordinate index in the horizontal direction, and k represents the coordinate index in the vertical direction; Step S23: according to the functional description of the surface layer in step S22, the terrain elevation data is used to assign a height value to each grid, and the bilinear interpolation method is used to perform elevation interpolation and smoothing calculation of unknown points to construct the surface layer; Step S24: Use GIS technology to overlay the underground pipe network and lifeline facilities on the surface layer and construct a spatial index. The location of the underground pipe network and lifeline facilities in each grid unit is determined through the spatial index to construct the underground pipe network layer and the lifeline facility layer, where the spatial index is: Among them, G pipe (i, j) represents the positional relationship between the underground pipe network and the grid unit, G facility (i, j) represents the positional relationship between the lifeline facilities and the grid cells.

5. The rapid simulation method for urban lifeline waterlogging risk assessment according to claim 4 is characterized by: In step S3, a method for precalculating water depth distribution data under different rainfall intensities using a numerical simulation method according to a shallow water equation includes: Step S31: pre-calculate the water depth distribution data under different rainfall intensities R, where the rainfall intensity R∈[R min , R max ], and use the grid unit to record the distribution of water depth h at each sampling point (x, y) at the rainfall intensity k (x, y); Step S32: according to the actual rainfall intensity R real Get the corresponding water depth distribution. When actual rainfall occurs, if the actual rainfall intensity R real The rainfall intensity R in step S31 k If they are not equal, the corresponding water depth distribution is obtained by interpolation in the pre-calculation scheme, and R k ≤R real <R k+1 , then the distribution of water depth under actual rainfall intensity is: Among them, h real (x, y) represents the actual rainfall intensity R real The depth distribution of water accumulation under k (x, y) represents the rainfall intensity R k The depth distribution of water accumulation, h k+1 (x, y) represents the rainfall intensity R k+1 Distribution of water depth.

6. The rapid simulation method for urban lifeline waterlogging risk assessment according to claim 5 is characterized by: In step S4, the method for calculating the flooding risk level of lifeline facilities includes: Step S41: according to the spatial position of the lifeline facility, obtaining the water depth value corresponding to the position of the lifeline facility from the water depth distribution grid; Step S42: Based on the acquired water depth value and empirical parameters at the location of the lifeline facility, determine the flooding level of the lifeline facility: Among them, h represents the water depth value.

7. The rapid simulation method for urban lifeline waterlogging risk assessment according to claim 6 is characterized by: In step S5, the method for visually displaying the water depth distribution data and the flooding risk level of lifeline facilities includes: step S51: mapping the water depth distribution data into a two-dimensional color map, and color-coding the lifeline facilities according to the lifeline risk level as follows: Where Color(f) represents the color code assigned to the facility according to the risk level of the lifeline facility; Step S52: superimpose the water depth distribution data and the flooding risk level of lifeline facilities into the GIS system.

8. A rapid simulation system for urban lifeline waterlogging risk assessment, characterized by: include: A data processing module is used to obtain key data of the study area, including high-resolution terrain data, underground pipe network distribution data, rainfall data, and urban lifeline system distribution data, and to perform interpolation processing on the high-resolution terrain data and network topology optimization processing on the underground pipe network distribution data; A grid model construction module is used to construct a multi-layered grid model including a surface layer, an underground pipe network layer and a lifeline facility layer based on the key data obtained and processed, wherein each layer of the grid contains information on spatial resolution, physical properties and interaction relationships; The water depth calculation module is used to pre-calculate the water depth distribution data under different rainfall intensities using a numerical simulation method based on a multi-layer superimposed grid model according to the shallow water equation. When the actual rainfall intensity is obtained, the water depth value under the actual rainfall intensity is interpolated using the pre-calculation method; A facility risk calculation module, which is used to superimpose the water depth value with the lifeline facility to calculate the flooding risk level of the lifeline facility, wherein the lifeline facility is a component of the urban lifeline system; The visualization module is used to visualize the water depth distribution data and the flooding risk level of lifeline facilities.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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