Waterlogging and ponding intelligent scheduling method for urban important infrastructure

Abstract

The invention relates to the technical field of urban inland inundation prevention and control, in particular to an intelligent inland inundation and ponding scheduling method for urban important infrastructures. The method comprises the following steps: collecting and processing basic data including urban terrain, pipe network, hydrology, rainfall, ponding and the like; establishing a one-dimensional pipe network and two-dimensional earth surface coupled urban inland inundation model, and verifying and calibrating the model; the method comprises the following steps: setting a protection target and a ponding allowable range in combination with important infrastructures and a ponding buffer area, and performing initialization simulation to obtain an initial ponding condition; scheduling parameters such as pump station flow and valve opening and closing are dynamically adjusted through an optimization algorithm, an optimal scheduling parameter group is generated, and a scheduling strategy is executed; and through comparative analysis of real-time monitoring data and a simulation result, a model initial field is dynamically adjusted, and iterative optimization is performed until a ponding control target is achieved. The method can effectively reduce the waterlogging and ponding risk of urban important infrastructures, and improves the scheduling efficiency and emergency capability of a drainage system.

Description

Technical Field

[0001] The present invention relates to the technical field of urban waterlogging prevention and control, and particularly to an intelligent scheduling method for waterlogging in important urban infrastructure. Background Art

[0002] The problem of urban waterlogging has become the main challenge faced by the current urban flood control and drainage system. Especially under extreme weather conditions, the suddenness and diffusion speed of waterlogging seriously threaten the normal operation of important urban infrastructure (such as transportation hubs, power facilities, etc.). The existing waterlogging prevention and control mainly rely on passive waterlogging model simulation. Although it can identify the location and degree of waterlogging occurrence, due to the lack of an effective dynamic scheduling mechanism, the drainage strategy is passive and lagging, and only passive defense can be achieved.

[0003] In addition, the traditional intelligent drainage method for urban pipe networks mainly focuses on monitoring the internal state of the pipe networks and realizes optimization by adjusting the pipe network drainage strategy. However, this method is mostly limited to the pipe network system itself, ignores the dynamic interaction between the one-dimensional pipe network and surface waterlogging, and cannot simulate the formation and evolution process of surface waterlogging, resulting in insufficient ability to prevent and control the waterlogging risk of important infrastructure. Summary of the Invention

[0004] Based on this, it is necessary for the present invention to provide an intelligent scheduling method for waterlogging in important urban infrastructure to solve at least one of the above technical problems.

[0005] To achieve the above object, an intelligent scheduling method for waterlogging in important urban infrastructure includes the following steps:

[0006] Step S1: Obtain the geographical basic data of the urban area, the pipe network data of the urban area, the hydrological data of the urban area, the rainfall data of the urban area, and the waterlogging data of the urban area; perform a topological check on the urban area pipe network based on the urban area pipe network data to obtain the urban area pipe network inspection data; integrate the surface characteristics of the urban area according to the geographical basic data of the urban area to obtain the urban area surface characteristic data; perform data preprocessing on the urban area rainfall data and the urban area waterlogging data respectively to obtain the rainfall data of the urban area to be analyzed and the waterlogging data of the urban area to be analyzed;

[0007] Step S2: Construct a one-dimensional pipe network model according to the urban area pipe network inspection data; construct a two-dimensional surface model according to the urban area surface characteristic data; couple the one-dimensional pipe network model and the two-dimensional surface model to obtain a coupled waterlogging model, and use the ant colony algorithm to automatically calibrate and optimize the coupled waterlogging model to obtain an urban waterlogging model;

[0008] Step S3: Obtain the important infrastructure data of the urban area, and calculate the waterlogging range of the important infrastructure in the urban area based on the surface feature data of the urban area, so as to obtain the waterlogging range data of the urban important infrastructure; Select the urban waterlogging buffer area based on the surface feature data of the urban area, so as to obtain the urban waterlogging buffer area data, and set the allowable waterlogging range of the waterlogging buffer area for the urban waterlogging buffer area data, so as to obtain the allowable waterlogging range data of the waterlogging buffer area;

[0009] Step S4: Conduct urban waterlogging simulation on the rainfall data of the urban area through the urban waterlogging model, so as to obtain the first urban waterlogging simulation data; Divide the important infrastructure data of the urban area and the urban waterlogging buffer area data based on the first urban waterlogging simulation data, so as to obtain the waterlogging data of the important infrastructure area and the waterlogging data of the urban waterlogging buffer area; Determine the waterlogging scheduling parameters for the waterlogging data of the important infrastructure area and the waterlogging data of the urban waterlogging buffer area according to the waterlogging range data of the urban important infrastructure and the allowable waterlogging range data of the waterlogging buffer area, so as to obtain the urban area waterlogging scheduling parameter group, and upload it to the urban drainage management platform to execute the waterlogging scheduling task;

[0010] Step S5: Update the model parameters of the urban waterlogging model with the urban area waterlogging scheduling parameter group, so as to obtain the urban waterlogging scheduling model, and conduct urban waterlogging simulation on the rainfall data of the urban area through the urban waterlogging scheduling model, so as to obtain the second urban waterlogging simulation data; Obtain the real-time urban area waterlogging data, and conduct a difference analysis on the second urban waterlogging simulation data and the real-time urban area waterlogging data, so as to obtain a scheduling difference report;

[0011] Step S6: Update the model input of the urban waterlogging scheduling model according to the scheduling difference report, so as to obtain an updated urban waterlogging model. Based on the updated urban waterlogging model, re-execute Step S4 and Step S5, and continuously iterate and optimize the scheduling parameters through the difference analysis between the new simulation results and the real-time urban area waterlogging data until the difference meets the preset conditions.

[0012] Optionally, Step S1 is specifically as follows:

[0013] Step S11: Obtain the geographical basic data of the urban area, the pipe network data of the urban area, the hydrological data of the urban area, the rainfall data of the urban area, and the urban waterlogging data. Among them, the geographical basic data of the urban area includes the terrain data of the urban area and the land use type data of the urban area;

[0014] Step S12: Check the consistency of pipeline connection nodes for the pipeline network data in the urban area to obtain the pipeline connection consistency node data, and perform pipe segment continuity check based on the pipeline connection consistency node data to obtain the urban area pipeline network inspection data;

[0015] Step S13: Based on the urban area land use type data, conduct regional division according to the preset land use classification standard to obtain the land use type regional data;

[0016] Step S14: Integrate the regional surface characteristics of the land use type regional data and the urban area terrain data to obtain the urban area surface characteristic data;

[0017] Step S15: Perform data preprocessing on the urban area rainfall data and the urban area waterlogging data respectively to obtain the urban area rainfall data to be analyzed and the urban area waterlogging data to be analyzed.

[0018] Optionally, Step S2 is specifically as follows:

[0019] Step S21: Construct a one-dimensional pipeline network model based on the urban area pipeline network inspection data;

[0020] Step S22: Construct a two-dimensional surface model based on the urban area surface characteristic data;

[0021] Step S23: Match the time step of the coupled model for the one-dimensional pipeline network model and the two-dimensional surface model to obtain a set of time step coordinated models; perform water volume exchange calculation on the set of time step coordinated models to obtain a set of water volume exchange models;

[0022] Step S24: Couple the boundary conditions for the set of water volume exchange models to obtain a boundary condition coupled model; dynamically update the boundary condition coupled model according to the urban area rainfall data to be analyzed and the urban area waterlogging data to be analyzed to obtain a coupled waterlogging model;

[0023] Step S25: Use the ant colony algorithm to automatically calibrate and optimize the coupled waterlogging model to obtain an urban waterlogging model.

[0024] Optionally, Step S21 is specifically as follows:

[0025] Step S211: Extract the pipeline pump station parameter characteristics, pipeline parameter characteristics, and pipeline network liquid level characteristics from the urban area pipeline network inspection data to obtain the pipeline pump station data, pipeline network pipeline data, and pipeline network liquid level data;

[0026] Step S212: Perform formatted integration on the pipeline pump station data, pipeline network pipeline data, and pipeline network liquid level data to obtain a set of pipeline parameters;

[0027] Step S213: Construct a pipe network topology structure model based on the pipe network parameter set;

[0028] Step S214: Set the water flow exchange boundary conditions based on the pipe network liquid level data and the pipe network pump station data, so as to obtain the water flow exchange boundary conditions;

[0029] Step S215: Construct a one-dimensional pipe network model according to the pipe network topology structure model and the water flow exchange boundary conditions.

[0030] Optionally, step S22 is specifically as follows:

[0031] Step S221: Extract the regional elevation characteristics from the urban area surface feature data, so as to obtain the urban area elevation data;

[0032] Step S222: According to the land use type regional data and in combination with the preset corresponding relationship between land use and surface attributes, deduce the infiltration conditions and Manning coefficients of different regions, so as to obtain the regional infiltration condition data and the regional Manning coefficient data;

[0033] Step S223: Set the hydrological boundary conditions based on the urban area hydrological data, so as to obtain the hydrological boundary conditions;

[0034] Step S224: Construct a two-dimensional surface model according to the water volume exchange boundary conditions, the urban area elevation data, the regional infiltration condition data and the regional Manning coefficient data.

[0035] Optionally, step S3 is specifically as follows:

[0036] Step S31: Obtain the important infrastructure data of the urban area, and extract the geographical locations of the important infrastructure and the waterlogging prevention requirements from the important infrastructure data of the urban area, so as to obtain the important infrastructure geographical location data and the important infrastructure waterlogging prevention requirement data;

[0037] Step S32: Extract the surface features of the important infrastructure locations from the urban area surface feature data according to the important infrastructure geographical location data, so as to obtain the surface feature data of the important infrastructure locations;

[0038] Step S33: Calculate the upper limit of the waterlogging area and the upper limit of the waterlogging depth of the important infrastructure according to the surface feature data of the important infrastructure locations and the important infrastructure waterlogging prevention requirement data, so as to obtain the waterlogging range data of the urban important infrastructure;

[0039] Step S34: Select urban waterlogging buffer zones based on the surface feature data of urban areas, so as to obtain urban waterlogging buffer zone data, and integrate the surface features of the buffer zones for the urban waterlogging buffer zone data, so as to obtain the surface feature data of the waterlogging buffer zones;

[0040] Step S35: Set the threshold for the waterlogging area and the threshold for the waterlogging depth for the surface feature data of the waterlogging buffer zones, so as to obtain the data on the allowable range of waterlogging in the waterlogging buffer zones.

[0041] Optionally, step S34 is specifically as follows:

[0042] Extract the terrain elevation features, permeability features, and vegetation coverage features from the surface feature data of urban areas, so as to obtain the urban area vegetation coverage data, urban area permeability data, and urban area terrain elevation data;

[0043] Extract the pipe network distribution features from the urban area pipe network inspection data, so as to obtain the drainage pipe network distribution data;

[0044] Identify the low terrain areas from the urban area terrain elevation data, so as to obtain the low terrain area data;

[0045] Select the high vegetation coverage areas from the urban area vegetation coverage data, so as to obtain the high vegetation coverage area data;

[0046] Screen the high permeability areas from the urban area permeability data, so as to obtain the high permeability area data;

[0047] Select the pipe network coverage areas according to the drainage pipe network distribution data, so as to obtain the pipe network coverage area data;

[0048] Allocate priorities for land use types based on the urban area land use type data, so as to obtain the land use type priority data, and divide the secondary priority areas according to the land use type priority data, so as to obtain the land use secondary priority area data;

[0049] Perform a spatial overlay analysis on the low terrain area data, high vegetation coverage area data, high permeability area data, pipe network coverage area data, and land use secondary priority area data to generate urban waterlogging buffer zone data;

[0050] Integrate the surface features of the buffer zones for the urban area surface feature data based on the urban waterlogging buffer zone data, so as to obtain the surface feature data of the waterlogging buffer zones.

[0051] Optionally, step S4 is specifically as follows:

[0052] Step S41: Simulate urban waterlogging accumulation for the rainfall data of the urban area through the urban waterlogging model to obtain the first urban waterlogging accumulation simulation data;

[0053] Step S42: Based on the first urban waterlogging accumulation simulation data, divide the important infrastructure data and urban waterlogging buffer data in the urban area to obtain the important infrastructure area waterlogging accumulation data and the urban waterlogging buffer area waterlogging accumulation data, and set the waterlogging benchmark according to the important infrastructure area waterlogging accumulation data and the urban waterlogging buffer area waterlogging accumulation data to obtain the initial simulated waterlogging benchmark data;

[0054] Step S43: Extract the control parameters of the pump stations and valves from the pipeline inspection data of the urban area to obtain the pump station control parameter data and the valve control parameter data;

[0055] Step S44: Set the objective function and constraints according to the waterlogging range data of important urban infrastructure and the allowable waterlogging range data of the waterlogging buffer to obtain the fitness function and the constraint condition function;

[0056] Step S45: According to the initial simulated waterlogging benchmark data, the fitness function and the constraint condition function, generate an initial particle swarm based on the pump station control parameter data and the valve control parameter data, calculate the initial fitness values of the particles in the initial particle swarm, select the global optimal particle from the initial fitness values of the particles, and record the individual optimal solutions of each particle to obtain the global optimal particle and the individual optimal solutions of the particles;

[0057] Step S46: Through the constraint condition function, and perform iterative optimization according to the global optimal particle and the individual optimal solutions of the particles until the preset early stop condition is met to obtain the global optimal solution;

[0058] Step S47: Convert the waterlogging accumulation scheduling parameters for the global optimal solution to obtain the urban area waterlogging accumulation scheduling parameter group and upload it to the urban drainage management platform to execute the waterlogging accumulation scheduling task.

[0059] Optionally, Step S44 is specifically:

[0060] Step S441: Construct a penalty function based on the allowable waterlogging range data of the waterlogging buffer;

[0061] Step S442: Construct a fitness function according to the penalty function and the waterlogging range data of important urban infrastructure, where the fitness function is specifically:

[0062]

[0063] where f is the fitness function value, n is the total number of critical infrastructures, i is the index of the critical infrastructure, α is the global weight of the buffer penalty, and w i is the weight used to control the water depth of the i-th critical infrastructure in the fitness function, and k i is the weight used to control the water area of the i-th critical infrastructure in the fitness function, D i is the water depth of the i-th critical infrastructure, d i is the target water depth of the i-th critical infrastructure, A i is the water area of the i-th critical infrastructure, a i is the target water area of the i-th critical infrastructure, At is the total water area excluding the water accumulation buffer, and A tr is the target total water area excluding the water accumulation buffer, and K is the weight of the total water area;

[0064] In the present invention, a fitness function is constructed by referring to relevant materials and derivations. This function fully considers the global weight α of the buffer penalty that affects the fitness function value f, the weight w i used to control the water depth of the i-th critical infrastructure in the fitness function, the weight k i used to control the water area of the i-th critical infrastructure in the fitness function, the water depth D i of the i-th critical infrastructure, the target water depth d i of the i-th critical infrastructure, the water area A i of the i-th critical infrastructure, the target water area a i of the i-th critical infrastructure, the total water area A t excluding the water accumulation buffer, the target total water area A tr excluding the water accumulation buffer, and the weight K of the total water area, forming a functional relationship:

[0065]

[0066] Among them, The first part first calculates the deviations of the water depth and area (i.e., the difference between the actual value and the target value). The max(0, X) function is used to ensure that when the deviation is negative, it will not have a negative impact on the fitness function. That is, when the water depth or area does not exceed the target value, this term will not impose a penalty on the fitness. When the water depth or area exceeds the target value, it will impose a positive penalty on the fitness function. This term controls the difference between the water depth and area and the target value, ensuring that when optimizing the drainage system, attention is paid to the safety of the infrastructure and the impact of excessive water accumulation on the city is reduced. Through w i and k iEqual weight coefficients can adjust the priorities of waterlogging depth and area according to the specific requirements of different infrastructures, so as to achieve more refined control. K·max(0,A t ―A tr ) part reflects the total control of the waterlogging area in the whole system. When the actual waterlogging area A t exceeds the preset target A tr , a penalty value is generated, and this penalty value is proportional to the weight K. This means that if the total waterlogging area exceeds the target, the fitness value will increase, thus guiding the optimization algorithm to avoid large-area waterlogging as much as possible. This item ensures that the drainage system not only controls the waterlogging conditions of individual infrastructures, but also pays attention to the waterlogging conditions of the whole city or region, and avoids large-scale waterlogging disasters. By introducing the weight K of the total waterlogging area, the consistency of the drainage capacity and waterlogging control objectives of each region in the city can be ensured during the overall drainage system optimization process. The introduction of α·f buffer is aimed at ensuring the effectiveness of the buffer zone, avoiding excessive waterlogging concentration in the buffer zone, or allowing the waterlogging in the buffer zone to exceed the predetermined safety range, so as to avoid system collapse in extreme weather conditions. The buffer zone is used to temporarily hold the waterlogging that cannot be immediately drained, ensuring that in extreme rainfall conditions, the waterlogging will not directly threaten the safety of important infrastructures. By punishing the situation where the buffer zone capacity is exceeded, the effectiveness of the buffer zone can be ensured. The weight coefficient α can flexibly adjust the importance of the buffer zone in the whole drainage optimization, so as to give priority to ensuring the sufficient buffer zone capacity in extreme cases. This fitness function comprehensively evaluates the waterlogging depth and area of each infrastructure, the total waterlogging area, and the buffer zone capacity, ensuring that both local and global drainage objectives are taken into account during the optimization process. By appropriately setting the weight coefficients w i , k i , K, and α, the optimization strategy can be adjusted according to the specific requirements of different infrastructures, so as to reduce the water damage impact on the infrastructures and ensure the safety of urban operation.

[0067] Step S443: Construct a control parameter constraint condition function based on the pump station control parameter data and the valve control parameter data, so as to obtain the constraint condition function.

[0068] Optionally, step S45 is specifically as follows:

[0069] Step S451: Randomly combine the scheduling parameters based on the constraint condition function, the pump station control parameter data, and the valve control parameter data, so as to obtain the control scheduling parameter combination data;

[0070] Step S452: Initialize the particles according to the control scheduling parameter combination data, so as to obtain the initialized particle swarm;

[0071] Step S453: Simulate urban waterlogging accumulation for the rainfall data of urban areas and the initialized particle swarm through the urban waterlogging model, so as to obtain the initialized particle simulation data;

[0072] Step S454: Calculate the initial fitness of particles for the initialized particle simulation data through the fitness function, so as to obtain the initial fitness value of particles;

[0073] Step S455: Select the individual optimal solution of particles for the initial fitness value of particles, so as to obtain the individual optimal solution of particles, and screen the global optimal solution according to the individual optimal solution of particles, so as to obtain the global optimal particle.

[0074] The present invention effectively makes up for the lack of linkage between traditional waterlogging prediction and scheduling technologies through multi-dimensional data integration and intelligent scheduling means, and has significant beneficial effects. By acquiring and preprocessing urban area geographical basic data, pipe network data, hydrological data, rainfall data and waterlogging data, an accurate data input basis is provided for subsequent analysis. These data cover basic data such as urban topography, pipe network, hydrology, rainfall, and waterlogging, making the decision of waterlogging scheduling more scientific and accurate. In the process of pipe network model construction and coupled optimization, by combining the one-dimensional pipe network model with the two-dimensional surface model, and using the ant colony algorithm for automatic calibration and optimization, the optimal solution can be quickly searched under multi-variable and complex conditions, greatly improving the calibration efficiency and accuracy of the model. In view of the limitations of the existing system, the introduction of the waterlogging buffer zone provides an effective auxiliary measure for urban waterlogging prevention and control. The waterlogging buffer zone can serve as a buffer space between surface waterlogging and the drainage system, and effectively reduce the instantaneous pressure of the drainage system by absorbing the waterlogging load in a short period of time. Through the coupling simulation of the one-dimensional pipe network model and the two-dimensional surface model, combined with the optimization algorithm to dynamically control the dispatching parameters such as the pipe network pump station and valve, the accumulated water can be scientifically guided to the appropriate accumulated water buffer zone, making full use of the storage capacity of the buffer zone, avoiding the risk of accumulated water in important infrastructure areas, and realizing the refined and efficient management of waterlogging prevention and control. By analyzing the data of important infrastructure in urban areas and calculating the scope of accumulated water, it is possible to clearly identify the key infrastructure that may be threatened by waterlogging, and formulate reasonable preventive measures for it. Combined with the selection and optimization of the accumulated water buffer zone, a more flexible and efficient dispatching strategy can be provided for the urban flood control and drainage system, ensuring that important facilities can be protected in time when waterlogging occurs, avoiding or reducing losses. The waterlogging simulation and the optimization iteration of the dispatching parameters fully consider the actual rainfall conditions, accumulated water data and drainage network capacity, ensuring that the dispatching strategy can be adjusted in real time in the dynamically changing waterlogging environment. For example, by analyzing the waterlogging difference report, the dispatching parameters can be optimized in real time, the initial field conditions of the model can be updated, and the inefficiency or lag reaction of the traditional model can be avoided. Overall, the advantage of this method is that it integrates the coupling model of one-dimensional pipe network and two-dimensional surface, optimizes pipe network drainage, and introduces waterlogging buffer zone as a key auxiliary measure for waterlogging prevention and control. By optimizing the scheduling algorithm, it provides intelligent and accurate solutions for waterlogging problems in important urban infrastructure. It not only has the ability to predict waterlogging, but also scientifically guides waterlogging to the buffer zone by dynamically adjusting the drainage system to relieve the pressure on the drainage system. Active defense and intelligent optimization are achieved in drainage scheduling and waterlogging prevention and control management, effectively reducing the impact of waterlogging on important urban infrastructure. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0076] Figure 1 It is a schematic diagram of the step process of an intelligent scheduling method for urban waterlogging in important urban infrastructure according to the present invention;

[0077] Figure 2 It is a distribution map of urban area pipe networks in the urban area pipe network inspection data in step S1;

[0078] Figure 3 It is a schematic diagram of waterlogging in the urban area before waterlogging scheduling;

[0079] Figure 4 It is a schematic diagram of waterlogging in the urban area after waterlogging scheduling by the present invention;

[0080] The realization, functional features and advantages of the object of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. Specific embodiments

[0081] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work belong to the scope of protection of the present invention.

[0082] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, so repeated descriptions of them will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0083] It should be understood that although the terms "first", "second", etc. may be used here to describe each unit, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0084] To achieve the above object, please refer to Figures 1 to 4 , the present invention provides an intelligent scheduling method for urban waterlogging in important urban infrastructure, which can be an intelligent scheduling method for urban infrastructure waterlogging. The method includes the following steps:

[0085] Step S1: Obtain the urban regional geographical basic data, urban regional pipe network data, urban regional hydrological data, urban regional rainfall data, and urban regional waterlogging data; perform a topological check on the urban regional pipe network based on the urban regional pipe network data to obtain the urban regional pipe network inspection data; integrate the urban regional surface features according to the urban regional geographical basic data to obtain the urban regional surface feature data; perform data preprocessing on the urban regional rainfall data and the urban regional waterlogging data respectively to obtain the urban regional rainfall data to be analyzed and the urban regional waterlogging data to be analyzed;

[0086] In this embodiment, the geographical information of the urban area is obtained through the GIS system, including topography, geomorphology, hydrology, land use type, etc. The data format is GeoTIFF or Shapefile for subsequent analysis. Data including pipe positions, diameters, materials, pipe network nodes, etc. are extracted from the SCADA system of the urban drainage system or the municipal pipe network management platform. The data is stored in a database (such as PostGIS) and imported into a modeling tool for topological analysis. The recent 24-hour or long-term historical rainfall data is obtained through a meteorological station or a ground monitoring station. The data form is time series data (such as CSV format), including time, rainfall amount, and rainfall intensity. The waterlogging time, waterlogging depth, duration, etc. data are obtained from the waterlogging monitoring stations in the urban area, and the format is usually CSV or Excel. A pipe network topological check is performed using geographical information system (GIS) software or dedicated pipe network management software (such as EPANET, SWMM). The inspection content includes checking the consistency of pipe connections, the continuity of pipes, and verifying the integrity of the network topological structure. Combining the urban geographical basic data, analyze the surface features of the city, such as the distribution of urban green spaces, buildings, roads, and water bodies. Through remote sensing image processing technology, extract the boundary information of buildings, roads, and water bodies, and analyze it through GIS tools to obtain the surface cover type. Perform time series data filling, missing value imputation, outlier detection, etc. on the rainfall data and the waterlogging data to obtain the urban regional rainfall data to be analyzed and the urban regional waterlogging data.

[0087] Step S2: Construct a one-dimensional pipe network model based on the urban regional pipe network inspection data; construct a two-dimensional surface model based on the urban regional surface feature data; perform model coupling on the one-dimensional pipe network model and the two-dimensional surface model to obtain a coupled waterlogging model, and use the ant colony algorithm to automatically calibrate and optimize the coupled waterlogging model to obtain an urban waterlogging model;

[0088] In this embodiment, according to the pipe network topology inspection data, a one-dimensional pipe network model of the urban area is constructed using a one-dimensional pipe network modeling tool (such as SWMM). The model includes modules such as pipes, pump stations, and outfalls, and the parameters include the geometric attributes of the pipes (such as length, diameter), friction coefficient, sub-catchment areas, rainfall data, and the operating characteristics of the pump stations, etc. Combining with GIS tools and urban geographic basic data, a professional two-dimensional surface modeling tool (such as MIKE) is used to construct a two-dimensional surface model of the city. The two-dimensional surface model includes characteristic parameters such as urban terrain, surface permeability, and surface friction coefficient, which are used to simulate the surface runoff behavior after rainfall. The dynamic coupling of the one-dimensional pipe network model and the two-dimensional surface model is realized through programming. An interface program is written using Python or other programming languages to call the API of SWMM (such as pyswmm) and the data interface of the two-dimensional model to complete the data interaction and coupled simulation between the models. During the programming process, the flow rate of the one-dimensional pipe network and the water volume of the two-dimensional surface are used as the key input and output parameters, and the model boundary conditions are dynamically updated. In the optimization and calibration stage, for the key parameters in the coupled waterlogging model, the parameters of the SWMM pipe network model and the parameters of the two-dimensional surface model are adjusted respectively, and the ant colony algorithm is used to quickly realize the automatic calibration of the model parameters, and finally an optimized coupled waterlogging model is generated.

[0089] Step S3: Obtain the important infrastructure data of the urban area, and calculate the waterlogging range of the important infrastructure in the urban area based on the surface feature data of the urban area, so as to obtain the important infrastructure waterlogging range data of the city; select the urban waterlogging buffer area based on the surface feature data of the urban area, so as to obtain the urban waterlogging buffer area data, and set the allowable waterlogging range of the waterlogging buffer area for the urban waterlogging buffer area data, so as to obtain the allowable waterlogging range data of the waterlogging buffer area.

[0090] In this embodiment, according to the surface feature data of the urban area and the waterlogging prevention requirements of important infrastructure, the waterlogging range of important infrastructure (such as hospitals, power plants, transportation hubs, etc.) in the urban area is calculated, including the waterlogging depth and area. The calculation of the waterlogging range comprehensively considers the topographic features and functional protection requirements of the location where the facilities are located to ensure the scientificity and pertinence of the evaluation results. Based on the surface feature data of the urban area, including factors such as topography, elevation, vegetation coverage rate, permeability, distribution of drainage pipelines, and secondary priority of land use, a spatial overlay analysis method is used to select areas suitable as waterlogging buffer zones around important infrastructure and generate urban waterlogging buffer zone data. According to the functional requirements of important infrastructure, the maximum depth and duration of allowable waterlogging are set. For example, for key areas such as transportation hubs or power facilities, the allowable waterlogging depth and duration need to be strictly restricted to ensure the normal operation of the facilities; while for areas such as park green spaces, if they are selected as waterlogging buffer zones, the upper limits of the allowable waterlogging depth and duration can be appropriately increased to give full play to the storage and regulation capacity of the buffer zones and reduce the pressure on the overall drainage system.

[0091] Step S4: Perform urban waterlogging simulation on the rainfall data of the urban area through the urban waterlogging model to obtain the first urban waterlogging simulation data; based on the first urban waterlogging simulation data, divide the waterlogging of important infrastructure data and urban waterlogging buffer zone data in the urban area to obtain the waterlogging data of important infrastructure areas and the waterlogging data of urban waterlogging buffer zones; determine the waterlogging dispatching parameters for the waterlogging data of important infrastructure areas and the waterlogging data of urban waterlogging buffer zones according to the waterlogging range data of urban important infrastructure and the allowable waterlogging range data of the waterlogging buffer zones, so as to obtain a set of urban waterlogging dispatching parameters and upload them to the urban drainage management platform to execute the waterlogging dispatching task;

[0092] In this embodiment, an optimized urban waterlogging model is used to simulate the waterlogging accumulation situation by inputting future forecast rainfall data. The simulation results include the waterlogging depth and scope. Based on the first-round waterlogging simulation results, the urban area is divided into waterlogging accumulation areas. Geographic Information System (GIS) is used for spatial analysis to determine which areas have the most serious waterlogging and which infrastructure is most affected, serving as the initial input for algorithms such as Particle Swarm Optimization (PSO). An urban waterlogging scheduling model is constructed based on the PSO algorithm, aiming to minimize the waterlogging depth and area in the important infrastructure areas. A fitness function is constructed and a penalty function for the waterlogging scope and depth in the buffer area is set. According to the objective function, scheduling parameters such as the start-stop time, operating power of the pump station, and the opening and closing states of the drainage valves are optimized. Combining with the model constraints, particle initialization, iterative optimization, and fitness calculation are carried out. Finally, the global optimal solution is output, generating an optimized scheduling parameter configuration plan for the pump station and valves, that is, the urban area waterlogging accumulation scheduling parameter group, and the parameter group is uploaded to the urban drainage management platform to execute the waterlogging scheduling task.

[0093] Step S5: Update the model parameters of the urban waterlogging model with the urban area waterlogging accumulation scheduling parameter group, thereby obtaining the urban waterlogging scheduling model, and perform urban waterlogging accumulation simulation on the urban area rainfall data through the urban waterlogging scheduling model, thereby obtaining the second urban waterlogging accumulation simulation data; obtain the real-time urban area waterlogging data, and perform a difference analysis on the second urban waterlogging accumulation simulation data and the real-time urban area waterlogging data, thereby obtaining a scheduling difference report;

[0094] In this embodiment, the output urban area waterlogging accumulation scheduling parameter group is executed, and parameters such as the pump station flow rate and the opening and closing states of the valves are applied to the waterlogging model constructed in Step S2, converted into the input parameters of the model, and the urban waterlogging model is re-run. According to the model operation results, after running for a period of time, during the execution of the scheduling plan, compare the real-time monitored waterlogging data obtained from the monitoring station with the waterlogging situation under the optimized scheduling plan, especially the waterlogging situation in the important infrastructure and the waterlogging buffer area. Compare and analyze the real-time monitored waterlogging data and the model simulation results, analyze the deviations and their causes during the scheduling process, including model parameters (such as whether the pump station flow rate and valve opening are set reasonably), equipment operation status (such as whether the pump station operates according to the plan and whether the valve operation is normal), and external environment impacts (such as the actual rainfall not matching the forecast or sudden rainfall changes, etc.). Evaluate the effectiveness of the existing drainage plan. Finally, a feedback conclusion is formed to clarify the difference areas and reasons, and improvement suggestions for optimizing the scheduling parameters or modifying the model are proposed, providing a basis for subsequent optimization.

[0095] Step S6: Update the model input of the urban waterlogging dispatch model according to the dispatch difference report, so as to obtain an updated urban waterlogging model. Based on the updated urban waterlogging model, re-execute Step S4 and Step S5, and continuously iteratively optimize the dispatch parameters through the difference analysis between the new simulation results and the real-time urban area water accumulation data until the difference meets the preset conditions.

[0096] In this embodiment, when there is a deviation between the feedback dispatch result of the dispatch difference report and the measured result, update the model input according to the dispatch difference report, including initial conditions such as the latest rainfall forecast, surface water accumulation status, and outer river channel water level, re-execute Step S4 to generate a new set of dispatch parameters, ensure that the dispatch strategy adapts to the latest conditions through dynamic iteration, and then perform the difference analysis in Step S5 until the measured and simulated results are consistent during the operation.

[0097] Optionally, Step S1 is specifically:

[0098] Step S11: Obtain the geographical basic data of the urban area, the pipe network data of the urban area, the hydrological data of the urban area, the rainfall data of the urban area, and the waterlogging data of the urban area. Among them, the geographical basic data of the urban area includes the terrain data of the urban area and the land use type data of the urban area;

[0099] In this embodiment, obtain the geographical information of the urban area through remote sensing technology and geographic information system (GIS), including terrain, landform, hydrology, land use type, etc. The terrain data can be obtained through a digital elevation model (DEM), and the land use type data is obtained through the interpretation of satellite images and the current land use map. The land use type data and river data can also be generated based on land use surveys or classification data. For hydrological data, the hydrological data of this area, including precipitation, river flow, river water level, etc., can be obtained through the local hydrological bureau or water authority. For the pipe network data of the urban area, the pipe network data of the urban area includes pipe network layout, pipeline parameters, pump station operation characteristics, etc., and can be obtained by referring to the water conservancy and drainage pipe network drawings provided by the urban planning department and conducting actual pipe network surveys using intelligent sensors and underground detection technologies (such as terrestrial laser scanning). The rainfall data and waterlogging data are collected through meteorological monitoring stations and basin hydrological monitoring stations, and combined with rain gauges and ground water accumulation sensors. The urban area rainfall data includes duration rainfall data and real-time monitored rainfall data; the urban area waterlogging data includes historical waterlogging data and real-time monitored water accumulation data.

[0100] Step S12: Check the consistency of the pipe network connection nodes for the pipe network data of the urban area, so as to obtain the pipe network connection consistency node data, and perform pipe segment continuity checks according to the pipe network connection consistency node data, so as to obtain the urban area pipe network inspection data;

[0101] In this embodiment, based on the inspection data of the urban regional pipe network, a one-dimensional pipe network model is constructed using the SWMM modeling method. By extracting pipe network nodes (such as rainwater wells, outlets, pumping stations) and pipe segment information (such as pipe length, diameter, slope, roughness coefficient), a complete pipe network structure is set up. According to the division of sub-catchment areas, parameters such as the area, slope, and impervious ratio of each area are defined and associated with the corresponding nodes. Rainfall station data is used to define rainfall conditions, which are distributed to sub-catchment areas to simulate the dynamic changes in rainfall intensity and distribution. A complete one-dimensional pipe network model containing information such as nodes, pipe segments, sub-catchment areas, and rainfall stations is generated.

[0102] Step S13: Based on the urban regional land use type data, regional division is carried out according to the preset land use classification standard, so as to obtain the land use type regional data;

[0103] In this embodiment, the land use type data is classified from the natural resources and planning bureau where the target city is located according to the local land use classification standard.

[0104] Step S14: Integrate the land use type regional data and the urban regional terrain data for regional surface feature integration, so as to obtain the urban regional surface feature data;

[0105] In this embodiment, spatial overlay analysis is carried out on the land use type regional data and the urban regional terrain data through the GIS platform. In this process, the land use type data serves as the "background layer", and the terrain data serves as the "surface elevation layer". The combination of the two can more intuitively display the land use patterns under different terrain conditions. The spatial data of the two are fused through algorithms (such as weighted average method, spatial interpolation method, etc.) to generate new urban regional surface feature data.

[0106] Step S15: Respectively perform data preprocessing on the urban regional rainfall data and the urban regional waterlogging data, so as to obtain the urban regional rainfall data to be analyzed and the urban regional waterlogging data to be analyzed.

[0107] In this embodiment, preprocessing is performed on the rainfall data and the waterlogging data. The rainfall data is sourced from meteorological monitoring stations or rain gauges, and the waterlogging data is obtained through manually reported waterlogging ledgers and water accumulation monitoring devices. The preprocessing work includes data cleaning and missing value filling, such as using the interpolation method to supplement the missing rainfall data. For the waterlogging data, data smoothing processing (such as moving average method) is used to remove noise, and the frequency and duration of waterlogging events are determined through time series analysis.

[0108] Optionally, step S2 is specifically as follows:

[0109] Step S21: Construct a one-dimensional pipe network model according to the urban regional pipe network inspection data;

[0110] In this embodiment, based on the inspection data of the urban regional pipe network, a one-dimensional pipe network model is constructed using the SWMM modeling method. By extracting pipe network nodes (such as rainwater wells, pumping stations), pipe segment information (such as pipe length, diameter, slope, etc.), pumping stations, outfalls, sub-catchment areas, rainfall stations, etc., and setting the pipe network structure and related parameters (such as friction coefficient, initial water level), a complete one-dimensional pipe network model is generated, providing basic data for subsequent hydraulic simulation.

[0111] Step S22: Construct a two-dimensional surface model according to the urban regional surface feature data;

[0112] In this embodiment, according to the urban regional surface feature data, a two-dimensional surface grid model is generated using a digital elevation model (DEM), and surface characteristic parameters (such as roughness coefficient, impermeability rate, permeability, etc.) are extracted and assigned. Combining rainfall input and boundary conditions, a surface water dynamic model is set.

[0113] Step S23: Match the time step of the one-dimensional pipe network model and the two-dimensional surface model to obtain a set of time step coordinated models; perform water volume exchange calculations on the set of time step coordinated models to obtain a set of water volume exchange models;

[0114] In this embodiment, different time steps are used for the one-dimensional and two-dimensional models. The one-dimensional model is calculated using a larger specified time step (by "larger" it means Δt 1 D > Δt 2 D), and the two-dimensional pipe network model is calculated using a smaller adaptive time step for multiple calculations. This can reduce the computational amount of the two-dimensional model while ensuring that water volume data is exchanged between the two models at the same time point. That is: Δt 1 D = N * Δt 2 D; where, Δt 1 D is the time step of the one-dimensional pipe network model; Δt 2 D is the time step of the two-dimensional pipe network model; N is an integer, indicating that N two-dimensional model time step simulations are performed within one one-dimensional model time step. The water volume exchange calculation is as follows: when the pipe network water level exceeds the surface elevation of the well point, the overflow volume is calculated based on the water level difference and the overflow cross-sectional area of the pipe network model; when the surface water head height is greater than the well point water head height, the orifice outflow or weir flow formula is used to calculate the return flow of accumulated water. Assume that the time step of the one-dimensional pipe network model is 5 seconds, and the two-dimensional surface model will dynamically adjust the time step (such as 1 second, 0.5 second, etc.) according to simulation requirements within these 5 seconds to complete multiple calculations. By adaptively adjusting the time step, at the end of each one-dimensional model time step, its calculation time is accurately synchronized to the end point of the current one-dimensional step. At the end of each one-dimensional time step, the total simulation time of the two-dimensional model is synchronized with the one-dimensional model time, ensuring the timeliness and consistency of data exchange and realizing the overall coordinated operation of the model.

[0115] Step S24: Couple the boundary conditions of the water volume exchange model set to obtain a boundary condition coupled model; dynamically update the boundary condition coupled model according to the rainfall data and waterlogging data of the urban area to be analyzed, so as to obtain a coupled waterlogging model.

[0116] In this embodiment, the water volume exchange between the pipe network and the ground surface is realized through the well point positions of the pipes. The well points of the one-dimensional pipe network serve as the point source boundary conditions of the two-dimensional ground surface model. When the drainage capacity of the pipe network reaches the upper limit, the water volume overflows to the ground surface through the well points to simulate the accumulation of water; when the ground water level exceeds the elevation of the well points, the accumulated water flows back into the pipe network through the well points to realize drainage.

[0117] This step requires determining the boundary conditions of the water flow according to the geographical information of the city and the position of the pipe network. Then, the actual rainfall data, waterlogging data, surface water accumulation, and river water level monitoring data are used as dynamic inputs to adjust the input boundary conditions of the model. By dynamically updating the model, the precipitation situation, surface flow situation, and pipe network load can be reflected in real time, and then a coupled waterlogging model can be obtained.

[0118] Step S25: Use the ant colony algorithm to automatically calibrate and optimize the coupled waterlogging model to obtain an urban waterlogging model.

[0119] In this embodiment, by comparing the water accumulation depths of the measured water accumulation points and the model-simulated water accumulation points in the city, the root mean square error (RMSE) and the mean absolute error (MAE) are used for verification. RMSE measures the square error between the simulation and the measured water accumulation depth to evaluate the overall accuracy of the model; MAE calculates the average value of the absolute errors between the two to reflect the prediction deviation. If the error is within the allowable range, the model continues to run; if the error is large, the model parameters are adjusted. When the model verification error is large, the ant colony algorithm is used to automatically calibrate and optimize the model parameters, including the following steps: Initialize the key parameters of the model, including pipe network roughness, surface permeability, runoff coefficient, surface roughness, etc. The ant colony algorithm generates multiple parameter combinations, each combination representing a set of model parameters. The model simulates the urban waterlogging accumulation according to these parameters and compares with the measured water accumulation data to output the error value. Increase the "pheromone" weight for the parameter combinations with smaller errors, and preferentially retain these combinations as the optimization direction to gradually guide the subsequent parameter search process. Through multiple iterations, the model parameters converge to the optimal combination, minimizing the error between the surface water accumulation simulation result of the waterlogging model and the measured water accumulation depth data, and completing the parameter calibration and optimization.

[0120] Optionally, step S21 is specifically:

[0121] Step S211: Extract the parameter characteristics of the pipe network pump stations, the parameter characteristics of the pipelines, and the liquid level characteristics of the pipe network from the inspection data of the urban area pipe network, so as to obtain the pipe network pump station data, the pipe network pipeline data, and the pipe network liquid level data;

[0122] In this embodiment, when extracting data from the pipe network pump stations, attention is paid to the working status of the pump stations (such as start-stop time, operating efficiency, pump flow, etc.). For the extraction of pipeline parameters, the physical characteristics such as the diameter, material, length quantity, starting point elevation, and inner wall roughness of the pipeline are mainly concerned. For the pipe network liquid level data, real-time liquid level data is collected according to the liquid level gauges installed in the pipe network. Through data cleaning and preprocessing, noise and outliers are removed to ensure the obtained effective pipe network pump station data, pipe network pipeline data, and pipe network liquid level data.

[0123] Step S212: Format and integrate the pipe network pump station data, the pipe network pipeline data, and the pipe network liquid level data, so as to obtain a pipeline parameter set;

[0124] In this embodiment, a data processing tool is used to integrate the pipe network pump station data, the pipeline data, and the pipeline flow data in a unified format. Specifically, the pipe network pump station data can be integrated according to the time series to form a table of the operation status of the pump stations, including the operation rule data of each pump station under different working conditions. The physical parameters of the pipeline (such as pipeline length, diameter, material, friction coefficient, starting point elevation, etc.) are extracted from multiple data sources and integrated into a unified database structure. In this process, the Pandas library in Python can be used to clean and integrate data in different formats. The liquid level data of the key nodes of the pipe network collected by the liquid level sensors is time-synchronized and formatted into a unified time series data set to ensure the time consistency between the data. Through data fusion technology, a complete pipeline parameter set is generated, including pump station parameters, pipeline parameters, and liquid level data, forming a set of standardized data sets that can be used for further analysis. And a unified data storage format (such as CSV, JSON, or database table) is adopted. Finally, a standardized pipeline parameter set is output through a data processing program (such as pandas in Python) for subsequent analysis and modeling.

[0125] Step S213: Build a pipe network topology structure model based on the pipeline parameter set;

[0126] In this embodiment, based on the integrated pipeline parameter set, a pipe network topology structure model is built according to the SWMM modeling method. The model takes nodes and pipelines as basic units. Nodes are used to represent facilities such as inspection wells, pump stations, and access points, and pipelines are used to represent the drainage pipelines connecting the nodes. During the modeling process, the spatial position and attributes of the nodes are determined according to the pipeline parameter set, and the geometric characteristics (such as length, slope, diameter) and hydraulic characteristics (such as roughness coefficient) of the pipelines are used to describe the pipeline attributes.

[0127] Step S214: Set the water flow exchange boundary conditions based on the pipe network liquid level data and the pipe network pumping station data, so as to obtain the water flow exchange boundary conditions;

[0128] In this embodiment, according to the extracted pipe network liquid level data and pumping station operation data, the boundary conditions for water flow exchange are set to ensure the rationality and consistency of the boundary conditions. For the pumping station boundary conditions, based on the start-stop state, operation flow rate, and time data of the pumping station, the pumping station boundary conditions are defined to ensure accurate simulation of the water flow exchange between the pumping station and the pipe network. For the pipe liquid level boundary conditions, through the real-time monitored liquid level data (or historical liquid level data), combined with the operation characteristics of the pipe network, the inlet and outlet liquid level ranges of the pipe nodes are determined and input into the model as the boundary liquid level conditions.

[0129] Step S215: Construct a one-dimensional pipe network model according to the pipe network topology structure model and the water flow exchange boundary conditions.

[0130] In this embodiment, based on the constructed pipe network topology structure and the set water flow exchange boundary conditions, a one-dimensional hydraulic model of the pipe network is constructed. This model generally assumes that the water flow along the pipeline is approximately one-dimensional flow, and factors such as pipeline flow resistance, friction loss, and local resistance are considered. Specifically, during implementation, first divide the pipe network into multiple small segments in the model, and each small segment of the pipeline is regarded as a one-dimensional flow model, considering parameters such as the length, diameter, and slope of the pipeline. Then, determine the inlet and outlet boundary values of each pipe segment through water flow boundary conditions such as the flow rate of the pumping station. For the construction of the one-dimensional model, hydraulic equations (such as the Hagen - Poiseuille flow formula, energy equation, etc.) are usually used to describe the water flow state of each pipe segment. The pipe network is solved by numerical methods (such as the finite difference method or the finite element method) to obtain the flow rate and liquid level distribution of each point in the pipe network. Professional hydraulic simulation software (such as SWMM, etc.) can be used for simulation calculation and output the water flow state of the well points and pipe segments in the pipe network.

[0131] Optionally, step S22 is specifically:

[0132] Step S221: Extract the regional elevation characteristics of the urban area surface feature data, so as to obtain the urban area elevation data;

[0133] In this embodiment, a high-resolution digital elevation model (DEM) of the target area can be obtained from the local natural resources and planning bureau of the city, and the resolution is adjusted according to the area of the region to balance the simulation accuracy and computational efficiency: for areas exceeding 1000 square kilometers, a 2-meter resolution grid is used; for areas between 200 and 1000 square kilometers, a 5-meter resolution grid is used; and for areas below 200 square kilometers, a 10-meter resolution grid is used. The GIS tool is used to identify and label buildings and other artificial structures. The outlines of the buildings are outlined by manual vectorization, and combined with on-site measurements or building height data, and then overlaid on the original DEM to obtain the surface elevation data considering artificial buildings.

[0134] Step S222: Based on the land use type area data and combined with the preset correspondence between land use and surface attributes, deduce the infiltration conditions and Manning coefficients of different regions, so as to obtain the regional infiltration condition data and regional Manning coefficient data;

[0135] In this embodiment, the land use data within the urban area (such as buildings, roads, bare soil, grasslands, water bodies, etc.) is combined to deduce the infiltration conditions and Manning coefficients of different regions. First, the current land use data of the city (which can be obtained through remote sensing images, land use classification maps, or urban planning data) is used to classify different land use types. Then, according to the characteristics of different land types, the infiltration conditions and Manning coefficients of each region are determined according to the preset correspondence table between land use and surface attributes. For example, urban hardened road surfaces (such as asphalt or concrete) usually have low permeability, while green spaces or wetlands have high permeability. For the Manning coefficient, hardened surfaces generally take lower values (such as 0.015), while natural surfaces such as grasslands or farmlands take higher values (such as 0.030). Through the GIS tool, for regions of different land use types, combined with the corresponding infiltration conditions and Manning coefficients, the infiltration condition data and Manning coefficient data of each region are generated to provide parameters for the two-dimensional surface model.

[0136] Step S223: Set the hydrological boundary conditions based on the urban area hydrological data, so as to obtain the hydrological boundary conditions;

[0137] In this embodiment, the hydrological boundary conditions are set according to the precipitation, evaporation, flow and other data included in the urban area hydrological data. For example, for rivers or drainage systems in the urban area, the boundary flow or water level can be set according to the hydrological data to simulate rainwater discharge and runoff conditions. At the same time, the surface runoff within the region is calculated through rainfall, soil type and land use data. In actual operation, using hydrological simulation software (such as HEC-HMS or SWMM) can help accurately set the hydrological boundary conditions to ensure that they reflect the actual hydrological change laws.

[0138] Step S224: Construct a two-dimensional surface model based on the water volume exchange boundary conditions, urban area elevation data, regional infiltration condition data, and regional Manning coefficient data.

[0139] In this embodiment, a two-dimensional surface hydrological model is constructed by means of numerical simulation using the obtained water volume exchange boundary conditions, urban area elevation data, regional infiltration condition data, and Manning coefficient data. First, a suitable two-dimensional surface model (such as MIKE) is selected, and the aforementioned data is input into the model. At the initial stage of the model, a digital elevation model (DEM) of the urban area is generated based on the elevation data as the basic topographic information of the model. Then, the infiltration condition data and Manning coefficient data are used as key parameters of the model to calculate the velocity and flow direction of the water flow in the region. According to the set water boundary conditions, simulate how the precipitation flow is distributed in different topographic regions, how it flows along the drainage paths, and how it infiltrates through the permeability of different surfaces. Finally, the distribution of the water flow, including the ponding range and depth, is output through simulation.

[0140] Optionally, step S3 is specifically as follows:

[0141] Step S31: Obtain the important infrastructure data of the urban area, and extract the geographical locations of the important infrastructure and the waterlogging prevention requirements for the important infrastructure data of the urban area, so as to obtain the important infrastructure geographical location data and the important infrastructure waterlogging prevention requirement data;

[0142] In this embodiment, the important infrastructure (lifeline facility project) data of the urban area is obtained by using the urban geographic information system (GIS), including hospitals, power stations, transportation hubs, etc. These data can be obtained through the urban planning bureau, engineering companies, or third-party data providers (such as OpenStreetMap). The data formats can include map data (Shapefile, GeoJSON, etc.) and databases (PostGIS, Oracle Spatial, etc.). Use spatial analysis tools (such as ArcGIS, QGIS) to extract the geographical locations of these infrastructures and mark the specific longitude and latitude positions of each infrastructure. Based on the elevation data, historical waterlogging conditions, and facility functional characteristics of the area where the facility is located, comprehensively analyze its waterlogging prevention requirements. The prevention requirements for facilities with higher terrain are relatively low, while facilities in low-lying areas need to set higher protection standards, and the waterlogging prevention and control requirements for different important infrastructures are also different. For example, hospitals need to consider strict waterlogging area prevention requirements, while power plants need to focus on waterlogging depth prevention requirements.

[0143] Step S32: Extract the surface characteristics of the important infrastructure locations from the urban area surface characteristic data according to the important infrastructure geographical location data, so as to obtain the important infrastructure location surface characteristic data;

[0144] In this embodiment, in combination with the geographical location data of the infrastructure, the surface features near these locations are extracted through spatial analysis. For example, the terrain elevation and slope features around the infrastructure are extracted to determine whether waterlogging is likely to occur; the distribution density of drainage facilities, land use types, and surface material characteristics in the surrounding area are extracted, and combined with historical waterlogging data, to evaluate the waterlogging flow direction and possible waterlogging range around the facilities. The extracted surface feature data is associated and integrated with the facility location data to form the surface feature data of the important infrastructure locations.

[0145] Step S33: Calculate the upper limit of the waterlogging area and the upper limit of the waterlogging depth of the important infrastructure according to the surface feature data of the important infrastructure locations and the waterlogging prevention requirement data of the important infrastructure, so as to obtain the waterlogging range data of the urban important infrastructure;

[0146] In this embodiment, based on the historical waterlogging data, the internal waterlogging prevention threshold of itself, and the terrain height information, combined with the surface feature data of the important infrastructure, the upper limit of the waterlogging area and the upper limit of the waterlogging depth of each facility are evaluated and calculated. The waterlogging range data that the facility can withstand is directly calculated, including the waterlogging area and the waterlogging depth. For example, a certain hospital needs to ensure that the waterlogging depth does not exceed 0.3 meters, and the waterlogging area is controlled within 3,000 square meters. The waterlogging range data of each important infrastructure is obtained, including the upper limit of the waterlogging area and the upper limit of the waterlogging depth. These data will be used for subsequent buffer selection and waterlogging risk assessment.

[0147] Step S34: Select the urban waterlogging buffer based on the urban area surface feature data, so as to obtain the urban waterlogging buffer data, and integrate the surface features of the urban waterlogging buffer data to obtain the surface feature data of the waterlogging buffer;

[0148] In this embodiment, according to the surface feature data of the urban area, the areas that meet the requirements around are selected as the buffer. For example, low-lying areas, unhardened surfaces, green spaces or vegetation-covered areas are preferentially selected, and at the same time, the areas with connectivity to the drainage system and water storage capacity are considered as the waterlogging buffer. The waterlogging buffer is used as a buffer space between the surface waterlogging and the drainage system, which can absorb the short-term waterlogging load, disperse the surface waterlogging, and reduce the waterlogging pressure on the important infrastructure.

[0149] Step S35: Set the waterlogging area threshold and the waterlogging depth threshold for the surface feature data of the waterlogging buffer, so as to obtain the allowable waterlogging range data of the waterlogging buffer.

[0150] In this embodiment, based on the surface feature data and historical waterlogging event data of the waterlogging buffer zone, a threshold for the waterlogging area is set. For example, if the waterlogging area of a certain region exceeds 5000 square meters, it will affect the normal operation of the infrastructure. Therefore, a reasonable area threshold is set. Only when the waterlogging area is less than this threshold will the region be considered within an acceptable risk range. At the same time, the waterlogging depth threshold is set according to the characteristics of the buffer zone itself and the topographical features of the surrounding area. For example, in a buffer zone, if the waterlogging depth exceeds 0.5 meters, it may cause overflow to spread to the traffic artery. Therefore, the depth threshold is set to 0.5 meters to ensure that the waterlogging is controllable within the buffer zone. By setting the dual thresholds of area and depth, the allowable waterlogging range of the waterlogging buffer zone is defined.

[0151] Optionally, step S34 is specifically as follows:

[0152] Extract the terrain elevation characteristics, permeability characteristics, and vegetation coverage characteristics from the surface feature data of the urban area, so as to obtain the urban area vegetation coverage data, urban area permeability data, and urban area terrain elevation data;

[0153] In this embodiment, the terrain elevation characteristics extraction is based on the Digital Elevation Model (DEM). Through the raster analysis of the DEM data by GIS tools, the elevation values within the region are extracted. The vegetation coverage and permeability characteristics extraction are based on the land use classification data. Using the urban area land use data, calculate its vegetation coverage and permeability characteristics according to different types (such as green land, hardened pavement, bare land, water body, etc.). For example, the vegetation coverage of green land and grassland is relatively high and the permeability is good, while the permeability of hardened pavement is relatively low. Through the spatial analysis of the land use classification results by GIS tools, generate the vegetation coverage map and permeability distribution map of the urban area. Integrate the DEM elevation data with the vegetation coverage and permeability data derived from the land use categories.

[0154] Extract the pipe network distribution characteristics from the urban area pipe network inspection data, so as to obtain the drainage pipe network distribution data;

[0155] In this embodiment, through the analysis of the urban area pipe network inspection data, the spatial distribution characteristics of the drainage pipe network are extracted. The drainage pipe network is the core facility for realizing waterlogging scheduling, and the covered area determines whether the waterlogging can be guided to the buffer zone. The specific methods include: using GIS tools to read the pipe network data, extract the geographical distribution of pipe network nodes (such as rainwater wells, outlets) and connected pipes; combining information such as pipe diameter, material, and slope, calibrate the drainage range, and generate the drainage pipe network distribution map. This distribution map is used to clarify the pipe network covered area and ensure that the buffer zone location is within the controllable range of the drainage pipe network.

[0156] Identify low-lying areas in the urban area's terrain elevation data to obtain low-lying area data;

[0157] Classify the extracted urban area's terrain elevation data into highlands, midlands, and lowlands according to the elevation range. For example, by calculating the local elevation difference (such as the average elevation relative to the surrounding 500-meter range), identify low-lying areas that are lower than the surrounding areas. Use the "Reclassify" function of GIS tools (such as ArcGIS) to reclassify the elevation data and combine spatial analysis tools (such as "Focal Statistics") to identify local low-lying areas.

[0158] Select areas with high vegetation coverage in the urban area's vegetation coverage data to obtain high-vegetation-coverage area data;

[0159] In this embodiment, according to the urban vegetation coverage data derived from the land use type, screen areas with relatively high vegetation coverage, and label areas with a coverage rate exceeding 60% as high-vegetation-coverage areas. High-vegetation-coverage areas have good water absorption and buffering capabilities, providing support for the selection of waterlogging buffer areas.

[0160] Screen areas with high permeability in the urban area's permeability data to obtain high-permeability area data;

[0161] In this embodiment, according to the urban area's permeability data derived from the land use type, screen areas with relatively high permeability (such as green spaces, grasslands, bare lands, etc.), and label areas with relatively high permeability as high-permeability areas. High-permeability areas can effectively reduce surface runoff and alleviate the risk of waterlogging, providing support for the selection and design of waterlogging buffer areas. Extract eligible areas through GIS spatial analysis.

[0162] Select areas covered by the pipe network based on the drainage pipe network distribution data to obtain pipe network covered area data;

[0163] In this embodiment, by analyzing the drainage pipe network distribution data, screen areas covered by the drainage pipe network. Use GIS tools to determine the effective coverage area of the pipe network based on pipe network nodes (such as rainwater wells, drainage outlets) and the pipe service range. The covered area refers to the area where waterlogging scheduling can be achieved through the pipe network, and its boundary is determined by factors such as pipe service capacity and terrain conditions. The generated pipe network covered area data is used to ensure that the location of the waterlogging buffer area is within the controllable range of the drainage pipe network.

[0164] Allocate priorities to land use types based on the urban area's land use type data to obtain land use type priority data, and divide sub-priority areas according to the land use type priority data to obtain land use sub-priority area data;

[0165] In this embodiment, a priority is assigned to the land use type according to factors such as the scarcity of resources and environmental impacts. Ecologically sensitive areas are given priority for protection, and certain types of land (such as wetlands and forests) are assigned higher priorities for protection or restoration. The priority for infrastructure construction is set according to factors such as the location of the land, the need for infrastructure, and the transportation network during urban development. In certain specific application scenarios, different grades or priorities are assigned to the land use type. Some areas are divided into "priority development areas" or "secondary development areas", etc. Within these areas, different land use types have different priority development orders. Combining the urban land use type data, excluding important ecological protection areas, urban planning and development land, and other sensitive areas with higher priorities, the land use sub-priority area data is generated.

[0166] Perform a spatial overlay analysis on the low-lying area data, high vegetation coverage area data, high permeability area data, pipe network coverage area data, and land use sub-priority area data to generate urban waterlogging buffer zone data;

[0167] In this embodiment, the generation of the waterlogging buffer zone depends on the overlay of multiple spatial data such as low-lying areas, high vegetation coverage, high permeability areas, pipe network coverage, and land use sub-priority areas. Through GIS tools such as ArcGIS, using tools such as "Overlay" or "Spatial Analyst" in spatial analysis, the above different data sets are overlaid and analyzed to generate waterlogging buffer zone data.

[0168] Integrate the surface characteristics data of the urban area based on the urban waterlogging buffer zone data to obtain the surface characteristics data of the waterlogging buffer zone.

[0169] In this embodiment, for the integrated analysis of surface characteristics based on the waterlogging buffer zone, it is usually necessary to combine multiple surface characteristics data (such as terrain elevation, vegetation coverage rate, permeability, etc.) for comprehensive analysis. Through tools such as "Intersect" or "Union" in GIS, different feature layers are combined to extract the surface characteristics information within the waterlogging buffer zone to form the final surface characteristics data of the buffer zone. These data can be further used for waterlogging risk assessment and urban disaster prevention planning.

[0170] Optionally, step S4 is specifically as follows:

[0171] Step S41: Simulate urban waterlogging and ponding for the rainfall data of the urban area through an urban waterlogging model to obtain the first urban waterlogging and ponding simulation data;

[0172] In this embodiment, future predicted rainfall data is used to simulate and analyze a target area through an urban coupled waterlogging model to generate first urban waterlogging accumulation simulation data. This data includes the distribution of urban waterlogging, such as the water accumulation depth, range, and duration in each area, etc.

[0173] Step S42: Based on the first urban waterlogging accumulation simulation data, divide the urban area's important infrastructure data and urban water accumulation buffer data according to the in - area waterlogging accumulation, so as to obtain the important infrastructure area's waterlogging accumulation data and the urban water accumulation buffer area's waterlogging accumulation data, and set the water accumulation benchmark according to the important infrastructure area's waterlogging accumulation data and the urban water accumulation buffer area's waterlogging accumulation data, so as to obtain the initial simulated water accumulation benchmark data;

[0174] In this embodiment, by combining the first urban waterlogging accumulation simulation data with the location data of urban important infrastructure, calculate the initial water accumulation situation of each important infrastructure, including the water accumulation depth, area, and duration. Combining the simulation results and spatial analysis, divide the water accumulation area of the entire urban area into the water accumulation area of the important infrastructure area and the water accumulation area of the buffer area. Obtain the initial water accumulation benchmark data before scheduling intervention, including the total water accumulation area (excluding the water accumulation volume of the water accumulation buffer) and the detailed water accumulation situation of each important infrastructure area.

[0175] Step S43: Extract the control parameters of the pipe network pump stations and valves from the urban area pipe network inspection data, so as to obtain the pump station control parameter data and the valve control parameter data;

[0176] In this embodiment, extract the data of the urban area's drainage pipe network system from the pipe network inspection data, especially the control parameters of the pump stations and valves. The pipe network inspection data includes information such as the diameter, flow rate, slope of each drainage pipe, the treatment capacity of the pump stations, and the opening and closing control rules of the valves. For example, the flow control parameter of a certain pump station in the urban drainage pipe network is 5000m 3 / h, and the valve control parameter is to automatically open when the water accumulation depth exceeds 30 cm.

[0177] Step S44: Set the objective function and constraints according to the urban important infrastructure water accumulation range data and the water accumulation buffer area's allowable water accumulation range data, so as to obtain the fitness function and the constraint condition function;

[0178] In this embodiment, it is necessary to define the objective function and the constraint conditions. These objective functions are usually related to the minimization of urban waterlogging, for example, the reduction of the corresponding waterlogging depth of important infrastructure, the reduction of the waterlogging area, and the reduction of the total waterlogging area (excluding the waterlogging buffer zone). The constraint conditions include the waterlogging depth limit (such as the waterlogging depth of infrastructure shall not exceed 50 cm), the drainage capacity limit, and the waterlogging buffer capacity limit of specific areas. By setting these functions and conditions, reasonable objectives and limitations can be provided for subsequent optimization calculations.

[0179] Step S45: According to the initial simulated waterlogging benchmark data, fitness function, and constraint condition function, generate an initial particle swarm based on the pump station control parameter data and valve control parameter data, calculate the initial fitness values of the particles in the initial particle swarm, select the global optimal particle from the initial fitness values of the particles, and record the individual optimal solutions of each particle, so as to obtain the global optimal particle and the individual optimal solutions of the particles.

[0180] In this embodiment, based on the foregoing initial simulated waterlogging benchmark data, fitness function, and constraint condition function, optimization is performed through the particle swarm optimization (PSO) algorithm. The initialization of the particle swarm includes randomly generating a certain number of particles, and each particle represents a possible urban waterlogging scheduling scheme. The particles calculate their fitness values according to the control parameters (such as the pump station start time, valve control switch state, etc.). Then, the global optimal particle is selected from them, and the individual optimal solutions of each particle are recorded. Suppose there are 100 particles, and each particle represents a combination of pump station and valve control parameters (for example, the start time of pump station 1 is 15 minutes, and the opening degree of valve A is 50%). The particle swarm selects the best scheme by calculating the fitness values of each particle (such as the cumulative waterlogging depth or drainage efficiency).

[0181] Step S46: Through the constraint condition function, and perform iterative optimization according to the global optimal particle and the individual optimal solutions of the particles until the preset early stop condition is met, so as to obtain the global optimal solution.

[0182] In this embodiment, using the iterative mechanism of the particle swarm algorithm, based on the current global optimal solution and individual optimal solutions, multiple calculations and adjustments are performed to gradually optimize the control parameters of each particle. Through continuous iterative optimization of the fitness function, the particle swarm will continuously approach the optimal solution until the preset stop condition is met (for example, after a fixed number of iterations or reaching a certain accuracy standard). Suppose the initial optimal solution is that the start time of the pump station is 15 minutes and the valve opening degree is 50%. After several rounds of iteration, the particle swarm finds that when the start time of the pump station is extended to 20 minutes and the valve opening degree is increased to 60%, the waterlogging depth is the smallest and the fitness value is the highest, so this is the optimal solution.

[0183] Step S47: Convert the global optimal solution into the waterlogging and ponding scheduling parameters, so as to obtain the waterlogging and ponding scheduling parameter group in the urban area and upload it to the urban drainage management platform to execute the ponding scheduling task.

[0184] In this embodiment, the final global optimal solution (including the opening time of the pump station, the control parameters of the valve, etc.) needs to be converted into actual control parameters for the use of the urban drainage management platform. These scheduling parameters will be uploaded to the drainage management platform, and the drainage system will be scheduled in real time according to these parameters to cope with future rainfall events. Upload the control parameters in the global optimal solution (for example, the opening time of pump station 1 is 20 minutes, and the opening of valve A is 60%) to the urban drainage management platform. The platform can dynamically adjust the operation of the drainage system according to the real-time rainfall situation and the water level change in the basin to ensure that the ponding is effectively drained.

[0185] Optionally, step S44 is specifically:

[0186] Step S441: Construct a penalty function based on the water accumulation allowable range data in the water accumulation buffer;

[0187] In this embodiment, control the water accumulation area in the water accumulation buffer not to exceed and the water accumulation depth in the water accumulation buffer not to exceed Set the penalty function f buffer . The penalty function is:

[0188]

[0189] where h j is the water accumulation depth of the buffer, is the maximum allowable water accumulation depth of the buffer, A j is the water accumulation area of the buffer, is the maximum allowable water accumulation area of the buffer; w j is the depth of the buffer, k j is the area weight of the buffer, and j is the index of the buffer.

[0190] Step S442: Construct a fitness function according to the penalty function and the water accumulation range data of urban important infrastructure, where the fitness function is specifically:

[0191]

[0192] In the formula, f is the fitness function value, n is the total number of important infrastructure, i is the index of the important infrastructure, α is the global weight of the buffer penalty, w i is the weight used to control the water accumulation depth of the i-th important infrastructure in the fitness function, k iTo control the weight of the waterlogging area of the i-th critical infrastructure in the fitness function, D i is the waterlogging depth of the i-th critical infrastructure, d i is the waterlogging depth target of the i-th critical infrastructure, A i is the waterlogging area of the i-th critical infrastructure, a i is the waterlogging area target of the i-th critical infrastructure, A t is the total waterlogging area excluding the waterlogging buffer zone, A tr is the total waterlogging area target excluding the waterlogging buffer zone; K is the weight of the total waterlogging area;

[0193] In this embodiment, first, the scheduling objectives are determined. The waterlogging depth in each critical infrastructure area is minimized to below its respective target depth (for example, for Critical Infrastructure Target 1, the water depth should be below 20 cm, for Critical Infrastructure Target 2, the water depth should be below 30 cm, and so on); the waterlogging area is controlled not to exceed the target value (for example, for Critical Infrastructure Target 1, the area should be below 100 square meters, for Critical Infrastructure Target 2, the area should be below 100 square meters, and so on); the total waterlogging area (excluding the waterlogging buffer zone) of the entire simulation area including the critical infrastructure area is reduced to 50% of the original. Based on these scheduling objectives, penalty functions, and the waterlogging range data of urban critical infrastructure, a fitness function is constructed.

[0194] Step S443: Construct a control parameter constraint condition function based on the pump station control parameter data and valve control parameter data, thereby obtaining the constraint condition function.

[0195] In this embodiment, the control parameters are obtained according to the design flow rate, operating capacity of the pump station, and the opening and closing control data of the valve. For example, the maximum flow rate of a certain pump station is 2000 cubic meters per hour, and the opening control range of a certain valve is 0 - 100%. Based on these data, a constraint condition function is constructed to ensure that the parameters of the pump station and valve do not exceed their physical or technical limits. For example:

[0196]

[0197] Where: i is the pump station index; j is the valve index; Q i is the control flow rate of the pump station; Q max is the maximum allowable flow rate of the pump station; θ j is the valve opening; θ max is the maximum valve opening.

[0198] Optionally, step S45 is specifically:

[0199] Step S451: Randomly combine scheduling parameters based on the constraint condition function, pump station control parameter data, and valve control parameter data to obtain control scheduling parameter combination data;

[0200] In this embodiment, multiple particles are generated according to the pump station control parameter data and the valve control parameter data. Each particle represents a combination of scheduling parameters for the pump station and the valve (such as flow rate, switch state), randomly distributed within the parameter space of each particle and conforming to the constraint condition function. For example, randomly select the start-stop sequence of the pump station and the opening setting of the valve. The generation of the control parameter combination needs to ensure that the operation restrictions and system balance requirements are met. For example, the switch state of the pump station must match the opening of the valve to avoid conflicting combinations.

[0201] Step S452: Initialize the particles according to the control scheduling parameter combination data to obtain an initialized particle swarm;

[0202] In this embodiment, based on the obtained control scheduling parameter combination data, the particle swarm is initialized. Each particle represents a possible scheduling scheme, whose position is determined by the scheduling parameter combination, and the velocity represents the change amount for adjusting these parameters. For example, if the position of a particle is a vector of the start-stop sequence of a pump station and a set of valve opening settings, then the velocity of the particle can be a fine-tuning of these parameters. During initialization, the positions and velocities of all particles are randomly initialized, and the size of the particle swarm is set, for example, 50 - 100 particles, and the dimension of each particle is the number of pump stations plus the number of valves. Then continue the iteration, set to 300 times, to ensure there are enough search times to find a solution that meets the requirements of multiple regions. The individual learning factor and the social learning factor are both set to 2.0, and the inertia weight gradually decreases from 0.9 to 0.4.

[0203] Step S453: Use the urban waterlogging model to simulate the urban waterlogging and ponding for the urban area rainfall data and the initialized particle swarm to obtain initialized particle simulation data;

[0204] In this embodiment, the urban waterlogging model is used to simulate the waterlogging and ponding for the scheduling scheme of each initialized particle. The urban waterlogging model inputs rainfall data (such as rainfall intensity, duration, etc.) and the control scheduling parameters in the particle swarm (such as pump station start-stop time, valve opening) for simulation calculation. During the simulation process, the model predicts the ponding situation under different scheduling schemes through the water flow equation and the hydraulic calculation of the drainage network. For example, for a scheme represented by a certain particle, the opening of the pump station is increased during a heavy rain, thereby improving the drainage capacity and reducing the amount of ponding. The simulation results will output data such as the ponding depth and ponding area of different regions corresponding to different particles.

[0205] Step S454: Calculate the initial fitness of the particles for the initialized particle simulation data through the fitness function to obtain the initial fitness value of the particles;

[0206] In this embodiment, the waterlogging range and waterlogging depth of the critical infrastructure after each simulation, the waterlogging range and waterlogging depth of the buffer zone, and the total waterlogging area (excluding the total area of the buffer zone) are statistically calculated. These data are substituted into the aforementioned fitness function for calculation to obtain the fitness values of each particle.

[0207] Step S455: Select the individual optimal solution of the particle for the initial fitness value of the particle to obtain the individual optimal solution of the particle, and screen the global optimal solution according to the individual optimal solution of the particle to obtain the global optimal particle.

[0208] In this embodiment, based on the fitness value of each particle, the particle with the best fitness is selected as the "individual optimal solution". By recording the historical optimal fitness of each particle and comparing it with the current fitness value, if the current fitness value is better, the individual optimal solution is updated. Then, the individual optimal solutions of all particles are compared, and the particle with the optimal fitness is selected as the "global optimal solution". For example, if the pump station start-stop sequence and valve opening setting corresponding to a certain particle perform best in the simulation and meet the system operation constraints, then that particle is the global optimal solution. Through the iterative process, the speed and position of the particle are continuously updated until the global optimal solution is finally reached.

[0209] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be encompassed within the present invention.

[0210] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An intelligent dispatching method for urban waterlogging and waterlogging of important urban infrastructure, characterized in that: The following steps are involved: Step S1: Obtaining urban area geographic basic data, urban area pipe network data, urban area hydrological data, urban area rainfall data, and urban area waterlogging data; performing urban area pipe network topology inspection based on urban area pipe network data, thereby obtaining urban area pipe network inspection data; integrating urban area surface features based on urban area geographic basic data, thereby obtaining urban area surface feature data; Data preprocessing is performed according to the urban area rainfall data and the urban area waterlogging data, so as to obtain the urban area rainfall data to be analyzed and the urban area waterlogging data to be analyzed; Step S2: construct a one-dimensional pipe network model based on the urban area pipe network inspection data; construct a two-dimensional surface model based on the urban area surface feature data; The one-dimensional pipe network model and the two-dimensional surface model are coupled to obtain a coupled waterlogging model, and the coupled waterlogging model is automatically calibrated and optimized using an ant colony algorithm to obtain an urban waterlogging model. Step S3: Obtaining important infrastructure data in the urban area, and calculating the water accumulation range of the important infrastructure in the urban area according to the surface characteristic data of the urban area, thereby obtaining the water accumulation range data of the important infrastructure in the city; selecting the urban water accumulation buffer zone based on the surface characteristic data of the urban area, thereby obtaining the urban water accumulation buffer zone data, and setting the allowable range of water accumulation in the water accumulation buffer zone for the urban water accumulation buffer zone data, thereby obtaining the allowable range of water accumulation in the water accumulation buffer zone data; Step S4: Performing urban waterlogging simulation on the urban area rainfall data through the urban waterlogging model, thereby obtaining the first urban waterlogging simulation data; performing regional waterlogging division on the urban area important infrastructure data and the urban waterlogging buffer zone data based on the first urban waterlogging simulation data, thereby obtaining the important infrastructure area waterlogging data and the urban waterlogging buffer zone waterlogging data; determining the waterlogging dispatching parameters for the important infrastructure area waterlogging data and the urban waterlogging buffer zone waterlogging data according to the important infrastructure area waterlogging range data and the waterlogging allowable range data, thereby obtaining the urban area waterlogging dispatching parameter group, and uploading it to the urban drainage management platform to execute the waterlogging dispatching task; Step S5: updating the model parameters of the urban waterlogging model using the urban area waterlogging scheduling parameter group, thereby obtaining the urban waterlogging scheduling model, and performing urban waterlogging simulation on the urban area rainfall data through the urban waterlogging scheduling model, thereby obtaining second urban waterlogging simulation data; obtaining real-time urban area waterlogging data, and performing difference analysis on the second urban waterlogging simulation data and the real-time urban area waterlogging data, thereby obtaining a scheduling difference report; Step S6: Update the model input of the urban waterlogging scheduling model according to the scheduling difference report to obtain an updated urban waterlogging model. Based on the updated urban waterlogging model, re-execute steps S4 and S5, and continuously iterate and optimize the scheduling parameters through difference analysis between the new simulation results and the real-time urban area waterlogging data until the difference meets the preset conditions.

2. The intelligent dispatching method for urban waterlogging and important infrastructure according to claim 1 is characterized in that: Step S1 is specifically as follows: Step S11: Acquire urban area geographic basic data, urban area pipe network data, urban area hydrological data, urban area rainfall data and urban area waterlogging data, wherein the urban area geographic basic data includes urban area terrain data and urban area land use type data; Step S12: performing a pipe network connection node consistency check on the urban area pipe network data, thereby obtaining pipe network connection consistency node data, and performing a pipe section continuity check based on the pipe network connection consistency node data, thereby obtaining urban area pipe network inspection data; Step S13: Based on the urban area land use type data, regional division is performed according to a preset land use classification standard, thereby obtaining land use type regional data; Step S14: integrating the regional surface characteristics of the land use type regional data and the urban area terrain data, thereby obtaining the urban area surface characteristic data; Step S15: performing data preprocessing according to the urban area rainfall data and the urban area waterlogging data, respectively, so as to obtain the urban area rainfall data to be analyzed and the urban area waterlogging data to be analyzed.

3. The intelligent dispatching method for urban waterlogging and important infrastructure according to claim 1 is characterized in that: Step S2 is specifically as follows: Step S21: constructing a one-dimensional pipe network model based on the urban area pipe network inspection data; Step S22: constructing a two-dimensional surface model according to the surface feature data of the urban area; Step S23: performing coupling model time step matching on the one-dimensional pipe network model and the two-dimensional surface model, thereby obtaining a time step coordination model set; performing water exchange calculation on the time step coordination model set, thereby obtaining a coupled waterlogging model; Step S24: using an ant colony algorithm to automatically calibrate and optimize the coupled waterlogging model, thereby obtaining an urban waterlogging model.

4. The intelligent dispatching method for urban waterlogging and important infrastructure according to claim 3 is characterized in that: Step S21 is specifically as follows: Step S211: extracting pipe network pump station parameter feature, pipe network parameter feature and pipe network liquid level feature from the urban area pipe network inspection data, thereby obtaining pipe network pump station data, pipe network pipeline data and pipe network liquid level data; Step S212: Formatting and integrating the pipeline network pump station data, pipeline network pipeline data, and pipeline network liquid level data to obtain a pipeline parameter set; Step S213: constructing a pipe network topology model based on the pipe parameter set; Step S214: setting water flow exchange boundary conditions based on the pipe network liquid level data and the pipe network pump station data, thereby obtaining water flow exchange boundary conditions; Step S215: construct a one-dimensional pipe network model according to the pipe network topology model and water flow exchange boundary conditions.

5. The intelligent dispatching method for urban waterlogging and important infrastructure according to claim 2 is characterized in that: Step S22 is specifically as follows: Step S221: extracting regional elevation features from urban area surface feature data, thereby obtaining urban area elevation data; Step S222: according to the land use type regional data and in combination with the preset corresponding relationship between land use and surface attributes, the infiltration conditions and Manning coefficients of different regions are derived, thereby obtaining regional infiltration condition data and regional Manning coefficient data; Step S223: setting hydrological boundary conditions based on the urban area hydrological data, thereby obtaining hydrological boundary conditions; Step S224: construct a two-dimensional surface model according to the hydrological boundary conditions, the urban area elevation data, the regional infiltration condition data and the regional Manning coefficient data.

6. The intelligent dispatching method for urban waterlogging and important infrastructure according to claim 1 is characterized in that: Step S3 is specifically as follows: Step S31: acquiring important infrastructure data in the urban area, and extracting the geographical location of the important infrastructure and the waterlogging prevention demand of the important infrastructure in the urban area, thereby obtaining the geographical location data of the important infrastructure and the waterlogging prevention demand data of the important infrastructure; Step S32: extracting the surface features of the important infrastructure locations from the urban area surface feature data according to the geographical location data of the important infrastructure, thereby obtaining the surface feature data of the important infrastructure locations; Step S33: Calculate the upper limit of the waterlogging area and the upper limit of the waterlogging depth of the important infrastructure according to the surface feature data of the location of the important infrastructure and the waterlogging prevention demand data of the important infrastructure, so as to obtain the waterlogging range data of the important infrastructure in the city; Step S34: selecting an urban waterlogging buffer zone based on the urban area surface characteristic data, thereby obtaining urban waterlogging buffer zone data, and integrating the surface characteristics of the buffer zone on the urban waterlogging buffer zone data, thereby obtaining surface characteristic data of the waterlogging buffer zone; Step S35: setting a threshold value for the waterlogging area and depth of the waterlogging buffer zone on the surface characteristic data of the waterlogging buffer zone, thereby obtaining data on the permissible range of waterlogging in the waterlogging buffer zone.

7. The intelligent dispatching method for urban waterlogging and important infrastructure according to claim 6 is characterized in that: Step S34 is specifically as follows: Extracting terrain elevation features, permeability features and vegetation coverage features from the urban area surface feature data, thereby obtaining urban area vegetation coverage data, urban area permeability data and urban area terrain elevation data; Extract the pipe network distribution characteristics from the urban area pipe network inspection data to obtain the drainage pipe network distribution data; Identify low-relief areas on urban area terrain elevation data to obtain low-relief area data; Select high vegetation coverage area from urban area vegetation coverage data, so as to obtain high vegetation coverage area data; Screening the high-permeability areas of urban area permeability data to obtain high-permeability area data; Select the pipe network coverage area according to the drainage pipe network distribution data, so as to obtain the pipe network coverage area data; Based on the urban area land use type data, land use type priority is allocated to obtain land use type priority data, and sub-priority areas are divided according to the land use type priority data to obtain land use sub-priority area data; The data of low-lying areas, high vegetation coverage areas, high permeability areas, pipe network coverage areas and land use secondary priority areas are spatially overlaid and analyzed to generate urban waterlogging buffer zone data; Based on the urban waterlogging buffer zone data, the surface characteristics data of the urban area are integrated to obtain the surface characteristics data of the waterlogging buffer zone.

8. The intelligent dispatching method for urban waterlogging and important infrastructure according to claim 1 is characterized in that: Step S4 is specifically as follows: Step S41: performing urban waterlogging simulation on the urban area rainfall data through the urban waterlogging model, thereby obtaining first urban waterlogging simulation data; Step S42: Based on the first urban waterlogging simulation data, the important infrastructure data in the urban area and the urban waterlogging buffer zone data are divided into regional waterlogging data, thereby obtaining the important infrastructure regional waterlogging data and the urban waterlogging buffer zone waterlogging data, and the waterlogging benchmark is set according to the important infrastructure regional waterlogging data and the urban waterlogging buffer zone waterlogging data, thereby obtaining the initial simulated waterlogging benchmark data; Step S43: extracting the pipe network pump station and valve control parameters from the urban area pipe network inspection data, thereby obtaining pump station control parameter data and valve control parameter data; Step S44: setting the objective function and constraints according to the water accumulation range data of important urban infrastructure and the water accumulation buffer zone water accumulation range data, thereby obtaining the fitness function and the constraint condition function; Step S45: generating an initialized particle swarm based on the initial simulated water accumulation benchmark data, the fitness function and the constraint condition function, based on the pump station control parameter data and the valve control parameter data, calculating the initial fitness value of the particles of the initialized particle swarm, selecting the global optimal particle from the initial fitness value of the particles, and recording the individual optimal solution of each particle, thereby obtaining the global optimal particle and the individual optimal solution of the particle; Step S46: performing iterative optimization according to the constraint condition function and the global optimal particle and the optimal solution of the individual particle until the preset early stopping condition is met, thereby obtaining the global optimal solution; Step S47: convert the global optimal solution into waterlogging scheduling parameters to obtain a waterlogging scheduling parameter group for the urban area, and upload it to the urban drainage management platform to execute the waterlogging scheduling task.

9. The intelligent dispatching method for urban waterlogging and important infrastructure according to claim 8 is characterized in that: Step S44 is specifically as follows: Step S441: constructing a penalty function based on the water accumulation allowable range data of the water accumulation buffer zone; Step S442: construct a fitness function based on the penalty function and the waterlogging range data of important urban infrastructure, where the fitness function is specifically: Where f is the fitness function value, n is the total number of important infrastructures, i is the index of important infrastructures, α is the global weight of the buffer penalty, and w i is the weight used to control the water depth of the i-th important infrastructure in the fitness function, k i is the weight used to control the waterlogging area of ​​the i-th important infrastructure in the fitness function, D i is the water depth of the i-th important infrastructure, d i is the water depth target of the i-th important infrastructure, A i is the waterlogging area of ​​the ith important infrastructure, a i is the waterlogging area target of the i-th important infrastructure, A t is the total waterlogging area excluding the waterlogging buffer zone, A tr is the total waterlogging area target excluding the waterlogging buffer zone, and K is the weight of the total waterlogging area; Step S443: construct a control parameter constraint condition function according to the pump station control parameter data and the valve control parameter data, thereby obtaining a constraint condition function.

10. The intelligent dispatching method for urban waterlogging and important infrastructure according to claim 8 is characterized in that: Step S45 is specifically as follows: Step S451: Randomly combine the dispatching parameters based on the constraint condition function, the pump station control parameter data and the valve control parameter data, so as to obtain the control dispatching parameter combination data; Step S452: Initialize particles according to the control scheduling parameter combination data to obtain an initialized particle group; Step S453: using the urban waterlogging model to simulate urban waterlogging on the urban area rainfall data and the initialized particle swarm, thereby obtaining initialized particle simulation data; Step S454: performing particle initialization fitness calculation on the initialized particle simulation data through a fitness function, thereby obtaining the particle initial fitness value; Step S455: Select the optimal solution of individual particles for the initial fitness values ​​of the particles, so as to obtain the optimal solution of individual particles, and select the global optimal solution according to the optimal solution of individual particles, so as to obtain the global optimal particle.

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