A method and system for preventing and controlling urban waterlogging disasters

Abstract

The present invention relates to the technical field of urban drainage and flood control, and specifically to a method and system for preventing and controlling urban waterlogging disasters. The method comprises the following steps: obtaining waterlogging disaster information of various areas within a city according to an urban disaster monitoring network; constructing a waterlogging disaster information processing mechanism according to the waterlogging disaster information and an urban drainage system, and obtaining target information for preventing and controlling urban waterlogging disasters based on the waterlogging disaster information processing mechanism; establishing a multidimensional analysis model for urban waterlogging disasters based on the target information and the urban drainage system, the multidimensional analysis model for urban waterlogging disasters comprising a drainage capacity assessment model, a system overflow analysis model and a drainage system infiltration rate prediction model; analyzing the drainage conditions of various areas within the city using the drainage capacity assessment model, the system overflow analysis model and the drainage system infiltration rate prediction model; and matching an urban waterlogging early warning plan in combination with the drainage conditions and the urban drainage system to achieve effective prevention and control of urban waterlogging disasters.

Description

Technical Field

[0001] The present invention relates to the technical field of urban drainage and flood prevention, and in particular to a method and system for preventing and controlling urban waterlogging disasters. Background Art

[0002] Extreme precipitation phenomena such as rainstorms have increased significantly, exceeding the carrying capacity of urban drainage systems. With the rapid expansion of cities, impervious areas have also increased, surface runoff has accelerated, and the natural drainage system has been destroyed, exacerbating the risk of urban waterlogging. At the same time, complex human activities have changed the characteristics of urban underlying surfaces, damaged water networks, and weakened natural drainage capacity, making urban hydrological processes more complex and unpredictable, which not only affects the spatiotemporal distribution characteristics of summer rainfall, but also exacerbates the complexity and uncertainty of urban waterlogging.

[0003] However, the current urban waterlogging prevention and control methods do not have the ability to deeply analyze the temporal and spatial evolution of urban compound flood disasters, and it is difficult to quantitatively evaluate the actual impact of urban underlying surface changes on rainfall characteristics and runoff production and convergence processes and their internal connections. In addition, the existing methods are also insufficient in integrating big data analysis and deep learning technology, and it is impossible to build an efficient and accurate flood disaster feature monitoring and identification framework, which limits the real-time and accuracy of urban flood prevention and control results.

[0004] Therefore, in order to respond to urban waterlogging disasters more effectively, it is necessary to explore new prevention and control strategies and technical means to adapt to the complex mechanisms of urban floods and improve the accuracy and timeliness of disaster warning and emergency response. Summary of the invention

[0005] In view of the shortcomings of existing methods and the needs of practical applications, the present invention analyzes the spatiotemporal evolution of urban waterlogging disasters based on historical data and modern monitoring methods, and quantitatively analyzes the impact of urban changes on rainfall characteristics and runoff production and convergence processes. In addition, a multidimensional analysis model for urban waterlogging disasters is constructed based on big data analysis technology and learning algorithms, which further improves the accuracy and timeliness of disaster warnings, and helps to achieve real-time and precision in urban waterlogging disaster prevention and control. On the one hand, the present invention provides a method for preventing and controlling urban waterlogging disasters, which includes the following steps: obtaining waterlogging disaster information of various areas within the city according to the urban disaster monitoring network; constructing a waterlogging disaster information processing mechanism based on the waterlogging disaster information and the urban drainage system, and obtaining target information for preventing and controlling urban waterlogging disasters based on the waterlogging disaster information processing mechanism; establishing a multi-dimensional analysis model for urban waterlogging disasters based on the target information and the urban drainage system, the multi-dimensional analysis model for urban waterlogging disasters including a drainage capacity assessment model, a system overflow analysis model, and a drainage system infiltration rate prediction model; using the drainage capacity assessment model, the system overflow analysis model, and the drainage system infiltration rate prediction model to analyze the drainage conditions of various areas within the city; matching the urban waterlogging early warning scheme in combination with the drainage conditions and the urban drainage system to achieve effective prevention and control of urban waterlogging disasters. The present invention constructs an efficient and scientific urban waterlogging disaster prevention and control system, realizes real-time monitoring, accurate early warning, and emergency response of waterlogging disasters, helps to reduce the impact of waterlogging disasters on urban operations and residents' lives, and further enhances urban resilience to promote sustainable development of cities.

[0006] Optionally, the waterlogging disaster information processing mechanism is constructed based on the waterlogging disaster information and the urban drainage system, and the target information for preventing and controlling urban waterlogging disasters is obtained based on the waterlogging disaster information processing mechanism, including: setting a dynamic time step of waterlogging disaster information based on the waterlogging disaster information processing mechanism; dividing the urban drainage system based on the waterlogging disaster information processing mechanism to obtain unit grid information of the urban drainage system. The present invention sets a dynamic time step and divides the unit grid based on the waterlogging disaster information processing mechanism, which can significantly improve the early warning accuracy of urban waterlogging disasters, optimize resource allocation, and enhance the adaptability of the system.

[0007] Optionally, the construction of a waterlogging disaster information processing mechanism based on the waterlogging disaster information and the urban drainage system, and obtaining target information for preventing and controlling urban waterlogging disasters based on the waterlogging disaster information processing mechanism include: establishing a dynamic time step optimization model based on the dynamic time step and the waterlogging disaster information; and obtaining target information for preventing and controlling urban waterlogging disasters based on the dynamic time step optimization model and the waterlogging disaster information. The present invention establishes a dynamic time step optimization model based on the dynamic time step and the waterlogging disaster information, and obtains target information for preventing and controlling urban waterlogging disasters based on the model, which provides a more scientific and reliable basis for decision makers and helps to formulate more effective prevention and control measures and emergency plans.

[0008] Optionally, the dynamic time step optimization model satisfies the following relationship:

[0009] ,

[0010] in, represents the time step after dynamic optimization, represents the initial time step corresponding to the waterlogging disaster information, represents the Courant number, represents the momentum correction factor for the time step, represents the average size of the unit grid in the urban drainage system, represents the unit grid reference depth, Indicates that the fluid is in the drainage system x Single-width flow in one direction, Indicates that the fluid is in the drainage system y Single-width flow in one direction, The dynamic time step optimization model of the present invention comprehensively considers multiple factors to obtain a dynamic time step, which can more accurately reflect the development trend and potential risks of urban waterlogging disasters and significantly improve the early warning and prevention and control capabilities of urban waterlogging disasters.

[0011] Optionally, the drainage capacity evaluation model satisfies the following relationship:

[0012]

[0013] in, It represents the flow rate of surface water into any unit grid in the urban drainage system. represents the number of rainwater outfalls corresponding to any unit grid, Represents the first The discharge volume of each rainwater outfall is The present invention monitors and evaluates the drainage capacity of the unit grid in real time, can timely detect poor drainage or blockage, and issue early warnings, which helps relevant departments to quickly initiate emergency response mechanisms and reduce the probability and extent of losses caused by waterlogging disasters.

[0014] Optionally, the system overflow analysis model satisfies the following relationship:

[0015] ,

[0016] in, express t The maximum overflow volume of any unit grid in the drainage system at the time step, Represents a cell grid The orifice flow coefficient, Represents a cell grid of water storage area, represents the acceleration due to gravity, Represents a cell grid The water head, Represents a cell grid The surface grid water level at the corresponding location;

[0017] ,

[0018] in, express t+1 The maximum overflow volume of any unit grid in the drainage system at the time step, represents the total water storage capacity of any unit grid, represents the acceleration due to gravity, Represents a cell grid The surface grid water level at the corresponding location, Represents a cell grid The grid cell height, represents the current time step corresponding to any unit grid, Indicates the next time step corresponding to any unit grid. The present invention accurately analyzes the overflow volume of the unit grid, which can enhance the understanding of the operating status and potential risks of the urban drainage system.

[0019] Optionally, the drainage system infiltration rate prediction model satisfies the following relationship:

[0020]

[0021] in, represents the maximum infiltration rate of the urban drainage system, represents the saturated hydraulic conductivity, represents the rainfall intensity after interception by the urban canopy, Represents the total water shortage of the shallow soil layer in the city, represents the adjustable parameter weights of the urban drainage system, represents the coefficient related to the time period conversion, The infiltration rate prediction model of the present invention comprehensively considers multiple factors such as saturated hydraulic conductivity, canopy interception rainfall intensity, shallow soil water shortage, system adjustable parameter weight, time period conversion coefficient and infiltration rate attenuation coefficient, and can more accurately predict the maximum infiltration rate of the drainage system.

[0022] Optionally, the use of the drainage capacity assessment model, the system overflow analysis model and the drainage system infiltration rate prediction model to analyze the drainage conditions of various areas within the city includes: obtaining the flow of urban surface water entering any unit grid through the drainage capacity assessment model; obtaining the maximum overflow of the current time step and the maximum overflow of the next time step of any unit grid using the system overflow analysis model; and obtaining the maximum infiltration rate of the urban drainage system based on the drainage system infiltration rate prediction model. The present invention can comprehensively evaluate the performance of the urban drainage system. The drainage capacity assessment model provides flow information of surface water entering the drainage system, the system overflow analysis model further reveals the overflow of the drainage system at different time steps, and the infiltration rate prediction model takes into account the natural infiltration capacity of the drainage system for rainwater, which helps to fully understand the performance of the urban drainage system.

[0023] Optionally, the use of the drainage capacity analysis model, the drainage system overflow analysis model and the drainage system infiltration rate prediction model to analyze the drainage conditions of various areas within the city includes: combining the flow of urban surface water into any unit grid, the maximum overflow of any unit grid, the maximum infiltration rate of the drainage system, the target information of waterlogging disasters and the urban drainage system to analyze the drainage conditions of various areas within the city. The present invention not only takes into account the basic capacity of the drainage system, but also combines the target information of waterlogging disasters, so that the evaluation results are more comprehensive and detailed, which is conducive to understanding the drainage capacity of different areas of the city under different conditions, so as to make more accurate warnings and judgments.

[0024] In the second aspect, in order to efficiently execute the urban waterlogging disaster prevention and control method provided by the present invention, the present invention also provides an urban waterlogging disaster prevention and control system, including a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the urban waterlogging disaster prevention and control method as described in the first aspect of the present invention. The urban waterlogging disaster prevention and control system of the present invention has a compact structure and stable performance, and can stably execute the urban waterlogging disaster prevention and control method provided by the present invention, thereby improving the overall applicability and practical application ability of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of the urban waterlogging disaster prevention and control method of the present invention;

[0026] Figure 2 It is a schematic diagram of the physical information neural network model analysis process in the urban waterlogging disaster prevention and control method of the present invention;

[0027] Figure 3 This is a structural diagram of the urban waterlogging disaster prevention and control system of the present invention. DETAILED DESCRIPTION

[0028] See also Figure 1 The present invention deeply analyzes the evolution of urban waterlogging disasters in time and space dimensions based on historical disaster data and modern monitoring technology, quantitatively evaluates urban rainfall characteristics and rainwater runoff processes, and uses big data analysis technology and machine learning algorithms to construct a multi-dimensional analysis model for urban waterlogging disasters, which helps to improve the early warning accuracy and response speed of urban waterlogging disasters. The present invention provides a method for preventing and controlling urban waterlogging disasters, which includes the following steps:

[0029] S1. Obtain waterlogging disaster information in various areas within the city based on the urban disaster monitoring network. The specific implementation steps and contents are as follows;

[0030] In this embodiment, a city that is representative in terms of geography, climate, and socio-economic conditions is selected as the research and analysis object. The target city can reflect the common phenomena and characteristic information of different areas within the city when encountering waterlogging disasters.

[0031] In order to build an efficient and comprehensive integrated monitoring system, the embodiment deeply analyzes the unique geographical, environmental and social characteristics of each area within the target city to ensure that the layout of the monitoring network is both accurate and comprehensive. Among them, monitoring equipment is deployed in key areas, flood-prone areas, low-lying areas and other locations in the target city, including but not limited to waterlogging monitoring instruments, buried waterlogging monitoring instruments, pipe network flow monitoring instruments, pipe network water level monitoring instruments, smart manhole cover sensors, etc. The above equipment can monitor important parameters such as rainfall, pipe network flow, and road water level changes in real time. At the same time, 5G / 4G communication technology is used to transmit the data information collected by the monitoring equipment to the background monitoring center in real time to ensure the accuracy and real-time nature of urban waterlogging disaster information.

[0032] At the same time, we work closely with multiple key departments such as urban planning, water conservancy, and meteorology, and deploy relevant monitoring equipment in high-risk areas for urban flooding, especially historical waterlogging points, which can collect and capture key data such as rainfall intensity, water level fluctuations, etc. in various areas within the target city in real time, providing information support for disaster warning and emergency response.

[0033] In terms of monitoring scope, this embodiment is different from the traditional disaster information monitoring method. The embodiment expands the perspective to the key disaster-prone factors of key cities, including but not limited to densely populated areas, vulnerable nodes of the transportation network, water resistance of buildings, etc. At the same time, through visits and surveys, authoritative data analysis, on-site field observations and other means, to obtain waterlogging disaster information in various areas within the city, so as to have a deeper understanding of the potential threat of waterlogging to the urban drainage system and even the overall urban safety.

[0034] In this embodiment, a comprehensive and efficient urban disaster monitoring network is constructed to provide a strong guarantee for the monitoring and prevention of waterlogging disasters in various areas within the city. This not only helps to improve the city's disaster response capabilities, but also provides data support for waterlogging disaster management in other cities.

[0035] Furthermore, the method for obtaining waterlogging disaster information in various areas within the city in this embodiment is only an optional condition of the present invention. In other embodiments, the method for obtaining urban waterlogging disaster information can be flexibly adjusted according to the actual situation within the city and the needs of disaster information collection. Different cities have their own unique geographical, climatic, and socio-economic backgrounds. The above factors will have a profound impact on the formation and impact of waterlogging disasters. By flexibly adjusting the information collection method, it is possible to better adapt to environmental differences and ensure the accuracy and pertinence of monitoring information.

[0036] S2. Construct a waterlogging disaster information processing mechanism based on waterlogging disaster information and urban drainage systems, and obtain target information for preventing and controlling urban waterlogging disasters based on the waterlogging disaster information processing mechanism. The specific implementation steps and contents are as follows:

[0037] In this embodiment, a waterlogging disaster information processing mechanism is constructed mainly based on waterlogging disaster information and urban drainage system. The dynamic time step of waterlogging disaster information is set according to the waterlogging disaster information processing mechanism. The specific contents are as follows:

[0038] Setting the dynamic time step of urban waterlogging disaster information is a key link. The setting of the dynamic time step aims to flexibly adjust the time interval of information processing according to the real-time development of the disaster, the frequency of data updates and the requirements of processing capacity, so as to ensure the timeliness, accuracy and representativeness of urban waterlogging disaster information.

[0039] First, set the initial time step.

[0040] According to the historical data and factual experience of the urban disaster monitoring network, an initial time step is set, which should be able to balance the timeliness of information processing and the utilization of computing resources. For example, in the early stage of a disaster or when data updates are not frequent, a relatively long time step can be set, such as every hour or every two hours.

[0041] Next, analysis and adjustments are made based on real-time waterlogging disaster information in various areas within the city.

[0042] By analyzing the real-time collected urban waterlogging disaster data, such as rainfall, water level change trends, drainage system load, etc., the development speed and impact range of urban waterlogging disasters in different areas can be determined; and according to the analysis results, the time step can be dynamically adjusted. If the urban waterlogging disaster develops rapidly, the water level rises rapidly, or the drainage system is close to saturation, the time step should be shortened to update the monitoring information more frequently to ensure timely response.

[0043] Then, data updating and frequency matching are required.

[0044] Considering the data update frequency of the relevant monitoring equipment in the urban disaster monitoring network, if the monitoring equipment can provide data in real time or at a high frequency, the time step can be shortened accordingly to make full use of the monitoring data; if the data update frequency is low, the time step should not be too short to avoid processing errors caused by insufficient data. Furthermore, it is necessary to evaluate the processing capacity of the current urban disaster monitoring network, including but not limited to computing resources, storage resources, and network resources, to ensure that within the set time step, the urban waterlogging disaster prevention and control system can timely process and analyze the collected waterlogging disaster information.

[0045] In this embodiment, a feedback mechanism for the waterlogging disaster information processing mechanism is established based on the actual information collection effect and the development of urban disasters. By evaluating the processing results and disaster response effects, the setting conditions of the waterlogging disaster information processing mechanism are continuously adjusted and optimized, so that the system can automatically adjust according to the real-time changes of the disaster and the changes in its own processing capabilities to achieve the best information processing effect.

[0046] Reasonable setting of dynamic time steps helps to capture the short-term fluctuations and long-term trends of urban waterlogging disasters, and provides strong support for the formulation of more scientific and reasonable response strategies. Through the implementation of the above content, it can be ensured that the waterlogging disaster information processing mechanism can scientifically set the dynamic time step of waterlogging disaster information according to the real-time development of the disaster and the needs of system processing capacity, realize the timely processing and accurate analysis of waterlogging disaster information, and provide strong support for the prevention and response of urban waterlogging disasters.

[0047] In another optional embodiment, the urban drainage system is divided based on the above-mentioned waterlogging disaster information processing mechanism to obtain unit grid information of the urban drainage system, and the specific content is as follows:

[0048] The layout data of the urban drainage system can be obtained based on the urban disaster monitoring network. In the embodiment, the layout plan information of the drainage system of the target city is obtained based on the urban disaster monitoring network, including but not limited to the location, scale, operation status and other information of facilities such as drainage pipes, pumping stations, reservoirs, and drainage outlets. On the other hand, it is necessary to obtain the geographic spatial data of the city, such as topography, water system distribution, land use type, etc., which helps to more accurately understand the spatial distribution and geographical environment background of the drainage system.

[0049] According to the functional characteristics of the urban drainage system and the urban geographical features, the drainage system is divided into different functional areas, such as rainwater collection area, drainage trunk area, pump station service area, etc. The functional area division and geographical feature information of the drainage system are used, combined with urban geographic spatial data, to formulate the principles of unit grid division. The division of unit grids should take into account factors such as the connection relationship of drainage pipes, terrain changes, and land use types, to ensure the relative independence and integrity of the drainage system within the grid.

[0050] In the embodiment, GIS geographic information system technology is used to divide the drainage system into several unit grids according to the unit grid division principle of the urban drainage system. Each grid should contain complete drainage facilities such as pipelines, pumping stations, etc. and corresponding geographic spatial information. At the same time, detailed information records are established for each unit grid, including grid number, geographical location, area, type and quantity of drainage facilities included, historical waterlogging conditions and other information. The implementation information will serve as the basic data for subsequent urban waterlogging disaster monitoring, early warning and emergency response. Based on the implementation steps, the unit grid information of the target city drainage system can be obtained, thereby realizing the refined management of the urban drainage system.

[0051] Then, based on the above dynamic time step and waterlogging disaster information, a dynamic time step optimization model is established, and the target information for preventing and controlling urban waterlogging disasters is obtained based on the dynamic time step optimization model and waterlogging disaster information. The specific contents are as follows:

[0052] In the embodiment, an information neural network model is introduced to optimize the waterlogging disaster information, time step information and unit grid information of each area within the city.

[0053] The above-mentioned physical information neural network model fully integrates deep learning technology and physical principles, and can analyze and solve complex physical phenomena, especially the prediction and simulation problems involving partial differential equations.

[0054] In this embodiment, a physical information neural network model is constructed and trained based on the waterlogging disaster information of various areas within the city. The physical information neural network model is used to predict and simulate the dynamic evolution process of urban waterlogging disasters. The waterlogging disaster information including but not limited to flood inundation conditions at historical time points, real-time rainfall, changes in outer river water levels, gate pump operating status, and operation conditions of deep drainage tunnels are integrated into the input layer. The above input data can comprehensively reflect the key factors of urban waterlogging disasters.

[0055] In the hidden layer of the physical information neural network model, physical analysis functions are embedded to accurately describe the natural laws of water flow movement, water balance, etc. within the city. At the same time, the model is used to automatically learn and identify the complex physical mechanisms and nonlinear relationships of urban flooding disasters. The above process relies on the driving force of urban flooding disaster information, ensuring the accuracy and consistency of the information optimization results.

[0056] In this embodiment, the analysis process of the physical information neural network model can be found in Figure 2 .

[0057] Finally, the output of the physical information neural network model can be used to predict and output the waterlogging disaster information at a certain time step, thereby providing decision support for urban waterlogging disaster prevention and control. In the embodiment, historical and real-time data need to be supervised, learned and trained to ensure that the physical information neural network model can continuously optimize waterlogging disaster information and improve the reliability of urban waterlogging disaster prevention and control methods.

[0058] In the embodiment, a dynamic time step optimization model is established based on the physical information neural network model, and the following relationship is satisfied:

[0059] ,

[0060] in, represents the time step after dynamic optimization, represents the initial time step corresponding to the waterlogging disaster information, represents the Courant number, represents the momentum correction factor for the time step, represents the average size of the unit grid in the urban drainage system, represents the unit grid reference depth, Indicates that the fluid is in the drainage system x Single-width flow in one direction, Indicates that the fluid is in the drainage system y Single-width flow in one direction, Represents the acceleration due to gravity.

[0061] The Courant number can be used to measure the stability of numerical calculations. It specifically refers to the relative relationship between the time step and the space step. It plays an important role in regulating the stability and convergence of calculations in optimization models. The size of the Courant number directly affects the stability and convergence of numerical calculations. The larger the Courant number, the more grids the fluid particles pass through in one time step during the calculation process, which will speed up the calculation, but it may also make the calculation process unstable and the convergence worse. On the contrary, the smaller the Courant number, the more stable the calculation process, but the calculation speed will also decrease accordingly.

[0062] The above Courant number needs to satisfy the following relationship:

[0063]

[0064] in, represents the Courant number, represents the time interval during each iteration of the optimization model, represents the characteristic velocity, Indicates the size of the city unit grid.

[0065] The characteristic speed can be the maximum flow velocity in the fluid, the speed of sound, or other speeds related to the problem. In the optimization model, it may be necessary to consider the velocity components in multiple directions and take the maximum value or some form of average.

[0066] The momentum correction coefficient of the time step can be used to correct the calculation error of the dynamic time step. The time step is a subdivision unit of time in the simulation or calculation process. In molecular dynamics simulation, fluid dynamics simulation or any physical process that needs to gradually track the changes of the system over time, the time step is a basic parameter in the simulation algorithm, which determines the frequency of system state updates in each time interval. In the embodiment, the momentum correction coefficient is used to ensure the validity of the time step optimization result.

[0067] The average size of the unit grid in the urban drainage system mainly depends on the city size, terrain characteristics, population density, drainage demand and the actual needs of urban management. In practical applications, it is necessary to comprehensively consider and determine factors such as urban management needs, terrain characteristics, population density, drainage demand and spatial distribution characteristics of the drainage network.

[0068] The reference depth of the unit grid in the urban drainage system is determined according to actual needs and simulation accuracy. At the same time, the reference depth of the unit grid needs to be determined according to the specific conditions and terrain conditions of the urban drainage system.

[0069] In the drainage system, the single-width flow rate of the fluid in the x and y directions refers to the seepage or flow rate per unit width in the x or y direction, which can describe the flow characteristics of the fluid in the unit width section.

[0070] The single-width flow rate refers to the seepage or flow rate per unit width, which reflects the flow intensity of the fluid per unit width. In the drainage system, it can be used to evaluate the flow of fluids at different locations or in different directions. Therefore, in a two-dimensional plane, the x-direction and the y-direction represent two vertical directions on the horizontal plane. In the drainage system, the single-width flow rate in the above two directions may vary depending on factors such as terrain, pipeline layout, and water flow velocity.

[0071] Finally, the target information for preventing and controlling urban waterlogging disasters can be obtained based on the dynamic time step optimization model and waterlogging disaster information.

[0072] In the embodiment, the dynamic time step optimization model can be used to adjust the time step in the simulation or calculation process according to the real-time waterlogging disaster information of each area within the city. In waterlogging disaster prevention and control, the above model can dynamically adjust and optimize the simulation time step according to the real-time waterlogging disaster information such as rainfall intensity, water flow speed, drainage system status, etc. of the target city, so as to more accurately predict and respond to urban waterlogging.

[0073] In an optional embodiment, by analyzing and sorting out the waterlogging disaster information of different unit grids in the urban drainage system based on the time step after dynamic optimization, the target information for preventing and controlling urban waterlogging disasters can be obtained. First, the time step of the drainage system is adjusted and optimized in real time using a dynamic time step optimization model. The above optimization model will dynamically select the most appropriate time step for simulation based on real-time monitoring information such as rainfall intensity, water flow velocity, and drainage pipe status in the target city, ensuring that subtle changes in the system can be captured during the simulation process while maintaining computational efficiency.

[0074] Then, based on the waterlogging disaster information processing mechanism, the urban drainage system is divided into multiple unit grids. Each grid represents a relatively independent drainage area. The division of unit grids needs to be based on factors such as terrain, drainage pipe layout, and historical waterlogging records to ensure that the drainage characteristics and waterlogging risks within each grid are relatively consistent.

[0075] Finally, based on the optimized time step and unit grid of the urban drainage system, the waterlogging disaster information of each unit grid is extracted, sorted and adjusted. The above information includes but is not limited to the rainfall, water accumulation depth and range, river outlet flow and flooding conditions of each grid. By combining the waterlogging disaster information of each unit grid, the target information for preventing and controlling urban waterlogging disasters can be obtained.

[0076] Furthermore, in the present embodiment, the method for processing urban waterlogging disaster information is only an optional condition of the present invention. In one or some other embodiments, the method for processing urban waterlogging disaster information can be replaced according to the specific conditions of monitoring the urban drainage system and the target data requirements. There are differences in the terrain, climate, and drainage system conditions of the city. Timely replacement of the information processing method can ensure that the urban waterlogging disaster prevention and control method continues to adapt to changes in demand and maintain its effectiveness and reliability.

[0077] S3. A multi-dimensional analysis model for urban waterlogging disasters is established based on the above target information and the urban drainage system. The multi-dimensional analysis model for urban waterlogging disasters includes a drainage capacity assessment model, a system overflow analysis model, and a drainage system infiltration rate prediction model. The specific implementation steps and contents are as follows:

[0078] In an optional embodiment, a drainage capacity assessment model is established based on the target information of urban waterlogging disaster prevention and control and the unit grid information of the urban drainage system, aiming to quantify the drainage capacity of each unit grid in order to better understand and predict the risk of urban waterlogging.

[0079] The above drainage capacity evaluation model satisfies the following relationship:

[0080]

[0081] in, It represents the flow rate of surface water into any unit grid in the urban drainage system. represents the number of rainwater outfalls corresponding to any unit grid, Represents the first The discharge volume of each rainwater outfall is Represents the well overflow flow corresponding to any unit grid.

[0082] The flow rate of surface water entering any unit grid in the urban drainage system refers to the total amount of water flowing into a unit grid in the urban drainage system through natural rainfall, surface runoff, upstream drainage system or other channels within a specific time period. This flow rate is one of the important parameters for evaluating the drainage capacity and waterlogging risk of different grids in the urban drainage system.

[0083] The number of rainwater outfalls corresponding to any unit grid in the urban drainage system refers to the total number of rainwater outfalls that are directly connected to or serve a specific unit grid in the urban drainage system. The above-mentioned rainwater outfalls are an important part of the drainage system, which can collect surface water from the grid area and direct it into the drainage pipe to achieve effective discharge of rainwater and prevent the occurrence of urban waterlogging disasters.

[0084] The discharge rate of different rainwater outfalls connected to any unit grid refers to the amount of water discharged from the unit grid and entering the drainage pipe through different rainwater outfalls within a specific time period. The above discharge rate is one of the key parameters for evaluating the drainage capacity of the unit grid and the risk of waterlogging. When rainfall or other surface water flows into the unit grid, part or all of the water will be collected and discharged through the drainage facilities in the grid. As an important node connecting the grid and the drainage pipe, the discharge rate of the rainwater outfall directly reflects the speed and efficiency of the water flow in the grid being discharged.

[0085] The well overflow corresponding to any unit grid in the urban drainage system refers to the amount of water overflowed by the overflow well or other wells with overflow function associated with the unit grid under specific conditions. The above overflow is one of the important indicators for evaluating the performance of the drainage system and the risk of grid waterlogging.

[0086] In another optional embodiment, a system overflow analysis model is established based on target information for preventing and controlling urban waterlogging disasters and unit grid information of the urban drainage system.

[0087] In the embodiment, the overflow of the urban drainage system is analyzed, and an overflow analysis model of the drainage system is constructed. The above model contains two key calculation formulas, which are used to calculate the maximum overflow of any unit grid under the current time step, and predict the maximum overflow of the next time step based on the current state.

[0088] The maximum overflow analysis model for any cell grid at the current time step.

[0089] The model needs to fully consider factors such as the input flow of the unit grid, the drainage capacity of the drainage system, and the change of water storage in the grid. The above system overflow analysis model satisfies the following relationship:

[0090] ,

[0091] in, express t The maximum overflow volume of any unit grid in the drainage system at the time step, Represents a cell grid The orifice flow coefficient, Represents a cell grid of water storage area, represents the acceleration due to gravity, Represents a cell grid The water head, Represents a cell grid The surface grid water level at the corresponding location;

[0092] In the analysis of drainage systems, t The maximum overflow of any cell grid within a time step refers to the overflow rate at a specific time step. t Internal, unit grid i The maximum overflow volume is the volume of water that cannot be discharged in time by the drainage system. The above volume is usually caused by the input of external water sources such as rainfall and upstream water exceeding the processing capacity of the drainage system in the grid and the water storage capacity of the grid itself.

[0093] The orifice flow coefficient of a unit grid refers to the ratio of the actual flow rate flowing through the orifices in different unit grids under certain conditions to the theoretical flow rate. It can describe the orifice flow characteristics and is of great significance for evaluating the performance of drainage systems, predicting overflow, and performing flow control.

[0094] The water storage area of ​​a unit grid in an urban drainage system refers to the ground area within the grid that can store rainwater or other water sources, including but not limited to low-lying areas, reservoirs, wetlands, parks and green spaces, and other areas with natural or artificial water storage capacity.

[0095] The head of a unit grid refers to the mechanical energy of unit weight of liquid in the unit grid, which mainly includes three parts: position head, pressure head and velocity head. The sum of the above three parts constitutes the head of the unit grid. The position head in the unit grid reflects the vertical position of the liquid in the unit grid.

[0096] The surface grid water level at the corresponding position of the unit grid refers to the vertical height of the free surface of the water body relative to a certain reference plane in the surface area covered by the unit grid. The above water level will change over time and is affected by many factors, such as rainfall, evaporation, groundwater level changes, river flow changes, and human activities. When monitoring and analyzing the surface grid water level, it is necessary to comprehensively consider the effects of various factors to improve the accuracy and reliability of monitoring and analysis.

[0097] And based on the maximum overflow of any unit grid at the current time step, the maximum overflow of the next time step is further predicted. The specific contents are as follows:

[0098] ,

[0099] in, express t+1 The maximum overflow volume of any unit grid in the drainage system at the time step, represents the total water storage capacity of any unit grid, represents the acceleration due to gravity, Represents a cell grid The surface grid water level at the corresponding location, Represents a cell grid The grid cell height, represents the current time step corresponding to any unit grid, Indicates the next time step corresponding to any unit grid.

[0100] t+1 The maximum overflow of any unit grid in the time step drainage system refers to the maximum water overflow that may be reached in the same unit grid in the next time step in the future.

[0101] The total water storage capacity of any unit grid refers to the total volume of water that can be accommodated by all spaces within the unit grid that can store rainwater, groundwater or other water sources. It will change with rainfall, evaporation, groundwater level changes, human activities and time.

[0102] In this embodiment, the total water storage of any unit grid satisfies the following relationship:

[0103]

[0104] in, represents the total water storage capacity of any unit grid of the urban drainage system, represents the maximum overflow of any unit grid, Represents the water storage capacity of any unit grid.

[0105] The grid cell height of a unit grid refers to the size or dimension of each grid cell in the vertical direction after the continuous spatial area is discretized into several small, regular grid cells when dividing a drainage system or terrain model. It is used to describe the position and shape of the grid cell in three-dimensional space and is an important parameter in applications such as terrain analysis, water flow simulation, and drainage system design.

[0106] In an optional embodiment, after obtaining the values ​​of all variables based on the urban waterlogging disaster prevention and control target information, the relevant code can be written and modified to run the maximum overflow prediction model for the next time step, and the relevant content is as follows:

[0107] # Known parameters

[0108] Q_i_max = 1000.0 # Total water storage capacity of the unit grid, unit:

[0109] a = 9.81 # Gravitational acceleration, unit:

[0110] h_i = 5.0 # Surface grid water level of the unit grid, unit: m

[0111] l_i_plus_1 = 2.0 # Grid cell height of the unit grid, unit: m

[0112] delta_t_i = 1.0 # Current time step, unit: s

[0113] delta_t_i_plus_1 = 0.5 # Next time step, unit: s

[0114] gamma = 0.1 # Water flow resistance coefficient

[0115] # Calculate the maximum overflow for the next time step

[0116] Q_t_plus_1_i_star_imax = (Q_i_max - a * (h_i + l_i_plus_1) * delta_t_i) / (1 + a * h_i * delta_t_i_plus_1 * gamma)

[0117] print(f"The maximum overflow of the next time step is: Q_t_plus_1_i_star_imax:.2f} m^3")

[0118] In the embodiment, the entire prediction process can be automated by executing the prediction model through code, reducing manual intervention and improving prediction efficiency. Automated processing can quickly respond to data updates and perform predictions in real time or near real time. On the other hand, using code to run the prediction model can ensure the consistency of each prediction process, making the prediction results repeatable.

[0119] In another optional embodiment, a drainage system infiltration rate prediction model is established based on the target information of preventing and controlling urban waterlogging disasters and the unit grid information of the urban drainage system.

[0120] According to the properties and characteristics of the infiltration rate related data, a suitable prediction model type is selected. For the infiltration rate prediction, machine learning models such as random forests, gradient boosting trees, neural networks, etc. or physical models can be considered. In the embodiment, the input parameters and output targets of the prediction model are screened and set based on the urban rainfall characteristic information, the urban soil layer conditions, and the actual situation of the urban drainage system. In the process of building the prediction model, further steps such as feature selection, parameter tuning, and cross-validation are required to improve the accuracy and generalization ability of the prediction model.

[0121] In addition, the training set data is needed to train the drainage system infiltration rate prediction model so that the prediction model can learn the mapping relationship between input features and output targets. At the same time, indicators such as the loss function and accuracy of the prediction model can be monitored to evaluate the training effect of the model.

[0122] The above drainage system infiltration rate prediction model satisfies the following relationship:

[0123]

[0124] in, represents the maximum infiltration rate of the urban drainage system, represents the saturated hydraulic conductivity, represents the rainfall intensity after interception by the urban canopy, Represents the total water shortage of the shallow soil layer in the city, represents the adjustable parameter weights of the urban drainage system, represents the coefficient related to the time period conversion, Represents the attenuation coefficient of the infiltration rate.

[0125] The maximum infiltration rate of an urban drainage system refers to the maximum amount of water that can penetrate into the soil through a unit area of ​​soil surface per unit time under specific conditions. This indicator is of great significance for evaluating the performance of urban drainage systems and preventing and controlling urban waterlogging disasters. Specifically, the maximum infiltration rate is affected by many factors, including the physical properties of the soil, rainfall intensity, soil moisture content, vegetation coverage, and the design and operation of the drainage system.

[0126] Saturated hydraulic conductivity can also be called soil saturated hydraulic conductivity, which refers to the water flow rate per unit area passing through the soil per unit time when the water in the soil reaches saturation. It can describe the ability of soil to move water under saturation and is an important indicator for evaluating soil permeability. Based on this, we can better understand the water movement law of urban drainage systems and provide a scientific basis for urban drainage system design, groundwater management and environmental protection.

[0127] The total water shortage of urban shallow soil layers involves multiple factors, including urban soil moisture conditions, precipitation, groundwater level, vegetation cover, soil type, and human activities. The total water shortage of urban shallow soil layers refers to the difference between the actual amount of water held in the urban shallow soil layer and its ideal or target amount in a specific period of time. The above difference is affected by many factors, such as insufficient precipitation, excessive evaporation, excessive groundwater exploitation, and high vegetation transpiration.

[0128] The adjustable parameter weights of urban drainage systems refer to the quantitative expression of the importance or influence of different parameters such as drainage pipe diameter, pumping capacity of pumping stations, and reservoir capacity on the overall system performance during the design and operation of the drainage system. The setting and optimization of the above parameter weights are of great significance for improving the efficiency of the drainage system, reducing operating costs, and enhancing flood control and drainage capabilities.

[0129] The coefficient related to the time period conversion is the ratio of the highest daily maximum hourly rainfall to the average hourly rainfall, which reflects the change of urban rainfall within a specific time step.

[0130] The attenuation coefficient of infiltration rate refers to the rate parameter that describes the process of soil infiltration rate changing over time, from the maximum infiltration rate to the stable infiltration rate or the minimum infiltration rate. It reflects the process in which the pores between soil particles in urban soil are gradually filled with water due to the increase of soil moisture content during rainfall, resulting in a gradual decrease in soil infiltration capacity.

[0131] The attenuation coefficient of the infiltration rate can usually be used to construct an exponential attenuation function that describes the change of the infiltration rate over time. In the embodiment, the attenuation coefficient satisfies the following relationship:

[0132]

[0133] in, express The infiltration rate for the time step, represents the initial infiltration rate of the city, represents the urban stable infiltration rate, Represents the attenuation coefficient of the infiltration rate.

[0134] The initial infiltration rate is the infiltration rate of urban soil in a dry state.

[0135] The stable infiltration rate is the infiltration rate when the urban soil reaches saturation.

[0136] The magnitude of the infiltration rate attenuation coefficient is affected by many factors, including but not limited to the physical properties of urban soil, vegetation coverage, rainfall intensity, and previous soil moisture content. The looser the soil texture, the greater the porosity, and the better the vegetation coverage, the smaller the attenuation coefficient may be, because the above factors are conducive to the penetration and storage of water in the soil. On the contrary, when the soil is compact, the porosity is small, the vegetation coverage is poor, or the rainfall intensity is high, the attenuation coefficient may be large, because the soil's ability to absorb and penetrate water is limited.

[0137] Furthermore, in this embodiment, a simplified Python function framework is set up to calculate the key parts of the drainage system infiltration rate prediction model, and its specific content is as follows:

[0138] def calculate_max_infiltration_rate(k, q1, Q2, phi, tau, lambda_):

[0139] """

[0140] Calculate the maximum infiltration rate of an urban drainage system.

[0141] parameter:

[0142] k (float): saturated hydraulic conductivity

[0143] q1 (float): Rainfall intensity after interception by the urban canopy

[0144] Q2 (float): Total water shortage in the shallow soil layer of the city

[0145] phi (float): adjustable parameter weight of the urban drainage system

[0146] tau (float): coefficient related to time period conversion

[0147] lambda_ (float): The decay coefficient of the infiltration rate

[0148] return:

[0149] float: Maximum infiltration rate of urban drainage system (IR_max)

[0150] """

[0151] numerator = k * q1 * (Q2 - Q2 * np.exp(-phi * tau))

[0152] denominator = q1 + np.exp(-phi * tau)

[0153] IR_max = numerator / denominator * (1 - lambda_)

[0154] return IR_max

[0155] # Example call (requires installation of numpy library)

[0156] import numpy as np

[0157] # Assumed parameter values

[0158] k = 0.1 # Saturated hydraulic conductivity

[0159] q1 = 50 # Rainfall intensity after canopy interception (mm / h)

[0160] Q2 = 100 # Total water shortage in shallow soil layer (mm)

[0161] phi = 0.5 # Adjustable parameter weight

[0162] tau = 1 # Time period conversion factor

[0163] lambda_ = 0.2 # Infiltration rate attenuation coefficient

[0164] # Calculate the maximum infiltration rate

[0165] max_infiltration_rate = calculate_max_infiltration_rate(k, q1, Q2,phi, tau, lambda_)

[0166] print(f"Maximum infiltration rate: {max_infiltration_rate:.2f} mm / h")

[0167] Furthermore, in the present embodiment, the combination content of the multidimensional analysis model for urban waterlogging disasters is only an optional condition of the present embodiment. In one or some other embodiments, the multidimensional analysis model for urban waterlogging disasters can be adjusted according to the actual situation of the target city and the waterlogging disaster analysis objectives, and the specific conditions such as the rainfall characteristics, topography, and drainage system conditions of the target city can be analyzed to construct a waterlogging disaster analysis model that is closer to the actual situation of the city, which can more accurately reflect the causes, evolution process, and impacts of urban waterlogging, thereby improving the accuracy of the analysis.

[0168] S4. Analyze the drainage conditions of various areas within the city using the above drainage capacity assessment model, system overflow analysis model and drainage system infiltration rate prediction model. The specific implementation steps and contents are as follows:

[0169] In this embodiment, the flow of urban surface water entering any unit grid is obtained through the drainage capacity assessment model; the maximum overflow of the current time step and the maximum overflow of the next time step of any unit grid are obtained using the system overflow analysis model; and the maximum infiltration rate of the urban drainage system is obtained based on the drainage system infiltration rate prediction model.

[0170] Based on the target information of urban waterlogging disaster prevention and control, key information such as urban topographic maps, historical rainfall data, soil type distribution, drainage network layout and capacity, unit grid division, etc. are extracted, and the key data are cleaned and sorted to ensure the accuracy and consistency of the data.

[0171] The flow of urban surface water entering any unit grid is obtained through the drainage capacity assessment model.

[0172] The drainage network layout, rainfall discharge conditions and specific information of the unit grid are taken as input parameters, and the drainage capacity assessment model is used to simulate the dynamic flow process of surface water on the urban surface during rainfall, and then the surface water flow rate received by each unit grid is calculated, thereby obtaining the surface water flow entering each grid in the urban drainage system at a specific time step.

[0173] In the embodiment, a system overflow analysis model is adopted, and the surface water flow of the unit grid, the working status of the drainage pipe, and the basic properties of the unit grid are used as key input parameters. The model is used to calculate the maximum overflow generated by each unit grid in the current time step, while taking into account the actual drainage capacity limitations of the drainage network and the dynamic factors such as the time required for the water flow to be transmitted in the pipe network system. Furthermore, the overflow analysis model also predicts the maximum overflow corresponding to different unit grids in the next time step. Finally, the maximum overflow data of each unit grid in the urban system at continuous time steps are obtained, and the above data provide an important basis for the management of urban flood control and drainage.

[0174] The maximum infiltration rate of the urban drainage system is obtained based on the drainage system infiltration rate prediction model.

[0175] In order to quantify and evaluate the natural infiltration and absorption efficiency of rainwater by the urban drainage system, a drainage system infiltration rate prediction model is established in the embodiment. The above model integrates the diversity of urban soil types, the changing characteristics of rainfall intensity, and the unique structural and functional properties of the urban drainage system as input conditions, and predicts the maximum infiltration rate level that the urban drainage system can achieve through analysis and calculation. The maximum infiltration rate can intuitively reveal the maximum rainwater absorption potential that the soil layer can bear during rainfall, thereby providing solid data support and scientific decision-making basis for the design and optimization of urban drainage networks and the formulation and adjustment of flood control measures.

[0176] Finally, the drainage conditions of various areas within the city are analyzed by combining the flow of urban surface water entering any unit grid, the maximum overflow flow of any unit grid, the maximum infiltration rate of the drainage system, the target information of urban waterlogging disasters and the urban drainage system.

[0177] The embodiment integrates the flow of urban surface water entering each unit grid, the maximum overflow of each unit grid under specific conditions, the maximum infiltration capacity of the drainage system, waterlogging disaster target information and the urban drainage system to conduct a comprehensive analysis of the drainage conditions of various areas within the city.

[0178] Based on the multi-dimensional analysis model of urban waterlogging disasters, the drainage efficiency of various areas within the city was analyzed and evaluated in an all-round and in-depth manner. In the above process, the output results of the multi-dimensional model, including drainage capacity assessment, system overflow prediction and drainage system infiltration rate, were systematically collected and integrated to construct a comprehensive data analysis framework. Subsequently, combined with the specific environmental conditions and drainage system characteristics of different areas of the city, the drainage response of different areas within the city under different rainfall patterns was analyzed in detail, taking into account key factors such as surface water inflow, system overflow and natural soil infiltration.

[0179] Based on the above analysis results, the waterlogging risk level of each area within the city was further evaluated, high-risk areas were clearly marked, and potential weaknesses and risks were accurately identified. The above implementation steps are not only conducive to a deep understanding of the current status of the urban drainage system, but also provide strong scientific support and decision-making guidance for the implementation and continuous optimization of specific measures for urban flood control and drainage.

[0180] Furthermore, in the present embodiment, the analysis method of the drainage conditions of various areas within the city is only an optional condition of the present embodiment. In one or some other embodiments, the analysis method can be adjusted according to the capacity of the urban drainage system and the actual conditions of various areas of the city, targeting the specific needs of the urban drainage system. It can make more effective use of existing resources and ensure the accuracy and pertinence of the urban waterlogging disaster analysis results.

[0181] S5. Combine the drainage conditions and urban drainage system to match the urban waterlogging early warning plan to achieve effective prevention and control of urban waterlogging disasters. The specific implementation steps and contents are as follows:

[0182] In the embodiment, the relevant analysis results and target data are sorted out. Among them, the urban drainage system information, including pipeline layout, pipe diameter, pump station capacity, drainage facility status, etc., is sorted out based on the above-mentioned multi-dimensional analysis model of urban waterlogging disasters, and the drainage performance of various areas within the city under different rainfall conditions is sorted out, mainly including surface water inflow, overflow, infiltration, etc. At the same time, the meteorological data under the same time step is sorted out, including but not limited to rainfall, rainfall intensity, rainfall duration, etc., which is helpful to accurately analyze and warn of relevant changes and trends in urban waterlogging disasters.

[0183] Based on drainage system data and regional drainage conditions, the waterlogging risk level of each region is assessed, high-risk areas and potential problem points are identified, and the city is divided into different early warning areas based on the waterlogging disaster risk assessment results. Each area formulates corresponding early warning strategies based on its waterlogging risk level.

[0184] Different levels of warning signals are set according to the rainfall intensity and waterlogging risk prediction results in different areas within the city. The embodiment mainly includes blue, yellow, orange and red. Different colors correspond to different degrees of waterlogging risks, and the danger level index gradually increases. For the classification of warning signals in the embodiment, please refer to Table 1.

[0185] Table 1 Classification standard of urban waterlogging risk level

[0186] Risk Level Color Indicators Low blue middle yellow Higher Orange Red

[0187] In this embodiment, it is necessary to pre-set the early warning triggering conditions for urban waterlogging disaster prevention and control, and clarify the triggering conditions of each warning level, such as rainfall reaching a certain threshold, water accumulation in high-risk areas, etc. At the same time, a rapid and effective early warning information release mechanism is established in the urban waterlogging disaster prevention and control system, and early warning information is released to the public through multiple channels such as radio, television, mobile phone text messages, and social media.

[0188] Detailed emergency plans are formulated for different warning levels, including but not limited to personnel evacuation, traffic control, drainage facility scheduling, etc. In the embodiment, the Internet of Things, big data and other technical means can be used to monitor the operating status and rainfall of the urban drainage system in real time, and adjust the warning plan and emergency measures in time according to the actual situation. Furthermore, after the urban waterlogging event, it is necessary to evaluate the effectiveness of the warning plan and emergency response measures for urban waterlogging disasters, and continuously optimize the urban waterlogging disaster prevention and control mechanism, warning plan and emergency response measures according to the evaluation results, improve the prevention and control capabilities of urban waterlogging disasters, so as to achieve effective prevention and control of urban waterlogging disasters and effectively ensure the safety and stable operation of the city.

[0189] See also Figure 3 In an optional embodiment, in order to efficiently execute the urban waterlogging disaster prevention and control method provided by the present invention, the present invention also provides an urban waterlogging disaster prevention and control system, in which the input device, processor, output device and memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the specific steps of the relevant embodiments of the urban waterlogging disaster prevention and control method provided by the present invention. The urban waterlogging disaster prevention and control system of the present invention has a complete structure, objective stability, and can efficiently execute the urban waterlogging disaster prevention and control method of the present invention, thereby improving the overall applicability and practical application ability of the present invention.

[0190] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for preventing and controlling urban waterlogging disasters, characterized in that: The steps include: Obtain waterlogging disaster information in various areas within the city through the urban disaster monitoring network; Building a waterlogging disaster information processing mechanism based on the waterlogging disaster information and the urban drainage system, and obtaining target information for preventing and controlling urban waterlogging disasters based on the waterlogging disaster information processing mechanism; Establishing a multi-dimensional analysis model for urban waterlogging disasters based on the target information and the urban drainage system, wherein the multi-dimensional analysis model for urban waterlogging disasters includes a drainage capacity assessment model, a system overflow analysis model, and a drainage system infiltration rate prediction model; Analyzing the drainage conditions of various areas within the city using the drainage capacity assessment model, the system overflow analysis model and the drainage system infiltration rate prediction model; In combination with the drainage conditions and the urban drainage system, an urban waterlogging early warning scheme is matched to achieve effective prevention and control of urban waterlogging disasters, and an urban drainage system and urban drainage system information corresponding to the urban drainage system are introduced. The urban drainage system includes pipelines, pipe diameters, pumping stations and drainage facilities. The urban drainage system information includes pipeline layout, pipe diameter, pumping station capacity and drainage facility status; The drainage capacity evaluation model satisfies the following relationship: in, It represents the flow rate of surface water into any unit grid in the urban drainage system. represents the number of rainwater outfalls corresponding to any unit grid, Represents the first The discharge volume of each rainwater outfall is Indicates the well overflow corresponding to any unit grid; The system overflow analysis model satisfies the following relationship: ， in, express t The maximum overflow volume of any unit grid in the drainage system at the time step, Represents a cell grid The orifice flow coefficient, Represents a cell grid of water storage area, represents the acceleration due to gravity, Represents a cell grid The water head, Represents a cell grid The surface grid water level at the corresponding location; ， in, express t+1 The maximum overflow volume of any unit grid in the drainage system at the time step, represents the total water storage capacity of any unit grid, represents the acceleration due to gravity, Represents a cell grid The surface grid water level at the corresponding location, Represents a cell grid The grid cell height, represents the current time step corresponding to any unit grid, Indicates the next time step corresponding to any unit grid; The drainage system infiltration rate prediction model satisfies the following relationship: ， in, represents the maximum infiltration rate of the urban drainage system, represents the saturated hydraulic conductivity, represents the rainfall intensity after interception by the urban canopy, Represents the total water shortage of the shallow soil layer in the city, represents the adjustable parameter weights of the urban drainage system, represents the coefficient related to the time period conversion, represents the attenuation coefficient of the infiltration rate; The use of the drainage capacity assessment model, the system overflow analysis model and the drainage system infiltration rate prediction model to analyze the drainage conditions of various areas within the city includes: The flow rate of urban surface water entering any unit grid is obtained through the drainage capacity assessment model; The system overflow analysis model is used to obtain the maximum overflow of the current time step and the maximum overflow of the next time step of any unit grid; Obtaining the maximum infiltration rate of the urban drainage system based on the drainage system infiltration rate prediction model; The use of the drainage capacity analysis model, the drainage system overflow analysis model and the drainage system infiltration rate prediction model to analyze the drainage conditions of various areas within the city also includes: The drainage conditions of various areas within the city are analyzed by combining the flow of urban surface water entering any unit grid, the maximum overflow flow of any unit grid, the maximum infiltration rate of the drainage system, the target information of urban waterlogging disasters and the urban drainage system.

2. The urban waterlogging disaster prevention and control method according to claim 1, characterized in that: The waterlogging disaster information processing mechanism is constructed based on the waterlogging disaster information and the urban drainage system, and the target information for preventing and controlling urban waterlogging disasters is obtained based on the waterlogging disaster information processing mechanism, including: Setting a dynamic time step of waterlogging disaster information according to the waterlogging disaster information processing mechanism; The urban drainage system is divided based on the waterlogging disaster information processing mechanism to obtain unit grid information of the urban drainage system.

3. The urban waterlogging disaster prevention and control method according to claim 2, characterized in that: The waterlogging disaster information processing mechanism is constructed based on the waterlogging disaster information and the urban drainage system, and the target information for preventing and controlling urban waterlogging disasters is obtained based on the waterlogging disaster information processing mechanism, including: Establishing a dynamic time step optimization model based on the dynamic time step and the waterlogging disaster information; Target information for preventing and controlling urban waterlogging disasters is obtained based on the dynamic time step optimization model and the waterlogging disaster information.

4. The urban waterlogging disaster prevention and control method according to claim 3, characterized in that: The dynamic time step optimization model satisfies the following relationship: ， in, represents the time step after dynamic optimization, represents the initial time step corresponding to the waterlogging disaster information, represents the Courant number, represents the momentum correction factor for the time step, represents the average size of the unit grid in the urban drainage system, represents the unit grid reference depth, Indicates that the fluid is in the drainage system x Single-width flow in one direction, Indicates that the fluid is in the drainage system y Single-width flow in one direction, Represents the acceleration due to gravity.

5. An urban waterlogging disaster prevention and control system, characterized in that: The system includes a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the urban waterlogging disaster prevention and control method as described in any one of claims 1 to 4.

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