A method for high-density urban waterlogging evolution and early warning based on digital twin

Abstract

The present invention relates to the field of digital twin technology, and in particular, to a method for high-density urban waterlogging evolution and early warning based on digital twins. The method includes the following steps: real-time waterlogging depth, river water level, meteorological monitoring, and drainage pipe network sensors installed in the city are used to collect the corresponding real-time waterlogging depth, real-time river water level, real-time meteorological conditions, and real-time drainage pipe network water level conditions of the city and perform digital twin construction to generate an urban waterlogging digital twin model; obtain the distribution of the drainage pipe network and perform drainage grid division to generate an urban drainage grid model; obtain the law of historical waterlogging events and perform waterlogging area prediction analysis and waterlogging evolution coupling analysis to generate an urban waterlogging evolution distribution field; perform waterlogging spatio-temporal evolution analysis and waterlogging early warning processing on the urban waterlogging evolution distribution field to execute corresponding urban waterlogging emergency response work. The present invention can provide accurate waterlogging evolution prediction and effective early warning.

Description

Technical Field

[0001] The present invention relates to the technical field of digital twins, and in particular, to a method for high-density urban waterlogging evolution and early warning based on digital twins. Background Art

[0002] The occurrence frequency and intensity of extreme climate events such as rainstorms and typhoons are increasing continuously, bringing huge impacts to urban infrastructure, residents' lives and economic activities. Especially in high-density cities, due to the tense land use, heavy load of the drainage system and complex underground facilities, the waterlogging phenomenon is more serious and it is often difficult to predict and respond effectively in a timely manner. In this context, as an advanced urban management tool, digital twin technology has gradually been introduced into the fields of urban water affairs and disaster management. By creating a virtual copy of the physical world, digital twin technology can real-time simulate and monitor various changes in the urban environment. Especially in complex hydrological and drainage systems, by integrating multi-dimensional data such as urban geographic information, meteorological data, drainage facilities, traffic flow, and terrain into a digital model, digital twin can achieve real-time dynamic simulation of the urban waterlogging evolution process, thereby improving the timeliness and accuracy of waterlogging early warning. However, although there are already some waterlogging prediction and management methods based on hydrological models, these methods still have certain limitations when facing the complex environment of high-density cities. They are difficult to process real-time changing meteorological data and the operating status of the drainage pipe network, and cannot fully consider the mutual influence between different factors, thus lacking a global analysis of the waterlogging evolution process and resulting in low waterlogging early warning efficiency. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a method for high-density urban waterlogging evolution and early warning based on digital twins to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for high-density urban waterlogging evolution and early warning based on digital twins includes the following steps:

[0005] Step S1: Real-time collect the real-time waterlogging depth, real-time river water level, real-time meteorological conditions and real-time drainage pipe network water level status of the city through waterlogging depth sensors, river water level sensors, meteorological monitoring devices and drainage pipe network sensors installed in the city, and perform digital twin construction based on the real-time waterlogging depth, real-time meteorological conditions and real-time drainage pipe network water level status of the city in combination with the land use type, terrain and geomorphic data and building road information model of the city to generate an urban waterlogging digital twin model;

[0006] Step S2: Obtain the distribution of the drainage pipe network corresponding to the city, and based on the distribution of the drainage pipe network corresponding to the city and the real-time river water level, divide the drainage grid of the urban waterlogging digital twin model to generate an urban drainage grid model; obtain the law of historical waterlogging events corresponding to the city, and based on the law of historical waterlogging events corresponding to the city, conduct waterlogging area prediction and analysis for each urban drainage grid partition in the urban drainage grid model to obtain the urban waterlogging prediction area;

[0007] Step S3: Obtain the measured waterlogging depth, river water level, rainfall process in the past period of time, and drainage pipe network status at the current moment corresponding to the urban waterlogging prediction area through the urban waterlogging digital twin model as the real-time monitoring data corresponding to the urban waterlogging prediction area, and conduct waterlogging evolution coupling analysis on the urban waterlogging prediction area based on the real-time monitoring data corresponding to the urban waterlogging prediction area to generate an urban waterlogging evolution distribution field;

[0008] Step S4: Conduct waterlogging spatio-temporal evolution analysis on the urban waterlogging evolution distribution field to obtain the development trend of urban waterlogging evolution at different spatio-temporal nodes of the city; based on the development trend of urban waterlogging evolution at different spatio-temporal nodes of the city, conduct waterlogging early warning processing on the corresponding urban waterlogging evolution distribution field to generate an urban waterlogging early warning distribution field to execute the corresponding urban waterlogging emergency response work.

[0009] Furthermore, Step S1 includes the following steps:

[0010] Step S11: Conduct real-time monitoring of urban waterlogging in the city through waterlogging depth sensors installed at various location nodes in the city to obtain the real-time waterlogging depth corresponding to the city;

[0011] Step S12: Conduct real-time monitoring of the river water level in the city through river water level sensors installed at various river locations in the city to obtain the real-time river water level corresponding to the city, including the water level height and the corresponding time;

[0012] Step S13: Conduct real-time meteorological monitoring of the city through meteorological monitoring equipment installed at various location nodes in the city to obtain the real-time meteorological conditions corresponding to the city, including the rainfall amount and the rainfall duration;

[0013] Step S14: Conduct real-time monitoring of the drainage pipe network in the city through drainage pipe network liquid level and flow velocity sensors installed at various location nodes in the city to obtain the real-time drainage pipe network water level status corresponding to the city, including the liquid level corresponding to the drainage pipe network node and the drainage flow velocity;

[0014] Step S15: Obtain the land use type, terrain and landform data, and building road information model corresponding to the city, and perform digital twin construction based on the real-time waterlogging depth, real-time meteorological conditions, and real-time water level status of the drainage pipe network corresponding to the city, combined with the land use type, terrain and landform data, and building road information model corresponding to the city, so as to generate a digital twin model of urban waterlogging.

[0015] Further, step S2 includes the following steps:

[0016] Step S21: Obtain the terrain and landform, building density, and drainage facility layout status corresponding to the city, and obtain the distribution of the drainage pipe network corresponding to the city according to the terrain and landform, building density, and drainage facility layout status corresponding to the city;

[0017] Step S22: Evaluate the drainage carrying capacity of the drainage pipe network at the corresponding distribution positions in the digital twin model of urban waterlogging based on the distribution of the drainage pipe network corresponding to the city and the real-time river water level, so as to obtain the drainage pipe network carrying capacity matrix of the city;

[0018] Step S23: Obtain the geographical space, drainage pipe network layout, and water flow characteristics at the corresponding distribution positions through the digital twin model of urban waterlogging, and conduct water flow load assessment and analysis according to the geographical space, drainage pipe network layout, and water flow characteristics at the corresponding distribution positions, so as to obtain the drainage water flow load at the corresponding distribution positions of the city;

[0019] Step S24: Based on the drainage pipe network carrying capacity at the corresponding distribution positions in the drainage pipe network carrying capacity matrix of the city and combined with the drainage water flow load at the corresponding distribution positions of the city, perform drainage grid carrying capacity matching division on the digital twin model of urban waterlogging, so as to generate a drainage grid model of the city;

[0020] Step S25: Obtain the historical waterlogging event pattern corresponding to the city, and conduct waterlogging area prediction and analysis on each urban drainage grid partition in the drainage grid model of the city based on the historical waterlogging event pattern corresponding to the city, so as to obtain the waterlogging prediction area of the city.

[0021] Further, step S22 includes the following steps:

[0022] Obtain the drainage pipe network diameter and drainage pipe network slope at the corresponding distribution positions through the distribution of the drainage pipe network corresponding to the city;

[0023] Conduct drainage flow velocity statistical analysis on the drainage pipe network at the corresponding distribution positions in the digital twin model of urban waterlogging to obtain the drainage pipe network flow velocity at the corresponding distribution positions of the city;

[0024] Obtain the time - series fluctuations of the river water levels at the corresponding distribution positions in the urban waterlogging digital twin model through the real - time river water levels corresponding to the cities, and conduct an analysis of the outlet flow constraint limit on the drainage pipe networks at the corresponding distribution positions in the urban waterlogging digital twin model based on the time - series fluctuations of the river water levels at the corresponding distribution positions in the cities, so as to obtain the time - varying constraints of the corresponding river water levels in the cities on the outlet flow of the drainage pipe networks;

[0025] Based on the pipe diameters of the drainage pipe networks, the pipe slopes of the drainage pipe networks, the flow velocities of the drainage pipe networks, and the time - varying constraints of the corresponding river water levels in the cities on the outlet flow of the drainage pipe networks at the corresponding distribution positions in the cities, use the drainage pipe network bearing capacity measurement calculation formula to conduct a bearing capacity evaluation calculation on the drainage pipe networks at the corresponding distribution positions in the urban waterlogging digital twin model, so as to obtain the drainage pipe network bearing capacity coefficients at the corresponding distribution positions in the cities;

[0026] Construct a drainage capacity matrix for the drainage pipe networks at the corresponding distribution positions in the urban waterlogging digital twin model according to the drainage pipe network bearing capacity coefficients at the corresponding distribution positions in the cities, so as to obtain the urban drainage pipe network bearing capacity matrix.

[0027] Furthermore, the specific drainage pipe network bearing capacity measurement calculation formula is:

[0028] ;

[0029] ;

[0030] In the formula, is the drainage pipe network bearing capacity coefficient of the city at the distribution position coordinates ; is the three - dimensional spatial region covered by the drainage pipe network, is the abscissa parameter of the distribution position, is the ordinate parameter of the distribution position, is the vertical coordinate parameter of the distribution position, is the time - variable parameter, is the initial time of the constraint influence integral, is the end time of the constraint influence integral, is the time - varying constraint of the corresponding river water level in the city on the outlet flow of the drainage pipe network at time ; is the river water level of the city at the distribution position coordinates and at time ; is the linear influence coefficient of the river water level on the outlet flow of the drainage pipe network, is the quadratic non - linear influence coefficient of the river water level on the outlet flow of the drainage pipe network, is the exponential function, is the time of the city The outlet flow rate of the drainage pipe network at a certain moment is the initial flow rate of the drainage pipe network outlet is the decay control parameter of the drainage pipe network flow response time is the water flow rate at the distribution location coordinates of the city is the distribution location coordinates of the city where the maximum allowable water flow rate is the distribution location coordinates of the city where the pipe diameter of the drainage pipe network is the influence weight factor borne by the pipe diameter is the distribution location coordinates of the city where the pipe slope of the drainage pipe network is the influence weight factor borne by the pipe slope is the distribution location coordinates of the city where the flow velocity of the drainage pipe network is the influence weight factor borne by the flow velocity is the correction coefficient of the bearing capacity coefficient of the drainage pipe network

[0031] Furthermore, the water flow load assessment and analysis based on the geospatial, drainage pipe network layout, and water flow characteristics at the corresponding distribution location in step S23 includes the following steps:

[0032] Conduct a load constraint analysis of the drainage space layout based on the geospatial and drainage pipe network layout at the corresponding distribution location to obtain the load constraint conditions of the drainage space layout at the corresponding distribution location of the city;

[0033] Conduct a water flow dynamic distribution analysis based on the water flow characteristics at the corresponding distribution location to generate a water flow dynamic distribution flow field at the corresponding distribution location of the city;

[0034] Conduct a water flow load assessment and analysis on the water flow dynamic distribution flow field at the corresponding distribution location of the city based on the load constraint conditions of the drainage space layout at the corresponding distribution location of the city to obtain the drainage water flow load at the corresponding distribution location of the city.

[0035] Furthermore, step S24 includes the following steps:

[0036] Step S241: Based on the drainage network carrying capacity at the corresponding distribution position within the urban drainage network carrying capacity matrix, perform drainage carrying capacity matching analysis on the drainage water flow load at the corresponding distribution position in the city. If the drainage network carrying capacity at the corresponding distribution position is greater than or equal to the drainage water flow load, then determine it as the drainage carrying capacity balance matching point for the corresponding drainage network carrying capacity; if the drainage network carrying capacity at the corresponding distribution position is less than the drainage water flow load, then perform re-carrying capacity matching analysis at a distribution position one level out parallel to it until a drainage carrying capacity balance matching point is determined.

[0037] Step S242: Connect equivalent drainage grids based on the drainage carrying capacity balance matching points with different drainage network carrying capacities to generate the urban drainage grid boundaries corresponding to different drainage network carrying capacities.

[0038] Step S243: Based on the urban drainage grid boundaries corresponding to different drainage network carrying capacities, perform drainage grid carrying capacity matching division on the urban waterlogging digital twin model to generate an urban drainage grid model.

[0039] Furthermore, the waterlogging area prediction analysis for each urban drainage grid partition within the urban drainage grid model based on the historical waterlogging event rules corresponding to the city in Step S25 includes the following steps:

[0040] Construct a two-dimensional surface model of waterlogging diffusion according to the historical waterlogging event rules corresponding to the city to reflect the diffusion of surface waterlogging after overflow in each drainage grid partition in the city and its impact on the waterlogging distribution in the surrounding areas, and generate a two-dimensional surface rule model of urban waterlogging diffusion.

[0041] Based on the two-dimensional surface rule model of urban waterlogging diffusion, perform waterlogging event frequency measurement for each corresponding urban drainage grid partition within the urban drainage grid model to obtain the waterlogging event frequency corresponding to each urban drainage grid partition.

[0042] Connect high-incidence waterlogging grid areas for the corresponding urban drainage grid partitions within the urban drainage grid model based on the waterlogging event frequency corresponding to each urban drainage grid partition to obtain high-incidence waterlogging grid areas in the city.

[0043] Obtain the terrain slope distribution and ground water seepage efficiency between the corresponding drainage grid partitions within the urban drainage grid model according to the high-incidence waterlogging grid areas in the city.

[0044] Based on the terrain slope distribution and ground water seepage efficiency between the corresponding drainage grid partitions, perform waterlogging area prediction analysis on the high-incidence waterlogging grid areas in the city to obtain the waterlogging prediction areas in the city.

[0045] Furthermore, Step S3 includes the following steps:

[0046] Step S31: Obtain the measured water depth, river water level, rainfall process in the past period, and drainage pipe network status corresponding to the urban waterlogging prediction area at the current moment through the urban waterlogging digital twin model as the real-time monitoring data corresponding to the urban waterlogging prediction area, and use the real-time monitoring data corresponding to the start time of the simulation as the initial condition data for the waterlogging prediction area;

[0047] Step S32: Determine the rainfall time series by obtaining the rainfall amount and rainfall duration corresponding to the future forecast time of the urban waterlogging prediction area through the urban waterlogging digital twin model, and obtain the rainfall time series corresponding to the future forecast time of the urban waterlogging prediction area;

[0048] Step S33: Obtain the terrain, drainage capacity, and climate conditions corresponding to the urban waterlogging prediction area through the urban waterlogging digital twin model, where the climate conditions include rainfall amount, evaporation amount, and river channel soil permeability, and estimate the change in river water level at the future forecast time for the corresponding urban waterlogging prediction area based on the terrain, drainage capacity, and climate conditions corresponding to the urban waterlogging prediction area, so as to obtain the river water level change data corresponding to the future forecast time of the urban waterlogging prediction area;

[0049] Step S34: Run the urban waterlogging digital twin model in Step S2 with the rainfall time series and river water level change data corresponding to the future forecast time of the urban waterlogging prediction area as input data based on the initial condition data of the waterlogging prediction area, so as to generate the future forecast simulation process corresponding to the urban waterlogging model;

[0050] Step S35: Use the real-time monitoring data corresponding to the urban waterlogging prediction area to perform real-time correction and waterlogging rolling prediction on the urban waterlogging digital twin model in the future forecast simulation process corresponding to the urban waterlogging model, so as to generate the urban waterlogging rolling prediction digital model;

[0051] Step S36: Obtain the waterlogging water level and drainage pipe network status corresponding to the urban waterlogging prediction area through the urban waterlogging rolling prediction digital model, and perform rainfall-water level spatio-temporal correlation analysis on the rainfall time series corresponding to the future forecast time of the urban waterlogging prediction area based on the waterlogging water level corresponding to the urban waterlogging prediction area, so as to generate the rainfall-water level spatio-temporal correlation distribution field corresponding to the urban waterlogging prediction area;

[0052] Step S37: Perform drainage efficiency attenuation analysis on the urban waterlogging prediction area corresponding to the urban waterlogging rolling prediction digital model based on the drainage pipe network status corresponding to the urban waterlogging prediction area, and generate the drainage pipe network efficiency attenuation distribution field corresponding to the urban waterlogging prediction area;

[0053] Step S38: Based on the rainfall-water level spatio-temporal correlation distribution field corresponding to the urban waterlogging prediction area and the drainage pipe network efficiency attenuation distribution field, perform a coupling analysis of the waterlogging evolution of the urban waterlogging prediction area corresponding to the digital model of urban waterlogging rolling prediction to generate an urban waterlogging evolution distribution field.

[0054] Further, step S37 includes the following steps:

[0055] Step S371: Obtain the drainage pipe network pressure and drainage treatment efficiency at the corresponding distribution location through the drainage pipe network condition corresponding to the urban waterlogging prediction area;

[0056] Step S372: Conduct a drainage pipe network efficiency evaluation analysis on the drainage pipe network pressure and drainage treatment efficiency at the corresponding distribution location of the urban waterlogging prediction area to obtain the drainage efficiency of the drainage pipe network at the corresponding distribution location of the urban waterlogging prediction area;

[0057] Step S373: Obtain the sediment accumulation amount and drainage pipe breakage rate at the corresponding distribution location of the urban waterlogging prediction area, and perform a drainage efficiency attenuation analysis on the drainage efficiency of the drainage pipe network at the corresponding distribution location of the urban waterlogging prediction area based on the sediment accumulation amount and drainage pipe breakage rate to obtain the drainage efficiency attenuation degree at the corresponding distribution location of the urban waterlogging prediction area;

[0058] Step S374: Based on the drainage efficiency attenuation degree at the corresponding distribution location of the urban waterlogging prediction area, perform an efficiency attenuation distribution analysis on the urban waterlogging prediction area to generate a drainage pipe network efficiency attenuation distribution field corresponding to the urban waterlogging prediction area.

[0059] Advantages of the present invention:

[0060] The method for high-density urban waterlogging evolution and early warning based on digital twin proposed by the present invention, compared with the prior art, the beneficial effects of the present application are that in urban waterlogging prediction and management, real-time data of waterlogging depth, river water level, meteorological monitoring and drainage pipe network are crucial. By installing various sensors in the city (waterlogging depth sensors, river water level sensors, meteorological monitoring equipment and drainage pipe network sensors), these data can be obtained in real time, thereby providing reliable input information for subsequent waterlogging prediction. By combining these real-time data with urban land use types, terrain and geomorphic data, and building road information models, a precise digital twin model of urban waterlogging can be generated. Digital twin technology can virtualize the city and its infrastructure in the physical world, dynamically reflect the changing trends and influencing factors of urban waterlogging, and establish a two-way interaction between the virtual and the real. Through the digital twin model, urban managers can more intuitively and real-time observe the occurrence and development of waterlogging, so as to more accurately simulate the evolution of waterlogging in different geographical environments. The construction of the digital twin model makes the city not just a static physical entity, but a dynamic system that can provide real-time feedback and prediction. This real-time, global, and precise data support can help decision-makers timely understand the health status of the urban drainage system, thus being able to fully consider the mutual influence between different factors, and can process real-time changing meteorological data and the operation status of the drainage pipe network, reducing the frequency and impact of waterlogging disasters. Secondly, by obtaining and analyzing the distribution of the urban drainage pipe network, the digital twin model of urban waterlogging can be further refined, especially by dividing the drainage grid based on the drainage pipe network and river water level. This grid model divides the city into multiple drainage units, facilitating the individual analysis and monitoring of the waterlogging situation in each area. By analyzing the laws of historical waterlogging events in each drainage grid area, more accurate prediction of waterlogging areas can be achieved, thereby providing refined guidance for the prevention and control of urban waterlogging. Specifically, first, based on the distribution of the urban drainage pipe network, the drainage capacity and drainage efficiency of each area can be accurately simulated. By dividing the city into multiple grids, the waterlogging risk situation of a single area can be accurately determined, and potential waterlogging hazards can be discovered in a timely manner, especially in areas where the drainage system is relatively weak or prone to waterlogging. Through the analysis of the laws of historical waterlogging events, combined with the meteorological conditions and drainage capacity of the area, it is helpful to accurately predict the possible waterlogging areas in the future, reducing the losses and impacts during waterlogging.Then, by combining real-time monitoring data (such as waterlogging depth, river water level, rainfall process, etc.) with the status of the urban drainage network, the process of waterlogging evolution and possible future waterlogging scenarios can be predicted more accurately. The coupled analysis of waterlogging evolution can reveal the whole process of urban waterlogging from formation to development, and predict the distribution of waterlogging at different time nodes. The generated waterlogging evolution distribution field provides a scientific basis for the emergency response to urban waterlogging, and can help decision-makers timely understand the waterlogging evolution trend in different regions, so as to take targeted drainage measures and diversion plans. The key to this analysis method lies in its multi-dimensional analysis ability, which can comprehensively consider various factors (such as rainfall intensity, river water level change, drainage network condition, etc.) to make a more accurate evolution prediction of waterlogging. Through the generation of the waterlogging evolution distribution field, urban managers can clearly see the change trend of waterlogging in the future period of time, and then take corresponding emergency measures, such as dispatching the drainage system, diverting traffic, setting up warnings in waterlogging areas, etc. Especially when the city faces extreme weather events (such as heavy rain, typhoon, etc.), the waterlogging evolution analysis can give early warnings, avoid the spread of disasters, and reduce the difficulty of post-disaster recovery. Finally, through the spatio-temporal evolution analysis of the waterlogging evolution distribution field and the generation of the waterlogging warning distribution field, the key to this analysis is that it can help decision-makers comprehensively understand the spatio-temporal characteristics of the waterlogging phenomenon, identify the high-incidence time, location and development trend of waterlogging, so as to improve the timeliness and efficiency of waterlogging warning. The spatio-temporal evolution analysis of waterlogging can reveal the dynamic changes of waterlogging areas at different time nodes, provide a decision-making basis for urban managers, and enable disaster prevention measures to be deployed at the best time. By combining the trend of waterlogging evolution, the urban management department can formulate targeted emergency response plans for different time periods and regions, make flood prevention preparations in advance, and timely start emergency diversion, drainage acceleration and other measures. This analysis not only helps to identify the current risk areas of waterlogging, but also can predict the possible development of future waterlogging, thus providing a long-term planning basis for disaster prevention and mitigation work in high-density cities. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Other features, objects and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments read in conjunction with the accompanying drawings:

[0062] Figure 1 It is a schematic flow chart of the steps of the method for waterlogging evolution and warning in high-density cities based on digital twins of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] 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 efforts belong to the scope of protection of the present invention.

[0064] To achieve the above object, please refer to Figure 1 , the present invention provides a method for high-density urban waterlogging evolution and early warning based on digital twin. In the embodiments of the present invention, please refer to Figure 1 As shown, it is a schematic diagram of the step flow of the method for high-density urban waterlogging evolution and early warning based on digital twin of the present invention. In this example, the method for high-density urban waterlogging evolution and early warning based on digital twin includes the following steps:

[0065] Step S1: Real-time collect the real-time waterlogging depth, real-time river water level, real-time meteorological conditions, and real-time drainage pipe network water level status of the city through waterlogging depth sensors, river water level sensors, meteorological monitoring equipment, and drainage pipe network sensors installed in the city, and build a digital twin based on the real-time waterlogging depth, real-time meteorological conditions, and real-time drainage pipe network water level status of the city in combination with the land use type, terrain, and landform data of the city and the building road information model to generate an urban waterlogging digital twin model;

[0066] In the embodiments of the present invention, through a variety of sensor devices deployed in the city, including waterlogging depth sensors, river water level sensors, meteorological monitoring equipment, and drainage pipe network sensors, the water level data of the city are collected in real time. These data include: the real-time waterlogging depth of each area of the city, the real-time water level of each river, local meteorological conditions (such as rainfall, wind speed, temperature, etc.), and the water level status of the drainage pipe network. The real-time collection of this information is achieved through the Internet of Things (IoT) system. The sensors transmit the data to the central data processing platform in real time through a wireless network. In addition, it is necessary to obtain the terrain, landform data of the city, as well as land use types (such as residential areas, commercial areas, green spaces, etc.) and building and road layout information through Geographic Information System (GIS) technology. These information helps to better understand the waterlogging risk of the city. By integrating these multi-source data, a digital twin model is constructed to simulate the possible situations of urban waterlogging. The digital twin model is a virtual mapping of the real environment of the city, which contains the above sensor data, geographic information data, and the functional model of the urban drainage system. This model provides a tool for subsequent waterlogging prediction, analysis, and early warning, which can dynamically update and reflect the waterlogging status of the city in real time, and finally generate an urban waterlogging digital twin model.

[0067] Step S2: Obtain the distribution of the drainage pipe network corresponding to the city, and based on the distribution of the drainage pipe network corresponding to the city and the real-time river water level, divide the drainage grid of the digital twin model of urban waterlogging to generate an urban drainage grid model; obtain the historical waterlogging event pattern corresponding to the city, and based on the historical waterlogging event pattern corresponding to the city, conduct waterlogging area prediction analysis for each urban drainage grid partition in the urban drainage grid model to obtain the urban waterlogging prediction area;

[0068] In the embodiment of the present invention, it is necessary to obtain the distribution information of the drainage pipe network of the city. These information are usually provided by the urban planning department or obtained through the survey and modeling of the existing pipe network system. Based on the distribution of these drainage pipe networks, combined with the real-time river water level data, the drainage grid is divided. The drainage grid division is to divide the city into multiple small areas according to the drainage basin of the drainage pipe network. The drainage performance and waterlogging risk of each area are analyzed independently. Through grid management, more accurate emergency plans and waterlogging risk assessments can be formulated for each small area. Then, combined with the historical waterlogging event pattern of the city, a corresponding two-dimensional surface model of urban waterlogging diffusion is constructed to reflect the diffusion of surface waterlogging after the overflow of each drainage grid partition in the city and its impact on the waterlogging distribution of the surrounding areas, so as to analyze the historical rainfall data, waterlogging depth data and the response of the drainage system, and realize the waterlogging area prediction analysis for each drainage grid area. The extraction of the historical waterlogging event pattern can be completed through big data analysis and machine learning techniques. Using the urban historical meteorological data, waterlogging data and pipe network response data, a waterlogging prediction model is constructed to predict the waterlogging possibility and waterlogging depth in a specific area under specific rainfall or river water level conditions. The result of this prediction analysis will be further refined to the drainage grid, indicating which areas may be waterlogged and conducting a waterlogging risk assessment for each grid, and finally obtaining the urban waterlogging prediction area.

[0069] Step S3: Obtain the measured waterlogging depth, river water level, rainfall process in the past period of time and the condition of the drainage pipe network corresponding to the urban waterlogging prediction area as the real-time monitoring data corresponding to the urban waterlogging prediction area through the digital twin model of urban waterlogging, and based on the real-time monitoring data corresponding to the urban waterlogging prediction area, conduct waterlogging evolution coupling analysis for the urban waterlogging prediction area to generate an urban waterlogging evolution distribution field;

[0070] In the embodiments of the present invention, real-time monitoring data corresponding to the waterlogging prediction area is obtained through the waterlogging digital twin model, including the measured waterlogging depth, the real-time river water level, the historical rainfall process, and the current status of the drainage pipe network. This data is real-time monitored by devices such as sensors and weather stations and input into the data platform. Then, based on this real-time monitoring data, a coupled analysis of waterlogging evolution is carried out using a hydrological and hydraulic model (such as the SWMM or MIKE21 model). This analysis model combines multiple factors such as rainfall, river water level, drainage pipe network status, and geographical features, simulates the whole process of waterlogging from the start of rainfall to flowing into the river or being discharged by the drainage system, predicts the distribution of waterlogging at different time points and different regions through numerical calculation and dynamic simulation, generates a waterlogging evolution distribution field, clarifies the temporal and spatial variation trends of the waterlogging area, and finally generates an urban waterlogging evolution distribution field.

[0071] Step S4: Conduct a spatio-temporal evolution analysis of the urban waterlogging evolution distribution field to obtain the urban waterlogging evolution development trend at different spatio-temporal nodes of the city; based on the urban waterlogging evolution development trend at different spatio-temporal nodes of the city, perform waterlogging early warning processing on the corresponding urban waterlogging evolution distribution field to generate an urban waterlogging early warning distribution field, so as to execute the corresponding urban waterlogging emergency response work.

[0072] In the embodiments of the present invention, through the spatio-temporal evolution analysis of the waterlogging evolution distribution field after previous waterlogging evolution, the purpose is to reveal the change trend of the waterlogging area at different time nodes. Through the spatio-temporal evolution analysis, the expansion trend of waterlogging, the peak water level, and the key affected areas can be obtained. This analysis uses a spatio-temporal data modeling method, combines historical waterlogging data and real-time data, uses time series analysis and spatial interpolation methods to predict the change trajectory of waterlogging, conducts early warning processing on the waterlogging evolution situation at each time node, and dynamically generates an urban waterlogging early warning distribution field by setting parameters such as waterlogging level, waterlogging range, and urban drainage capacity and combining real-time monitoring data. The early warning distribution field will indicate the severity of waterlogging in different regions and the impact on infrastructure such as transportation and buildings, and trigger emergency response measures through an intelligent decision-making system. For example, when the waterlogging risk in a certain area reaches a preset threshold, the emergency response process will be automatically started, such as arranging to repair the drainage pipe network, dredging traffic, or starting emergency drainage equipment and other measures to reduce the impact of waterlogging on urban operation, and finally execute the corresponding urban waterlogging emergency response work.

[0073] Further, step S1 includes the following steps:

[0074] Step S11: Conduct real-time monitoring of urban waterlogging in the city through waterlogging depth sensors installed at various location nodes in the city to obtain the corresponding real-time waterlogging depth of the city;

[0075] In the embodiments of the present invention, first, water depth sensors are deployed at position nodes such as the main waterlogging-prone areas, low-lying areas, and transportation hubs in the city. These sensors measure the water depth and upload data to the city management center in real time. The specific installation method is to fix the sensors at a predetermined position underground or below the road surface to ensure that the change in water depth can be detected in a timely manner during rainfall. The sensors usually use ultrasonic or laser ranging technology to accurately measure the water surface height of the waterlogging, ensuring the accuracy and real-time nature of the data. The monitoring system can transmit the data to the data center through wireless transmission technology (such as LoRa, NB-IoT, etc.), and perform comprehensive analysis in combination with the data of other sensors to understand the waterlogging situation at different locations in real time. The water depth data will be transmitted to the control platform in a manner of being updated once a minute, and finally the real-time water depth corresponding to the city is obtained.

[0076] Step S12: Real-time monitoring of the river water level in the city is carried out through river water level sensors installed at various river positions in the city to obtain the real-time river water level corresponding to the city, including the water level height and the corresponding time.

[0077] In the embodiments of the present invention, river water level sensors are installed at the main river positions in the city (including rivers, lakes, and their tributaries, etc.). These sensors can be water level gauges, pressure sensors, or buoy sensors. Each sensor continuously monitors the change in the river water level according to its installation position and records the real-time water level data. These sensors are set in different depth areas of the water body, and the water level is calculated by the change in water pressure of the sensors. After the data is collected, the water level data is transmitted to the data center through wireless communication technology. In the collected data, in addition to the water level height, a timestamp is also recorded, forming a set of time series data for analyzing the change trend of the water level, and finally the real-time river water level corresponding to the city is obtained, including the water level height and the corresponding time.

[0078] Step S13: Real-time meteorological monitoring of the city is carried out through meteorological monitoring equipment installed at various position nodes in the city to obtain the real-time meteorological conditions corresponding to the city, including the rainfall amount and the rainfall duration.

[0079] In the embodiments of the present invention, meteorological monitoring devices are deployed in different areas of the city, especially in areas where rainfall is frequent or extreme weather is likely to occur. These devices include rain gauges, temperature and humidity sensors, wind speed and direction sensors, etc. The rain gauge measures the rainfall intensity and duration in real time through an automated method, and the data is uploaded to the meteorological monitoring system in real time through a wireless network. During the rainfall monitoring process, the system records the start time of rainfall, the rainfall intensity, and the rainfall duration, providing basic data for subsequent water level monitoring and early warning. In addition, the meteorological monitoring devices also record real-time meteorological data such as temperature, humidity, and wind speed. These information play an important role in predicting the evaporation of rainwater and other climate change factors, and finally obtain the corresponding real-time meteorological conditions of the city, including rainfall amount and rainfall duration.

[0080] Step S14: Conduct real-time monitoring of the urban drainage pipe network through the liquid level and flow velocity sensors installed at various position nodes in the city to obtain the corresponding real-time drainage pipe network water level conditions of the city, including the liquid level corresponding to the drainage pipe network nodes and the drainage flow velocity.

[0081] In the embodiments of the present invention, the urban drainage pipe network system is monitored in real time by installing liquid level sensors and flow velocity sensors at different key nodes (such as pipe branches, important road intersections, etc.). The liquid level sensor can measure the water level inside the pipe, usually using a pressure sensor or a floating liquid level gauge. The flow velocity sensor measures the water flow velocity in the pipe and calculates the drainage volume in combination with the pipe cross-sectional area. These sensors are installed at key positions in the pipe system to ensure that the drainage conditions of the pipe network can be monitored. The real-time data is sent to the data center through a wireless communication network. The system can predict the load and possible blockage conditions of the drainage pipe network according to the change trends of the flow velocity and liquid level. During rainfall, it can evaluate the operation conditions of each drainage pipe network node in real time and predict which areas may have problems such as poor drainage or waterlogging. Finally, the corresponding real-time drainage pipe network water level conditions of the city are obtained, including the liquid level corresponding to the drainage pipe network nodes and the drainage flow velocity.

[0082] Step S15: Obtain the land use type, terrain and landform data, and building and road information models corresponding to the city, and construct a digital twin based on the corresponding real-time waterlogging depth, real-time meteorological conditions, and real-time drainage pipe network water level conditions of the city, combined with the land use type, terrain and landform data, and building and road information models corresponding to the city, to generate an urban waterlogging digital twin model.

[0083] In the embodiments of the present invention, by integrating real-time data from waterlogging depth, meteorological monitoring, and drainage pipe networks, these data are aggregated into a data processing platform. After data preprocessing, cleaning, and fusion, a unified urban hydro-meteorological dataset is formed. In addition, in order to achieve accurate digital twin modeling, it is also necessary to collect geographical information data of the city, such as land use types, terrain, geomorphic data, and building and road information. These data can be obtained through remote sensing satellite images, geographic information systems (GIS), and ground surveys. Based on these multi-source data, a virtual urban model is established using digital twin technology, and through the combination of physical models and data-driven models, the urban hydro-environment in different scenarios is simulated. By analyzing the waterlogging evolution process of the city under different rainfall intensities, meteorological conditions, and drainage pipe network states, possible waterlogging areas can be accurately predicted, early warning information can be released in a timely manner, and it can guide urban managers to make emergency decisions. The digital twin model can not only reflect the current state of urban waterlogging in real time, but also provide the waterlogging evolution trend in a future period based on historical data and predicted data, assisting urban flood prevention and control planning and construction, and finally generating a digital twin model of urban waterlogging.

[0084] Further, step S2 includes the following steps:

[0085] Step S21: Obtain the terrain and geomorphology, building density, and drainage facility layout status corresponding to the city, and obtain the drainage pipe network distribution corresponding to the city according to the terrain and geomorphology, building density, and drainage facility layout status corresponding to the city;

[0086] In the embodiments of the present invention, the terrain and geomorphology information of the city is obtained through remote sensing image data and geographic information system (GIS) technology, and using lidar (LiDAR) data or digital elevation model (DEM), the features such as the terrain undulation, mountains, hills, and low-lying areas of the city can be accurately extracted to form a topographic map of the city. Then, combined with the building density information, the building coverage of each area can be calculated through urban planning data or building contour data, and the distribution of high-density building areas and low-density building areas can be determined. The acquisition of building density data can rely on high-resolution satellite images or three-dimensional models of buildings. For the drainage facility layout status, the layout information of the drainage pipe network can be obtained through the urban infrastructure database. If there is no complete pipe network information in the database, the spatial distribution of urban drainage facilities can be gradually constructed through the combination of remote sensing images and ground surveys. By analyzing the relationship between the layout of the drainage pipe network and the terrain and geomorphology and building density of the city, the drainage pipe network distribution model of the city can be further deduced. This model will detail important information such as the distribution, size, and flow rate of drainage pipes, and finally obtain the drainage pipe network distribution corresponding to the city.

[0087] Step S22: Based on the distribution of the drainage pipe network corresponding to the city and the real-time river water level, evaluate the drainage carrying capacity of the drainage pipe network at the corresponding distribution positions in the urban waterlogging digital twin model to obtain the urban drainage pipe network carrying capacity matrix;

[0088] In the embodiment of the present invention, by combining real-time meteorological data and river water level monitoring data, real-time hydrological information is obtained through Internet of Things (IoT) devices. This information includes precipitation, river water level, water flow rate, etc. Then, based on the real-time river water level data, using the urban drainage pipe network distribution model, the drainage carrying capacity is evaluated. Specifically, first, factors such as the diameter, material, slope, and geographical location of each drainage pipe need to be considered. The water flow carrying capacity of each pipe is calculated through a hydraulic model (such as the Saint-Venant equation or Manning's formula). The evaluation of the drainage pipe network's carrying capacity should also be corrected by combining the blockage situation of the drainage pipe network and the aging situation of the pipes. Through this evaluation, a detailed drainage pipe network carrying capacity matrix can be obtained. Each item in the matrix represents the water flow processing capacity of the drainage pipe network at this position under the current river water level, and finally, the urban drainage pipe network carrying capacity matrix is obtained.

[0089] Step S23: Obtain the geographical space, drainage pipe network layout, and water flow characteristics at the corresponding distribution positions through the urban waterlogging digital twin model, and conduct a water flow load assessment and analysis based on the geographical space, drainage pipe network layout, and water flow characteristics at the corresponding distribution positions to obtain the drainage water flow load at the corresponding distribution positions in the city;

[0090] In the embodiment of the present invention, by using the previously constructed digital twin model of urban waterlogging, detailed information about each drainage pipe network distribution position is obtained, including the geographical space characteristics of this position (such as slope, terrain type, etc.), the layout of the drainage pipe network (such as the positions, quantities, and states of pipes, diversion wells, inspection wells, etc.), and the related water flow characteristics (such as water flow velocity, flow rate, precipitation intensity, etc.). Computational Fluid Dynamics (CFD) simulation is used, combined with the hydraulic model of the drainage pipe network, to simulate the water flow behavior under different precipitation conditions, and evaluate the changes in water flow velocity and flow rate when the water flow passes through different pipe network segments, so as to obtain the water flow load. During this process, a hydraulic analysis software for the urban drainage system (such as MIKE 21, HEC-RAS, etc.) is used for water flow load analysis to generate the drainage water flow load data for each position, and finally, the drainage water flow load at the corresponding distribution positions in the high-density city is obtained.

[0091] Step S24: Based on the drainage pipe network carrying capacity at the corresponding distribution positions in the urban drainage pipe network carrying capacity matrix and combined with the drainage water flow load at the corresponding distribution positions in the city, conduct a drainage grid carrying capacity matching division on the urban waterlogging digital twin model to generate an urban drainage grid model;

[0092] In the embodiment of the present invention, by combining the drainage pipe network bearing capacity matrix with the water flow load data, the bearing capacity matching analysis is carried out for each drainage pipe network node. Specifically, in the digital twin model, for each drainage grid (i.e., a small area in the city), combining the bearing capacity of the drainage pipe network in this grid and the actual water flow load, the matching analysis of the bearing capacity and the load is carried out. If the bearing capacity of the drainage pipe network in a certain area is greater than or equal to the water flow load in this area, then grid processing needs to be carried out for this area, adjust the drainage pipe network design or strengthen the drainage facilities. In this process, GIS and a hydraulic model can be combined, and machine learning or optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) can be used to carry out the optimal drainage grid division to generate the corresponding drainage grid model of the city. Each drainage grid corresponds to specific bearing capacity and load conditions, and finally the urban drainage grid model is generated.

[0093] Step S25: Obtain the historical waterlogging event rules corresponding to the city, and based on the historical waterlogging event rules corresponding to the city, conduct waterlogging area prediction analysis for each urban drainage grid partition in the urban drainage grid model to obtain the urban waterlogging prediction area.

[0094] In the embodiment of the present invention, by collecting the waterlogging event data in the history of the city, including information such as waterlogging time, waterlogging depth, waterlogging duration, etc., these data can be obtained through channels such as the city meteorological bureau, drainage management department, ground monitoring system, and historical flood archives. Through the analysis of these historical data, the rules of waterlogging events can be extracted, such as which areas are vulnerable to rainstorms, which areas have the most serious waterlogging overflows, etc. And by combining these historical waterlogging event rules, the waterlogging prediction analysis can be carried out on the urban drainage grid model. Specifically, by constructing a two-dimensional surface model of waterlogging diffusion to simulate the diffusion of waterlogging in different geographical space areas. This model can predict the diffusion and spread process of waterlogging in different areas by considering factors such as the overflow of the drainage pipe network, surface morphology, and precipitation intensity. Based on this model, the waterlogging area of each drainage grid can be predicted to identify the areas where waterlogging may occur, helping to deploy drainage facilities in advance. This process will generate an accurate urban waterlogging prediction area, and finally obtain the urban waterlogging prediction area.

[0095] Furthermore, step S22 includes the following steps:

[0096] Obtain the diameter and slope of the drainage pipe network at the corresponding distribution location through the drainage pipe network distribution corresponding to the city;

[0097] In the embodiment of the present invention, through the spatial information of the drainage pipe network in the digital twin model of the city, the specific location distribution of each drainage pipe is extracted. This process uses geographic information system (GIS) data and the design drawings of the urban drainage system to map the specific location, pipe orientation, and related pipe characteristics of the urban drainage pipe network onto the digital twin platform. Based on this data, the pipe diameter and slope of each pipe can be accurately identified. Specifically, using light detection and ranging (LIDAR) data or high-definition satellite images, combined with on-site measured data, the diameter, slope, and elevation difference between the upstream and downstream of each pipe are calculated, and finally the pipe diameter and pipe slope of the drainage pipe network at the corresponding distribution location are obtained.

[0098] Preferably, statistical analysis of the drainage flow velocity of the drainage pipe network at the corresponding distribution location in the digital twin model of urban waterlogging is carried out to obtain the drainage flow velocity of the drainage pipe network at the corresponding distribution location in the city.

[0099] In the embodiment of the present invention, statistical analysis of the drainage flow velocity is carried out through the real-time monitoring data of the urban drainage pipe network. The acquisition of the flow velocity data depends on flow meters, pressure sensors, and water level monitoring instruments in the pipes. These devices regularly feed the real-time data in the drainage pipe network back to the digital twin platform. In the system, first, the water flow velocity in each pipe is monitored and recorded in real time. The instantaneous flow rate is obtained through the flow meter, and the flow velocity is calculated in combination with the cross-sectional area of the pipe. Then, using the flow velocity statistical analysis algorithm, statistical analysis such as the mean value, maximum value, minimum value, and fluctuation value of the flow velocity at each location is carried out to obtain the flow velocity data of each pipe section, and finally the drainage flow velocity of the drainage pipe network at the corresponding distribution location in the city is obtained.

[0100] Preferably, the time series fluctuation of the river water level at the corresponding distribution location in the digital twin model of urban waterlogging is obtained through the real-time river water level corresponding to the city, and based on the time series fluctuation of the river water level at the corresponding distribution location in the city, an analysis of the outlet flow constraint limit of the drainage pipe network at the corresponding distribution location in the digital twin model of urban waterlogging is carried out to obtain the time-varying constraint of the river water level corresponding to the city on the outlet flow of the drainage pipe network.

[0101] In the embodiment of the present invention, by the real-time water level change of the river channel, the influence on the outlet flow of the drainage pipe network is simulated and analyzed. First, by docking with the urban hydrological monitoring system, the water level data of the river channel are obtained in real time, and these data can reflect the temporal fluctuations of the river channel water level. Specifically in implementation, high-precision water level monitoring instruments and sensors are used to regularly obtain the real-time water level information of the river channel. In the digital twin model, these real-time river channel water level data are connected to the outlet of the drainage pipe network to analyze the flow carrying capacity of the drainage pipe network under different water level conditions. If the river channel water level rises, the flow of the drainage pipe network will be restricted. Especially when the river channel water level approaches or exceeds the outlet of the pipe, the water flow will reverse or the flow rate will decrease. Therefore, based on the data of temporal fluctuations, a time-varying flow constraint model is constructed, and through mathematical analysis and numerical simulation, the constraint limitations that the outlet flow of the drainage pipe network should be subject to under different river channel water level conditions are determined, and finally the time-varying constraint of the corresponding river channel water level of the city on the outlet flow of the drainage pipe network is obtained.

[0102] Preferably, based on the pipe diameter of the drainage pipe network at the corresponding distribution position in the city, the pipe slope of the drainage pipe network, the flow velocity of the drainage pipe network, and the time-varying constraint of the corresponding river channel water level of the city on the outlet flow of the drainage pipe network, the bearing capacity evaluation calculation of the drainage pipe network at the corresponding distribution position in the urban waterlogging digital twin model is carried out by using the bearing capacity measurement calculation formula of the drainage pipe network to obtain the bearing capacity coefficient of the drainage pipe network at the corresponding distribution position in the city.

[0103] In the embodiment of the present invention, by combining the three-dimensional space area covered by the drainage pipe network, the abscissa parameter of the distribution position, the ordinate parameter of the distribution position, the vertical coordinate parameter of the distribution position, the time-varying constraint of the river channel water level on the outlet flow of the drainage pipe network, the river channel water level, the linear influence coefficient of the river channel water level on the outlet flow of the drainage pipe network, the quadratic nonlinear influence coefficient, the outlet flow of the drainage pipe network, the initial outlet flow of the drainage pipe network, the drainage network flow response time decay control parameter, the water flow, the maximum allowable water flow, the pipe diameter of the drainage pipe network, the bearing influence weight factor of the pipe diameter, the pipe slope of the drainage pipe network, the bearing influence weight factor of the pipe slope, the flow velocity of the drainage pipe network, the bearing influence weight factor of the flow velocity, and related parameters, a suitable bearing capacity measurement calculation formula of the drainage pipe network is formed to carry out the bearing capacity evaluation calculation of the drainage pipe network at the corresponding distribution position in the urban waterlogging digital twin model, so as to calculate the bearing capacity coefficient of the drainage pipe at each position. The bearing capacity coefficient represents the maximum drainage flow that this section of the drainage pipe can withstand under normal operation, and finally the bearing capacity coefficient of the drainage pipe network at the corresponding distribution position in the city is obtained.

[0104] Preferably, according to the bearing capacity coefficient of the drainage pipe network at the corresponding distribution position in the city, a drainage capacity matrix of the drainage pipe network at the corresponding distribution position in the urban waterlogging digital twin model is constructed to obtain the urban drainage pipe network bearing capacity matrix.

[0105] In an embodiment of the present invention, a drainage capacity matrix at the city level is constructed based on the drainage network bearing capacity coefficients at the corresponding distribution positions of the city obtained by previous quantification. The rows and columns of this matrix represent the drainage network positions in different regions, and each element in the matrix represents the bearing capacity of the drainage network at this position under different scenarios, including drainage demands under various climate conditions such as normal rainfall, heavy rainfall, typhoons, etc. During the process of matrix construction, the drainage network is first divided into several regional units according to geographical regions, and then, in combination with historical precipitation data and the actual performance of the drainage network, the drainage capacity of each unit under various climate scenarios is evaluated. The drainage capacity matrix shows the bearing capacity of the drainage network in the whole city at different positions and under different weather conditions, and finally, the drainage network bearing capacity matrix of the city is obtained.

[0106] Furthermore, the specific calculation formula for the drainage network bearing capacity metric is as follows:

[0107] ;

[0108] ;

[0109] In the formula, is the drainage network bearing capacity coefficient of the city at the distribution position coordinates , is the three-dimensional spatial region covered by the drainage network, is the abscissa parameter of the distribution position, is the ordinate parameter of the distribution position, is the vertical coordinate parameter of the distribution position, is the time variable parameter, is the initial time of the constraint influence integral, is the end time of the constraint influence integral, is the time-varying constraint of the corresponding river channel water level on the drainage network outlet flow at the time of the city, is the river channel water level of the city at the distribution position coordinates and at the time of the city, is the linear influence coefficient of the river channel water level on the drainage network outlet flow, is the quadratic non-linear influence coefficient of the river channel water level on the drainage network outlet flow, is the exponential function, is the drainage network outlet flow of the city at the time of the city, is the initial flow of the drainage network outlet, is the decay control parameter of the drainage network flow response time, is the water flow at the distribution location coordinates of the city and is the maximum allowable water flow at the distribution location coordinates of the city ; is the pipe diameter of the drainage pipe network at the distribution location coordinates of the city ; is the weight factor for the influence of the pipe diameter ; is the pipe slope of the drainage pipe network at the distribution location coordinates of the city ; is the weight factor for the influence of the pipe slope ; is the water flow velocity of the drainage pipe network at the distribution location coordinates of the city ;

[0110] The present invention obtains a calculation formula for measuring the bearing capacity of a drainage pipe network through the use of a specific mathematical model and verification, which is used to evaluate and calculate the bearing capacity of the drainage pipe network at the corresponding distribution location in the digital twin model of urban waterlogging. The role of this calculation formula for measuring the bearing capacity of the drainage pipe network in the digital twin model of urban waterlogging is very crucial. It comprehensively considers the physical characteristics of the drainage pipe network (such as pipe diameter, slope, and flow velocity) and external factors (such as the time-varying constraints of river water level and drainage network outlet flow). By these factors, the bearing capacity of the drainage pipe network under different time and space conditions is evaluated. Specifically, the formula contains various factors affecting the bearing capacity of the drainage pipe network. The physical characteristics of the drainage pipe network (pipe diameter, pipe slope, and flow velocity: pipe diameter , a larger pipe diameter can provide more drainage flow, so its influence on the bearing capacity of the drainage pipe network is linear, and the weight factor represents the degree of this influence; pipe slope , the greater the slope, the higher the flow velocity of the water and the stronger the drainage capacity. The influence of the slope is adjusted by the weight factor ; flow velocity , the flow velocity directly affects the drainage capacity. A higher flow velocity indicates a stronger drainage capacity. The influence of the flow velocity is measured by the weight factor ; the time series fluctuation of the river water level , through the constraint of the time-varying river water level on the drainage network flow, the impact of the external water environment on the drainage network can be more realistically reflected. The fluctuation of the river water level not only affects the flow rate but also the pressure and flow conditions of the drainage network; the ratio of the water flow rate to the maximum flow rate directly affects the working state of the drainage network, and too large a water flow rate can lead to overloading of the network. The carrying capacity of the drainage network is affected by time and the external environment, especially the changes in the river water level and drainage flow rate. By introducing time-varying constraints, the formula can consider the dynamic impact of the river water level on the drainage network at different time periods. In addition, the integral term ∫_A in the formula represents the distribution of the carrying capacity of the drainage network within the three-dimensional spatial region A. By considering the physical characteristics, flow velocity, and constraint factors of the drainage network at different spatial positions, the carrying capacity of the drainage network at different positions within the entire urban area can be obtained. This spatial assessment is of great significance for the prevention of urban waterlogging and the optimal design of the drainage system. In summary, the formula fully considers the carrying capacity coefficient of the drainage network at the distribution position coordinates of the drainage network at the three-dimensional spatial region covered by the drainage network abscissa parameter of the distribution position ordinate parameter of the distribution position vertical coordinate parameter of the distribution position time variable parameter initial time of the constraint impact integral end time of the constraint impact integral at time the time-varying constraint of the corresponding river water level on the outlet flow of the drainage network at the distribution position coordinates of the city and at time the river water level at time linear impact coefficient of the river water level on the outlet flow of the drainage network quadratic non-linear impact coefficient of the river water level on the outlet flow of the drainage network exponential function at time the outlet flow of the drainage network initial outlet flow of the drainage network drainage network flow response time decay control parameter at the distribution position coordinates of the city the water flow rate at the distribution position coordinates of the city the maximum allowable water flow rate at the distribution position coordinates of the city the pipe diameter of the drainage network pipe diameter carrying impact weight factor , the distribution location coordinates of the city , the pipeline slope of the drainage pipeline network at , the pipeline slope bearing influence weight factor , the distribution location coordinates of the city , the flow velocity of the drainage pipeline network at , the flow velocity bearing influence weight factor , the correction coefficient of the bearing capacity coefficient of the drainage pipeline network , among which, through the horizontal coordinate parameter of the distribution location , the vertical coordinate parameter of the distribution location , the vertical coordinate parameter of the distribution location , the time variable parameter , the initial time of the constraint influence integral , the end time of the constraint influence integral , the distribution location coordinates of the city and at time , the river water level at the moment , the linear influence coefficient of the river water level on the outlet flow of the drainage pipeline network , the quadratic non - linear influence coefficient of the river water level on the outlet flow of the drainage pipeline network , the exponential function , the outlet flow of the drainage pipeline network at the moment of time of the city , the initial outlet flow of the drainage pipeline network , the decay control parameter of the drainage pipeline network flow response time constitute a time - varying constraint of the corresponding river water level on the outlet flow of the drainage pipeline network at the moment of time of the city of the functional relationship , according to the bearing capacity coefficient of the drainage pipeline network at the distribution location coordinates of the city and the mutual correlation relationship between the above - mentioned parameters constitutes a functional relationship:

[0111] ;

[0112] This formula can realize the evaluation and calculation process of the bearing capacity of the drainage pipeline network at the corresponding distribution location in the digital twin model of urban waterlogging. At the same time, through the introduction of the correction coefficient of the bearing capacity coefficient of the drainage pipeline network, it can be adjusted according to the error situation in the calculation process, so as to improve the accuracy and applicability of the bearing capacity measurement calculation formula of the drainage pipeline network.

[0113] Further, the water flow load assessment and analysis according to the geospatial, drainage pipeline network layout and water flow characteristics at the corresponding distribution location in step S23 includes the following steps:

[0114] Conduct a load constraint analysis of the drainage space layout based on the geospatial data and the layout of the drainage pipe network at the corresponding distribution locations, so as to obtain the load constraint conditions for the drainage space layout at the corresponding distribution locations in the city.

[0115] In the embodiment of the present invention, through the use of the geospatial data of the urban area and combined with the detailed layout of the drainage pipe network for comprehensive analysis, the specific operations include obtaining the digital elevation model (DEM) data of the area to accurately obtain the topographic and geomorphic information, and through the GIS (Geographic Information System) tool, accurately locate the drainage pipe network of the city to obtain the orientation, diameter, depth of each drainage pipe and the location of the water outlet. In addition, it is also necessary to obtain the layout of drainage pipes such as rainwater pipe networks and sewage pipe networks in the city through the basic data of the municipal drainage plan. Based on the above data, use the hydrodynamic model to conduct a constraint analysis of the load of the drainage space layout. This process involves the capacity limitations of the input drainage pipe network, such as the water passing capacity of the drainage pipe (usually determined by the cross-sectional area, slope, material, etc. of the pipe), and the influence of the topographic and geomorphic features, such as the risk of water accumulation in low-lying areas. By simulating different precipitation intensities and drainage demands, determine the load conditions of each area under different precipitation conditions, so as to clarify the load-bearing limit of the drainage system in the high-density areas in the city, and finally obtain the load constraint conditions for the drainage space layout at the corresponding distribution locations in the city.

[0116] Preferably, conduct a water flow dynamic distribution analysis based on the water flow characteristics at the corresponding distribution locations to generate a water flow dynamic distribution flow field at the corresponding distribution locations in the city.

[0117] In the embodiment of the present invention, by obtaining the precipitation, drainage pipe network layout, terrain and other hydrological data of the area, these data will be used as input parameters for the water flow dynamic distribution analysis. In this process, numerical simulation methods are used to study the dynamic distribution of water flow. Commonly used numerical simulation tools include SWMM (Storm Water Management Model) or HEC-RAS and other water flow simulation software. These software can simulate the water flow behavior under different precipitation scenarios in the urban area. First, it is necessary to define the simulated water flow characteristics, including the intensity and temporal variation of precipitation, ground infiltration characteristics, and surface runoff characteristics, etc. By inputting these characteristics into the model, simulate and analyze the flow process after the precipitation reaches the ground, and determine the flow direction, velocity and change trend of the water flow, especially the flow pattern in the urban area. These dynamic water flow characteristics will be presented as a water flow field in space, including the flow velocity distribution, flow rate distribution and low-lying areas where water accumulation occurs. During the analysis process, the water flow field will be continuously iteratively updated to reflect the changes in water flow under different precipitation conditions, and finally generate a water flow dynamic distribution flow field at the corresponding distribution locations in the city.

[0118] Preferably, based on the drainage space layout load constraint conditions at the corresponding distribution positions of the city, the water flow load assessment and analysis are carried out on the water flow dynamic distribution flow field at the corresponding distribution positions of the city to obtain the drainage water flow load at the corresponding distribution positions of the city.

[0119] In the embodiment of the present invention, by combining the layout load constraint conditions of the drainage pipe network according to the previously obtained data and analysis results, the load assessment and analysis are carried out on the water flow dynamic distribution flow field. The specific operation steps include matching the flow field of the water flow dynamic distribution with the load limit of the drainage pipe network, and simulating the load conditions of the drainage pipe network under different scenarios. First, the dynamic change factors such as the flow velocity, flow rate, and precipitation intensity of the water flow are combined with the spatial layout constraints of the drainage pipe network and input into the fluid mechanics simulation system to calculate the flow capacity and drainage load of each drainage pipe. During this process, the load limit of the pipe in the urban area is mainly considered. For example, a drainage pipe with a smaller diameter cannot effectively handle a large amount of runoff after heavy rain, resulting in the risk of waterlogging in local areas. The drainage load in different regions under specific precipitation conditions can be quantified, and finally the drainage water flow load at the corresponding distribution positions of the city is obtained.

[0120] Further, step S24 includes the following steps:

[0121] Step S241: Based on the drainage pipe network bearing capacity at the corresponding distribution position in the urban drainage pipe network bearing capacity matrix, the drainage bearing matching analysis is carried out on the drainage water flow load at the corresponding distribution position of the city. If the drainage pipe network bearing capacity at the corresponding distribution position is greater than or equal to the drainage water flow load, it is determined as the drainage bearing equilibrium matching point corresponding to the drainage pipe network bearing capacity; if the drainage pipe network bearing capacity at the corresponding distribution position is less than the drainage water flow load, it is re-carried out for bearing matching analysis at a distribution position one parallel outward until the drainage bearing equilibrium matching point is determined;

[0122] In an embodiment of the present invention, by combining the previously established matrix of the carrying capacity of the urban drainage pipe network, which shows the carrying capacity of the drainage pipe network at various locations in the city, and the carrying capacity value at each location is based on factors such as the design standards of the pipe network, historical carrying data, and future load predictions. Then, the drainage water flow load of the city is matched one by one with the carrying capacity of the pipe network. Each drainage water flow load value is calculated based on factors such as rainfall, geographical location, and historical water flow in the drainage pipes. For each distribution location, if the carrying capacity of the drainage pipe network is greater than or equal to the drainage water flow load at that location, then that location is the drainage carrying equilibrium matching point, indicating that the drainage pipe network at that location can carry the water flow load. If the carrying capacity of the drainage pipe network at the corresponding location is less than the drainage water flow load at that location, the carrying analysis at that location will automatically be moved to an adjacent or parallel distribution location, and the new carrying capacity at that location will be matched again with the water flow load at that location. This process is repeated until a drainage pipe network location that can carry the water flow load is found. This process of re-carrying matching involves optimizing and adjusting multiple pipe network connection points around, and is automatically adjusted by an algorithm to ensure that all areas can be matched with appropriate drainage capabilities, thus avoiding the phenomenon of excessive drainage pressure in local areas.

[0123] Step S242: Connect the equivalent drainage grids according to the drainage carrying equilibrium matching points with different drainage pipe network carrying capacities to generate the urban drainage grid boundaries corresponding to different drainage pipe network carrying capacities.

[0124] In an embodiment of the present invention, by connecting the drainage grids according to the drainage carrying equilibrium matching points with different drainage pipe network carrying capacities, the core of this step is to divide the areas with different carrying capacities into several drainage grids. First, the previously identified drainage carrying equilibrium matching points are used as the boundaries of the drainage grids. Using a geographic information system (GIS) tool for processing, the urban area will be divided into multiple grid units, and the carrying capacity of each grid will be adjusted according to the carrying capacity of the pipe network and the water flow load within the grid. Based on these carrying matching points, the boundaries of different grids are connected to ensure that the drainage network can smoothly transfer and distribute water flow between different areas, thereby forming the urban drainage grid boundaries that can reflect the real drainage network, and finally connecting to generate the urban drainage grid boundaries corresponding to different drainage pipe network carrying capacities.

[0125] Step S243: Based on the urban drainage grid boundaries corresponding to different drainage pipe network carrying capacities, perform drainage grid carrying matching division on the urban waterlogging digital twin model to generate an urban drainage grid model.

[0126] In the embodiments of the present invention, by performing load matching division of the urban waterlogging digital twin model based on the established drainage grid boundaries, first, the digital twin model of the city is imported into the simulation software. The software can perform real-time simulation of the city's drainage pipe network, topography, climate conditions, etc. According to the previously obtained drainage grid boundaries, each area in the digital twin model is matched with the corresponding drainage grid, and the bearing capacity of each drainage grid is accurately mapped to the corresponding area in the digital twin model. During the drainage grid division process, spatial analysis and optimization algorithms are used to accurately match the city's drainage network with the bearing capacity of each drainage grid, ensuring that the drainage capacity of each grid unit is not lower than the load demand of the area. Especially in urban dense areas or areas with large drainage loads, a higher density of grid division is adopted to ensure the efficiency and early warning ability of the drainage system. Through these precise grid divisions, the refined management level of the drainage system can be effectively improved, and potential areas with excessive drainage pressure or insufficient drainage can be discovered in a timely manner, thereby dividing and forming a drainage grid model with efficient drainage capacity, and finally generating an urban drainage grid model.

[0127] Further, the prediction and analysis of the waterlogging area for each urban drainage grid partition in the urban drainage grid model based on the historical waterlogging event rules corresponding to the city includes the following steps:

[0128] Construct a two-dimensional surface model of waterlogging diffusion according to the historical waterlogging event rules corresponding to the city to reflect the diffusion of surface waterlogging after overflow in each drainage grid partition in the city and its impact on the waterlogging distribution in the surrounding areas, and generate a two-dimensional surface rule model of urban waterlogging diffusion;

[0129] In the embodiments of the present invention, by collecting and analyzing historical waterlogging event data, a two-dimensional surface model of waterlogging diffusion based on the laws of urban historical waterlogging events is established. First, select urban waterlogging event data within a certain time range (such as the past five or ten years), including information such as the time, location, waterlogging depth, and duration of waterlogging occurrence. These data will be used as inputs and modeled using multi-dimensional urban data such as meteorology, geography, and drainage systems. In specific implementation, numerical simulation methods such as the finite element method (FEM) or the finite difference method (FDM) can be used to simulate the waterlogging diffusion process by constructing a two-dimensional grid model of the city. Each grid represents an area within the city, and it simulates how waterlogging spreads from the source along the ground to the surrounding areas. The waterlogging diffusion on the surface is mainly affected by multiple factors, including terrain slope, soil permeability, urban hardening degree, etc. All these factors need to be accurately described and calculated through physical models. The model also needs to consider the precipitation intensity, drainage capacity, and the impact of drainage facilities, and use a hydrodynamic model to calculate the waterlogging distribution and diffusion trend in different areas after overflow, and finally generate a two-dimensional surface law model of urban waterlogging diffusion.

[0130] Preferably, based on the two-dimensional surface law model of urban waterlogging diffusion, the waterlogging event frequency is measured for each corresponding urban drainage grid partition in the urban drainage grid model to obtain the waterlogging event frequency corresponding to each urban drainage grid partition.

[0131] In the embodiments of the present invention, using the previously generated two-dimensional surface law model of urban waterlogging diffusion, combined with the urban drainage grid model, the waterlogging event frequency is measured for each drainage grid partition. The drainage grid model divides the city into several small grid units, and each unit represents a drainage area. Usually, considering the functional division and drainage capacity differences in urban areas, the grid division should have a certain degree of fineness. In specific operation, the historical waterlogging events in each drainage grid partition are simulated and analyzed using the two-dimensional waterlogging diffusion model, and the frequency of waterlogging occurrence in this grid partition within a specific time period is calculated. The frequency of waterlogging occurrence in each grid partition can be obtained by statistically analyzing the spatio-temporal distribution of waterlogging events. During this process, attention should be paid to the waterlogging duration, waterlogging depth in different drainage areas and their impact on neighboring areas. For example, if a certain grid has had five waterlogging events in the past ten years, then the waterlogging event frequency of this grid is 0.5 times / year. This frequency value can reflect the load situation of the drainage system in this area and possible drainage hidden dangers, and finally obtain the waterlogging event frequency corresponding to each urban drainage grid partition.

[0132] Preferably, based on the waterlogging event frequency corresponding to each urban drainage grid partition, the high-incidence waterlogging grid areas in the urban drainage grid model are connected to obtain the urban high-incidence waterlogging grid areas.

[0133] In an embodiment of the present invention, by identifying and connecting high-incidence areas of drainage grids according to the frequency of waterlogging events, the specific implementation method is to sort all grids according to the frequency of waterlogging events of each drainage grid, and select the grids with higher frequencies as "high-incidence waterlogging areas". A threshold can be set. When the frequency of waterlogging events of a drainage grid exceeds this threshold, the grid is identified as a high-incidence waterlogging grid area. To further identify high-incidence waterlogging areas, spatial clustering algorithms (such as DBSCAN, K-means, etc.) can be used to connect adjacent high-frequency waterlogging grids to form a connected area. For example, the distribution of some grids with higher frequencies of waterlogging events shows a certain spatial coherence, and the clustering algorithm can merge these high-frequency grids into a whole, called "high-incidence waterlogging grid area". This area reflects the areas in the city with more serious waterlogging problems, and finally the high-incidence waterlogging grid areas in the city are obtained.

[0134] Preferably, according to the high-incidence waterlogging grid areas in the city, obtain the terrain slope distribution and ground water seepage efficiency between the corresponding drainage grid partitions in the urban drainage grid model;

[0135] In an embodiment of the present invention, by analyzing the terrain slope and ground water seepage efficiency of the high-incidence waterlogging grid areas in the city, the specific operation is to first extract the terrain data of the urban area from the digital elevation model (DEM), calculate the terrain slope distribution of each drainage grid partition. Areas with larger slope values cause the water flow to accelerate, affecting the drainage effect, while areas with smaller slopes are prone to waterlogging. Then, by using soil permeability data or using ground cover data (for example: the coverage of grasslands, roads, buildings, etc.), combined with the permeability coefficients of soil and buildings, calculate the ground water seepage efficiency of each drainage grid partition. Areas with high water seepage efficiency are more likely to have water penetrate underground, while areas with low water seepage efficiency are prone to waterlogging. This process usually uses GIS technology, remote sensing data, and soil science data to analyze the urban ground characteristics and perform spatial distribution calculations within the drainage grids, and finally obtain the terrain slope distribution and ground water seepage efficiency between the drainage grid partitions.

[0136] Preferably, based on the terrain slope distribution and ground water seepage efficiency between the corresponding drainage grid partitions, conduct a prediction analysis of the waterlogging areas in the high-incidence waterlogging grid areas in the city to obtain the urban waterlogging prediction areas.

[0137] In the embodiments of the present invention, data obtained from previous analyses is integrated to predict and analyze waterlogging areas. First, based on the distribution of high-incidence waterlogging grid areas, combined with the terrain slope and ground seepage efficiency information of each grid, physical models and statistical models are used to predict the waterlogging risk. For example, hydrological models (such as the SWMM model or the HEC-HMS model) are used to simulate the water flow direction and waterlogging conditions under different slopes and seepage efficiency conditions during rainstorms. The model inputs include the slope, seepage rate of the drainage grid partition, and frequency data of historical waterlogging events, and the output is the waterlogging depth and water flow path of each drainage grid under a specific precipitation. Further, the waterlogging areas can be identified through the simulation results, and these areas are usually associated with low-lying areas, small slopes, and low seepage rates. To improve the prediction accuracy, machine learning algorithms such as random forest and support vector machine (SVM) can be combined to train the historical data to predict possible future waterlogging areas. In addition, through various scenario simulations and risk assessments, the change trend of the waterlogging area can be determined, and finally the urban waterlogging prediction area is obtained.

[0138] Further, step S3 includes the following steps:

[0139] Step S31: Obtain the measured waterlogging depth, river water level, rainfall process in the past period, and drainage pipe network status corresponding to the urban waterlogging prediction area at the current moment through the urban waterlogging digital twin model as the real-time monitoring data corresponding to the urban waterlogging prediction area, and use the real-time monitoring data corresponding to the start moment of the simulation as the initial condition data of the waterlogging prediction area;

[0140] In the embodiments of the present invention, data collection is performed on the urban waterlogging prediction area through the urban waterlogging digital twin model. This step includes four main parts: the waterlogging depth at the current moment, the river water level, the rainfall process, and the drainage pipe network status. For the waterlogging depth, the waterlogging situation in the area is obtained by means of ground measurement stations and remote sensing technologies (such as lidar, satellite images, or drone scans), and the depth is calculated in combination with the known drainage outlet flow data. For the river water level, real-time water level data is collected through water level monitoring sensors installed in the river and drainage systems, and these data are synchronized and updated with the digital twin system through Internet of Things technology. In terms of the rainfall process in the past period, the historical precipitation data provided by meteorological stations is combined with the real-time precipitation data of ground meteorological sensors to help calculate the precipitation situation at the current moment. The drainage pipe network status is understood through the sensor data of the urban drainage pipe network system, the intelligent flow monitoring system, and the data transmitted back by online sensors, and the current flow and blockage status of the drainage pipe network. All the above real-time monitoring data will be used as inputs to determine the initial conditions of the urban waterlogging prediction area, and finally the initial condition data of the waterlogging prediction area is obtained.

[0141] Step S32: Obtain the rainfall amount and rainfall duration corresponding to the future forecast time for the urban waterlogging prediction area through the urban waterlogging digital twin model to determine the rainfall time series, and obtain the rainfall time series corresponding to the future forecast time for the urban waterlogging prediction area.

[0142] In the embodiment of the present invention, the rainfall forecast for the future period is processed through the urban waterlogging digital twin model. Specifically, information such as future rainfall amount and rainfall duration is obtained through a meteorological forecast model (such as a high-resolution weather forecast model or a regional climate model). These data are usually obtained through meteorological satellites or meteorological radars. Combining with the precipitation forecast generated by the meteorological forecast system, the rainfall time series is determined. For the determination of the rainfall duration, based on the analysis of historical meteorological data and the forecast model for rainfall events, combined with the climate characteristics of the specific area, such as common rainfall patterns (short-term heavy rainfall or long-term light rainfall) and other factors, the future rainfall time series is deduced. These rainfall time series data will be converted into precipitation forecasts at time steps and used as input data for subsequent steps, and finally the rainfall time series corresponding to the future forecast time for the urban waterlogging prediction area is obtained.

[0143] Step S33: Obtain the terrain, drainage capacity, and climate conditions corresponding to the urban waterlogging prediction area through the urban waterlogging digital twin model, where the climate conditions include rainfall amount, evaporation amount, and river channel soil permeability, and estimate the change in the river channel water level at the future forecast time for the corresponding urban waterlogging prediction area according to the terrain, drainage capacity, and climate conditions corresponding to the urban waterlogging prediction area, so as to obtain the river channel water level change data corresponding to the future forecast time for the urban waterlogging prediction area.

[0144] In the embodiment of the present invention, through a detailed analysis of the geographical environment and climate conditions affecting urban waterlogging, in this process, first, the terrain data of the urban waterlogging prediction area needs to be obtained. The elevation data of the area is obtained through remote sensing technology (such as drones, aerial photography, lidar, etc.), and combined with topographic maps for digital processing to construct an accurate digital elevation model (DEM). Secondly, the drainage capacity is evaluated through the data of the municipal drainage system, focusing on analyzing the bearing capacity, flow rate, and blockage situation of the drainage pipe network, and evaluating its drainage efficiency based on the existing urban drainage pipe network model and on-line monitoring data. For the climate conditions, first, the historical meteorological data of the area, including rainfall amount, evaporation amount, temperature, humidity, etc., is obtained, and the future climate conditions are predicted by combining meteorological station data with historical records and climate change models. The soil permeability is analyzed emphatically, and it can be accurately evaluated through soil permeability tests, groundwater monitoring, and geological exploration data. All these data will be used as input and transmitted into the digital twin model to estimate the change in the river channel water level at the future forecast time, and finally the river channel water level change data corresponding to the future forecast time for the urban waterlogging prediction area is obtained.

[0145] Step S34: Based on the initial condition data of the waterlogging prediction area, the rainfall time series and the river water level change data of the urban waterlogging prediction area corresponding to the future forecast time are used as input data to run the urban waterlogging digital twin model in step S2 to generate a future forecast simulation process corresponding to the urban waterlogging model;

[0146] In an embodiment of the present invention, the monitoring data, rainfall time series and river water level change data in steps S31 and S32 are taken as input to execute the urban waterlogging digital twin model for simulation and prediction. The model is based on a combination of a basin hydrological model, a terrain model, a drainage capacity model and a meteorological forecast model, and takes into account multiple factors such as terrain, climate, precipitation, etc. to dynamically simulate the waterlogged area. During the simulation process, the digital twin model will predict the changes in waterlogging after future rainfall events through numerical calculation and time series analysis. Specifically, the model numerically simulates the evolution process of urban waterlogging based on the input rainfall time series and real-time river water level change data, calculates the diffusion of waterlogging, waterlogging depth, and changes in drainage capacity, and combines the simulated water level changes to obtain a waterlogging distribution map at the future forecast time. The output data in the simulation process is the simulation result of urban waterlogging at the future forecast time, and finally generates a future forecast simulation process corresponding to the urban waterlogging model.

[0147] Step S35: using the real-time monitoring data corresponding to the urban waterlogging prediction area to perform real-time correction and waterlogging rolling forecast on the urban waterlogging digital twin model in the future forecast simulation process corresponding to the urban waterlogging model, so as to generate an urban waterlogging waterlogging rolling forecast digital model;

[0148] In an embodiment of the present invention, real-time correction of the urban waterlogging digital twin model is performed by using real-time monitoring data. As the actual rainfall process progresses, real-time monitoring data (such as precipitation, water level changes, etc.) will be continuously provided to the digital twin system. At this time, the model will compare the new real-time data with the previous simulation data, detect differences and automatically adjust the model parameters to ensure the accuracy and timeliness of the model. This process is achieved through data assimilation technology, that is, the observed data is input into the model in a timely manner, and the model state is corrected through optimization algorithms (such as Kalman filtering or particle filtering). Based on the correction of these real-time data, the model will generate a rolling forecast, that is, through continuously updated data input, the urban waterlogging situation in the future period of time is continuously calculated to provide more accurate real-time warning information. This real-time correction process will ensure that the waterlogging model is always calculated based on the latest monitoring data, improve the accuracy of the forecast, and then realize dynamic warning of the waterlogged area, and finally generate a digital model of urban waterlogging rolling forecast.

[0149] Step S36: Obtain the waterlogging levels and the status of the drainage pipe network corresponding to the urban waterlogging prediction areas through the rolling prediction digital model of urban waterlogging. Based on the waterlogging levels corresponding to the urban waterlogging prediction areas, conduct rainfall-water level spatio-temporal correlation analysis on the rainfall time series at the future prediction moments corresponding to the urban waterlogging prediction areas, so as to generate the rainfall-water level spatio-temporal correlation distribution field corresponding to the urban waterlogging prediction areas;

[0150] In the embodiment of the present invention, based on the generated rolling prediction digital model of urban waterlogging, the current status of the waterlogging levels and the drainage pipe network is obtained. This digital model determines the waterlogging levels of each area of the city by integrating data such as real-time waterlogging depth, pressure and flow of the drainage pipe network, and evaluates the operating status of the drainage pipe network. Then, according to the waterlogging levels of the urban waterlogging prediction areas, combined with the rainfall time series at the future prediction moments of these areas, rainfall-water level spatio-temporal correlation analysis is carried out. This analysis uses time series data analysis techniques. Based on the correlation data of historical rainfall and waterlogging levels, spatio-temporal correlation models between rainfall and waterlogging levels are established through regression analysis or machine learning algorithms (such as random forest, support vector machine, etc.). Through these models, the waterlogging levels of different areas at specific future moments can be predicted, and then the impact degree of future rainfall can be predicted. This analysis not only considers the intensity of rainfall, but also takes into account the spatial distribution characteristics of rainfall and the response of the drainage pipe network, further improving the accuracy of waterlogging level prediction, and finally generating the rainfall-water level spatio-temporal correlation distribution field corresponding to the urban waterlogging prediction areas.

[0151] Step S37: Conduct drainage efficiency attenuation analysis on the urban waterlogging prediction areas corresponding in the rolling prediction digital model of urban waterlogging based on the status of the drainage pipe network corresponding to the urban waterlogging prediction areas, and generate the drainage pipe network efficiency attenuation distribution field corresponding to the urban waterlogging prediction areas;

[0152] In the embodiment of the present invention, first, based on the actual operating conditions of the drainage pipe network in the urban waterlogging prediction areas, including data such as the diameter, flow velocity, slope, blockage condition and overload degree of the pipes, a mathematical model of the drainage system is established. Then, these data are used to evaluate the drainage efficiency of the drainage pipe network under different rainfall intensities, and the focus is on analyzing the degree of decline in the drainage capacity of the pipe network under extreme weather conditions, that is, the attenuation of drainage efficiency. The attenuation analysis can simulate the flow change of the drainage pipe network under specific rainfall amounts by establishing a hydraulic model, and methods such as hydraulic and hydrological models (such as SWMM model) are used to predict the working status of the drainage pipe network at different positions in the waterlogging area, and then analyze the distribution of drainage efficiency attenuation of the drainage pipe network. The purpose of this step is to evaluate the drainage capacity of different regions and different types of drainage pipe networks under extreme rainfall, generate the drainage efficiency attenuation distribution field, and finally generate the drainage pipe network efficiency attenuation distribution field corresponding to the urban waterlogging prediction areas.

[0153] Step S38: Based on the rainfall-water level spatio-temporal correlation distribution field and the drainage network efficiency attenuation distribution field corresponding to the urban waterlogging prediction area, perform a coupling analysis of the water accumulation evolution in the urban waterlogging prediction area corresponding to the digital model of urban waterlogging rolling prediction to generate an urban water accumulation evolution distribution field.

[0154] In the embodiment of the present invention, by combining the rainfall-water level spatio-temporal correlation distribution field and the drainage network efficiency attenuation distribution field, the evolution process of water accumulation is predicted, and then the water accumulation distribution in each area within a certain period in the future is calculated. In this step, first, through the rainfall-water level spatio-temporal correlation distribution field obtained in the previous stage, the influence law of different rainfall intensities on the water levels in various areas of the city is clarified, and by combining the drainage network efficiency attenuation distribution field, the influence of the attenuation effect of the drainage network on the water accumulation evolution under future rainfall conditions is evaluated. The coupling analysis of water accumulation evolution uses a multi-dimensional hydraulic model or a multi-field coupling simulation model, comprehensively considering factors such as rainfall amount, water level change, and drainage network efficiency, and adopts a dynamic simulation method to predict the water accumulation change trend in each area within a certain period in the future. This analysis relies on the continuous input of real-time monitoring data, and the model dynamically updates the water accumulation evolution results according to the latest rainfall prediction and the efficiency status of the drainage network. Through the coupling analysis, an water accumulation evolution distribution field within several hours to several days in the future can be generated, potential waterlogging risk areas can be identified, and the analysis results are presented in the form of a spatial distribution map to help decision-makers understand the evolution trend of urban water accumulation. Through the rolling prediction method, not only can an accurate early warning be provided for the current water accumulation evolution, but also support can be provided for future rainfall and waterlogging risk assessment, and finally an urban water accumulation evolution distribution field is generated.

[0155] Further, step S37 includes the following steps:

[0156] Step S371: Obtain the drainage network pressure and drainage treatment efficiency at the corresponding distribution position through the drainage network condition corresponding to the urban waterlogging prediction area;

[0157] In the embodiment of the present invention, by using digital twin technology to construct a digital model of the urban drainage pipe network, the model should have the spatial layout of facilities such as drainage pipes, pump stations, and inspection wells throughout the network and the physical parameters of the pipes. Then, based on remote sensing data and geographic information system (GIS) technology, real-time rainfall data and waterlogging area information of the urban waterlogging prediction area are obtained. On this basis, a hydrological and hydraulic model is used for real-time simulation to calculate the pressure distribution of the drainage pipe network. This simulation model takes into account factors such as different rainfall intensities, terrain, and the diameter, slope, and flow rate of drainage pipes, and combines the influence of obstacles such as urban roads and buildings on the water flow to obtain the drainage pressure at each pipe network distribution location and the drainage capacity of the pipe network. At the same time, combining the design standards, operating status, and past drainage records of the drainage pipes, the processing efficiency of each drainage point is calculated through data analysis tools (such as Python, MATLAB, etc.). Specifically, a linear regression or machine learning model can be used, combined with historical rainfall and actual drainage volume, to obtain the processing efficiency of the drainage system under different conditions, and finally the drainage pipe network pressure and drainage processing efficiency at the corresponding distribution location are obtained.

[0158] Step S372: Conduct a drainage pipe network efficiency evaluation and analysis on the drainage pipe network pressure and drainage processing efficiency at the corresponding distribution location of the urban waterlogging prediction area to obtain the drainage efficiency of the drainage pipe network at the corresponding distribution location of the urban waterlogging prediction area;

[0159] In the embodiment of the present invention, through comprehensive analysis of the pipe network pressure and drainage efficiency data of each previously obtained drainage distribution location point, the urban hydraulic model is used to evaluate the efficiency of each drainage node. The specific method is to quantitatively analyze the drainage capacity of the drainage pipe network through the relationship between pressure and flow rate, using fluid mechanics equations (such as Bernoulli's equation) and the control volume method. The key to the drainage pipe network efficiency evaluation lies in the dynamic analysis of pressure-flow-time for each node, combined with the actual drainage task volume, to evaluate the flow velocity, flow rate of the pipeline, and the drainage capacity of the system. To more accurately evaluate the drainage efficiency, factors such as different types of drainage pipe materials and pipe ages also need to be introduced, and operation and maintenance data and historical monitoring data are used for comparison and verification. Different drainage performances in different situations can be simulated through numerical simulation tools (such as OpenFOAM, SWMM, etc.) to evaluate the drainage efficiency of the drainage pipe network in each area under the existing conditions, and finally the drainage efficiency of the drainage pipe network at the corresponding distribution location of the urban waterlogging prediction area is obtained.

[0160] Step S373: Obtain the sediment accumulation amount and the drainage pipe breakage rate at the corresponding distribution positions of the urban waterlogging prediction area, and conduct a drainage efficiency attenuation analysis on the drainage efficiency of the drainage pipe network at the corresponding distribution positions of the urban waterlogging prediction area based on the sediment accumulation amount and the drainage pipe breakage rate, so as to obtain the drainage efficiency attenuation degree at the corresponding distribution positions of the urban waterlogging prediction area;

[0161] In the embodiment of the present invention, through a combination of remote sensing monitoring and manual inspection, the sediment accumulation situation of the drainage pipe network within the urban waterlogging prediction area is obtained, and the sediment thickness and accumulation amount of the pipeline are obtained through in-pipe sensors, inspection well monitoring and regular inspection reports. Then, in combination with historical data such as flow rate and pressure, the influence of the sediment in the drainage pipe network on the flow is modeled by using hydraulic simulation, and the influence degree on the drainage efficiency of the pipe network is calculated. Next, in combination with the breakage rate data of the drainage pipes, the influence of pipe breakage on the drainage capacity is evaluated. The breakage rate can be obtained relying on on-site inspections (such as acoustic inspections, endoscopic inspections, etc.) and historical maintenance records. By comparing and analyzing the local repair of the damaged pipes with the drainage efficiency of the entire pipe network, the attenuation effect of breakage on the drainage capacity is obtained. The corrosion-breakage model and the fluid mechanics model are combined to evaluate the specific attenuation degree of pipe breakage and sediment accumulation on the drainage efficiency. On this basis, through numerical simulation technology, considering the drainage efficiency attenuation under different breakage rates and sediment accumulation amounts, the drainage efficiency attenuation values of each area are obtained. These attenuation values reflect the actual efficiency loss degree of the drainage pipe network under different regions and different conditions, and finally the drainage efficiency attenuation degree at the corresponding distribution positions of the urban waterlogging prediction area is obtained.

[0162] Step S374: Conduct an efficiency attenuation distribution analysis on the urban waterlogging prediction area based on the drainage efficiency attenuation degree at the corresponding distribution positions of the urban waterlogging prediction area, and generate a drainage pipe network efficiency attenuation distribution field corresponding to the urban waterlogging prediction area.

[0163] In the embodiments of the present invention, by spatially processing the drainage efficiency attenuation data at each previously obtained distribution location, using Geographic Information System (GIS) technology, mapping the attenuation degree onto the urban map, and combining with the spatial distribution of the high-density urban waterlogging prediction areas, a spatial distribution field of the drainage network efficiency attenuation is generated. This distribution field will show which areas are more affected by the drainage network efficiency attenuation and the high-risk areas of waterlogging. When analyzing the efficiency attenuation distribution, a heat map or contour map can be used to visually present the attenuation distribution, helping urban managers identify the weak areas of the drainage system and providing data support for the next step of waterlogging warning and pipe network optimization. At the same time, by combining meteorological forecasts and precipitation models, future precipitation events can be simulated to predict which areas of the drainage network will experience more severe efficiency attenuation under future precipitation conditions, and finally a spatial distribution field of the drainage network efficiency attenuation corresponding to the urban waterlogging prediction areas is generated.

[0164] 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 will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A high-density urban waterlogging evolution and early warning method based on digital twins, characterized in that: The following steps are involved: Step S1: The real-time waterlogging depth, real-time river water level, real-time meteorological conditions and real-time drainage network water level of the city are collected in real time by installing waterlogging depth sensors, river water level sensors, meteorological monitoring equipment and drainage network sensors in the city, and a digital twin is constructed based on the real-time waterlogging depth, real-time meteorological conditions and real-time drainage network water level of the city in combination with the land use type, terrain and geomorphology data and building road information model of the city, so as to generate a digital twin model of urban waterlogging; Step S2: Obtain the drainage network distribution corresponding to the city, and divide the urban waterlogging digital twin model into drainage grids based on the drainage network distribution corresponding to the city and the real-time river water level to generate an urban drainage grid model; obtain the historical waterlogging event pattern corresponding to the city, and perform waterlogging area prediction and analysis on each urban drainage grid partition in the urban drainage grid model based on the historical waterlogging event pattern corresponding to the city to obtain the urban waterlogging prediction area; Step S3: The current measured water depth, river water level, rainfall process in the past period, and drainage network status corresponding to the urban waterlogging prediction area are obtained through the urban waterlogging digital twin model as the real-time monitoring data corresponding to the urban waterlogging prediction area, and the urban waterlogging prediction area is subjected to waterlogging evolution coupling analysis based on the real-time monitoring data corresponding to the urban waterlogging prediction area to generate the urban waterlogging evolution distribution field. Step S3 includes the following steps: Step S31: The current measured water depth, river water level, rainfall process in the past period, and drainage network status corresponding to the urban waterlogging prediction area are obtained through the urban waterlogging digital twin model as real-time monitoring data corresponding to the urban waterlogging prediction area, and the real-time monitoring data corresponding to the simulation start time is used as the initial condition data of the waterlogging prediction area; Step S32: Obtain the rainfall amount and rainfall duration in the urban waterlogging prediction area corresponding to the future forecast time through the urban waterlogging digital twin model to determine the rainfall time series, and obtain the rainfall time series in the urban waterlogging prediction area corresponding to the future forecast time; Step S33: obtaining the terrain, drainage capacity and climate conditions corresponding to the urban waterlogging prediction area through the urban waterlogging digital twin model, wherein the climate conditions include rainfall, evaporation and river soil permeability, and estimating the river water level change of the corresponding urban waterlogging prediction area at the future forecast time according to the terrain, drainage capacity and climate conditions corresponding to the urban waterlogging prediction area, so as to obtain the river water level change data corresponding to the urban waterlogging prediction area at the future forecast time; Step S34: Based on the initial condition data of the waterlogging prediction area, the rainfall time series and the river water level change data of the urban waterlogging prediction area corresponding to the future forecast time are used as input data to run the urban waterlogging digital twin model in step S2 to generate a future forecast simulation process corresponding to the urban waterlogging model; Step S35: using the real-time monitoring data corresponding to the urban waterlogging prediction area to perform real-time correction and waterlogging rolling forecast on the urban waterlogging digital twin model in the future forecast simulation process corresponding to the urban waterlogging model, so as to generate an urban waterlogging waterlogging rolling forecast digital model; Step S36: obtaining the water level and drainage network status corresponding to the urban waterlogging prediction area through the urban waterlogging waterlogging rolling forecast digital model, and performing rainfall-water level spatiotemporal correlation analysis on the rainfall time series corresponding to the future forecast time in the urban waterlogging prediction area based on the water level corresponding to the urban waterlogging prediction area, so as to generate a rainfall-water level spatiotemporal correlation distribution field corresponding to the urban waterlogging prediction area; Step S37: Based on the drainage pipe network status corresponding to the urban waterlogging prediction area, drainage efficiency attenuation analysis is performed on the urban waterlogging prediction area corresponding to the urban waterlogging prediction digital model, and a drainage pipe network efficiency attenuation distribution field corresponding to the urban waterlogging prediction area is generated; Step S38: Based on the rainfall-water level spatiotemporal correlation distribution field and the drainage network efficiency attenuation distribution field corresponding to the urban waterlogging prediction area, a waterlogging evolution coupling analysis is performed on the urban waterlogging prediction area corresponding to the urban waterlogging prediction digital model to generate an urban waterlogging evolution distribution field; Step S4: Analyze the spatiotemporal evolution of urban waterlogging evolution distribution field to obtain the evolution and development trend of urban waterlogging at different spatiotemporal nodes; perform waterlogging early warning processing on the corresponding urban waterlogging evolution distribution field based on the evolution and development trend of urban waterlogging at different spatiotemporal nodes to generate an urban waterlogging early warning distribution field to execute corresponding urban waterlogging emergency response work.

2. The high-density urban waterlogging evolution and early warning method based on digital twin according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: performing real-time monitoring of urban waterlogging by installing waterlogging depth sensors at various location nodes in the city to obtain the corresponding real-time waterlogging depth of the city; Step S12: Real-time monitoring of the river water level in the city is performed by installing river water level sensors at various river locations in the city to obtain the real-time river water level corresponding to the city, including the water level height and the corresponding time; Step S13: Performing real-time weather monitoring of the city through weather monitoring equipment installed at various location nodes in the city to obtain real-time weather conditions corresponding to the city, including rainfall and rainfall duration; Step S14: The drainage network of the city is monitored in real time by means of the drainage network liquid level and flow rate sensors installed at various location nodes of the city, so as to obtain the real-time drainage network water level status corresponding to the city, including the liquid level and drainage flow rate corresponding to the drainage network nodes; Step S15: Obtain the land use type, terrain and geomorphology data, and building road information model corresponding to the city, and construct a digital twin based on the real-time waterlogging depth, real-time meteorological conditions, and real-time drainage network water level conditions corresponding to the city in combination with the land use type, terrain and geomorphology data, and building road information model corresponding to the city to generate a digital twin model of urban waterlogging.

3. The high-density urban waterlogging evolution and early warning method based on digital twin according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Obtain the topography, building density and drainage facility layout of the city, and obtain the drainage network distribution of the city according to the topography, building density and drainage facility layout of the city; Step S22: Based on the drainage network distribution corresponding to the city and the real-time river water level, the drainage carrying capacity of the drainage network at the corresponding distribution position in the urban waterlogging digital twin model is evaluated to obtain the urban drainage network carrying capacity matrix; Step S23: obtaining the geographic space, drainage network layout and water flow characteristics at the corresponding distribution location through the urban waterlogging digital twin model, and performing water flow load assessment and analysis based on the geographic space, drainage network layout and water flow characteristics at the corresponding distribution location to obtain the drainage water flow load at the corresponding distribution location of the city; Step S24: performing drainage grid load matching and division on the urban waterlogging digital twin model based on the drainage network load capacity at the corresponding distribution position in the urban drainage network load capacity matrix and the drainage flow load at the corresponding distribution position in the city, so as to generate an urban drainage grid model; Step S25: Obtain the historical waterlogging event patterns corresponding to the city, and perform waterlogging area prediction analysis on each urban drainage grid partition in the urban drainage grid model based on the historical waterlogging event patterns corresponding to the city, so as to obtain the urban waterlogging prediction area.

4. The high-density urban waterlogging evolution and early warning method based on digital twin according to claim 3 is characterized in that: Step S22 includes the following steps: Obtain the drainage network pipe diameter and drainage network pipe slope at the corresponding distribution location through the drainage network distribution of the city; Conduct a statistical analysis of the drainage flow rate of the drainage network at the corresponding distribution locations in the urban waterlogging digital twin model to obtain the drainage network flow rate at the corresponding distribution locations in the city; The real-time river water level corresponding to the city is used to obtain the time-series fluctuation of the river water level at the corresponding distribution position in the urban waterlogging digital twin model, and based on the time-series fluctuation of the river water level at the corresponding distribution position in the city, the outlet flow constraint analysis of the drainage network at the corresponding distribution position in the urban waterlogging digital twin model is performed to obtain the time-varying constraint of the corresponding river water level in the city on the outlet flow of the drainage network; Based on the time-varying constraints of the drainage network pipe diameter, drainage network pipe slope, drainage network flow rate and the corresponding river water level in the city on the drainage network outlet flow, the drainage network carrying capacity measurement calculation formula is used to evaluate and calculate the carrying capacity of the drainage network at the corresponding distribution position in the urban waterlogging digital twin model, so as to obtain the drainage network carrying capacity coefficient at the corresponding distribution position in the city; According to the carrying capacity coefficient of the drainage network at the corresponding distribution location in the city, the drainage capacity matrix of the drainage network at the corresponding distribution location in the urban waterlogging digital twin model is constructed to obtain the carrying capacity matrix of the urban drainage network.

5. The high-density urban waterlogging evolution and early warning method based on digital twin according to claim 4 is characterized in that: The specific calculation formula for the drainage network carrying capacity measurement is: ； ； In the formula, The coordinates of the city's distribution location The carrying capacity coefficient of the drainage network at It is the three-dimensional space area covered by the drainage network. is the horizontal coordinate parameter of the distribution position, is the ordinate parameter of the distribution location, is the vertical coordinate parameter of the distribution location, is the time variable parameter, To constrain the initial time of the integration, To constrain the impact of the integral end time, For the city in time The time-varying constraint of the river water level on the outlet flow of the drainage network at the time. The coordinates of the city's distribution location and in time The river water level at the time, is the linear influence coefficient of the river water level on the outlet flow of the drainage network, is the quadratic nonlinear influence coefficient of the river water level on the outlet flow of the drainage network, is an exponential function, For the city in time The drainage network outlet flow at time, is the initial flow rate at the outlet of the drainage network, is the drainage network flow response time attenuation control parameter, The coordinates of the city's distribution location The water flow rate at The coordinates of the city's distribution location The maximum permissible water flow rate at The coordinates of the city's distribution location The diameter of the drainage network pipe at is the weight factor affecting the pipe diameter load, The coordinates of the city's distribution location The slope of the drainage network pipes at is the weight factor affecting the pipeline slope load, The coordinates of the city's distribution location The flow rate of the drainage network at is the flow velocity bearing weight factor, It is the correction factor of the drainage network bearing capacity coefficient.

6. The high-density urban waterlogging evolution and early warning method based on digital twin according to claim 3 is characterized in that: The water flow load assessment analysis in step S23 according to the geographical space, drainage network layout and water flow characteristics at the corresponding distribution location includes the following steps: According to the geographical space and drainage network layout at the corresponding distribution location, drainage space layout load constraint analysis is carried out to obtain the drainage space layout load constraint conditions at the corresponding distribution location of the city; According to the water flow characteristics at the corresponding distribution positions, the water flow dynamic distribution analysis is performed to generate the water flow dynamic distribution flow field at the corresponding distribution positions in the city; Based on the drainage space layout load constraints at the corresponding distribution locations in the city, the water flow load evaluation and analysis is carried out on the dynamic distribution flow field of the water flow at the corresponding distribution locations in the city to obtain the drainage water flow load at the corresponding distribution locations in the city.

7. The high-density urban waterlogging evolution and early warning method based on digital twin according to claim 3 is characterized in that: Step S24 includes the following steps: Step S241: Based on the drainage pipe network carrying capacity at the corresponding distribution position in the urban drainage pipe network carrying capacity matrix, the drainage water flow load at the corresponding distribution position of the city is subjected to drainage bearing matching analysis. If the drainage pipe network carrying capacity at the corresponding distribution position is greater than or equal to the drainage water flow load, it is determined as the drainage bearing balance matching point of the corresponding drainage pipe network carrying capacity; if the drainage pipe network carrying capacity at the corresponding distribution position is less than the drainage water flow load, a new bearing matching analysis is performed at a distribution position parallel to the outside until a drainage bearing balance matching point is determined. Step S242: connecting equal-value drainage grids according to drainage load balance matching points of different drainage network carrying capacities to generate urban drainage grid boundaries corresponding to different drainage network carrying capacities; Step S243: Based on the urban drainage grid boundaries corresponding to different drainage network carrying capacities, the urban waterlogging digital twin model is divided into drainage grid carrying capacity matching to generate an urban drainage grid model.

8. The high-density urban waterlogging evolution and early warning method based on digital twin according to claim 3 is characterized in that: The step S25 of performing waterlogging area prediction analysis on each urban drainage grid partition in the urban drainage grid model based on the historical waterlogging event law corresponding to the city includes the following steps: A two-dimensional surface model of waterlogging diffusion is constructed based on the historical waterlogging events corresponding to the city, so as to reflect the diffusion of surface waterlogging after overflow in each drainage grid partition in the city and its impact on the distribution of waterlogging in the surrounding areas, and generate a two-dimensional surface law model of urban waterlogging diffusion; Based on the two-dimensional surface law model of urban waterlogging diffusion, the frequency of waterlogging events is measured for each urban drainage grid partition in the urban drainage grid model to obtain the frequency of waterlogging events corresponding to each urban drainage grid partition; Based on the frequency of waterlogging events corresponding to each urban drainage grid partition, the corresponding urban drainage grid partitions in the urban drainage grid model are connected to the high-incidence waterlogging grid areas to obtain the high-incidence waterlogging grid areas in the city; According to the high-incidence waterlogging grid areas in the city, the terrain slope distribution and ground water infiltration efficiency between the corresponding drainage grid partitions in the urban drainage grid model are obtained; Based on the terrain slope distribution and ground infiltration efficiency between the corresponding drainage grid divisions, a waterlogging area prediction analysis is conducted on the high-incidence waterlogging grid areas in the city to obtain the urban waterlogging prediction area.

9. The high-density urban waterlogging evolution and early warning method based on digital twin according to claim 1 is characterized in that: Step S37 includes the following steps: Step S371: obtaining the drainage network pressure and drainage treatment efficiency at the corresponding distribution location according to the drainage network status corresponding to the urban waterlogging prediction area; Step S372: performing drainage network performance evaluation and analysis on the drainage network pressure and drainage treatment efficiency at the corresponding distribution positions in the urban waterlogging prediction area, and obtaining the drainage network drainage efficiency at the corresponding distribution positions in the urban waterlogging prediction area; Step S373: obtaining the sediment accumulation amount and the drainage pipe damage rate at the corresponding distribution position of the urban waterlogging prediction area, and performing drainage efficiency attenuation analysis on the drainage efficiency of the drainage pipe network at the corresponding distribution position of the urban waterlogging prediction area based on the sediment accumulation amount and the drainage pipe damage rate, to obtain the drainage efficiency attenuation degree at the corresponding distribution position of the urban waterlogging prediction area; Step S374: performing an efficiency attenuation distribution analysis on the urban waterlogging prediction area based on the drainage efficiency attenuation degree at the corresponding distribution positions in the urban waterlogging prediction area, and generating a drainage network efficiency attenuation distribution field corresponding to the urban waterlogging prediction area.

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