A method for rapid prediction and diagnosis of urban tunnel waterlogging disaster based on numerical simulation

Abstract

The application discloses a kind of disaster prediction and diagnosis method, especially a kind of fast prediction and diagnosis method of urban tunnel waterlogging disaster based on numerical simulation, including the following contents, collect and arrange the data required for urban undercrossing tunnel waterlogging simulation;Combined with the data collected in the numerical simulation software to establish a model, the waterlogging disaster simulation is carried out to obtain the tunnel waterlogging simulation result;On the basis of the tunnel waterlogging simulation result, the waterlogging disaster risk is predicted, and the time-varying physical quantity of tunnel water accumulation process and drainage process is extracted, and the waterlogging disaster mode is diagnosed in combination with the time-varying physical quantity of inflow process.The present application constructs the dimensionless inflow impact curve and drainage constraint curve, and converts the complex tunnel hydrodynamic process into quantitative indicators reflecting the system water supply and demand game.Based on these time series indicators, the present application accurately analyzes and divides the tunnel waterlogging into three different driving modes of drainage constraint dominant type, inflow impact dominant type and composite disaster type.

Description

Technical Field

[0001] This invention relates to a disaster prediction and diagnosis method, and in particular to a rapid prediction and diagnosis method for urban tunnel flooding disasters based on numerical simulation. Background Technology

[0002] Urban underpasses, as a crucial component of modern urban transportation networks, often serve as critical transportation hubs. However, because underpasses are typically located in low-lying areas and directly connected to urban roads, they have large catchment areas and steep slopes. During extreme rainfall, rainwater flowing from the ground quickly converges at the bottom of the tunnel. The development of water accumulation within the tunnel is closely related to the inflow rate at the entrance, the water diffusion process inside the tunnel, and the drainage capacity of the pumping station. Once the water accumulation rate exceeds the pumping capacity, severe flooding can easily occur, potentially leading to major safety accidents such as vehicle submersion and personal injury. Therefore, accurately assessing the development trend of tunnel water accumulation to provide a scientific basis for management departments to make prudent decisions regarding the timing of tunnel closures and shutdowns is a critical challenge in urban flood control efforts.

[0003] Numerical simulation technology can extrapolate the spatiotemporal evolution of water flow based on boundary conditions, making it an effective means to compensate for the lag in real-time monitoring. Among existing urban stormwater simulation models, the SWMM model focuses on one-dimensional pipe network calculations, making it difficult to directly reflect the depth and extent of surface inundation; the MIKE series models suffer from insufficient stability in multi-module coupled calculations. In contrast, the InfoWorks ICM model, with its stable one-dimensional pipe network and two-dimensional surface coupled calculation capabilities, is widely used in urban flooding simulations. In existing InfoWorks ICM-based simulations of water accumulation in urban underpasses, a one-dimensional-two-dimensional coupled modeling approach is typically used: the drainage pipe network is simulated in one dimension, while the tunnel interior and surrounding surface are divided into two-dimensional grids to simulate the surface runoff process. Although this coupled calculation method can reflect the two-dimensional diffusion pattern of water flow within the tunnel with good accuracy, its limitations lie in the fact that the two-dimensional hydrodynamic calculations inside the tunnel require extremely high accuracy of topographic data and fine mesh division, resulting in a huge overall computational load and long calculation time. In emergency command scenarios facing sudden heavy rainfall, this time-consuming simulation method is difficult to quickly output prediction results within the rainfall window, and cannot meet the real-time decision-making needs of emergency response. Summary of the Invention

[0004] The technical problem this invention aims to solve is to address the shortcomings of existing technologies by providing a rapid prediction and diagnosis method for urban tunnel flooding disasters based on numerical simulation. This rapid prediction and diagnosis method simplifies the complex tunnel space into a one-dimensional pipe network system for modeling. While preserving key hydraulic conduction characteristics, this method significantly reduces the computational load on the model, thereby achieving rapid and advanced prediction of urban underpass flooding risks. It can support rapid prediction of tunnel disaster states during rainfall and help managers determine tunnel flooding disaster patterns.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0006] A rapid prediction and diagnosis method for urban tunnel flooding disasters based on numerical simulation includes the following:

[0007] Collect and organize the data required for simulating flooding in urban underpasses;

[0008] Based on the collected data, a generalized one-dimensional model of urban underpass tunnels was established in numerical simulation software. The urban underpass tunnel generalized one-dimensional model was used to simulate waterlogging disasters and obtain the simulation results of tunnel waterlogging.

[0009] Based on the simulation results of tunnel flooding, the risk of flooding disaster is predicted, and the time-varying physical quantities of the tunnel water accumulation process and drainage process are extracted. Combined with the time-varying physical quantities of the inflow process, the flooding disaster mode is diagnosed.

[0010] Furthermore, the data required for simulating urban underpass flooding includes tunnel geometric features, road surface elevation data, distribution data of drainage facilities, characteristic parameters of drainage facilities, and water level of drainage bodies.

[0011] Furthermore, the process of establishing the generalized one-dimensional model of the urban underpass is as follows:

[0012] In numerical simulation software, geometric feature points on the tunnel's central axis are extracted from the tunnel's geometric feature data and used as nodes. Based on the elevation information corresponding to the nodes and the shape of the tunnel's end face, an equivalent pipeline network for the tunnel foundation is established.

[0013] The topology of the equivalent pipeline network of the tunnel foundation is adjusted, and the side ditch and the adjusted equivalent pipeline network of the tunnel foundation are generalized into a one-dimensional pipeline network.

[0014] Based on the water exchange relationship between the tunnel surface and the side ditch, the elevation of the side ditch in the one-dimensional pipe network is adjusted and water exchange nodes are set.

[0015] Set the Manning coefficient and the blockage coefficient in the one-dimensional pipe network;

[0016] Based on the characteristic parameters of the drainage facilities, head-flow curves for the intercepting ditch and the drainage pumping station are established respectively. Based on the head-flow curves of the intercepting ditch and the drainage pumping station and the distribution data of the drainage facilities, the collection well and pipeline are modeled to complete the establishment of the generalized one-dimensional model of the urban underpass.

[0017] Furthermore, the specific details of the urban flooding disaster simulation are as follows:

[0018] To set the catchment boundary conditions, specific data of inflow time-series flow rate are input at the inlet node of the generalized one-dimensional model of the urban underpass. To set the drainage boundary conditions, specific data of tunnel drainage water level are input at the outlet node of the generalized one-dimensional model of the urban underpass. To set the water accumulation conditions, water accumulation conditions are set by inputting water depth data at the lowest node of the generalized one-dimensional model of the urban underpass. Based on the set catchment boundary conditions, drainage boundary conditions, and water accumulation conditions, the water flow diffusion in the tunnel is simulated in numerical simulation software using the generalized one-dimensional model of the urban underpass, thereby simulating urban flooding disaster and obtaining the tunnel flooding simulation results.

[0019] Furthermore, the inflow process line and the outflow process line are obtained in the following ways:

[0020] Predictions are obtained by inputting rainfall forecast data from the area above the tunnel into a deep learning network.

[0021] and / or

[0022] This information is obtained through real-time monitoring of the water inflow and drainage level of the water bodies where the drainage outlets of urban underpasses are located.

[0023] The water depth data was obtained through a sensor at the lowest point of the tunnel.

[0024] Furthermore, the specific details of the urban flooding disaster risk prediction are as follows:

[0025] Extract the maximum water accumulation peak during the simulation period from the tunnel flooding simulation results. and the corresponding peak water accumulation time ; will the Compared with the preset disaster threshold Perform a comparison: if the stated If the above is true, then it is predicted that there will be no flooding; if the above is true... If so, then it is predicted that an urban flooding disaster will occur.

[0026] Furthermore, the specific details of diagnosing the flooding disaster patterns are as follows:

[0027] Based on the characteristic parameters of the drainage facilities, the design limits of the tunnel drainage system are obtained. ;

[0028] Extract the time-varying inflow rate during the simulation period from the tunnel flooding simulation results. and time-varying drainage flow ;

[0029] The peak impact of the urban flooding disaster was calculated. and overload duration ,

[0030] ;

[0031] ;

[0032] ;

[0033] The average drainage load of the tunnel under waterlogging disaster is calculated using the following formula. and high load duration ,

[0034] ;

[0035] ;

[0036] ;

[0037] When the And the At that time, the diagnosis of urban flooding disaster pattern was that drainage constraints were the dominant factor;

[0038] When the , And the , At that time, the diagnosis of urban flooding disaster pattern was that it was dominated by inflow impact.

[0039] When the , And the The above At that time, the diagnosis of the urban flooding disaster pattern was a complex disaster type.

[0040] The present invention has the following beneficial effects: First, in terms of engineering applicability and superiority, the present invention abstracts and generalizes the complex tunnel space into a one-dimensional open channel network system for modeling. While fully reflecting the water exchange modeling of the drainage system in the tunnel, it also takes into account the numerical calculation speed, enabling rapid and advanced simulation of the diffusion of floodwater in the tunnel. It can support management departments to quickly predict the risk of flooding in the tunnel and reserve sufficient time window for emergency response.

[0041] Secondly, in terms of theoretical innovation, this invention breaks through the limitations of traditional flood warning systems, which can only output the superficial phenomenon of water depth. It pioneers a flood etiology diagnosis system based on hydrodynamic evolution mechanisms. Existing monitoring or numerical simulation technologies mostly only provide results on whether water accumulation has occurred and its specific depth, failing to reveal the fundamental physical causes of disasters. This invention creatively constructs dimensionless inflow impact curves and drainage constraint curves, transforming the complex tunnel hydrodynamic process into quantitative indicators reflecting the supply and demand dynamics of the system's water. Based on these time-series indicators, this invention accurately analyzes and classifies tunnel flooding into three different driving modes: drainage constraint-dominated, inflow impact-dominated, and combined disaster-causing. This classification method scientifically reveals the disaster-causing mechanism under different rainfall characteristics, enabling the model to possess in-depth etiology diagnosis capabilities. Furthermore, this dimensionless assessment system is not limited by specific tunnel geometry, exhibiting strong universality and a high degree of theoretical innovation.

[0042] Overall, this invention provides a scientific and practical basis for differentiated and refined emergency rescue decision-making for urban underpass tunnels. It effectively establishes a closed-loop decision-making process encompassing risk prediction, causal diagnosis, and precise intervention, directly guiding flood control and emergency response departments to shift from passive response to precise policy implementation. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the rapid prediction and diagnosis method in an embodiment of the present invention.

[0044] Figure 2 This is a schematic diagram of the modeling of a generalized one-dimensional tunnel underpass in an embodiment of the present invention.

[0045] Figure 3 This is a time-series diagram of the water ingress-drainage-water accumulation response in the tunnel using a generalized one-dimensional model of the urban underpass tunnel in this embodiment of the invention. Detailed Implementation

[0046] The present invention will now be described in further detail with reference to the accompanying drawings and specific preferred embodiments.

[0047] In the description of this invention, it should be understood that the terms "left side," "right side," "upper part," "lower part," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. "First," "second," etc., do not indicate the importance of the components, and therefore should not be construed as a limitation of this invention. The specific dimensions used in this embodiment are only for illustrating the technical solution and do not limit the scope of protection of this invention.

[0048] like Figure 1As shown, a rapid prediction and diagnosis method for urban tunnel flooding disasters based on numerical simulation includes the following:

[0049] Data required for simulating urban underpass flooding was collected and organized. This data includes tunnel geometric features, road surface elevation data, drainage facility distribution data, drainage facility characteristic parameters, and drainage water levels. In this embodiment, point cloud data of the urban tunnel and its pumping stations were obtained through on-site surveying. Three-dimensional models of the tunnel and pumping stations were created. The central axis of the tunnel road surface model was extracted from the GIS. Inflection points were rationally arranged according to the tunnel road surface curvature and elevation changes, and inflection points were added at each cross-ditch location. Road surface elevation data and location information were saved to the inflection point attributes. New pumping station ground points were created, and pumping station surface elevation data and location information were stored to prepare data for subsequent numerical modeling. Data was also collected through methods such as consulting drawings in archives and conducting on-site surveys. The geometric features of the storm drain grate in the transverse ditch are obtained to determine the geometric dimensions and bottom elevation information of the collection well under the grate, the pipes connecting the transverse ditch and the pump room, and the collection wells of the storm water pump room and the wastewater pump room. It should be noted that, considering that the highest point of the water-retaining hump at the tunnel entrance and exit constitutes a natural surface runoff watershed, the above-mentioned 3D tunnel modeling and subsequent generalized modeling both take the highest point of the water-retaining hump as the outer boundary. The modeling range includes the open section and buried section of the tunnel within the highest point, and also includes the storm water pump room and wastewater pump room inside it.

[0050] Based on the collected data, a generalized one-dimensional model of urban underpass tunnels was established in numerical simulation software. The specific details are as follows:

[0051] In numerical simulation software, geometric feature points on the tunnel's central axis are extracted from the tunnel's geometric feature data and used as nodes. Based on the elevation information corresponding to the nodes and the tunnel's end face shape, an equivalent pipe network for the tunnel foundation is established. The pipe network topology of the equivalent pipe network is adjusted, and the side ditches are generalized together with the adjusted equivalent pipe network into a one-dimensional pipe network. Based on the water exchange relationship between the tunnel pavement and the side ditches, the elevation of the side ditches in the one-dimensional pipe network is adjusted, and water exchange nodes are set. The Manning coefficient and blockage coefficient in the one-dimensional pipe network are set. Based on the characteristic parameters of the drainage facilities, head-flow curves for the cross-ditch and the drainage pumping station are established respectively. Based on the head-flow curves of the cross-ditch and the drainage pumping station, combined with the distribution data of the drainage facilities, the collection wells and pipelines are modeled to complete the establishment of a generalized one-dimensional model of the urban underpass tunnel. In this embodiment, in the numerical model (the present invention preferably uses InfoWorks), The ICM model simplifies the complex tunnel space by abstracting and constructing a generalized one-dimensional model of the underpass tunnel. Specifically: the tunnel centerline inflection points extracted in step 1 are imported as model nodes, and the connections between nodes form an equivalent pipe network; to realistically reflect the gravity overflow process of water flow on the tunnel's longitudinal slope, the pipe type of all road surface pipe networks is set to "Channel," and the system type of nodes and pipes is set to "Overland." Linear interpolation is performed using the model's inference tools combined with inflection point elevation data to assign accurate pipe bottom elevations to each node and pipe network; the flood type of the nodes corresponding to the cross-cutting ditch is defined as "Gully / Inlet" to simulate the cross-cutting ditch's water collection function. Based on the obtained geometric features of the rainwater grates and cross-cutting ditches, a corresponding "head-flow relationship table" is constructed and assigned to... This node is used to accurately simulate the hydraulic diversion and overflow process of surface runoff falling through rainwater grates into the underground water collection system; the pump station system is abstracted as a topology structure of "collection well node - pump connector - pressure main pipe"2; specifically, the collection well is generalized as a "storage tank node" (Storage), and the accurate bottom elevation of the well is set according to the obtained geometric dimensions and its corresponding "elevation-capacity data sequence" is input; the pump is generalized as a pump connector, and the corresponding "head-flow relationship table" is created and input according to the pump operating parameters to set the pumping capacity, while the pump opening and closing water levels are set according to the actual operation of the tunnel pump station; finally, the solution model of the drainage main pipe is set as "pressure main pipe" (ForceMain) to simulate the full-pipe pressure flow state when the pump is pumping, and the pipe network head loss type is set to Fix. Between the inspection well nodes of the cross-cutting ditch and the water collection well nodes of the pump house, a "storm pipe" is used for topology connection, and the geometric properties are filled in according to the actual pipe dimensions to complete the modeling, such as... Figure 2 As shown.

[0052] A generalized one-dimensional model of an urban underpass tunnel was used to simulate urban flooding disasters. The simulation results are as follows:

[0053] To set the catchment boundary conditions, specific inflow time-series flow data are input at the inlet node of the generalized one-dimensional model of the urban underpass. At the outlet node, specific drainage water level data is input to set the drainage boundary conditions. At the lowest node, water depth data is input to set the water accumulation conditions. Based on these catchment, drainage, and water accumulation conditions, the water flow diffusion within the tunnel is simulated using numerical simulation software, thereby simulating urban flooding and obtaining simulation results. Rainfall forecast data from the area above the tunnel is input into a deep learning network for prediction and / or real-time monitoring of the inflow rate and drainage water level of the urban underpass. Water depth data is collected by sensors at the lowest point of the tunnel.

[0054] Based on the simulation results of tunnel waterlogging, the risk of waterlogging disaster is predicted, and the time-varying physical quantities of the tunnel water accumulation process and drainage process are extracted. Combined with the time-varying physical quantities of the inflow process, the waterlogging disaster-causing pattern is diagnosed.

[0055] The specific details of this embodiment are as follows:

[0056] Using the software's data extraction function, the time-varying total inflow rate is extracted from the inlet node defined in the model. The actual total drainage volume is extracted from the outflow node of the pump house sump. The design limit of the system's drainage capacity is obtained by combining the design parameters of the tunnel's drainage facilities. ;Calculate the time-varying water accumulation in the tunnel based on the water balance equation. Its mathematical expression is: .

[0057] Based on the calculation results of physical quantities, the maximum peak water level is extracted and compared with a preset disaster-causing critical threshold to determine the disaster status, and the flooding process is divided into a water accumulation development stage and a water drainage stage. Specifically: the maximum peak water level during the simulation period is extracted. and the corresponding peak water accumulation time ,like Figure 3 As shown. Compared with the preset disaster threshold In comparison, this embodiment uses the impact on tunnel driving safety as the disaster-causing condition, corresponding to a tunnel water depth of 15cm at the lowest point. When a vehicle is submerged in water exceeding 15cm, it may stall. Furthermore, the critical threshold can be determined based on the height of the electrical cabinet or other custom water depths. ; If so, it is determined that there will be no flooding; if If the water level reaches its peak, it is determined that an urban flooding disaster will occur; based on the starting point of water receding (i.e., the time when the water level reaches its peak). The flooding process is divided into two stages: [0, This is the stage of water accumulation development. The water accumulation and drainage stage is the stage where the disaster development process is mainly constrained by the tunnel's drainage capacity. There are multiple possible causes of disaster during the water accumulation and drainage stage, and it is necessary to determine the cause of disaster based on the dynamic process of water inflow, water accumulation, and drainage.

[0058] For each stage of water accumulation, dimensionless inflow impact curves and drainage constraint curves are established using extracted time-varying physical quantities, such as... Figure 3 As shown, the corresponding inflow impact indices (such as peak impact and overload duration) and drainage constraint indices (such as average drainage load and high load duration) are extracted. Specifically: The inflow impact curve is established and the inflow impact indices are extracted. First, the inflow impact curve is defined. This curve essentially reflects the transient impact load on the tunnel drainage system caused by external rainfall runoff. It's important to note that under extreme rainfall conditions, the inflow rate of surface runoff can be far greater than the maximum pumping capacity of the pumping station. Therefore, the vertical axis of this curve can exceed 1.0. Based on this, two inflow impact indicators are extracted: peak impact value... Its physical meaning is the maximum inflow rate, that is Overload duration Its physical meaning is when the inflow exceeds the system's drainage design limit for an extended period, i.e. Then, drainage constraint curves are established and drainage constraint indices are extracted to define the drainage constraint curves. This curve represents the working load rate of the tunnel's own drainage and intake facilities. Limited by the pump power, the capacity of the drainage and intake wells, and pipeline conditions, its theoretical maximum value can only approach 1.0 (i.e., 100% full load and full power operation). Based on this, two drainage constraint indicators are extracted: average drainage load... Its physical meaning is the average load of the drainage system during the water accumulation development stage, that is... High load duration Its physical meaning is the time when the drainage facilities are operating at more than 90% capacity during the water accumulation stage. .

[0059] Based on the extracted indicators, multi-dimensional threshold condition judgments are performed to identify the fundamental physical driving mechanism leading to excessive water accumulation in tunnels. The flooding disaster pattern is quantitatively diagnosed as drainage-constrained, inflow-impact-driven, or a combination of both, thus providing precise guidance for emergency rescue decision-making during disasters. Specifically: when the following conditions are met... and At that time, the diagnosis was that drainage constraints were the main cause of the flooding, indicating that the flooding was mainly due to the pumping stations operating at high or even full capacity for extended periods, resulting in a bottleneck in drainage capacity. This is more common during prolonged rainfall. Emergency management personnel should dispatch mobile pumping trucks to the pumping stations in advance to support capacity expansion. , and , At that time, the diagnosis was that the disaster was mainly caused by inflow impact, indicating that the pumping station had not yet utilized its full drainage capacity. However, the surface runoff generated by the short-term heavy rainfall surged into the tunnel at an extremely high flow rate, exceeding the safety threshold. This is common in sudden, short-term heavy rainstorms. After rainfall, emergency personnel should intercept vehicles and urgently deploy flood barriers to block the inflow before the water volume reaches the critical threshold. When the conditions are met... , and and The diagnosis was a complex disaster type, where there was an inflow impact exceeding the limit and the pumping station drainage system had reached the limit of full-load operation. The highest level of response needed to be activated, and simultaneous intervention of two-end sealing and forced drainage was implemented.

[0060] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all fall within the protection scope of the present invention.

Claims

1. A rapid prediction and diagnosis method for urban tunnel flooding disasters based on numerical simulation, characterized in that: Includes the following: Collect and organize the data required for simulating flooding in urban underpasses; Based on the collected data, a generalized one-dimensional model of urban underpass tunnels was established in numerical simulation software. The urban underpass tunnel generalized one-dimensional model was used to simulate waterlogging disasters and obtain the simulation results of tunnel waterlogging. Based on the simulation results of tunnel flooding, the risk of flooding disaster is predicted, and the time-varying physical quantities of the tunnel water accumulation process and drainage process are extracted. Combined with the time-varying physical quantities of the inflow process, the flooding disaster mode is diagnosed.

2. The rapid prediction and diagnosis method for urban tunnel flooding disaster based on numerical simulation according to claim 1, characterized in that: The data required for simulating urban underpass flooding includes the tunnel's geometric features, road surface elevation, distribution of drainage facilities, characteristic parameters of drainage facilities, and water level of the drainage body.

3. The rapid prediction and diagnosis method for urban tunnel flooding disaster based on numerical simulation according to claim 2, characterized in that: The process of establishing the generalized one-dimensional model of the urban underpass tunnel is as follows: In numerical simulation software, geometric feature points on the tunnel's central axis are extracted from the tunnel's geometric feature data and used as nodes. Based on the elevation information corresponding to the nodes and the cross-sectional shape of the tunnel, an equivalent pipeline network for the tunnel foundation is established. The topology of the equivalent pipeline network of the tunnel foundation is adjusted, and the side ditch and the adjusted equivalent pipeline network of the tunnel foundation are generalized into a one-dimensional pipeline network. Based on the water exchange relationship between the tunnel surface and the side ditch, the elevation of the side ditch in the one-dimensional pipe network is adjusted and water exchange nodes are set. Set the Manning coefficient and the blockage coefficient in the one-dimensional pipe network; Based on the characteristic parameters of the drainage facilities, head-flow curves for the intercepting ditch and the drainage pumping station are established respectively. Based on the head-flow curves of the intercepting ditch and the drainage pumping station and the distribution data of the drainage facilities, the collection well and pipeline are modeled to complete the establishment of the generalized one-dimensional model of the urban underpass.

4. The rapid prediction and diagnosis method for urban tunnel flooding disaster based on numerical simulation according to any one of claims 1-3, characterized in that: The specific details of predicting the risk of urban flooding disasters are as follows: Extract the maximum water accumulation peak during the simulation period from the tunnel flooding simulation results. and the corresponding peak water accumulation time ; will the Compared with the preset disaster threshold Perform a comparison: if the stated If so, it is predicted that there will be no flooding; if the aforementioned If so, then it is predicted that an urban flooding disaster will occur.

5. The rapid prediction and diagnosis method for urban tunnel flooding disaster based on numerical simulation according to any one of claims 2-3, characterized in that: The specific details of diagnosing the flooding disaster pattern are as follows: Based on the characteristic parameters of the drainage facilities, the design limits of the tunnel drainage system are obtained. ; Extract the time-varying inflow rate during the simulation period from the tunnel flooding simulation results. and time-varying drainage flow ; The peak impact of the urban flooding disaster was calculated. and overload duration , The average drainage load of the tunnel under waterlogging disaster was calculated. and high load duration , When the And the At that time, the diagnosis of urban flooding disaster pattern was that drainage constraints were the dominant factor; When the , And the , At that time, the diagnosis of urban flooding disaster pattern was that it was dominated by inflow impact. When the , And the The above At that time, the diagnosis of the urban flooding disaster pattern was a complex disaster type.

6. The rapid prediction and diagnosis method for urban tunnel flooding disaster based on numerical simulation as described in claim 5, characterized in that: The peak impact and overload duration The calculation formula is as follows: ； ； 。 7. The rapid prediction and diagnosis method for urban tunnel flooding disaster based on numerical simulation as described in claim 5, characterized in that: The average drainage load and high load duration The calculation formula is as follows: ； ； 。 8. The rapid prediction and diagnosis method for urban tunnel flooding disaster based on numerical simulation according to any one of claims 1-3, characterized in that: The specific details of the urban flooding disaster simulation are as follows: To set the catchment boundary conditions, specific data of inflow time-series flow rate are input at the inlet node of the generalized one-dimensional model of the urban underpass. To set the drainage boundary conditions, specific data of tunnel drainage water level are input at the outlet node of the generalized one-dimensional model of the urban underpass. To set the water accumulation conditions, water accumulation conditions are set by inputting water depth data at the lowest node of the generalized one-dimensional model of the urban underpass. Based on the set catchment boundary conditions, drainage boundary conditions, and water accumulation conditions, the water flow diffusion in the tunnel is simulated in numerical simulation software using the generalized one-dimensional model of the urban underpass, thereby simulating urban flooding disaster and obtaining the tunnel flooding simulation results.

9. The rapid prediction and diagnosis method for urban tunnel flooding disaster based on numerical simulation according to claim 8, characterized in that: The inflow time-series flow rate and the water level of the tunnel drainage body are obtained in the following ways: Predictions are obtained by inputting rainfall forecast data from the area above the tunnel into a deep learning network. and / or This information is obtained through real-time monitoring of the water inflow and drainage level of the water bodies where the drainage outlets of urban underpasses are located. The water depth data was obtained through a sensor at the lowest point of the tunnel.

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