Multi-dimensional situation deduction method for flood disaster prevention in closed or semi-closed space

Abstract

The invention discloses a multi-dimensional situation deduction method for flood disaster prevention in a closed or semi-closed space. The method comprises the following steps: S1, collecting and acquiring geographical and building data and hydrological and meteorological data related to a specified underground space; s2, creating a 3D digital model of the specified underground space according to geographical and building data; s3, scene simulation is carried out in the specified 3D digital model according to the hydrological and meteorological data, a fluid dynamic model is established, the mutual influence relation between the specified underground space and the flood factors is analyzed according to the fluid dynamic model, and the associated characteristics of flood disaster occurrence of the specified underground space in the time sequence are calculated according to the mutual influence relation. And different prevention and treatment schemes are implemented according to the associated characteristics. According to the invention, multi-dimensional simulation and analysis are carried out according to different rainfall scenes, flood risks under different conditions can be evaluated, high-risk areas can be identified more accurately, and specific prevention and control schemes are provided according to different scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of flood control and disaster prevention, and particularly relates to a multi-dimensional situation deduction method for flood control and disaster prevention in enclosed or semi-enclosed spaces. Background Art

[0002] The three-dimensional, diversified and multi-dimensional construction of urban spaces has led to the emergence of a large number of enclosed or semi-enclosed spaces. In particular, the available spaces located below the ground and surface water can often become the "second-degree spaces" of the city, such as: underground rail transit, underground garages and civil air defense projects, underground commercial complexes, underground utility tunnels, underpass flyovers, tunnels, passageways, sunken plazas, etc.

[0003] With the development and expansion of China's cities, compared with the traditional independently constructed underground spaces, urban planning and construction place more emphasis on the interconnection of underground spaces, encourage the interconnection between rail transit stations and adjacent underground spaces, and create an underground space complex with contiguous networking and systematic development. However, with the connection and expansion of underground facilities, the distribution of urban underground spaces presents complexity, and the difficulty of disaster response has increased greatly.

[0004] Since the underground space is much more restricted by the environment than the ground space, with a closed or semi-closed spatial form, it is in an unfavorable position for defense. When encountering flood disasters such as waterlogging, engineering accidents, and water disasters caused by geological disasters, once the response is not timely or the countermeasures are inappropriate, it is extremely easy to cause huge disaster losses and casualties; in the past urban flood simulations, the modeling work for the process of underground space water inflow simulation was complex and required a large amount of data; the weir flow formula was mostly used to calculate the water volume entering the underground space, which described the flood process relatively simply; it was only applicable to fine-scale simulations in a small area and was difficult for large-scale modeling and simulation at the urban scale.

[0005] For example, the existing patent application number: CN202310124926.0 discloses a rapid simulation method for underground space storage and regulation during urban extreme floods, which relates to the technical field of flood control and disaster reduction science and technology, including: S1. Collect the building distribution of the city and set characteristic elevation values based on the building distribution of the city; S2. Identify the buildings according to the characteristic elevation values to obtain the size of the underground space of the buildings as the water storage capacity of the buildings; S3. Based on the water storage capacity of the buildings, use the Saint-Venant equations to calculate the water inflow volume of the flood into the underground space of the buildings; S4. Based on the water inflow volume, calculate the urban underground space storage and regulation model through the shallow water equations, and use this model to complete the rapid simulation of underground space storage and regulation during urban extreme floods; this method still has certain limitations in some aspects:

[0006] 1. Dependence on the accurate acquisition of the distribution of urban buildings. If the data is inaccurate or incomplete, it may lead to inaccurate calculation of the water storage capacity and water inflow in the underground space.

[0007] 2. Using the characteristics of the ideal river channel model for simplified calculation may not fully reflect the water flow characteristics in actual situations, especially in the complex and changeable urban environment.

[0008] 3. The differences in the underground space characteristics (such as uses, structures, ventilation, etc.) of different buildings may not be fully considered in the model, resulting in deviations in the simulation results.

[0009] 4. Just analyzing the water inflow cannot accurately issue disaster early warnings before disasters occur.

[0010] Therefore, this application specifically proposes a multi-dimensional situation deduction method for flood and waterlogging disasters in enclosed or semi-enclosed spaces to solve the above technical problems. Summary of the Invention

[0011] The main purpose of the present invention is to provide a multi-dimensional situation deduction method for flood and waterlogging disasters in enclosed or semi-enclosed spaces to solve the technical problems proposed in the background art.

[0012] The present invention adopts the following technical solutions to solve the above technical problems:

[0013] A multi-dimensional situation deduction method for flood and waterlogging disasters in enclosed or semi-enclosed spaces, comprising the following steps:

[0014] S1. Collect and obtain geographical and building data, hydrological and meteorological data related to the specified underground space.

[0015] S2. Create a 3D digital model of the specified underground space according to the geographical and building data.

[0016] S3. Conduct scenario simulations in the specified 3D digital model according to the hydrological and meteorological data, establish a hydrodynamic model, analyze the mutual influence relationship between the specified underground space and flood factors according to the hydrodynamic model, calculate the correlation characteristics of flood disasters occurring in the specified underground space in time series according to the mutual influence relationship, and implement different prevention and control schemes according to the correlation characteristics.

[0017] S4. According to the simulation results in step S3, obtain the areas prone to flood, generate a risk map in combination with the risk assessment under different rainfall conditions, and through the analysis of the flood factors inside the underground space, combined with geological conditions, building materials and seepage conditions, and plus the corresponding time series analysis, accurately evaluate the flood risk faced by the underground space.

[0018] Preferably, the geographical and architectural data in the step S1 further includes the characteristics affecting flood disasters within the surrounding area of the specified space, which is used to designate the special zone through the digital elevation model and integrate it into the 3D digital model constructed in the step S2;

[0019] The digital elevation model includes:

[0020] U i =(x i ,y i ,z i )

[0021] where i = 1, 2, 3,..., n, in the formula, x i and y i are plane coordinates, and z i is the corresponding elevation value.

[0022] Preferably, the 3D digital model in the step S2 includes surface elevation data including terrain undulation and drainage slopes, the position and elevation data of the connection ports, the geometric characteristics of the connection ports, the underground space layout and dimensions.

[0023] Preferably, the flood factors in the step S3 include: external flood factors including surface and underground connection ports and underground space structures, and internal flood factors including underground space structures.

[0024] Preferably, the specific operation process of the step S3 includes:

[0025] S31. Collect and update historical and real-time rainfall data according to the preset time, and conduct a preliminary precipitation assessment to capture the rainfall pattern in the short term;

[0026] S32. Use a hydrological model to simulate the rainfall into the ground, and based on the simulation results, determine the water level rise of the connection ports under different rainfall conditions. According to the rainfall amount and the change of the connection port water level, generate a flood risk time series graph, mark the risk changes under different rainfall conditions, and establish a risk assessment matrix to evaluate the probability of flood occurrence under specific rainfall amounts and connection port elevations;

[0027] S33. Evolve through internal flood factors to construct a multi-factor model including time series analysis, which is used to input specified variables to evaluate the time of flood occurrence.

[0028] Preferably, the specific assessment process for the preliminary precipitation assessment in the step S31 includes:

[0029] According to the historical and real-time rainfall data, the data needs to be updated hourly or minute by minute to capture the rainfall pattern in the short term;

[0030] Conduct a time series analysis of rainfall to determine the water level changes at the underground connection ports under different rainfall intensities and durations;

[0031] Use the precipitation within a unit time and combine it with a 3D digital model to calculate the time when water enters the connection port;

[0032] Set an inlet threshold, that is, when the water level at the connection port reaches a specified height, it is considered that a flood has occurred, and a precipitation assessment is carried out. The assessment formula is:

[0033]

[0034] where H t is the water level height at the connection port when a flood occurs, H0 is the initial water level height, and R is the precipitation per unit time.

[0035] Preferably, the flood occurrence probability assessment process in step S32 specifically includes:

[0036] Use classification algorithms such as Logistic regression or random forest, set a binary classification problem where the occurrence of a flood disaster is 1 and the non-occurrence is 0, and evaluate the possibility of a flood disaster occurring with model independent variables including precipitation, drainage volume, and terrain slope. There is:

[0037]

[0038] where P(H) is the possibility of a flood disaster occurring, e is a constant, β0, β1, β2, β3 are the influence coefficients of different model parameters, R is the precipitation, B is the terrain slope, and A is the drainage volume.

[0039] Preferably, by constructing a multi-factor model including time series analysis, the possibility of a flood disaster occurring at the underground space and the external connection ports can be evaluated more systematically and accurately. At the same time, the time and location of the disaster occurring at each connection port can be accurately obtained, and the connection ports can be strengthened or waterproof measures can be implemented through prevention and control; it can effectively prevent flood disasters from occurring in the underground space.

[0040] Preferably, the specific operation process of step S33 includes:

[0041] Establish a seepage dynamic model to analyze how water is distributed and accumulated in the underground space after rainfall. The model formula is:

[0042]

[0043] where Q is the seepage water volume, k is the permeability coefficient of the soil, s is the seepage area, Δh is the water level difference at both ends of the soil layer, and l is the length of the water flow path;

[0044] By simulating the water seepage situation under different rainfall scenarios, a model is established to predict that if water seepage and flooding occur in the underground space within a specified time after rainfall, there are:

[0045]

[0046] Among them, T is the predicted water seepage time, t is the starting water inlet time, Q is the seepage water volume, and c is the drainage capacity.

[0047] On the other hand, the present invention also discloses a computer-readable storage medium storing a computer program, which when executed by a processor causes the processor to execute the steps of the above method.

[0048] On yet another aspect, the present invention also discloses a computer device including a memory and a processor, the memory storing a computer program, which when executed by the processor causes the processor to execute the steps of the above method.

[0049] From the above technical solutions, the present invention provides a multi-dimensional situation deduction method for flood and waterlogging disasters in a closed or semi-closed space. Compared with the prior art, the present invention has the following advantages:

[0050] 1. By combining building information modeling and computational fluid dynamics simulation, the present invention can more accurately reflect the structure of the subway station and its behavior under flood conditions. At this time, through multi-dimensional simulation and analysis according to different rainfall scenarios, the flood risk in different situations can be evaluated.

[0051] 2. By adopting a multi-factor model combined with a machine learning algorithm to analyze the probability and possible time of flood and waterlogging disasters, the risk of different regions can be accurately evaluated. Compared with the prior art, the present invention can more accurately identify high-risk regions and provide specific prevention and control solutions according to different scenarios.

[0052] 3. Based on rainfall intensity and underground water level, etc., the present invention predicts the probability of flood occurrence and water level change, making the emergency response more scientific and accurate. Therefore, by combining real-time data monitoring and a dynamic response mechanism, the risk can be predicted in time and countermeasures can be taken before the flood and waterlogging disaster occurs. Moreover, by collecting real-time meteorological, hydrological and underground water level data, the system can dynamically adjust the prediction model to provide a more accurate risk assessment and early warning.

[0053] 4. The present invention not only considers the internal structure and environmental factors of the underground space, but also systematically analyzes the influence of external flood factors. Through the coupled analysis of internal and external factors, a comprehensive flood prevention solution can be provided, rather than simply dealing with specific factors.

[0054] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Of course, any product implementing the present invention does not necessarily need to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings forming a part of this application are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0056] Figure 1 is a schematic diagram of the overall process of the present invention;

[0057] Figure 2 in which (a) is a schematic diagram of the interrelationship (coupling) between the internal and external flood and waterlogging environments in the underground space of the present invention;

[0058] Figure 2 in which (b) is a schematic diagram of the interrelationship (coupling) between the flood and waterlogging yellow wine subsystems of the underground space body of the present invention;

[0059] Figure 3 is a schematic diagram of the spatial data processing flow structure of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0061] In the embodiments, refer in detail to Figures 1 to 3 .

[0062] Since most of the enclosed or semi-enclosed spaces for conventional flood and waterlogging prevention and control are underground spaces, the multi-dimensional situation deduction methods for flood and waterlogging prevention and control in enclosed or semi-enclosed spaces proposed in the embodiments of the present invention are all based on underground spaces for deduction operations.

[0063] As Figure 1 and Figure 2 shown: The multi-dimensional situation deduction method for flood and waterlogging prevention and control in digital underground spaces includes the following steps:

[0064] S1: Data collection, obtaining geographical and building data, hydrological and meteorological data related to the underground space;

[0065] S2: Digital modeling to create a 3D digital model of the underground space based on geographical and architectural data;

[0066] S3: Multidimensional situation simulation. Conduct scenario simulation in the 3D digital model according to hydrological and meteorological data, establish a hydrodynamic model, analyze the interaction relationship between the underground space and flood factors based on the hydrodynamic model, calculate the correlation characteristics of flood disasters occurring in the underground space over time according to the interaction relationship, and implement different prevention and control schemes according to the correlation characteristics;

[0067] S4. According to the simulation results in step S3, identify which areas are most prone to flooding, and generate a risk map by combining risk assessments under different rainfall conditions; through the analysis of flood factors inside the underground space, combined with geological conditions, building materials, and seepage conditions, plus corresponding time series analysis, the flood risk faced by the underground space can be accurately evaluated.

[0068] As Figure 2 shown: The urban underground space flood disaster prevention and control system is affected by objective factors such as the uncertainty of the external environment, the uncertainty of underground space flooding, the uncertainty of the underground space water flow path, and the uncertainty of human activities in the adjacent environment.

[0069] Therefore, the flood disaster prevention and control system is not only reflected in the coupling response relationship between the internal and external environments of the underground space, but there is also an interaction relationship between the response states of each subsystem within the system; external environmental factors determine the rationality of the underground space location and the safety of disaster prevention. Taking flood disasters as an example, the elements of disaster occurrence are closely related to the regional natural factors and flood control and drainage capabilities in the external environment. The flood control and drainage capabilities and the status of related facilities in the area where the underground space is located determine the exposure and danger of the external environment. The complexity of the coupling of the underground space body flood environment subsystem and the flood prevention status determine the vulnerability of bearing disasters. Each subsystem is independent and closely related. Based on the principle of the structure characteristics of the disaster system, the relationships between the internal and external flood environments and the underground space body flood environment subsystem with the underground space as the object are shown in Figures (a) and (b) respectively. The key to preventing flood disasters in the underground space lies in decomposing the external flood environment and the underground space body flood environment subsystem.

[0070] It should be noted that in this embodiment, the obtained geographical and architectural data include the geographical and architectural data of the underground space itself and the geographical and architectural data of the surrounding area. The geographical and architectural data of the underground space itself can be obtained during construction and improved after construction, and it can form a 3D digital model; the geographical and architectural data of the surrounding area of the underground space include: characteristics such as the surrounding terrain, buildings, and municipal drainage networks that affect flood disasters, and these characteristics are specified for special areas through a digital elevation model and integrated into the 3D digital model of the underground space;

[0071] The digital elevation model includes:

[0072] U i =(x i ,y i ,z i )

[0073] where i = 1, 2, 3, …, n, and in the formula, x i and y i are planar coordinates, and z i is the corresponding elevation value;

[0074] After constructing the 3D digital model, analyze the flood factors in the underground space. The flood factors include: the surface - underground connection openings and the underground space structure. The surface - underground connection openings are external flood factors for water intake into the underground space, and they can be alleviated or solved through the flood - control system of the underground space; while the underground space structure can be understood as an internal flood factor of the underground space, specifically the occurrence of internal water seepage and other situations in the underground space, which requires considering the geological factors around the underground space.

[0075] In step S3, specifically, it can be realized that: evolve the scenarios of flood disasters occurring in the underground space according to the external and internal flood factors of the underground space;

[0076] Among them, the evolution according to the external flood factors includes:

[0077] In the 3D digital model, the surface elevation data includes terrain undulations, drainage slopes, etc., the positions and heights of the connection openings, clarify the geometric characteristics of each connection opening, especially the height relative to the surface, and the underground space layout includes the layout and dimensions of each pipeline, tunnel, space, etc.;

[0078] Collect historical and real-time rainfall data. The data needs to be updated hourly or minutely to capture short-term rainfall patterns. Through time series analysis of rainfall amounts, determine the water level changes at the underground connection ports under different rainfall intensities and durations. Using the precipitation amount per unit time and combining with the elevation of the connection ports in the 3D digital model, calculate when water will enter the connection ports. Set an inlet threshold, that is, when the water level of the connection port reaches a certain height, it is regarded as a flood occurrence. It can be evaluated through the following formula:

[0079]

[0080] Among them, H t is the water level height of the connection port when a flood occurs, H0 is the initial water level height, and R is the precipitation amount per unit time;

[0081] Use a hydrological model (such as HEC-HMS, SWMM, etc.) to conduct a simulation of rainfall into the ground. Based on the simulation results, determine the water level rise of the connection ports under different rainfall conditions (such as short-term heavy rainfall and continuous rainfall). Generate a time series graph of flood risk according to the rainfall amount and the water level change of the connection ports, mark the risk changes under different rainfall conditions, establish a risk assessment matrix, and evaluate the probability of flood occurrence and its possible time under specific rainfall amounts and connection port elevations;

[0082] Use classification algorithms such as Logistic regression or random forest to set a binary classification problem where the occurrence of a flood disaster is 1 and the non-occurrence is 0; the independent variables of the model include factors such as precipitation amount, drainage amount, and terrain slope, and there are:

[0083]

[0084] Among them, P(H) is the possibility of a flood disaster occurring, e is a constant, β0, β1, β2, β3 are the influence coefficients of different model parameters respectively, R is the precipitation amount, B is the terrain slope, and A is the drainage amount.

[0085] By constructing a multi-factor model including time series analysis, it is possible to more systematically and accurately evaluate the possibility of flood disasters occurring at the underground space and its external connection ports. At the same time, the time and location of disasters occurring at each connection port can be accurately obtained, and the connection ports can be strengthened or waterproof measures can be implemented through prevention and control; it can effectively prevent flood disasters from occurring in the underground space.

[0086] And at this time, by adopting a multi-factor model combined with machine learning algorithms to analyze the probability and possible time of flood disasters occurring, it is possible to accurately evaluate the risks in different regions. Compared with the existing technologies (such as the existing technical solutions mentioned in the background art), the present invention can more accurately identify high-risk regions and provide specific prevention and control solutions according to different scenarios.

[0087] In one embodiment of the present invention, the process of evolving according to internal flood factors includes:

[0088] Collect historical and real-time rainfall data, which needs to be updated hourly or minutely to capture short-term rainfall patterns. By performing time series analysis on rainfall amounts, determine the hydrogeological conditions of the geological soil around the underground space under different rainfall intensities and durations. For example, establish a water level monitoring system near the underground space to collect real-time data on changes in the groundwater level to facilitate analysis of the impact of rising water levels on the underground space; based on soil assessment, analyze the permeability of different soil types (such as sandy soil, clay, pebbles, etc.) to identify the sources of water seepage in the underground space: water seepage caused by rising groundwater levels; the saturation degree of the surrounding soil and its contact surface with the underground space; leakage caused by failures of underground pipelines or other facilities.

[0089] Among them, through specific geological analysis:

[0090] (a) Geological exploration: Conduct a detailed geological survey to obtain information on the underground soil layers, including the thickness of the geological layers, the physical properties of the soil, and the presence of faults or fissures, etc.;

[0091] (b) Hydrogeology: Analyze the hydrogeological conditions of groundwater flow, including flow direction, flow velocity, and characteristics of the aquifer, etc., to predict the movement trend of groundwater during heavy rainfall;

[0092] (c) Dynamic analysis of internal flood factors: Include combining factors such as rainfall amount, groundwater level, and soil moisture, establish a seepage dynamic model, and analyze how water is distributed and accumulated in the underground space after rainfall. The model formula is:

[0093]

[0094] Among them, Q is the seepage water volume, k is the soil permeability coefficient, s is the seepage area, Δh is the water level difference at both ends of the soil layer, and l is the length of the water flow path;

[0095] (d) By simulating the seepage situation under different rainfall scenarios, establish a model to predict possible water seepage and flooding in the underground space within a specified time after rainfall, with:

[0096]

[0097] Among them, T is the predicted seepage time, t is the start time of water inflow, Q is the seepage water volume, and c is the drainage capacity.

[0098] In summary, the solution of the present invention predicts the probability of flood occurrence and water level changes based on rainfall intensity, groundwater level, etc., making the emergency response more scientific and accurate. Therefore, by combining real-time data monitoring and dynamic response mechanisms, risks can be predicted in a timely manner and countermeasures can be taken before the occurrence of flood disasters. Moreover, by collecting meteorological, hydrological, and groundwater level data in real time, the system can dynamically adjust the prediction model to provide more accurate risk assessment and early warning.

[0099] Furthermore, in a specific embodiment of the present invention, when implementing the above-mentioned multi-dimensional situation deduction method for flood prevention and control in underground spaces based on digitization in a subway station, there are:

[0100] (1) Collection of underground space building and geographical data:

[0101] (1a) Building data:

[0102] Use laser scanning and photogrammetry techniques to obtain detailed data of the existing buildings and structural features (such as walls, floors, stairs, lift shafts, etc.) of the subway station; obtain the underground structure diagram, including the dimensions, positions, and depths of the main channels, entrances and exits, and various connected pipelines.

[0103] (1b) Geographical data:

[0104] Collect elevation data and terrain data of the area where the subway station is located, represent them using a digital elevation model (DEM), and accurately analyze the surrounding environment through a GIS system; obtain information about the surrounding environment, such as the layout of the urban drainage system (rainwater pipe network, sewage pipe network) and its drainage capacity, to evaluate potential flood risks.

[0105] (2) Collection of hydrological and meteorological data:

[0106] Integrate the meteorological monitoring data of the local area, including historical rainfall records and real-time rainfall monitoring (ground rain gauges and real-time data from weather stations can be used); monitor the change of the groundwater level, and obtain the groundwater level data in real time by installing groundwater level monitoring wells.

[0107] (3) Construction of a 3D digital model:

[0108] Use BIM software (such as Revit or Tekla) to convert the collected building and geographical data into a 3D model; add the layout of groundwater level monitoring wells and drainage systems to combine them with the actual building structure; mark the corresponding heights and geometric characteristics for key connection ports (such as subway station entrances and exits, ventilation openings, etc.).

[0109] (4) Establishment of a hydrodynamic model:

[0110] Use CFD software (such as ANSYS Fluent or OpenFOAM) to simulate the water flow dynamics caused by surface rainfall and analyze how water enters the subway station; model the relationship between the water flow rate between the surface and the underground space under heavy rain exposure and analyze the flow path of groundwater.

[0111] (5) Simulation of the impact of external flood factors: Establish different scenario simulations according to different rainfall scenarios. Use historical rainfall data and real-time rainfall intensity and visualize them in a 3D model; analyze the impacts of various rainfall intensities (such as 5 mm / h, 10 mm / h, and 30 mm / h) and predict the water level changes at each connection port under these conditions;

[0112] (6) Calculation of the water level at the connection port: Calculate the water level change at the surface connection port according to the rainfall amount: Set the water inlet threshold at the connection port and predict when water inlet will occur based on the rainfall amount per unit time; Use the following formula for evaluation:

[0113]

[0114] where h(t) is the water level height at the connection port, h0 is the initial water level height, R is the precipitation amount per unit time, and B is the cross-sectional area of the connection port;

[0115] Combine the elevation data of the connection ports in the 3D model to generate a time series graph of flood risk and mark the water inlet risks of each connection port under different rainfall conditions (such as short-term heavy rainfall and continuous rainfall).

[0116] (7) Analysis of internal flood factors: Establish a water level monitoring system in the underground space and predict the groundwater level and seepage situation based on real-time rainfall and time series data; Conduct experimental tests on the permeability performance of different soil types (such as sandy soil, clay, etc.) and analyze the movement of water inside the underground space, and calculate through the soil permeability model; Combine factors such as rainfall amount, groundwater level, and soil humidity to establish a seepage dynamics model.

[0117] Calculate the seepage volume using the permeability coefficient and the length of the water flow path, and we have:

[0118]

[0119] where Q is the seepage volume, k is the permeability coefficient of the soil, s is the seepage area, Δh is the water level difference between both ends of the soil layer, and l is the length of the water flow path;

[0120] Predict the possible time of seepage and flooding inside the underground space after rainfall through the model, using the following formula:

[0121]

[0122] Among them, T is the predicted seepage time, t is the starting water inlet time, Q is the seepage water volume, and c is the drainage capacity;

[0123] Input variables such as precipitation, drainage volume, and terrain slope into the multi-factor model, and use Logistic regression to calculate the probability of flood occurrence. There is:

[0124]

[0125] Among them, P(H) is the possibility of flood disaster occurrence, e is a constant, β0, β1, β2, and β3 are the influence coefficients of different model parameters respectively, R is the precipitation, B is the terrain slope, and A is the drainage volume.

[0126] (8) Calculation of flood occurrence time and probability: Establish a multi-factor model based on precipitation, drainage volume, and terrain slope. Input relevant variables into the model, and use algorithms such as Logistic regression for training and optimization; through historical data and simulation results, evaluate the water inlet risk and possible inundation time of the connection port under specific rainfall conditions.

[0127] (9) Implement prevention and control measures: For high-risk connection ports (such as areas close to concentrated rainfall), design and install waterproof and water-blocking dams, and use valves and pump stations for active drainage; establish an emergency warning mechanism. When the rainfall intensity reaches the preset threshold, the system automatically issues an alarm and notifies the operation and maintenance personnel through mobile terminals.

[0128] In summary, the flood prevention and control plan for this underground space can more accurately reflect the structure of the subway station and its behavior under flood conditions through the combination of building information modeling and computational fluid dynamics simulation. At this time, multi-dimensional simulation and analysis are carried out according to different rainfall scenarios, and the flood risks in different situations can be evaluated.

[0129] In addition, the solution of the present invention not only considers the internal structure and environmental factors of the underground space, but also systematically analyzes the influence of external flood factors. Through the coupled analysis of internal and external factors, a comprehensive flood prevention solution can be provided, rather than simply dealing with a certain specific factor.

[0130] Moreover, the above digital flood prevention and control plan for the underground space also provides a comprehensive, scientific, and efficient solution for flood management in the underground space by introducing advanced real-time monitoring, dynamic modeling, multi-dimensional analysis, and response mechanisms, which not only improves the ability to identify and manage flood risks, but also enhances the safety of urban public facilities and the ability to respond to emergencies;

[0131] This specific example demonstrates how to utilize digital modeling and hydrodynamic simulation to analyze and prevent flood disasters in subway stations; by collecting and analyzing geographical, architectural, hydrological, and meteorological data, constructing a 3D digital model, and combining advanced simulation and analysis tools, flood risks can be systematically evaluated, and effective prevention and control measures can be designed and implemented;

[0132] This method is not only applicable to subway stations but can also be extended to the prevention and control of flood disasters in other underground spaces.

[0133] On the other hand, based on the above embodiments, it should also be noted that the present invention also discloses a water level monitoring system in an underground space for predicting the specific implementation process of the underground water level and seepage conditions according to real-time rainfall and time series data.

[0134] Wherein:

[0135] L1. Monitoring system deployment:

[0136] L11. Equipment selection: Install pressure water level gauges and ultrasonic sensors in key areas of the underground space (such as subway station entrances, drainage outlets) to monitor the underground water level in real time;

[0137] L12. Layout planning: Determine the distribution density of monitoring points in combination with the terrain and connection port locations of the underground space.

[0138] L2. Data collection and transmission:

[0139] Use Internet of Things technology to connect the monitoring devices to the data transmission network to achieve real-time collection and upload;

[0140] At this time, water level, rainfall intensity, and other environmental data are collected every 5 minutes; (the rainfall intensity here can be obtained from the meteorological department);

[0141] L3. Use a time series model (such as ARIMA) to predict the trend of water level changes, and there is:

[0142] W t = W t-1 + ΔR t

[0143] Wherein, W t is the current water level, W t-1 is the water level at the previous time, and ΔR t is the impact of rainfall on the water level per unit time, obtained by subtracting the drainage volume per unit time from the rainfall storage volume per unit time.

[0144] For example, in a certain underground space, there are three water level monitoring points, and the data examples of real-time monitoring and uploading are as follows:

[0145]

[0146] Predicting water level using the ARIMA model:

[0147] Assume ΔR t = 0.1, then:

[0148] W t+1 = W t + ΔR t = 2.7 + 0.1 = 2.8

[0149] Predict that the water level at monitoring point 1 reaches 2.8 after one hour;

[0150] If the warning threshold for monitoring point 1 is 5, it can be reached after 22 hours of continuous rainfall; of course, in actual situations, changes in rainfall intensity will also change the impact of rainfall volume per unit time on the water level. For example, if the rainfall intensity increases to 25 mm / h, ΔR t will increase accordingly, which is obtained by subtracting the drainage volume per unit time from the storage volume of rainfall per unit time.

[0151] L4. Compare the predicted water level value with the set warning threshold, generate a flood risk assessment report, and notify relevant personnel through the mobile terminal.

[0152] L5. Conduct experimental tests on the permeability of different soil types (such as sandy soil, clay, etc.), analyze the movement of water in the underground space, and calculate through the soil permeability model; the specific implementation process of establishing a dynamic water infiltration model by combining factors such as rainfall, groundwater level, and soil moisture:

[0153] L51. Soil experimental test: Sampling and classification: Collect soil samples (sandy soil, clay, pebbles, etc.) around the underground space, classify them and send them to the laboratory for testing;

[0154] L52. Permeability test: Use a permeameter to measure the soil permeability coefficient, such as:

[0155] Sandy soil: k = 10 -4 cm / s;

[0156] Clay: k = 10 -7 cm / s;

[0157] Pebbles: k = 10 -3 cm / s

[0158] Calculate the soil water infiltration volume based on Darcy's law:

[0159]

[0160] Assume the infiltration area of the sandy soil area is 100 m 2, the soil layer thickness is 0.5 m, the water level difference is 1 m, and the seepage volume calculation formula is:

[0161]

[0162] L6. Combine the rainfall intensity and soil moisture to establish a dynamic water seepage model:

[0163] Use a numerical simulation tool (such as COMSOL Multiphysics) to simulate the water seepage path based on the permeability coefficient of the soil layer. The path of water seeping from the ground surface into the underground space is distributed in a network shape, mainly concentrated in the positions where the soil thickness is smaller or the aquifer connection is closer. Based on this, the water content of the soil layer around the underground space and the pressure on the underground space can be obtained;

[0164] Combine the rainfall amount and soil moisture to establish a dynamic water seepage model:

[0165] W t =∫Q t dt

[0166] W t is the cumulative seepage volume of the soil layer around the underground space.

[0167] L7. Input the water content, pressure, and seepage calculation results into the computational fluid dynamics (CFD) model to simulate the movement path and cumulative position of water, highlight the high-permeability areas (such as sandy soil or pebble areas), and put forward improvement suggestions for the areas where the seepage volume exceeds the standard (such as low-lying areas or positions with poor waterproof performance), such as increasing drainage equipment or strengthening waterproof measures.

[0168] Establish a multi-factor model based on precipitation, drainage volume, and terrain slope, and use algorithms such as Logistic regression for training and optimization; through historical data and simulation results, evaluate the specific implementation process of the water inlet risk and possible flooding time of the connection port under specific rainfall conditions:

[0169] Collect historical rainfall, terrain slope, and drainage capacity data:

[0170] Time Rainfall intensity Slope coefficient Drainage capacity 2024.12.20.11.00 50 0.2 50

[0171] L8. Use the Logistic regression formula to predict the flood probability:

[0172]

[0173] Among them, P(H) is the possibility of flood disaster occurrence, e is a constant, β0, β1, β2, β3 are the influence coefficients of different model parameters, R is the precipitation, B is the terrain slope, and A is the drainage volume;

[0174] Use historical data to fit a Logistic regression model and optimize the coefficients β0, β1, β2, and β3 through the maximum likelihood estimation method;

[0175] Example of optimized regression coefficients: β0 = -1.5, β1 = 0.3, β2 = 0.8, β3 = 0.5;

[0176] For the conditions of the previous example: P = 0.23;

[0177] That is: the probability of flood occurrence is obtained as 0.23.

[0178] Through this process, the Logistic regression model can combine historical data and real-time monitoring data to accurately predict the water inlet risk and possible time of the underground space connection opening.

[0179] On the other hand, the present invention also discloses a computer-readable storage medium storing a computer program, which when executed by a processor causes the processor to execute the steps of the above method.

[0180] On yet another aspect, the present invention also discloses a computer device including a memory and a processor, the memory storing a computer program, which when executed by the processor causes the processor to execute the steps of the above method.

[0181] In another embodiment provided by the present application, there is also provided a computer program product containing instructions, which when run on a computer causes the computer to execute the multi-dimensional situation deduction method for flood prevention and control in any closed or semi-closed space in the above embodiments.

[0182] It can be understood that the system provided by the embodiments of the present invention corresponds to the method provided by the embodiments of the present invention, and the explanations, examples, and beneficial effects of related content can refer to the corresponding parts in the above method.

[0183] The embodiments of the present application also provide an electronic device including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus.

[0184] The memory is used to store a computer program;

[0185] The processor is used to implement the multi-dimensional situation deduction method for flood prevention and control in the above closed or semi-closed space when executing the program stored in the memory.

[0186] The communication bus mentioned in the above electronic device can be a peripheral component interconnect standard (PCI) bus or an extended industry standard architecture (EISA) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0187] The communication interface is used for communication between the above-mentioned electronic device and other devices.

[0188] The memory may include a random access memory (RAM), or may also include a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0189] The aforementioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0190] It should also be noted that the electronic device further includes a terminal device, which may also be referred to as a terminal, a user equipment, a mobile station, a mobile terminal, etc. The terminal device may be a mobile phone, a smart TV, a wearable device, a tablet computer, a computer with wireless transceiver function, a virtual reality terminal device, an augmented reality terminal device, a wireless terminal in industrial control, a wireless terminal in unmanned driving, a wireless terminal in remote surgery, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, and so on. The specific technologies and specific device forms adopted by the terminal device in the embodiments of the present application are not limited.

[0191] In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line DSL) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state drive SSD), etc.

[0192] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0193] In addition, it should be noted that if there are directional indications (such as up, down, left, right, front, back...) involved in the embodiments of the present invention, then such directional indications are only used to explain the relative positional relationship and movement conditions between components in a specific posture. If this specific posture changes, then the directional indications will also change accordingly.

[0194] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, then such descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the meaning of "and / or" appearing throughout the text includes three parallel scenarios. Taking "A and / or B" as an example, it includes Scenario A, or Scenario B, or the scenario where both A and B are satisfied simultaneously. In addition, in the embodiments of the present invention, "a plurality of" means two or more. In addition, the technical solutions between various embodiments can be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

Claims

1. A multi-dimensional situation deduction method for flood prevention in closed or semi-closed spaces, characterized in that: The following steps are involved: S1. Collect and obtain geographic and architectural data, hydrological and meteorological data of the designated space; S2. Construct a 3D digital model of a designated space based on geographic and architectural data; S3. Establish a fluid dynamics model in the 3D digital model based on the hydrological and meteorological data, analyze the mutual influence relationship between the specified space and flood factors based on the fluid dynamics model, calculate the correlation characteristics of flood disasters in the specified space in time series based on the mutual influence relationship, and simulate the seepage situation under different rainfall conditions.

2. The multi-dimensional situation deduction method for flood prevention in closed or semi-closed spaces as claimed in claim 1, characterized in that: The geographic and architectural data in step S1 also include features that affect flood disasters within the designated space perimeter, which are used for special zone designation through a digital elevation model and incorporated into the 3D digital model constructed in step S2.

3. The multi-dimensional situation deduction method for flood prevention in a closed or semi-closed space as claimed in claim 1, characterized in that: The designated space is set as an underground space.

4. The multi-dimensional situation deduction method for flood prevention in a closed or semi-closed space as claimed in claim 3, characterized in that: The 3D digital model in step S2 includes surface elevation data including terrain undulations and drainage slopes, the location and elevation data of the connection opening, the geometric characteristics of the connection opening, and the underground space layout and dimensions.

5. The multi-dimensional situation deduction method for flood prevention in a closed or semi-closed space as claimed in claim 4, characterized in that: The flood factors in step S3 include: external flood factors including the connecting opening between the surface and the underground and the underground space structure, and internal flood factors including the underground space structure.

6. The multi-dimensional situation deduction method for flood prevention in closed or semi-closed spaces as claimed in claim 4, characterized in that: The specific operation process of the S3 step includes: S31. Collect and update historical and real-time rainfall data according to preset time and conduct preliminary precipitation assessment to capture rainfall patterns in the short term; S32. Use the hydrological model to simulate the water inflow from rainfall to the ground, generate a flood risk time series diagram, and establish a risk assessment matrix to assess the probability of flooding under specific rainfall and connection port elevation; S33. By evolving the internal flood factors, a multi-factor model including time series analysis is constructed to input specified variables to evaluate the time of flood occurrence.

7. The multi-dimensional situation deduction method for flood prevention in a closed or semi-closed space as claimed in claim 6, characterized in that: The specific evaluation process of performing preliminary precipitation evaluation in step S31 includes: Capture short-term rainfall patterns based on historical and real-time rainfall data; Conduct time series analysis of rainfall to determine the changes in water levels at underground connections under different rainfall intensities and durations; Using the precipitation per unit time and the 3D digital model, the time leading to water inflow at the connecting port is calculated; The water inflow threshold is set, that is, when the water level at the connection port reaches the specified height, it is considered that a flood has occurred, and a precipitation assessment is performed. The assessment formula is: Among them, H t is the water level of the connecting port when flood occurs, H0 is the initial water level, and R is the precipitation per unit time.

8. The multi-dimensional situation deduction method for flood prevention in a closed or semi-closed space as claimed in claim 6, characterized in that: The probability assessment process of flood occurrence in step S32 specifically includes: Using the classification algorithm, we set the binary classification problem where the occurrence of flood disasters is 1 and the non-occurrence is 0. The model independent variables including precipitation, drainage, and terrain slope are used to evaluate the possibility of flood disasters: Among them, P(H) is the possibility of flood disasters, e is a constant, β0, β1, β2, and β3 are the influence coefficients of different model parameters, R is precipitation, B is terrain slope, and A is drainage.

9. The multi-dimensional situation deduction method for flood prevention in a closed or semi-closed space as claimed in claim 6, characterized in that: The specific operation process of step S33 includes: A water seepage dynamic model is established to analyze how water is distributed and accumulated in the underground space after rainfall. The model formula is: Among them, Q is the infiltration water volume, k is the permeability coefficient of the soil, s is the permeable area, Δh is the water level difference at both ends of the soil layer, and l is the length of the water flow path; By simulating the seepage under different rainfall conditions, a model is established to predict the occurrence of seepage and flooding in the underground space within a specified time after rainfall: Among them, T is the predicted seepage time, t is the water inflow start time, Q is the infiltration water volume, and c is the drainage capacity.

10. The multi-dimensional situation deduction method for flood prevention in a closed or semi-closed space as claimed in claim 1, characterized in that: The following S4 steps are also included: S4. Based on the simulation results in step S3, obtain areas prone to flooding, and generate a risk map based on risk assessment under different rainfall conditions to assess the flood risk faced by the designated space.

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