Tunnel waterlogging early warning method, device, equipment, storage medium and program product

Abstract

This application discloses a method, device, equipment, storage medium, and program product for tunnel flooding early warning, relating to the field of tunnel early warning technology. The method includes: based on a simulation model, determining the tunnel passage time required for vehicles to pass through the tunnel when the tunnel flooding level reaches the maximum flooding threshold; calculating the current flooding level and the current congestion level to determine the current tunnel early warning level; if the current tunnel early warning level reaches the pre-alarm level, determining the maximum flooding warning time when the tunnel flooding level reaches the maximum flooding threshold using the simulation model; determining the actual warning time based on the difference between the maximum flooding warning time and the tunnel passage time; and deducing the flooding level corresponding to the actual warning time based on the simulation model; and issuing a final alarm when the tunnel flooding level reaches the warning level. This application improves the accuracy of tunnel early warning.

Description

Technical Field

[0001] This application relates to the field of tunnel early warning technology, and in particular to tunnel water accumulation early warning methods, devices, equipment, storage media and program products. Background Technology

[0002] Urban tunnels, as key hubs in urban transportation networks, play a vital role in the normal operation of the transportation system. However, due to the unique structure and enclosed nature of tunnels, they face certain safety risks, especially the problem of tunnel water accumulation. Researching early warning methods for urban tunnel water accumulation has significant practical implications and far-reaching social impact. It will not only help solve traffic safety hazards caused by tunnel water accumulation but also provide strong support for improving tunnel traffic efficiency and building a safer and more efficient urban tunnel traffic early warning system.

[0003] In related technologies, there is a common problem that the hydrological system and the transportation system are independent of each other. The water level simulation results fail to be dynamically coupled with the actual vehicle operation behavior, making it difficult to reflect the impact of water accumulation on traffic capacity and vehicle safe passage time. At the same time, fixed water depth thresholds lack consideration of traffic flow changes and vehicle passage time, which can easily lead to delayed or over-warning. Therefore, there is an urgent need for a coupled modeling and dynamic threshold back-calculation method that integrates hydrological evolution and traffic behavior characteristics to improve the scientificity and timeliness of urban tunnel water accumulation early warning. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device, equipment, storage medium and program product for early warning of tunnel water accumulation, which aims to solve the technical problem of inaccurate early warning mechanisms for tunnel water accumulation in related technologies.

[0005] To achieve the above objectives, this application provides a method for early warning of tunnel water accumulation, the method comprising: A simulation model is constructed that correlates tunnel water accumulation with traffic conditions. Based on the simulation model, the tunnel passage time required for vehicles to pass through the tunnel when the water accumulation reaches the maximum water accumulation threshold is determined. Monitor and obtain information on water accumulation and traffic conditions inside the tunnel, calculate the current level of water accumulation and congestion, and determine the current warning level for the tunnel based on the current level of water accumulation and congestion. If the current tunnel warning level reaches the pre-alarm level, the maximum water accumulation warning time when the tunnel water accumulation reaches the maximum water accumulation threshold will be determined through the simulation model. Based on the difference between the maximum water accumulation warning time and the tunnel passage time, the actual warning time is determined, and the water accumulation level corresponding to the actual warning time is deduced based on the simulation model. When the water accumulation level in the tunnel reaches the warning level, the final alarm is issued.

[0006] In one embodiment, the steps of constructing a simulation model relating tunnel flooding to traffic conditions, and determining the tunnel passage time required for vehicles to pass through the tunnel when the tunnel flooding level reaches the maximum flooding threshold based on the simulation model, include: The changes in tunnel water accumulation were simulated using rainstorm management model software to obtain a dataset of water level changes. The water level change dataset is input into traffic simulation software to build a car-following model, simulate traffic flow under tunnel water accumulation, and obtain vehicle speed simulation data. Based on vehicle speed simulation data, the tunnel passage time required for a vehicle to pass through the tunnel when the tunnel water accumulation reaches the maximum water accumulation threshold is determined.

[0007] In one embodiment, the car-following model is represented as: Where t is time; The maximum safe speed of the vehicle; The speed of the vehicle in front; The speed of the following vehicle is a quantity that needs to be measured; is a quadratic fitting function for the water depth; This refers to the depth of the accumulated water. For the car behind The position at that moment; For the car behind The speed of time; For time step; The distance between the leading vehicle and the followers; This refers to the driver's reaction time; For the desired speed; The speed of the following vehicle; This is the car's maximum acceleration; For time step; This is an uncertainty factor that allows for deviations from optimal driving conditions.

[0008] In one embodiment, the steps of monitoring and acquiring water accumulation and traffic conditions within the tunnel, calculating the current level of water accumulation and the current level of congestion, and determining the current tunnel warning level based on the current level of water accumulation and the current level of congestion include: Determine the road occupancy rate and vehicle throughput rate of the tunnel within the monitoring time window, and calculate the current level of congestion; Determine the changes in water depth in the tunnel within the monitoring time window, and calculate the current water level based on the ratio of water depth to the maximum water threshold. An early warning model is constructed, and the current level of water accumulation and congestion is combined with the early warning model to determine the current early warning level of the tunnel.

[0009] In one embodiment, the steps of constructing an early warning model include: Construct a decision tree to obtain multiple combinations of tunnel water accumulation levels and traffic congestion levels; Assign an early warning level to each combination, determine multiple early warning levels and the corresponding ranges of tunnel water accumulation and traffic congestion for each level, and construct an early warning model.

[0010] Secondly, to achieve the above objectives, this application further provides a tunnel water accumulation early warning device, the device comprising: The simulation module is used to build a simulation model that relates tunnel water accumulation to traffic conditions. Based on the simulation model, the tunnel passage time required for vehicles to pass through the tunnel when the tunnel water accumulation reaches the maximum water accumulation threshold is determined. The monitoring module is used to monitor and acquire information on water accumulation and traffic conditions inside the tunnel, calculate the current level of water accumulation and congestion, and determine the current tunnel warning level based on the current level of water accumulation and congestion. The early warning determination module is used to determine the maximum water accumulation warning time when the current tunnel early warning level reaches the pre-alarm level through a simulation model. The alarm calculation module is used to determine the actual warning time based on the difference between the maximum water accumulation warning time and the tunnel passage time, and to deduce the warning water accumulation level corresponding to the actual warning time based on the simulation model. When the water accumulation level in the tunnel reaches the warning water accumulation level, the final alarm is issued.

[0011] Thirdly, to achieve the above objectives, this application further provides a tunnel flooding early warning device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the above-described tunnel flooding early warning method.

[0012] Fourthly, to achieve the above objectives, this application further provides a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the above-described tunnel flooding early warning method.

[0013] Fifthly, to achieve the above objectives, this application further provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the above-described tunnel flooding early warning method.

[0014] One or more technical solutions proposed in this application have at least the following technical effects: This application integrates the SWMM flooding simulation model and the SUMO traffic simulation model, and simulates traffic conditions under tunnel flooding based on the improved Krauss car-following model, realizing joint simulation of tunnel flooding and traffic conditions. Subsequently, it proposes a tunnel flooding early warning index system based on congestion and flooding indicators, and constructs a hierarchical early warning model. On this basis, this application optimizes the early warning threshold and time according to different tunnel conditions, and designs an early warning release and response device, which can identify potential dangers before the flooding reaches the threshold of traditional early warning methods. Attached Figure Description

[0015] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the tunnel water accumulation early warning method in the embodiments of this application.

[0018] Figure 2 This is a schematic diagram of the module connection for a tunnel water accumulation early warning device.

[0019] Figure 3 This is a schematic diagram of a tunnel water accumulation early warning device.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The main solution of this application embodiment is as follows: By constructing a coupled simulation model that correlates the tunnel water accumulation process with traffic operation status, water level change data is obtained using a rainstorm management model, and the car-following model in traffic simulation is combined to simulate vehicle speed and traffic capacity under different water accumulation conditions, determining the vehicle passage time corresponding to the maximum water accumulation threshold; in actual operation, the tunnel water accumulation depth and traffic flow parameters are monitored in real time, the current water accumulation level and congestion level are calculated, and the current warning level is determined based on a pre-constructed decision tree warning model; when the pre-alarm level is reached, the simulation model is used to deduce the actual warning time and corresponding warning water accumulation level that meet the safety margin requirements, and a final alarm is issued when the water accumulation reaches the warning water accumulation level, thereby realizing dynamic threshold back-calculation and early warning based on safe passage time, improving the accuracy and timeliness of tunnel water accumulation warning. Here, the safety margin refers to the time redundancy or other equivalent parameter margin reserved to ensure that vehicles that have entered the tunnel after the warning is issued can still complete passage.

[0024] Specifically, this application provides a method for early warning of tunnel water accumulation, referring to... Figure 1 In this embodiment, the tunnel water accumulation early warning method includes steps S10 to S40: Step S10: Construct a simulation model that correlates tunnel water accumulation with traffic conditions. Based on the simulation model, determine the tunnel passage time required for vehicles to pass through the tunnel when the tunnel water accumulation reaches the maximum water accumulation threshold.

[0025] Step S20: Monitor and obtain the water accumulation and traffic conditions inside the tunnel, calculate the current water accumulation level and the current congestion level, and determine the current tunnel warning level based on the current water accumulation level and the current congestion level.

[0026] Step S30: If the current tunnel warning level reaches the pre-alarm level, then the maximum water accumulation warning time when the tunnel water accumulation reaches the maximum water accumulation threshold is determined by the simulation model.

[0027] Step S40: Based on the difference between the maximum water accumulation warning time and the tunnel passage time, determine the actual warning time, and deduce the warning water accumulation level corresponding to the actual warning time based on the simulation model. When the tunnel water accumulation level reaches the warning water accumulation level, issue the final alarm.

[0028] It should be noted that tunnel water accumulation refers to the process of water depth changing over time inside a tunnel under conditions of rainfall or insufficient drainage capacity; traffic conditions refer to the comprehensive characterization of vehicle operation status inside the tunnel, usually composed of parameters such as vehicle speed, traffic volume, road occupancy, and vehicle throughput; simulation model refers to a computational model that couples the evolution process of tunnel water accumulation with the traffic flow operation mechanism, used to describe the vehicle operation characteristics and traffic capacity changes under different levels of water accumulation.

[0029] Specifically, the core logic of this embodiment lies in constructing a simulation model that couples tunnel water accumulation with traffic behavior, thereby realizing a dynamic correlation analysis between vehicle traffic capacity and water level evolution. Based on this, the tunnel passage time is used as a safety constraint, and the time difference between the maximum water accumulation warning time and the actual warning time is used to dynamically back-calculate the threshold, avoiding the problems of warning lag or over-warning caused by traditional fixed water depth thresholds.

[0030] Additionally, it should be noted that the maximum water accumulation threshold refers to the extreme water level at which vehicles can safely pass, while the tunnel passage time refers to the real-time dynamic time required for a vehicle to completely leave the tunnel after entering, assuming that water accumulation affects traffic flow speed in the simulation environment. The actual warning time is the advance warning time point determined based on the safety margin principle. It is calculated by subtracting the tunnel passage time from the maximum water accumulation warning time to ensure that vehicles already in the tunnel still have the safe time to complete passage after the final warning is issued. The warning water accumulation level is the water depth level corresponding to the actual warning time, which is obtained by back-calculating from the simulation model. When the actual monitored tunnel water accumulation level reaches the warning water accumulation level, the final warning is triggered.

[0031] In one feasible implementation, step S10 includes steps A10 to A30: Step A10: Simulate the changes in tunnel water accumulation using rainstorm management model software to obtain a data set of water level changes.

[0032] Step A20: Input the water level change dataset into the traffic simulation software. Collect driver speeds under different flood conditions using an image questionnaire method. Apply the least squares method to perform a second-order fitting on the obtained data. The fitted function related to the flood is: ,according to An improved car-following model was used to simulate traffic flow under conditions of water accumulation in tunnels, and vehicle speed simulation data was obtained.

[0033] Step A30: Based on vehicle speed simulation data, determine the tunnel passage time required for a vehicle to pass through the tunnel when the tunnel water accumulation reaches the maximum water accumulation threshold.

[0034] The car-following model is represented as follows: Where t is time; The maximum safe speed of the vehicle; The speed of the vehicle in front; The speed of the following vehicle is a quantity that needs to be measured; Let be a quadratic fitting function for the water depth. In one example, ; This refers to the depth of the accumulated water. For the car behind The position at that moment; For the car behind The speed of time; For time step; The distance between the leading vehicle and the followers; This refers to the driver's reaction time; For the desired speed; The speed of the following vehicle; This is the car's maximum acceleration; For time step; This is an uncertainty factor that allows for deviations from optimal driving conditions.

[0035] An improved car-following model can be used to simulate the impact of flooding on traffic flow. Through the `Traci` interface of `sumo` in Python, the car-following model under flooded conditions is improved to simulate traffic flow characteristics. The system determines whether there is a vehicle ahead on the road segment. If the preceding vehicle value returns 0, the speed is set to the minimum of the lane speed limit and the flooded speed. If a preceding vehicle value is returned, the improved car-following model is executed to calculate the safe speed, and then the desired speed is calculated based on the safe speed, thus updating the vehicle speed settings. After updating the vehicle state at the current moment, the simulation proceeds to the next time step and repeats the above calculation process, thereby achieving continuous simulation of traffic flow evolution over time.

[0036] Specifically, the Storm Water Management Model (SWMM) software is a hydrological and hydrodynamic simulation tool used to simulate the formation, confluence process, and drainage system response of rainfall runoff. It is used to characterize the dynamic process of water depth changes over time inside tunnels under given rainfall conditions and drainage capacity constraints. Tunnel water accumulation changes refer to the depth of water accumulation on the road surface within the tunnel, the rate of water accumulation growth, and its temporal evolution characteristics. The water level change dataset is a time-series data set obtained through simulation using the Storm Water Management Model software, containing water depths and related hydrological parameters at different time points, providing boundary conditions and environmental inputs for subsequent traffic simulations.

[0037] Traffic simulation software (SUMO, Simulation of Urban Mobility) is a computing platform used to simulate the driving behavior of vehicles and the evolution of traffic flow under specific road conditions. In this implementation, the water depth corresponding to different times is embedded as a road operating environment parameter into the traffic scenario, thereby constructing a traffic operation scenario coupled with the water state, and then determining a car-following model to describe the speed, distance and acceleration / deceleration relationship between vehicles in front and behind in the same lane.

[0038] Under conditions of water accumulation in tunnels, the depth of the water affects the maximum safe speed and desired speed of vehicles, thereby altering vehicle acceleration and spacing constraints. A car-following model can be used to simulate the change in vehicle speed over time, yielding vehicle speed simulation data. This vehicle speed simulation data refers to the calculated vehicle speed at various times under different levels of water accumulation and traffic flow conditions, reflecting the impact of water accumulation on traffic capacity. When the water accumulation in the simulation scenario reaches the maximum water accumulation threshold, the tunnel passage time can be determined by statistically analyzing the changes in vehicle speed under this condition and calculating the time required to complete the entire journey.

[0039] Understandably, this implementation method first performs hydrological process simulation to obtain a water level change dataset, and then uses this dataset as an environmental input to embed into traffic simulation software to build a car-following model, thereby realizing the coupled simulation of tunnel water accumulation and traffic flow behavior. Then, under the condition of maximum water accumulation threshold, vehicle speed simulation data is extracted and tunnel passage time is calculated, providing key parameter support for subsequent dynamic early warning time calculation based on safe passage time.

[0040] In another feasible implementation, step S20 includes steps B10 to B30: Step B10: Determine the road occupancy rate and vehicle throughput rate of the tunnel within the monitoring time window, and calculate the current congestion level.

[0041] Step B20: Determine the change in water depth in the tunnel within the monitoring time window, and calculate the current water level based on the ratio of water depth to the maximum water threshold.

[0042] Step B30: Construct an early warning model. Based on the current level of water accumulation and congestion, determine the current tunnel early warning level using the early warning model.

[0043] The steps for constructing the early warning model include steps C10 to C20: Step C10: Construct a decision tree to obtain multiple combinations of tunnel water accumulation levels and traffic congestion levels; Step C20: Assign a warning level to each combination, determine multiple warning levels and the corresponding range of tunnel water accumulation and traffic congestion for each warning level, and construct a warning model.

[0044] Specifically, a monitoring time window refers to a fixed time interval used for statistical analysis of traffic and waterlogging data, such as a sliding time period of several minutes, used to smooth out instantaneous fluctuations and reflect the phased operational status. In terms of quantitative assessment of traffic conditions, within the monitoring time window, the proportion of time or space occupied by vehicles in the monitored area determines the road occupancy rate, which characterizes traffic density. Based on the number of vehicles passing through a specific monitoring section of the tunnel per unit time, the vehicle throughput rate is determined to reflect traffic capacity. By comprehensively calculating the characteristics of these two dimensions, the current level of congestion is ultimately derived, thus accurately depicting the true situation of vehicles being obstructed within the tunnel.

[0045] In terms of quantitative assessment of hydrological conditions, this implementation method tracks changes in water depth in real time within a monitoring time window. To ensure a unified hazard assessment standard for water levels at different depths, a maximum water depth threshold is preset, which typically refers to the physical limit depth at which a vehicle (such as a sedan) can safely wade through the water. The system compares the actual water depth collected with this threshold to calculate a standardized current water level. This ratio processing transforms absolute water level height into a relative hazard index, providing a foundation for multi-dimensional data fusion.

[0046] After quantifying the two-dimensional indicators, a decision tree algorithm was introduced to construct the final early warning model. The decision tree comprehensively analyzed the cross-combinations of the current water accumulation and congestion levels across different numerical ranges. The model sets a clear parameter range for each cross-combination (e.g., "slight water accumulation and severe congestion" or "severe water accumulation and slight congestion") and maps it one-to-one to different early warning levels. Based on this early warning model, the system can automatically match the calculated water accumulation and congestion indicators and output the most accurate current tunnel early warning level.

[0047] In one example, an SWMM simulation model is constructed, sub-catchment areas are divided, a road model and a drainage model are generalized, rainfall parameters and other parameters are set, and the changes in water depth under different rainfall intensities and durations are simulated to obtain a water level change dataset. The dataset is then input into the SUMO simulation model in real time via TraCI to improve the Krauss car-following model. Vehicle parameters are set to simulate road traffic flow under different traffic volumes.

[0048] Congestion is quantified by calculating road occupancy and vehicle throughput, and water depth is converted into a water level index (DI) to obtain early warning indicators. 1) Calculate road occupancy: Based on the actual number of vehicles on the road segment, calculate the maximum number of vehicles the road segment can accommodate, and then obtain the road occupancy rate AN / Nd. The calculation formula is as follows: In the formula, Total space required for each vehicle; The length of the vehicle; Minimum safe distance; The number of vehicles that a single lane can accommodate; This is the total length of the tunnel; This represents the maximum number of vehicles that the road segment can accommodate. AN represents the number of lanes; AN represents the average number of vehicles on the road over a certain period of time. The actual number of vehicles per hour at a certain cross-section is measured, and the maximum traffic flow that the road can handle within a certain time period is calculated. The ratio of the two is the vehicle throughput rate. : In the formula, This refers to the number of vehicles passing through a certain cross-section in one hour. Given conditions, this represents the maximum number of vehicles that can pass through a road segment per hour. This refers to the headway of the train. Number of lanes; 3) Congestion index: The calculation formula is as follows: In the formula, Congestion index; , These are the weighting coefficients; Water accumulation index: Considering the safety of vehicles driving in water, the most unfavorable conditions are adopted. Using the wading performance of a passenger car as a reference, k=0.25 is set as the maximum water accumulation threshold for water accumulation warning, and the water depth is converted into a water accumulation severity index DI: In the formula, This refers to the depth of the accumulated water. For the first The depth of the water at any given time; This is the maximum flood warning threshold.

[0049] Step 3: Construct a tiered early warning model based on the early warning indicators. The early warning levels are divided into green, yellow, orange, and red warnings. This invention constructs a decision tree to obtain all possible combinations of water accumulation and traffic congestion levels, assigning a warning level to each combination; it also obtains the relationship between water accumulation and traffic congestion levels to construct warning levels. Green alert, yellow alert, orange alert, and red alert. The calculation formula is as follows: Waterlogging index The formula is as follows: The formula for constructing the congestion index is as follows: In the formula, g, y, o, and r correspond to green, yellow, orange, and red, respectively. The water accumulation level warning is indicated by the water depth DI. class; Indicates the level of water accumulation warning Level threshold; The congestion level warning of the Congestion Index (CI) is as follows: class; Indicates a level of congestion warning Level threshold; Calculate the warning level The formula is shown below: In the formula, Green alert; Yellow alert; Orange alert issued; Red alert issued; This invention uses threshold values ​​to classify the degree of water accumulation. It is 0.2. The threshold for crowding level classification is 0.6. It is 0.5. It is 0.7. It is 0.9.

[0050] Calculated by combining water depth and traffic congestion conditions , Early warning calculation diagram as follows Figure 2 ; Red alerts have the greatest impact on traffic safety and the normal operation of the road network, as they determine whether vehicles can safely pass through tunnels. Therefore, red alerts are optimized. When vehicles enter a tunnel, rising water levels can prevent them from passing safely, requiring precise calculations of time and water depth thresholds for early warning.

[0051] Based on the above analysis, when the water depth reaches 0.25 meters, the time required for a vehicle to pass through the tunnel in real time was obtained through simulation. Based on this time, the depth of water accumulation in the tunnel before this time can be deduced, and this water accumulation depth can be used as a new threshold to trigger a red alert for tunnel water accumulation. When the water depth reaches the threshold The time when =0.25 meters is Under these circumstances, the simulation yields the following real-time tunnel passage time: , The simulation duration for the early warning point is The time to reach the water depth threshold is ,but: ; Obtained through simulation models The water depth at that moment was ,Will Set a new water depth threshold; when the water depth reaches... A red alert is issued, meaning that the water accumulation and traffic conditions inside the tunnel are monitored in real time. Data such as traffic flow and speed are collected to assess traffic conditions and analyze and predict the data. Once water accumulation exceeds the safety threshold for a tiered warning, a tiered warning is immediately triggered. The warning issuance for tunnel water accumulation is customized based on the above warning model as follows: real-time monitoring of water accumulation and traffic conditions inside the tunnel; water level sensors are installed inside the tunnel to monitor the water depth in real time; data such as traffic flow and speed are collected to assess traffic conditions; data is analyzed and predicted; real-time monitoring data is input into the warning model to calculate the tunnel water accumulation level index (DI) and congestion level index (CI); based on the preset water accumulation level threshold (… ), congestion threshold ( , The system determines the warning level for water accumulation. When the water depth reaches the initial red warning threshold, it calculates the real-time time for vehicles to pass through the tunnel, calculates the actual red warning time and depth threshold, and issues an early warning.

[0052] This example uses the Chicago rainfall pattern method to design the rainfall pattern for the study area. The peak location coefficient is generally between 0.3 and 0.5. This invention uses a peak location coefficient of 0.5. Rainfalls with a return period of 10 years and 50 years are selected as simulation rainfall parameters. According to the vehicle outline dimensions specified in the Urban Road Design Code (CJJ37-2022), the length of a passenger car is 6m and the total length of a large vehicle is 12m. Other simulation parameters of the experiment are shown in Table 1.

[0053] Table 1. Partial Traffic Simulation Parameter Settings This model can issue a red alert in a timely manner before the water depth reaches the traditional warning threshold. With a traffic flow of 3000 vehicles / hour, a red alert was issued 132 seconds in advance for a 600m tunnel during heavy rainfall; and 264 seconds in advance for a 1200m tunnel during heavy rainfall. This real-time capability provides a valuable time window for emergency response.

[0054] In terms of comprehensiveness, the model takes into account multiple factors such as the degree of water accumulation and traffic congestion. In the case of a red alert, the warning threshold and time are optimized according to different tunnel conditions, which makes the warning more comprehensive and accurate and can better reflect the actual situation.

[0055] In terms of flexibility, the model can dynamically adjust according to different rainfall intensities and traffic conditions, issuing corresponding levels of warnings. This flexibility allows the model to adapt to various complex and changeable weather and traffic conditions.

[0056] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the tunnel water accumulation early warning method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0057] This application also provides a tunnel water accumulation early warning device, please refer to... Figure 2 The tunnel flooding early warning device includes: Simulation module 10 constructs a simulation model that correlates tunnel water accumulation with traffic conditions. Based on the simulation model, it determines the tunnel passage time required for vehicles to pass through the tunnel when the tunnel water accumulation reaches the maximum water accumulation threshold. Monitoring module 20 monitors and acquires the water accumulation and traffic conditions inside the tunnel, calculates the current water accumulation level and the current congestion level, and determines the current tunnel warning level based on the current water accumulation level and the current congestion level. The early warning determination module 30 determines the maximum water accumulation warning time when the current tunnel early warning level reaches the pre-alarm level through the simulation model if the current tunnel water accumulation level reaches the maximum water accumulation threshold. The alarm calculation module 40 determines the actual warning time based on the difference between the maximum water accumulation warning time and the tunnel passage time, and deduces the warning water accumulation level corresponding to the actual warning time based on the simulation model. When the tunnel water accumulation level reaches the warning water accumulation level, a final alarm is issued.

[0058] The tunnel flooding early warning device provided in this application, employing the tunnel flooding early warning method in the above embodiments, can solve the technical problem of inaccurate tunnel flooding early warning mechanisms in related technologies. Compared with related technologies, the beneficial effects of the tunnel flooding early warning device provided in this application are the same as those of the tunnel flooding early warning method provided in the above embodiments, and other technical features in the tunnel flooding early warning device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0059] This application provides a tunnel flooding early warning device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the tunnel flooding early warning method in the above embodiments.

[0060] The following is for reference. Figure 3 The diagram illustrates a structural schematic suitable for implementing the tunnel flooding warning device of the embodiments of this application. The tunnel flooding warning device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), tablets, PMPs (Portable Media Players), vehicle terminals (e.g., vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The tunnel flooding early warning device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0061] like Figure 3As shown, the tunnel flooding early warning device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 (ROM) or a program loaded from a storage device 1003 into a random access memory 1004 (RAM). The random access memory 1004 also stores various programs and data required for the operation of the tunnel flooding early warning device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 (I / O interface) is also connected to the bus 1005. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the tunnel flooding warning device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a tunnel flooding warning device with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0062] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0063] The tunnel flooding early warning device provided in this application, employing the tunnel flooding early warning method in the above embodiments, can solve the technical problem of inaccurate tunnel flooding early warning mechanisms in related technologies. Compared with related technologies, the beneficial effects of the tunnel flooding early warning device provided in this application are the same as those of the tunnel flooding early warning method provided in the above embodiments, and other technical features of this tunnel flooding early warning device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0064] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0065] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0066] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the tunnel flooding early warning method in the above embodiments.

[0067] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0068] The aforementioned computer-readable storage medium may be included in the tunnel flooding early warning device; or it may exist independently and not be assembled into the tunnel flooding early warning device.

[0069] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0070] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0071] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0072] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described tunnel flooding early warning method, which can solve the technical problem of inaccurate tunnel flooding early warning mechanisms in related technologies. Compared with related technologies, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the tunnel flooding early warning method provided in the above embodiments, and will not be repeated here.

[0073] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the tunnel flooding early warning method described above.

[0074] The computer program product provided in this application can solve the technical problem of inaccurate tunnel water accumulation early warning mechanisms in related technologies. Compared with related technologies, the beneficial effects of the computer program product provided in this application are the same as those of the tunnel water accumulation early warning method provided in the above embodiments, and will not be repeated here.

[0075] The above are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A tunnel water accumulation early warning method, characterized in that, The method includes: A simulation model is constructed that correlates tunnel water accumulation with traffic conditions. Based on the simulation model, the tunnel passage time required for vehicles to pass through the tunnel when the tunnel water accumulation reaches the maximum water accumulation threshold is determined. Monitor and obtain information on water accumulation and traffic conditions within the tunnel, calculate the current level of water accumulation and congestion, and determine the current tunnel warning level based on the current level of water accumulation and congestion. If the current tunnel warning level reaches the pre-alarm level, the maximum water accumulation warning time when the tunnel water accumulation reaches the maximum water accumulation threshold is determined by the simulation model. Based on the difference between the maximum flood warning time and the tunnel passage time, the actual warning time is determined, and the flood level corresponding to the actual warning time is deduced based on the simulation model. When the tunnel flood level reaches the warning flood level, a final alarm is issued.

2. The tunnel water accumulation early warning method as described in claim 1, characterized in that, The steps for constructing a simulation model relating tunnel flooding and traffic conditions, and determining the tunnel passage time required for vehicles to pass through the tunnel when the flooding level reaches the maximum flooding threshold, include: The changes in tunnel water accumulation were simulated using rainstorm management model software to obtain a dataset of water level changes. The water level change dataset is input into traffic simulation software to build a car-following model, simulate traffic flow under tunnel water accumulation, and obtain vehicle speed simulation data. Based on the vehicle speed simulation data, the tunnel passage time required for a vehicle to pass through the tunnel when the tunnel water accumulation reaches the maximum water accumulation threshold is determined.

3. The tunnel water accumulation early warning method as described in claim 2, characterized in that, The car-following model is represented as follows: Where t is time; The maximum safe speed of the vehicle; The speed of the vehicle in front; The speed of the following vehicle is a quantity that needs to be measured; is a quadratic fitting function for the water depth; This refers to the depth of the accumulated water. For the car behind The position at that moment; For the car behind The speed of time; For time step; The distance between the leading vehicle and the followers; This refers to the driver's reaction time; For the desired speed; The speed of the following vehicle; This is the car's maximum acceleration; For time step; This is an uncertainty factor that allows for deviations from optimal driving conditions.

4. The tunnel water accumulation early warning method as described in claim 1, characterized in that, The steps of monitoring and acquiring water accumulation and traffic conditions within the tunnel, calculating the current level of water accumulation and congestion, and determining the current tunnel warning level based on the current level of water accumulation and congestion include: Determine the road occupancy rate and vehicle throughput rate of the tunnel within the monitoring time window, and calculate the current level of congestion; Determine the change in water depth in the tunnel within the monitoring time window, and calculate the current water level based on the ratio of water depth to the maximum water threshold. An early warning model is constructed, and the current tunnel early warning level is determined based on the current water accumulation level and the current congestion level, combined with the early warning model.

5. The tunnel water accumulation early warning method as described in claim 4, characterized in that, The steps for constructing the early warning model include: Construct a decision tree to obtain multiple combinations of tunnel water accumulation levels and traffic congestion levels; Assign an early warning level to each combination, determine multiple early warning levels and the corresponding ranges of tunnel water accumulation and traffic congestion for each level, and construct an early warning model.

6. A tunnel flooding early warning device, characterized in that, The device includes: The simulation module is used to build a simulation model that correlates tunnel water accumulation with traffic conditions. Based on the simulation model, the tunnel passage time required for vehicles to pass through the tunnel when the tunnel water accumulation reaches the maximum water accumulation threshold is determined. The monitoring module is used to monitor and acquire the water accumulation and traffic conditions inside the tunnel, calculate the current water accumulation level and the current congestion level, and determine the current tunnel warning level based on the current water accumulation level and the current congestion level. The early warning determination module is used to determine the maximum water accumulation warning time when the current tunnel early warning level reaches the pre-alarm level through the simulation model; The alarm calculation module is used to determine the actual warning time based on the difference between the maximum water accumulation warning time and the tunnel passage time, and to deduce the warning water accumulation level corresponding to the actual warning time based on the simulation model. When the tunnel water accumulation level reaches the warning water accumulation level, a final alarm is issued.

7. A tunnel flooding early warning device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the tunnel flooding early warning method as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the tunnel water accumulation early warning method as described in any one of claims 1 to 5.

9. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the tunnel flooding early warning method as described in any one of claims 1 to 5.

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