Tunnel state monitoring method and system
Through the multi -tasking neural network model to integrate environment, structure, transportation and equipment parameters, the problem of low tunnel status monitoring accuracy in existing technology has been solved, and more efficient and accurate tunnel status monitoring is achieved.
Patent Information
- Application Number
- CN202411361575.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-27
- Publication Date
- 2025-01-28
AI Technical Summary
The prior art cannot accurately understand the true status of tunnel operations by monitoring only a single state factor in the tunnel, resulting in low monitoring accuracy.
Adopt a multi -task head neural network model, collect environmental parameters, structural security parameters, traffic parameters and tunnel equipment status parameters, extract the common feature parameters through the shared layer, and further extract and predict at the mission -specific layer. Results to obtain the overall status information of the tunnel.
By collecting multi -dimensional factors, the accuracy and efficiency of tunnel status monitoring have been improved, it can more accurately understand the operating status of the tunnel and enhance safety and operating stability.
Smart Images
Figure CN119377634A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of tunnel monitoring, and in particular to a tunnel status monitoring method and system. Background Art
[0002] With the rapid economic development and the advent of the automobile era, the mileage of highways is increasing every year. Since there are many mountains in some areas, the number of tunnels, which have high transportation efficiency and occupy less area, is also increasing.
[0003] Once an accident occurs in a highway tunnel, if no timely disposal measures are taken, it is very easy to cause a secondary accident, which in turn threatens the personal safety of drivers and passengers and causes significant property losses. Tunnel control is an important part of highway operation management decision-making. It is responsible for monitoring and managing, controlling the entire tunnel status, improving the stability and efficiency of tunnel operations, and has a very broad application prospect.
[0004] However, the existing technology determines the status of the tunnel by only monitoring a single status factor in the tunnel, which has low accuracy and is unable to obtain the true status of the tunnel operation. Summary of the invention
[0005] The purpose of the embodiments of the present invention is to provide a tunnel status monitoring method and system, which are used to completely or at least partially solve the technical problems existing in the above-mentioned prior art.
[0006] In order to achieve the above object, an embodiment of the present invention provides a tunnel status monitoring method, including:
[0007] Collect environmental parameters, structural safety parameters, traffic parameters of the tunnel to be monitored, and status parameters of equipment deployed in the tunnel;
[0008] The collected environmental parameters, structural safety parameters, traffic parameters and status parameters of equipment deployed in the tunnel are input into a pre-built multi-task head neural network model to obtain the status information of the tunnel to be monitored.
[0009] Optionally, the environmental parameters include at least: temperature, humidity, air pressure, oxygen concentration and harmful gas concentration and water quality;
[0010] The structural safety parameters include at least: ground settlement, vibration and displacement, support structure deformation and surrounding rock pressure;
[0011] The traffic parameters include at least: vehicle flow and speed, illumination, wind speed and wind direction;
[0012] The status parameters of the equipment arranged in the tunnel at least include: the operating status of the ventilation equipment, the operating status of the drainage equipment and the status of the fire-fighting equipment.
[0013] Optionally, the collected environmental parameters, structural safety parameters, traffic parameters and status parameters of equipment deployed in the tunnel are input into a pre-built multi-task head neural network model to obtain status information of the tunnel to be monitored, including:
[0014] Inputting the environmental parameters, structural safety parameters, traffic parameters, and state parameters of equipment deployed in the tunnel into a shared layer of a pre-built multi-task head neural network model, and extracting characteristic parameters common to the environmental parameters, structural safety parameters, traffic parameters, and state parameters of equipment deployed in the tunnel;
[0015] Input the feature parameters into the task-specific layer to perform further feature extraction and prediction according to different task requirements, and output the prediction results for different tasks through the output layer, wherein the task-specific layer is a plurality of independent sub-networks;
[0016] The prediction results of each independent sub-network output by the output layer are integrated to obtain the status information of the tunnel to be monitored.
[0017] Optionally, the construction process of the multi-task head neural network model includes:
[0018] Design a shared layer to extract common feature representations of environmental parameters, structural safety parameters, traffic parameters, and status parameters of equipment deployed in the tunnel;
[0019] Task-specific layer design: Design an independent sub-network for each task, where each sub-network receives the output of the shared layer and performs further feature extraction and prediction according to its own task requirements;
[0020] Output layer design: Design the corresponding output layer for each task-specific layer;
[0021] Loss function design: Design a loss function for each task, and perform weighted summation of the loss functions of all tasks to obtain the total loss function.
[0022] Optionally, the overall loss function is as follows:
[0023] Total Loss =αLoss Environment +βLoss Vibration +γLoss Traffic +δLoss Device
[0024] In the formula, Total Loss Represents the total loss function, Loss Environment Represents the loss function of the environment parameter task, Loss Vibration Represents the loss function of the structural vibration task, Loss TrafficRepresents the loss function of the traffic parameter task, Loss Device represents the loss function of the state parameter task of equipment deployment in the tunnel, α, β, γ, and δ are the weights of the loss function of the environmental parameter task, the loss function of the structural vibration task, the loss function of the traffic parameter task, and the loss function of the state parameter task of equipment deployment in the tunnel, respectively.
[0025] On the other hand, the present invention also provides a tunnel status monitoring system, comprising:
[0026] A collection unit, used to collect environmental parameters, structural safety parameters, traffic parameters of the tunnel to be monitored, and status parameters of equipment deployed in the tunnel;
[0027] The monitoring unit is used to input the collected environmental parameters, structural safety parameters, traffic parameters and status parameters of equipment deployed in the tunnel into a pre-built multi-task head neural network model to obtain the status information of the tunnel to be monitored.
[0028] Optionally, the monitoring unit is specifically used for:
[0029] Inputting the environmental parameters, structural safety parameters, traffic parameters, and state parameters of equipment deployed in the tunnel into a shared layer of a pre-built multi-task head neural network model, and extracting characteristic parameters common to the environmental parameters, structural safety parameters, traffic parameters, and state parameters of equipment deployed in the tunnel;
[0030] Input the feature parameters into the task-specific layer to perform further feature extraction and prediction according to different task requirements, and output the prediction results for different tasks through the output layer, wherein the task-specific layer is a plurality of independent sub-networks;
[0031] The prediction results of each independent sub-network outputted by the output layer are integrated to obtain the status information of the tunnel to be monitored.
[0032] Optionally, the monitoring unit is specifically used to construct a multi-task head neural network model:
[0033] Design a shared layer to extract common feature representations of environmental parameters, structural safety parameters, traffic parameters, and status parameters of equipment deployed in the tunnel;
[0034] Task-specific layer design: Design an independent sub-network for each task, where each sub-network receives the output of the shared layer and performs further feature extraction and prediction according to its own task requirements;
[0035] Output layer design: Design the corresponding output layer for each task-specific layer;
[0036] Loss function design: Design a loss function for each task, and perform weighted summation of the loss functions of all tasks to obtain the total loss function.
[0037] On the other hand, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned tunnel status monitoring method when executing the program.
[0038] On the other hand, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above-mentioned tunnel status monitoring method when executed by a processor.
[0039] Through the above technical solution, factors affecting the tunnel status in multiple dimensions are collected to improve the monitoring accuracy. Moreover, by utilizing the constructed multi-task head neural network model, the efficiency of tunnel monitoring is further improved.
[0040] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS
[0041] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:
[0042] Figure 1 is an implementation flow chart of a tunnel status monitoring method provided by an embodiment of the present invention;
[0043] Figure 2 It is a structural schematic diagram of a tunnel status monitoring system provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0044] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.
[0045] See also Figure 1 As shown, it is a flowchart of an implementation method of a tunnel status monitoring method provided by an embodiment of the present invention, including the following execution steps:
[0046] Step 100: Collect environmental parameters, structural safety parameters, traffic parameters of the tunnel to be monitored, and status parameters of equipment deployed in the tunnel.
[0047] Specifically, the environmental parameters include at least: temperature, humidity, air pressure, oxygen concentration and harmful gas concentration and water quality; the structural safety parameters include at least: surface settlement, vibration and displacement, support structure deformation and surrounding rock pressure; the traffic parameters include at least: vehicle flow and speed, illumination, wind speed and wind direction; the status parameters of the equipment deployed in the tunnel include at least: the operating status of the ventilation equipment, the operating status of the drainage equipment and the status of the fire-fighting equipment.
[0048] In some embodiments, the temperature of the tunnel has a great influence on the stability and service life of the tunnel structure. Humidity is an important parameter of the internal environment of the tunnel and has a great influence on the corrosion and damage of the internal structure and equipment of the tunnel. The air pressure inside the tunnel needs to be monitored in real time to ensure the air circulation and discharge inside the tunnel. Oxygen concentration and harmful gas concentration: The oxygen concentration and harmful gas concentration (such as carbon monoxide, hydrogen sulfide, etc.) inside the tunnel need to be monitored in real time to ensure the safety of staff and vehicles. The drainage system inside the tunnel needs to monitor the water quality to ensure that the drainage is environmentally friendly and safe.
[0049] In some embodiments, tunnel construction will have an impact on the surrounding environment, and the surrounding surface settlement needs to be monitored. Geological and topographic changes around the tunnel may cause vibration and displacement of the tunnel, which requires real-time monitoring. The deformation of the support structure includes the deformation of the tunnel lining, anchor rods and other support structures, which is an important indicator for evaluating the stability of the tunnel. The change of surrounding rock pressure may directly affect the stability and safety of the tunnel, and needs to be monitored in real time.
[0050] In some embodiments, the traffic volume and speed in the tunnel need to be monitored in real time to ensure smooth and safe traffic. The lighting in the tunnel has a great impact on the safety and comfort of the driver and needs to be monitored and adjusted in real time. The wind speed and direction in the tunnel have a great impact on the stability and driving safety of the vehicle and need to be monitored in real time.
[0051] In some embodiments, smoke and fire inside the tunnel are major threats to tunnel safety and require real-time monitoring and alarm. A video surveillance system needs to be installed inside the tunnel to monitor and record the situation inside the tunnel in real time so as to promptly detect and deal with problems. By monitoring sound and noise, the impact of the internal tunnel environment on surrounding residents and workers can be evaluated and necessary adjustments can be made.
[0052] In some embodiments, the operating status of the ventilation equipment is monitored in real time to ensure its normal operation to maintain the freshness and circulation of the air inside the tunnel, the operation of the drainage equipment is monitored to ensure that the accumulated water inside the tunnel can be discharged in time to prevent water damage, and the status of the fire-fighting equipment is monitored to ensure that it is available at any time to respond quickly to emergency situations such as fire.
[0053] Step 101: input the collected environmental parameters, structural safety parameters, traffic parameters and status parameters of equipment deployed in the tunnel into a pre-built multi-task head neural network model to obtain status information of the tunnel to be monitored.
[0054] Specifically, when executing step 101, the following steps may be specifically performed:
[0055] S1010: Input the environmental parameters, structural safety parameters, traffic parameters and status parameters of equipment deployed in the tunnel into the shared layer of a pre-built multi-task head neural network model, and extract the common characteristic parameters of the environmental parameters, structural safety parameters, traffic parameters and status parameters of equipment deployed in the tunnel.
[0056] S1011: Input the feature parameters into the task-specific layer to perform further feature extraction and prediction according to different task requirements, and output the prediction results for different tasks through the output layer, wherein the task-specific layer is a plurality of independent sub-networks.
[0057] S1012: The prediction results of each independent sub-network predicted by the output layer are integrated to obtain the status information of the tunnel to be monitored.
[0058] In some embodiments, before executing step 101, a process of constructing a multi-task head neural network model is also executed, including the following steps:
[0059] S1: Design a shared layer to extract the common feature representations of environmental parameters, structural safety parameters, traffic parameters, and status parameters of equipment deployed in the tunnel.
[0060] S2: Task-specific layer design: Design an independent sub-network for each task, where each sub-network receives the output of the shared layer and performs further feature extraction and prediction according to its own task requirements.
[0061] S3: Output layer design: Design the corresponding output layer for each task-specific layer.
[0062] S4: Loss function design: Design a loss function for each task, and perform weighted summation of the loss functions of all tasks to obtain the total loss function.
[0063] Specifically, the total loss function is as follows:
[0064] Total Loss =αLoss Environment +βLoss Vibration +γLoss Traffic +δLoss Device
[0065] In the formula, Total Loss Represents the total loss function, Loss Environment Represents the loss function of the environment parameter task, Loss Vibration Represents the loss function of the structural vibration task, Loss Traffic Represents the loss function of the traffic parameter task, Loss Device represents the loss function of the state parameter task of equipment deployment in the tunnel, α, β, γ, and δ are the weights of the loss function of the environmental parameter task, the loss function of the structural vibration task, the loss function of the traffic parameter task, and the loss function of the state parameter task of equipment deployment in the tunnel, respectively.
[0066] In some embodiments, before executing step 101, the features of different data sources are fused together by splicing before the data is input into the model, and then input into the network model together. Further feature fusion is performed at the task-specific layer in the network model. First, the data of each data source is preliminarily processed by its own sub-network, and then the output of the sub-network is fused at the task-specific layer of the network. Finally, the different output layers of the network model are fused. Each data source (each task) corresponds to an output head, and each output head performs prediction independently. Then, multiple prediction results are fused at the output layer.
[0067] In some embodiments, see Figure 2 FIG. 1 is a schematic diagram of a structure of a tunnel status monitoring system provided by an embodiment of the present invention, including:
[0068] A collection unit 20, used to collect environmental parameters, structural safety parameters, traffic parameters of the tunnel to be monitored, and status parameters of equipment deployed in the tunnel;
[0069] The monitoring unit 21 is used to input the collected environmental parameters, structural safety parameters, traffic parameters and status parameters of the equipment deployed in the tunnel into a pre-built multi-task head neural network model to obtain the status information of the tunnel to be monitored.
[0070] In some embodiments, the monitoring unit is specifically used to:
[0071] Inputting the environmental parameters, structural safety parameters, traffic parameters, and state parameters of equipment deployed in the tunnel into a shared layer of a pre-built multi-task head neural network model, and extracting characteristic parameters common to the environmental parameters, structural safety parameters, traffic parameters, and state parameters of equipment deployed in the tunnel;
[0072] Input the feature parameters into the task-specific layer to perform further feature extraction and prediction according to different task requirements, and output the prediction results for different tasks through the output layer, wherein the task-specific layer is a plurality of independent sub-networks;
[0073] The prediction results of each independent sub-network outputted by the output layer are integrated to obtain the status information of the tunnel to be monitored.
[0074] In some embodiments, the monitoring unit is specifically used to construct a multi-task head neural network model:
[0075] Design a shared layer to extract common feature representations of environmental parameters, structural safety parameters, traffic parameters, and status parameters of equipment deployed in the tunnel;
[0076] Task-specific layer design: Design an independent sub-network for each task, where each sub-network receives the output of the shared layer and performs further feature extraction and prediction according to its own task requirements;
[0077] Output layer design: Design the corresponding output layer for each task-specific layer;
[0078] Loss function design: Design a loss function for each task, and perform weighted summation of the loss functions of all tasks to obtain the total loss function.
[0079] On the other hand, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned tunnel status monitoring method when executing the program.
[0080] On the other hand, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the tunnel status monitoring method described in any one of the above embodiments are implemented.
[0081] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.
[0082] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0083] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0084] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0085] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0086] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.
[0087] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.
[0088] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.
[0089] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.
Claims
1. A tunnel status monitoring method, characterized in that: include: Collect environmental parameters, structural safety parameters, traffic parameters of the tunnel to be monitored, and status parameters of equipment deployed in the tunnel; The collected environmental parameters, structural safety parameters, traffic parameters and status parameters of equipment deployed in the tunnel are input into a pre-built multi-task head neural network model to obtain the status information of the tunnel to be monitored.
2. The tunnel status monitoring method according to claim 1, characterized in that: The environmental parameters include at least: temperature, humidity, air pressure, oxygen concentration and harmful gas concentration and water quality; The structural safety parameters include at least: ground settlement, vibration and displacement, support structure deformation and surrounding rock pressure; The traffic parameters include at least: vehicle flow and speed, illumination, wind speed and wind direction; The status parameters of the equipment arranged in the tunnel at least include: the operating status of the ventilation equipment, the operating status of the drainage equipment and the status of the fire-fighting equipment.
3. The tunnel status monitoring method according to claim 1, characterized in that: The collected environmental parameters, structural safety parameters, traffic parameters and status parameters of the equipment deployed in the tunnel are input into the pre-built multi-task head neural network model to obtain the status information of the tunnel to be monitored, including: Inputting the environmental parameters, structural safety parameters, traffic parameters, and state parameters of equipment deployed in the tunnel into a shared layer of a pre-built multi-task head neural network model, and extracting characteristic parameters common to the environmental parameters, structural safety parameters, traffic parameters, and state parameters of equipment deployed in the tunnel; Input the feature parameters into the task-specific layer to perform further feature extraction and prediction according to different task requirements, and output the prediction results for different tasks through the output layer, wherein the task-specific layer is a plurality of independent sub-networks; The prediction results of each independent sub-network outputted by the output layer are integrated to obtain the status information of the tunnel to be monitored.
4. The tunnel status monitoring method according to claim 3, characterized in that: The construction process of the multi-task head neural network model includes: Design a shared layer to extract common feature representations of environmental parameters, structural safety parameters, traffic parameters, and status parameters of equipment deployed in the tunnel; Task-specific layer design: Design an independent sub-network for each task, where each sub-network receives the output of the shared layer and performs further feature extraction and prediction according to its own task requirements; Output layer design: Design the corresponding output layer for each task-specific layer; Loss function design: Design a loss function for each task, and perform weighted summation of the loss functions of all tasks to obtain the total loss function.
5. The tunnel status monitoring method according to claim 4, characterized in that: The overall loss function is as follows: Total Loss =αLoss Environment +βLoss Vuvration +γLoss Traffic +δLoss Device In the formula, Total Loss Represents the total loss function, Loss Environment Represents the loss function of the environment parameter task, Loss Vibration Represents the loss function of the structural vibration task, Loss Traffic Represents the loss function of the traffic parameter task, Loss Device represents the loss function of the state parameter task of equipment deployment in the tunnel, α, β, γ, and δ are the weights of the loss function of the environmental parameter task, the loss function of the structural vibration task, the loss function of the traffic parameter task, and the loss function of the state parameter task of equipment deployment in the tunnel, respectively.
6. A tunnel status monitoring system, characterized in that: include: A collection unit, used to collect environmental parameters, structural safety parameters, traffic parameters of the tunnel to be monitored, and status parameters of equipment deployed in the tunnel; The monitoring unit is used to input the collected environmental parameters, structural safety parameters, traffic parameters and status parameters of equipment deployed in the tunnel into a pre-built multi-task head neural network model to obtain the status information of the tunnel to be monitored.
7. The tunnel status monitoring system according to claim 6, characterized in that: The monitoring unit is specifically used for: Inputting the environmental parameters, structural safety parameters, traffic parameters, and state parameters of equipment deployed in the tunnel into a shared layer of a pre-built multi-task head neural network model, and extracting characteristic parameters common to the environmental parameters, structural safety parameters, traffic parameters, and state parameters of equipment deployed in the tunnel; Input the feature parameters into the task-specific layer to perform further feature extraction and prediction according to different task requirements, and output the prediction results for different tasks through the output layer, wherein the task-specific layer is a plurality of independent sub-networks; The prediction results of each independent sub-network outputted by the output layer are integrated to obtain the status information of the tunnel to be monitored.
8. The tunnel status monitoring system according to claim 6, characterized in that: The monitoring unit is specifically used to construct a multi-task head neural network model: Design a shared layer to extract common feature representations of environmental parameters, structural safety parameters, traffic parameters, and status parameters of equipment deployed in the tunnel; Task-specific layer design: Design an independent sub-network for each task, where each sub-network receives the output of the shared layer and performs further feature extraction and prediction according to its own task requirements; Output layer design: Design the corresponding output layer for each task-specific layer; Loss function design: Design a loss function for each task, and perform weighted summation of the loss functions of all tasks to obtain the total loss function.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the tunnel status monitoring method according to any one of claims 1 to 5 are implemented.
10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the tunnel status monitoring method according to any one of claims 1 to 5 are implemented.