Tunnel fire alarm method based on coupling analysis of temperature, humidity and smoke sensors

Abstract

The invention provides a tunnel fire alarm method based on coupling analysis of temperature, humidity and smoke sensors, and belongs to the field of tunnel fire early warning, and the method comprises the following steps: installing a plurality of in-tunnel monitoring devices in a tunnel to collect data in the tunnel, carrying out the preprocessing of the collected data, and carrying out the early warning of the collected data; the preprocessing comprises data filtering and denoising; performing data feature extraction on the preprocessed data, and performing coupling analysis on the extracted feature data; threshold judgment is carried out on the data, according to processing judgment on the data, an alarm is given when a fire occurs in the tunnel, and the tunnel fire early-warning prompt device carries out sound-light alarm. The coupling analysis can effectively avoid misinformation of other special conditions and damage misinformation and abnormal misinformation of a certain single sensor, the setting of the deformation threshold value not only improves the preciseness of the coupling analysis, but also has preliminary understanding of the grade and the influence degree of the tunnel fire, and is beneficial for a manager to organize emergency rescue and material allocation.

Description

Technical Field

[0001] The invention relates to the field of tunnel fire warning, and in particular to a tunnel fire alarm method based on coupling analysis of temperature, humidity and smoke sensors. Background Art

[0002] Highway tunnel safety is extremely important. On the one hand, the space inside the tunnel is relatively closed and the ventilation conditions are limited. Once a fire occurs, smoke and toxic gases are difficult to be discharged quickly, and will permeate the entire tunnel in a short time, seriously threatening the lives of trapped people. In addition, high temperatures will also damage the tunnel structure, increasing the difficulty of rescue. On the other hand, the visibility in the tunnel is poor and the vehicles are traveling at a high speed. If there are emergencies, such as vehicle failures and traffic accidents, it is easy to cause a chain collision, causing heavy casualties and property losses. In addition, tunnels are usually major traffic arteries connecting important areas. Their safety issues will have a significant impact on regional traffic, leading to traffic congestion or even interruption, affecting economic development and people's travel. Therefore, it is necessary to attach great importance to highway tunnel safety, especially fire-related safety issues.

[0003] However, there are many difficulties in tunnel fire warning. First, the environmental factors are complex. The closed space of the tunnel makes it difficult for smoke to spread, which interferes with the work of the detector and reduces visibility, affecting the effect of visual detectors. The ventilation system will change the distribution of smoke and heat, and the airflow may also blow away the smoke, increasing the difficulty of early warning. At the same time, the electromagnetic interference generated by electrical equipment and communication facilities in the tunnel will affect the performance of the detector. Secondly, the characteristics of fire are changeable. Vehicles in the tunnel carry many flammable items, which burn quickly and have a short warning time. The location of the fire source is uncertain, and a variety of detection methods are required to cover it. In addition, the types of fires are diverse, requiring the early warning system to accurately identify them. Finally, the technical means are limited. Commonly used detectors have limitations in tunnels, and there are problems with the reliability and cost of new detectors. Communication difficulties may affect the transmission of early warning signals, and communication equipment is easily damaged during fires. The analysis of large amounts of data is complex, requiring high-performance equipment and software, and is costly. Therefore, it is necessary to design a tunnel fire alarm method based on the coupling analysis of temperature, humidity and smoke sensors to make tunnel fire warnings more accurate and timely. Summary of the invention

[0004] The purpose of the present invention is to provide a tunnel fire alarm method based on the coupled analysis of temperature, humidity and smoke sensors to solve the technical problems mentioned in the background technology. It can effectively give play to the advantages of various sensors, integrate and distinguish to improve the accuracy of early warning, and overcome the communication difficulties in the tunnel.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A tunnel fire alarm method based on coupling analysis of temperature, humidity and smoke sensors, the method comprising the following steps:

[0007] Step 1: Install several tunnel monitoring devices (2) in the tunnel (1) to collect data inside the tunnel, install an external tunnel reference monitoring device (3) outside the tunnel to collect data on environmental changes and use it as reference data for prediction, and set a tunnel fire warning device at a fixed distance in front of the tunnel entrance;

[0008] Step 2: Preprocess the collected data, including data filtering and denoising;

[0009] Step 3: Extract data features from the preprocessed data;

[0010] Step 4: Conduct coupling analysis on the extracted feature data;

[0011] Step 5: Perform threshold judgment on the data;

[0012] Step 6: Based on the data processing and judgment, when a fire occurs in the tunnel, an alarm is sounded and lighted by the tunnel fire warning prompt device, and the relevant departments are reported. At the same time, the tunnel fire is uploaded to the cloud for users to query in time.

[0013] Furthermore, in step 1, several tunnel monitoring devices in the tunnel are arranged at equal intervals, and adjacent tunnel monitoring devices communicate with each other, and transmit the collected data to the entrance of the tunnel one by one. At the same time, the data of each device is marked with the corresponding device number and the distance position of the device in the tunnel. When the data sent by the rear tunnel monitoring device to the front tunnel monitoring device cannot be fed back, the rear tunnel monitoring device will request communication with the first two tunnel monitoring devices, so as to avoid the communication failure of a tunnel monitoring device affecting the data collection and transmission of the entire tunnel.

[0014] Furthermore, in step 1, a temperature and humidity sensor, a smoke sensor and a wireless networking transmission module are provided in the monitoring device in the tunnel. The temperature and humidity sensor is used to collect temperature and humidity data of the environment, the smoke sensor is used to collect smoke data, and the wireless networking transmission module is used to transmit the collected data to an external processor. The reference monitoring device outside the tunnel collects reference data of environmental factors: reference temperature T0, reference humidity H0, reference smoke PM0, and the monitoring device inside the tunnel collects environmental factors such as tunnel temperature T0 inside the tunnel. 1～n 、Tunnel humidity H 1～n , tunnel smoke PM 1～n .

[0015] Furthermore, in step 1, the tunnel fire warning prompt device includes a solar energy storage device, an acoustic and light horn, an LED display screen, a wireless module, a support column and a data processing device. The solar energy storage device, the acoustic and light horn, the LED display screen, the wireless module and the data processing device are all arranged on the support column. The solar energy storage device is connected to the acoustic and light horn, the LED display screen, the wireless module and the data processing device for power supply. The acoustic and light horn, the LED display screen and the wireless module are all connected to the data processing device. The acoustic and light horn is used for sound and light alarm, the LED display screen is used for displaying fire data, and the wireless module is used to report the fire situation to relevant units and upload it to a cloud server.

[0016] Furthermore, in step 3, characteristic values ​​that are helpful in judging a fire are extracted from the preprocessed data to determine whether the temperature rises sharply or the smoke concentration increases abnormally, where ΔT1=T1-T0, ΔH1=H1-H0, and ΔPM1=PM1-PM0.

[0017] Furthermore, in step 4, the data from different sensors are fused and processed, and the multi-source data from the same type of sensors are locally fused using a correlation function or a data fusion algorithm to obtain the optimal fused data. When a fire occurs in a tunnel, three situations may occur: a sudden increase in temperature, a decrease in humidity, and a sudden increase in smoke concentration. If one of the data increases while the other data does not change significantly, further coupling analysis is required. At the same time, the change rates of the tunnel monitoring devices before and after the tunnel monitoring device with abnormal data also need to be immediately coupled analyzed, and the change rates of other tunnel monitoring devices should be proportional to the distance.

[0018] Furthermore, in step 5, it is determined whether the current environment is abnormal according to the preset threshold value. If the temperature or smoke concentration exceeds the set threshold value, it is considered that a fire may have occurred in the tunnel. Under normal circumstances, the temperature change rate ΔT≤10%, the humidity change rate ΔH≤15%, and the smoke change rate ΔPM≤10%. The hourly change rate of ΔT, ΔH, and ΔPM is less than 10%. When the change rate of the three sensors suddenly increases, that is, the hourly change rate exceeds 10% continuously, a fire occurs.

[0019] The different degrees of fire disasters determine the rate and magnitude of change of the current environmental factors in the tunnel. As long as the hourly change rate of ΔT, ΔH, and ΔPM and T are calculated, 1～n , H 1～n 、PM 1～n The maximum value of the fire can be used to determine the corresponding fire severity.

[0020] The present invention has the following beneficial effects due to the adoption of the above technical solution:

[0021] The coupling analysis of the present invention can effectively avoid false alarms in other special situations and false alarms of damage and abnormality of a single sensor. The setting of the deformation threshold not only improves the rigor of the coupling analysis, but also provides a preliminary understanding of the level and impact of tunnel fires, which is conducive to managers organizing rescue and material deployment. The LED automatic real-time warning screen can wirelessly connect to the monitoring station inside the tunnel to improve the timeliness of real-time alarms. The data collection equipment has the characteristics of lightweight, small size, light weight, and more convenient installation and disassembly. This means that the monitoring point station can be set up quickly in a short time, reducing interference with normal traffic in the tunnel. Moreover, daily maintenance work has become relatively simple, reducing the difficulty and workload of maintenance personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the tunnel fire warning data collection and installation structure of the present invention.

[0023] Numbers in the figure: 1-tunnel, 2-monitoring device inside the tunnel, 3-reference monitoring device outside the tunnel, 4-solar power storage device, 5-sound and light horn, 6-LED display screen, 7-wireless module, 8-support column. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and preferred embodiments. However, it should be noted that many details listed in the specification are only for the purpose of enabling the reader to have a thorough understanding of one or more aspects of the present invention, and these aspects of the present invention can be implemented even without these specific details.

[0025] like Figure 1 As shown, a tunnel fire alarm method based on temperature, humidity and smoke sensor coupling analysis comprises the following steps:

[0026] Step 1: Install several tunnel monitoring devices 2 in the tunnel 1 to collect data inside the tunnel, install tunnel reference monitoring devices 3 outside the tunnel to collect data on environmental changes and use them as reference data for prediction, and set up a tunnel fire warning device at a fixed distance in front of the tunnel entrance.

[0027] First, the temperature and humidity sensors and smoke sensors installed in the tunnel will collect environmental data in real time. These data include temperature, humidity, and smoke concentration. Temperature, humidity, and smoke monitoring require the establishment of a base station and several monitoring stations. The base station is located at the tunnel entrance and collects the baseline data of environmental factors: T0, H0, PM0; the monitoring station is located in the tunnel and is arranged at intervals of 20 to 50 meters according to demand, collecting environmental factors T inside the tunnel. 1～n , H 1～n 、PM1～n .

[0028] Several tunnel monitoring devices 2 in tunnel 1 are arranged at equal intervals, and adjacent tunnel monitoring devices 2 communicate with each other and transmit the collected data to the entrance of tunnel 1 one by one. At the same time, the data of each device is marked with the corresponding device number and the distance position of the device from tunnel 1. When the data sent by the rear tunnel monitoring device 2 to the front tunnel monitoring device 2 cannot be fed back, the rear tunnel monitoring device 2 will request communication with the first two tunnel monitoring devices 2, so as to avoid affecting the data collection and transmission of the entire tunnel when a communication failure occurs in a tunnel monitoring device 2.

[0029] The monitoring device 2 in the tunnel is equipped with a temperature and humidity sensor, a smoke sensor and a wireless networking transmission module. The temperature and humidity sensor is used to collect the temperature and humidity data of the environment, the smoke sensor is used to collect the smoke data, and the wireless networking transmission module is used to transmit the collected data to the external processor. The reference monitoring device 3 outside the tunnel collects the reference data of environmental factors: reference temperature T0, reference humidity H0, reference smoke PM0. The monitoring device 2 in the tunnel collects the environmental factors inside the tunnel, such as the tunnel temperature T 1～n 、Tunnel humidity H 1～n , tunnel smoke PM 1～n .

[0030] The tunnel fire early warning prompt device includes a solar energy storage device 4, an acoustic and light horn 5, an LED display screen 6, a wireless module 7, a support column 8 and a data processing device. The solar energy storage device 4, the acoustic and light horn 5, the LED display screen 6, the wireless module 7 and the data processing device are all arranged on the support column 8. The solar energy storage device 4 is connected to the acoustic and light horn 5, the LED display screen 6, the wireless module 7 and the data processing device for power supply. The acoustic and light horn 5, the LED display screen 6 and the wireless module 7 are all connected to the data processing device. The acoustic and light horn 5 is used for sound and light alarm, the LED display screen 6 is used for displaying fire data, and the wireless module 7 is used for reporting the fire situation to relevant units and uploading it to a cloud server.

[0031] Step 2: Preprocess the collected data, including data filtering and denoising. The raw data collected by the sensor usually needs to be preprocessed, such as filtering and denoising, to improve data quality, especially to eliminate abnormal readings and provide accurate input for subsequent analysis.

[0032] Step 3: Extract data features from the preprocessed data. Extract feature values ​​that are helpful in judging fire from the preprocessed data, such as whether the temperature rises sharply or whether the smoke density increases abnormally. ΔT1=T1-T0, ΔH1=H1-H0, ΔPM1=PM1-PM0... and so on.

[0033] Step 4: Conduct coupling analysis on the extracted feature data. The data from different sensors are fused and processed, and the multi-source data from the same type of sensors are locally fused using correlation functions or data fusion algorithms (such as DS evidence theory) to obtain the optimal fused data. Tunnel fires generally present three situations: rapid increase in temperature, decrease in humidity, and rapid increase in smoke concentration. If one of the data increases while the other data does not change significantly, further coupling analysis is required; at the same time, the change rates of other monitoring stations before and after the monitoring station with abnormal data also need to be coupled analyzed immediately. The change rate of other monitoring stations should be proportional to ΔL (the distance between other monitoring stations and the abnormal monitoring station).

[0034] Step 5: Make a threshold judgment on the data. Determine whether the current environment is abnormal based on the preset threshold. For example, if the temperature or smoke concentration exceeds the set threshold, the system will think that a fire may have occurred in the tunnel. Under normal circumstances in the tunnel, ΔT≤10%, ΔH≤15%, ΔPM≤10%, and the hourly change rate of ΔT, ΔH, and ΔPM is generally less than 10%. When the change rate of these three sensors increases sharply (the hourly change rate exceeds 10% continuously), there may be a fire.

[0035] The different degrees of fire disasters determine the rate and magnitude of change of the current environmental factors in the tunnel. Therefore, as long as the hourly change rate of ΔT, ΔH, and ΔPM and T are calculated, 1～n , H 1～n 、PM 1～n The maximum value of the fire can be used to determine the corresponding fire severity.

[0036] Step 6: Based on the data processing and judgment, when a fire occurs in the tunnel, an alarm is triggered, and the tunnel fire warning device gives an audible and visual alarm, reports to the relevant departments, and uploads the tunnel fire to the cloud for timely query by users. Once the system determines that there may be a fire in the tunnel, it will trigger an alarm and notify the personnel in the tunnel and the remote monitoring center so that emergency measures can be taken in time.

[0037] At the same time, machine learning optimization can also be carried out, using machine learning algorithms such as support vector machine (SVM) and artificial bee colony algorithm (ABC-SVM) to conduct a deeper analysis of sensor data to identify the characteristics of early fires and improve the accuracy and timeliness of fire detection.

[0038] Through the above steps, the coupled analysis of real-time monitoring data of temperature, humidity and smoke sensors can effectively determine whether a fire occurs in the tunnel. It can also send danger signals to the early warning information release equipment in real time and activate the sound and light alarms and LED screens in the tunnel and in front of the tunnel to indicate that there is a fire in the tunnel ahead. Vehicles should stop or detour immediately, providing important guarantees for the safe operation of the tunnel.

[0039] Improve accuracy through coupling analysis. Tunnel fires involve a variety of environmental parameter changes, which may cause false alarms to a certain extent. Coupling analysis can effectively avoid false alarms in other special situations and false alarms of damage and abnormality of a single sensor. The setting of deformation thresholds not only improves the rigor of coupling analysis, but also provides a preliminary understanding of the level and impact of tunnel fires, which is conducive to managers organizing rescue and material deployment.

[0040] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A tunnel fire alarm method based on coupling analysis of temperature, humidity and smoke sensors, characterized in that: The method comprises the following steps: Step 1: Install several tunnel monitoring devices (2) in the tunnel (1) to collect data inside the tunnel, install an external tunnel reference monitoring device (3) outside the tunnel to collect data on environmental changes and use it as reference data for prediction, and set a tunnel fire warning device at a fixed distance in front of the tunnel entrance; Step 2: Preprocess the collected data, including data filtering and denoising; Step 3: Extract data features from the preprocessed data; Step 4: Conduct coupling analysis on the extracted feature data; Step 5: Perform threshold judgment on the data; Step 6: Based on the data processing and judgment, when a fire occurs in the tunnel, an alarm is sounded and lighted by the tunnel fire warning prompt device, and the relevant departments are reported. At the same time, the tunnel fire is uploaded to the cloud for users to query in time.

2. The tunnel fire alarm method based on temperature, humidity and smoke sensor coupling analysis according to claim 1 is characterized in that: In step 1, a plurality of tunnel monitoring devices (2) in the tunnel (1) are arranged at equal intervals, and adjacent tunnel monitoring devices (2) communicate with each other, and transmit the collected data to the entrance of the tunnel (1) one by one. At the same time, the data of each device is marked with the corresponding device number and the distance position of the device in the tunnel (1). When the data sent by the rear tunnel monitoring device (2) to the front tunnel monitoring device (2) cannot be fed back, the rear tunnel monitoring device (2) will request to communicate with the front two tunnel monitoring devices (2), so as to avoid affecting the data collection and transmission of the entire tunnel when a communication failure occurs in a tunnel monitoring device (2).

3. The tunnel fire alarm method based on temperature, humidity and smoke sensor coupling analysis according to claim 1 is characterized in that: In step 1, a temperature and humidity sensor, a smoke sensor and a wireless networking transmission module are provided in the tunnel monitoring device (2), the temperature and humidity sensor is used to collect temperature and humidity data of the environment, the smoke sensor is used to collect smoke data, and the wireless networking transmission module is used to transmit the collected data to an external processor. The reference monitoring device (3) outside the tunnel collects reference data of environmental factors: reference temperature T0, reference humidity H0, reference smoke PM0, and the tunnel monitoring device (2) collects environmental factors such as tunnel temperature T0 inside the tunnel. 1～n 、Tunnel humidity H 1～n 、Tunnel smoke PM 1～n .

4. The tunnel fire alarm method based on temperature, humidity and smoke sensor coupling analysis according to claim 1 is characterized in that: In step 1, the tunnel fire early warning prompt device comprises a solar power storage device (4), an acoustic and light speaker (5), an LED display screen (6), a wireless module (7), a support column (8) and a data processing device. The solar power storage device (4), the acoustic and light speaker (5), the LED display screen (6), the wireless module (7) and the data processing device are all arranged on the support column (8). The solar power storage device (4) is connected to the acoustic and light speaker (5), the LED display screen (6), the wireless module (7) and the data processing device for power supply. The acoustic and light speaker (5), the LED display screen (6) and the wireless module (7) are all connected to the data processing device. The acoustic and light speaker (5) is used for sound and light alarm, the LED display screen (6) is used for displaying fire data, and the wireless module (7) is used for reporting the fire situation to relevant units and uploading it to a cloud server.

5. The tunnel fire alarm method based on temperature, humidity and smoke sensor coupling analysis according to claim 1 is characterized in that: In step 3, characteristic values ​​that are helpful in judging a fire are extracted from the preprocessed data to determine whether the temperature rises sharply or the smoke concentration increases abnormally, where ΔT1=T1-T0, ΔH1=H1-H0, and ΔPM1=PM1-PM0.

6. The tunnel fire alarm method based on temperature, humidity and smoke sensor coupling analysis according to claim 4 is characterized in that: In step 4, the data from different sensors are fused and processed, and the multi-source data from the same type of sensors are locally fused using a correlation function or a data fusion algorithm to obtain the optimal fused data. When a fire occurs in a tunnel, there will be three situations: a sudden increase in temperature, a decrease in humidity, and a sudden increase in smoke concentration. If one of the data increases while the other data does not change significantly, further coupling analysis is required. At the same time, the change rate of the tunnel monitoring device (2) before and after the tunnel monitoring device (2) with abnormal data also needs to be immediately coupled analyzed. The change rate of the other tunnel monitoring device (2) should be proportional to the distance.

7. The tunnel fire alarm method based on temperature, humidity and smoke sensor coupling analysis according to claim 1 is characterized in that: In step 5, the current environment is judged to be abnormal according to the preset threshold. If the temperature or smoke concentration exceeds the set threshold, it is considered that a fire may have occurred in the tunnel. Under normal circumstances, the temperature change rate ΔT≤10%, the humidity change rate ΔH≤15%, and the smoke change rate ΔPM≤10%. The hourly change rate of ΔT, ΔH, and ΔPM is less than 10%. When the change rate of the three sensors suddenly increases, that is, the hourly change rate exceeds 10% continuously, a fire occurs. The different degrees of fire disasters determine the rate and magnitude of change of the current environmental factors in the tunnel. As long as the hourly change rate of ΔT, ΔH, and ΔPM and T are calculated, 1～n , H 1～n 、PM 1～n The maximum value of the fire can be used to determine the corresponding fire severity.

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