Road tunnel environment monitoring system based on WiFi multi-stage bridging Internet of Things technology

Abstract

The utility model discloses a highway tunnel environment monitoring system based on WiFi multistage bridging Internet of Things technology, which comprises an environment data acquisition terminal, a data transmission system and a cloud server, and is characterized in that the environment data acquisition terminal comprises a plurality of acquisition terminals arranged in a tunnel along the length direction of the tunnel; the acquisition terminal comprises a data acquisition unit and a WiFi transmission unit, and the data acquisition unit is used for acquiring CO concentration, NO2 concentration, temperature and humidity, smoke concentration, ambient light and monitoring videos at a layout position, preprocessing the CO concentration, the NO2 concentration, the temperature and humidity, the smoke concentration, the ambient light and the monitoring videos into environmental data and sending the environmental data to the WiFi transmission unit; and the data transmission is used for acquiring environment data of all WiFi transmission units in the tunnel and sending the environment data to the cloud server. The tunnel environment monitoring system is especially suitable for an extra-long tunnel, realizes long-term and comprehensive monitoring of environment data in the tunnel, is comprehensive, intelligent and stable in monitoring, and provides intelligent response and scientific decision for construction, operation process, traffic management, emergency rescue and the like of the tunnel.

Description

TECHNICAL FIELD

[0001] The utility model relates to environmental monitoring system especially based on WiFi multistage bridging internet of things technology's highway tunnel environmental monitoring system. BACKGROUND

[0002] The special long tunnel has the problems of relative closure of tunnel section, poor light in the tunnel, low air quality, large environmental noise and high probability of traffic safety accidents compared with general road sections. It is difficult to conduct comprehensive environmental data monitoring and ventilation test on such tunnels, the monitoring cost is high, the test period is long, and there are great safety hazards for the early construction and later operation of the tunnel.

[0003] And the existing monitoring scheme and detection means mostly focus on the monitoring analysis of single environmental factor, lack long-term and comprehensive monitoring data, and for some special long tunnels and complex mountain tunnels, there are problems of difficult signal transmission, high monitoring cost and poor stability of monitoring equipment. SUMMARY

[0004] The utility model discloses a highway tunnel environmental monitoring system based on WiFi multistage bridging internet of things technology to solve the above problems.

[0005] In order to realize the above-mentioned purpose, the utility model adopts the technical scheme as follows: a highway tunnel environmental monitoring system based on WiFi multistage bridging internet of things technology, comprising an environmental data acquisition terminal, a data transmission system and a cloud server, the environmental data acquisition terminal comprises a plurality of acquisition terminals arranged along the length direction in the tunnel, the acquisition terminal comprises a data acquisition unit and a WiFi transmission unit, wherein the data acquisition unit is used for acquiring CO concentration, NO2 concentration, temperature and humidity, smoke concentration, environmental light and monitoring video at the arranged place, and pretreatment is carried out to send the environmental data to the WiFi transmission unit.

[0006] The data transmission system comprises a bridge unit, a switch and a 4G base station, wherein the bridge unit is used for acquiring the environmental data of all WiFi transmission units in the tunnel, and the environmental data is sent to the cloud server through the switch and the 4G base station.

[0007] As preferred, the acquisition terminals are uniformly arranged, and all the acquisition terminals cover the whole tunnel.

[0008] As preferred: the data acquisition unit comprises a microprocessor, a gas detection sensor connected with the microprocessor, a temperature and humidity sensor, a smoke concentration sensor, an ambient light sensor and a video monitoring camera, wherein the microprocessor is an STM32F103 microprocessor, the gas detection sensor is an MICS-6814 gas sensor for detecting CO concentration and NO2 concentration, the temperature and humidity sensor is a DHT11 temperature and humidity sensor, the smoke concentration sensor is an MQ-2 smoke sensor, the ambient light sensor is an AP3216C light sensor, and the video monitoring camera is an OV7725 camera; the WiFi transmission unit is an ESP8266 WiFi module.

[0009] As preferred: in the data acquisition unit, the preprocessing is completed by the microprocessor, including data filtering, denoising and / or compensation.

[0010] As preferred: the bridge unit comprises a plurality of bridges arranged along the length direction of the tunnel, and adopts a multi-stage bridging mode to transmit the environmental data.

[0011] Compared with the prior art, the advantages of the utility model lie in that the integrated gas detection sensor, temperature and humidity sensor, smoke concentration sensor, ambient light sensor and video monitoring camera and other devices collect various data in the tunnel, and the WiFi multi-stage bridging technology is used to solve the monitoring data transmission problem in the complex tunnel. Long-term and comprehensive monitoring of the environmental information in the tunnel is realized, and the monitoring is comprehensive, intelligent, stable and low in cost. The utility model can also realize intelligent monitoring of the tunnel by cooperating with a simple recognition algorithm, and can provide scientific management and decision-making data for the construction and operation of the tunnel. BRIEF DESCRIPTION OF DRAWINGS

[0012] Fig. 1 It is a whole structure schematic view of the utility model

[0013] Fig. 2 It is an environmental data acquisition terminal structure schematic view

[0014] Fig. 3 It is a data transmission system structure schematic view

[0015] Fig. 4 It is a layout schematic view of the utility model. DETAILED DESCRIPTION

[0016] The utility model will be further described in combination with examples below.

[0017] Example 1: see Figs. 1 to 4 A highway tunnel environmental monitoring system based on WiFi multi-stage bridging Internet of Things technology, comprising an environmental data acquisition terminal, a data transmission system and a cloud server.

[0018] The environment data collection terminal comprises a plurality of collection terminals arranged along the length direction of the tunnel, and the collection terminal comprises a data collection unit and a WiFi transmission unit, wherein the data collection unit is used for collecting the CO concentration, the NO2 concentration, the temperature and humidity, the smoke concentration, the ambient light and the monitoring video at the arranged position, and the preprocessed environment data is sent to the WiFi transmission unit;

[0019] The data transmission system comprises a bridge unit, a switch and a 4G base station, wherein the bridge unit is used for acquiring the environment data of all WiFi transmission units in the tunnel, and the environment data is sent to the cloud server through the switch and the 4G base station.

[0020] The collection terminals are uniformly arranged, and all the collection terminals cover the entire tunnel.

[0021] The data collection unit comprises a microprocessor, a gas detection sensor, a temperature and humidity sensor, a smoke concentration sensor, an ambient light sensor and a video monitoring camera connected with the microprocessor, wherein the microprocessor is an STM32F103 microprocessor, the gas detection sensor is an MICS-6814 gas sensor used for detecting the CO concentration and the NO2 concentration, the temperature and humidity sensor is a DHT11 temperature and humidity sensor, the smoke concentration sensor is an MQ-2 smoke sensor, the ambient light sensor is an AP3216C light sensor, and the video monitoring camera is an OV7725 camera; and the WiFi transmission unit is an ESP8266 WiFi module.

[0022] In the data collection unit, the preprocessing is completed by the microprocessor, including data filtering, denoising and / or compensation.

[0023] The bridge unit comprises a plurality of bridges arranged along the length direction of the tunnel, and the environment data is transmitted in a multi-level bridging mode. Since the tunnel is a long and narrow closed space, the WiFi signal is enhanced when propagating along the tunnel through multi-path reflection propagation, but the signal is weakened or lost at the corner, so it is necessary to realize the WiFi signal coverage in the tunnel through multi-level bridging. Generally, the WiFi signal coverage range of each sub-bridge is 50-200 meters, so the WiFi signal coverage range and the tunnel terrain are comprehensively considered when arranging. For example Fig. 4 A specific bridge unit setting and arrangement mode is given, for example Fig. 4As shown, 6 bridges are selected and hierarchical, and numbered respectively, the first level includes master bridge 01, sub-bridge 01, the second level includes master bridge 02, sub-bridge 02, the third level includes master bridge 03, sub-bridge 03, the master bridge and the sub-bridge are the same structure, only the layout time is set differently, the sub-bridge is used for signal receiving, and the master bridge is used for signal sending. Point-to-point wireless bridging is used between the sub-bridge and the master bridge of the same level, and reliable data transmission is carried out through the specified SSID and channel. Each master bridge is connected with the sub-bridge of the upper level through a network cable.

[0024] In the utility model, the transmission of environmental data is divided into two steps:

[0025] The first step is that the environmental data is sent to the nearest sub-bridge.

[0026] After the data acquisition unit collects the CO concentration, NO2 concentration, temperature and humidity, smoke concentration, environmental light and monitoring video, converts each signal into an analog voltage signal and a digital signal, and then pre-processes, obtains comprehensive environmental data, and then packs the data into a data packet according to the TCP / IP protocol, sends the data packet to the nearest sub-bridge, such as sub-bridge 01, sub-bridge 02 and sub-bridge 03.

[0027] The second step is to send the data packet to the cloud server.

[0028] After the sub-bridge receives the data packet, the data is sent to the corresponding master bridge, such as sub-bridge 03 sending the received data packet to master bridge 03. After each master bridge receives the data packet, it will be checked, and the data packet that passes the check will be treated as valid data, which is forwarded to the sub-bridge of the upper level through a network cable, such as master bridge 03 sending the valid data to sub-bridge 02 through a network cable. According to this method, all valid data is finally gathered to master bridge 01, and then sent to the cloud server through the 4G base station and the switch. The cloud server stores and processes the data, and provides real-time monitoring, alarm, prediction and other functions through the data analysis platform.

[0029] Embodiment 2: see Figs. 1 to 4 In the utility model environmental data acquisition terminal,

[0030] The gas detection sensor adopts MICS-6814 gas sensor to monitor the two kinds of pollutants CO and NO2 with high concentration in the tunnel, and the MICS-6814 gas sensor is a stable MEMS sensor, which is very suitable for such a bad environment as the tunnel. The sensor also has the characteristics of high precision, high sensitivity and low cost.

[0031] The temperature and humidity sensor adopts a DHT11 temperature and humidity sensor. The sensor has the advantages of small size, fast response, strong anti-interference ability, high cost performance, ultra-low power consumption, etc. The sensor internally contains a negative temperature coefficient NTC component for temperature measurement and a resistance type humidity sensing element for humidity measurement. It has four pins, one of which can be used for data transmission up to 20 meters.

[0032] The smoke concentration sensor is an MQ-2 smoke sensor, which is an important element for tunnel environment smoke concentration. The MQ-2 smoke sensor is suitable for various application scenarios, with a smoke concentration detection range of 100-10000ppm, low cost, high sensitivity, fast response, and good stability. When the MQ-2 smoke sensor is in an environment with smoke or flammable gas, the conductivity of the sensor increases with the increase of the concentration of smoke or flammable gas in the air. The STM32F103 microprocessor internally integrates a high-speed ADC for smoke concentration data acquisition, which can obtain real-time tunnel smoke concentration environment information.

[0033] The ambient light sensor is an AP3216C light sensor, which has the characteristics of high precision and high sensitivity. It can measure in a wide range of light intensity, with an effective measurement range of 0-1023Lux, and can provide accurate measurement results under different light conditions. At the same time, it also has the characteristics of low power consumption and fast response, with an effective acquisition rate of 50Hz.

[0034] The video monitoring camera is an OV7725 camera, which is a high-performance sensor integrating a 1 / 4 inch single-chip VGA camera and an image processor. OV7725 supports outputting images with a maximum of 3 million pixels (640x480 resolution), with excellent imaging quality, simple development, and a maximum image transmission rate of 60fps, which can meet the application requirements of low-illumination scenes such as tunnels.

[0035] The STM32F103 microprocessor is used to preprocess the data collected by the above-mentioned data acquisition unit according to existing technical means. For example, preprocessing includes Kalman filter data processing and corrosion iteration color tracking recognition.

[0036] Kalman filter data processing is used for filtering and optimizing the data collected by the gas detection sensor and the smoke concentration sensor. Compared with Wiener filter, temperature compensation algorithm, Kalman filter has small error, high accuracy, strong stability and fast response. First, the mean filter is used to eliminate the pulse noise of the data, and then the Kalman filter is used to optimize the data. For the gas detection sensor, when the environmental temperature and humidity are different, the corresponding characteristic curve will also have obvious deviation. The DHT11 temperature and humidity sensor is used to compensate the temperature and humidity of the smoke, CO and CO2 concentration measurement values through multiple measurement results, which can greatly compensate the measurement error caused by different temperature and humidity.

[0037] The corrosion iteration color tracking recognition algorithm is mainly based on the data of the camera to identify the traffic congestion in the tunnel. The OV7725 camera displays the images collected in the tunnel on the LCD display screen in real time. The STM32F103 microprocessor converts the RGB value of the LCD display screen into HSL through a conversion formula, compares the converted hue H, saturation S and brightness L with the HSL of the target color, and completes the color recognition. The corrosion iteration color tracking recognition algorithm is used to realize the tracking and recognition of the target color object in the image. The color tracking and recognition of the vehicle tail light in the tunnel can be realized by using the algorithm, and the vehicle congestion condition of the video monitoring section can be judged by calculating the tracking and recognition time.

[0038] After the environmental data is sent to the cloud server, the staff can obtain the data through the data analysis platform connected to the cloud server and process various environmental data as needed.

[0039] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A highway tunnel environmental monitoring system based on WiFi multi-level bridging IoT technology, comprising an environmental data acquisition terminal, a data transmission system, and a cloud server, characterized in that: The environmental data acquisition terminal includes several acquisition terminals deployed along the length of the tunnel. Each acquisition terminal includes a data acquisition unit and a WiFi transmission unit. The data acquisition unit is used to collect CO concentration, NO2 concentration, temperature and humidity, smoke concentration, ambient light and monitoring video at the deployment location, and preprocesses them into environmental data before sending them to the WiFi transmission unit. The data transmission system includes a bridge unit, a switch, and a 4G base station. The bridge unit is used to acquire environmental data from all WiFi transmission units in the tunnel and then transmits the environmental data to the cloud server via the switch and the 4G base station.

2. The highway tunnel environmental monitoring system based on WiFi multi-level bridging IoT technology according to claim 1, characterized in that: The data acquisition terminals are evenly distributed, and all the data acquisition terminals cover the entire tunnel.

3. The highway tunnel environmental monitoring system based on WiFi multi-level bridging IoT technology according to claim 1, characterized in that: The data acquisition unit includes a microprocessor, a gas detection sensor, a temperature and humidity sensor, a smoke concentration sensor, an ambient light sensor, and a video surveillance camera connected to the microprocessor. The microprocessor is an STM32F103 microprocessor, the gas detection sensor is a MICS-6814 gas sensor used to detect CO and NO2 concentrations, the temperature and humidity sensor is a DHT11 temperature and humidity sensor, the smoke concentration sensor is an MQ-2 smoke sensor, the ambient light sensor is an AP3216C light sensor, and the video surveillance camera is an OV7725 camera. The WiFi transmission unit is an ESP8266 WiFi module.

4. The highway tunnel environmental monitoring system based on WiFi multi-level bridging IoT technology according to claim 3, characterized in that: In the data acquisition unit, preprocessing is performed by a microprocessor, including data filtering, noise reduction, and / or compensation.

5. The highway tunnel environmental monitoring system based on WiFi multi-level bridging IoT technology according to claim 1, characterized in that: The network bridge unit includes multiple network bridges deployed along the tunnel length direction, and uses a multi-level bridging method to transmit environmental data.

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