Forest fire danger monitoring station system

Abstract

The utility model discloses a forest fire danger monitoring station system and a wireless sensor network technology. The system is mainly composed of a microprocessor, a fire factor sensing module, a power supply module, a communication module, a storage module, a display module and a protection module. And the communication module is connected with the microprocessor and is responsible for transmitting the collected fire danger factor data to a cloud platform section through a network, so that visual display and real-time monitoring of the data are realized. The storage module is used for storing historical fire danger factor data. And the display module is used for visually displaying the real-time fire danger factor data and carrying out fire danger alarm reminding. The power supply module provides stable power supply guarantee for the whole monitoring system. According to the utility model, the wireless sensor network technology and the NB-IoT technology are combined, and monitoring, data reporting and analysis of forest fire factor information in a specific area can be effectively realized.

Description

Technical Field

[0001] The utility model relates to wireless sensor network technology, wireless communication technology and Internet of Things technology, and belongs to the field of forest fire alarm, as well as early detection and early warning of fire in forest environments. Background Art

[0002] Forest fires are one of the most devastating natural disasters worldwide, characterized by their high frequency and wide-ranging impacts. Fires not only destabilize ecosystems but also cause enormous economic losses and casualties. According to statistics, approximately 200,000 forest fires occur worldwide each year, burning a total area of ​​350 to 450 million hectares. These fires often occur in remote areas where sparse populations and delayed information transmission often lead to large-scale disasters by the time they are discovered. Therefore, establishing an efficient forest fire risk monitoring and early warning system is crucial for early detection and rapid response to fires.

[0003] Traditional forest fire prevention methods primarily rely on ground patrols, observation towers, and manual alarms. However, these methods suffer from limited monitoring range, slow response times, and susceptibility to weather and terrain, making them inadequate for modern forest fire prevention. With advances in science and technology, a growing number of advanced forest fire prevention technologies are being applied in practical fire prevention efforts. For example, satellite monitoring, infrared thermal imaging, and digital land-based remote observation systems have garnered widespread attention and application.

[0004] However, these technologies still face numerous challenges in practical application. For example, while satellite patrol monitoring offers wide coverage, its information is updated infrequently. Fires are often already widespread by the time they are discovered, making timely early warning difficult. Infrared thermal imaging technology is easily obscured by vegetation, and the high cost of equipment makes it difficult to widely adopt. While digital land-based remote observation systems can provide high-definition monitoring, their high construction and maintenance costs and complex construction preclude widespread application in densely forested areas. Internationally, several countries, including Canada and the United States, have experimented with technologies such as satellite monitoring and drone early warning systems, but these remain limited by high costs and incomplete coverage. China is also actively exploring new forest fire prevention technologies, such as using drones equipped with monitoring equipment for aerial inspections, but this approach also suffers from issues such as insufficient monitoring accuracy and difficulty in detecting fires in a timely manner. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention proposes a forest fire risk monitoring station system. The system integrates a variety of fire risk factor sensors and combines intelligent analysis technology to achieve real-time monitoring and early warning of forest fire risks. Compared with traditional monitoring methods, the system significantly improves the scope and accuracy of monitoring, while reducing construction and operating costs, providing more efficient and reliable technical support for forest fire prevention. By collecting and analyzing key fire risk factor data such as soil moisture content, combustible moisture content, and meteorological factors in real time, the system can accurately assess fire risks, achieve early detection and rapid response, and overcome many limitations of existing technologies. The utility model adopts a wireless sensor network to collect data on multiple fire risk factors in real time, solving the problem of being unable to predict forest fire risks in a timely and high-precision manner.

[0006] The technical solution adopted by the utility model to solve its technical problems is:

[0007] A forest fire risk monitoring station system, the system comprises: a fire risk factor sensing module, a microprocessor, a storage module, a power supply module, a communication module, a display module, a protection module, and a bracket;

[0008] The fire risk factor perception module includes: multiple meteorological element sensors, soil moisture sensor, combustible moisture sensor, phenological analyzer, temperature and humidity sensor, Beidou / GPS dual positioning module; the data collected by the fire risk factor perception module is transmitted to the microprocessor, and the microprocessor transmits the received data to the storage module for storage; the power supply module supplies power to the remaining modules; the display module displays the parameter information of the fire risk factor perception module; the microprocessor transmits the data stored in the storage module to the cloud platform wirelessly through the communication module; the protection module is used to protect the entire fire risk factor perception module;

[0009] The protection module includes a guardrail and a protective chassis. The guardrail surrounds the forest fire risk monitoring station. The microprocessor, storage module, power supply module, communication module, and display module are arranged in the protective chassis; the multi-meteorological element sensor, phenological analyzer, and protective chassis are arranged on a bracket, and the bracket is fixed to the ground.

[0010] Furthermore, the power supply module includes a solar panel and a battery.

[0011] Furthermore, a soil moisture sensor is inserted into the soil, a combustible moisture sensor is arranged inside the combustibles around the fire risk monitoring station, and the soil moisture sensor and the combustible moisture sensor are connected to the microprocessor via a wiring harness.

[0012] Furthermore, the multi-meteorological element sensor uses the MX-S60 micro-weather station provided by the China Meteorological Administration, the phenological analyzer uses the RR-8140 surface combustible material phenological state sensor, and the positioning module uses the ATGM336H.

[0013] Furthermore, the microprocessor adopts STM32F103ZET6 single chip microcomputer.

[0014] Furthermore, the communication module is the narrowband Internet of Things chip BC260Y.

[0015] In order to ensure that the system can operate stably for a long time in a complex forest environment, the utility model has designed a power supply module and a protection system. The power supply module includes a solar panel, an energy storage lithium battery pack and a power management module. The solar panel converts light energy into electrical energy and stores it in the energy storage lithium battery pack. The power management module is responsible for controlling the charging and discharging process, maintaining voltage stability, and ensuring safe operation of the circuit. In addition, the system is designed with multi-level protection measures, including lightning rods and surge protectors to protect the system from the impact of lightning strikes and voltage fluctuations. The outer casing is made of waterproof and dustproof materials and is equipped with an insect-proof net to adapt to a variety of harsh environmental conditions. These protective measures effectively improve the reliability and durability of the system, ensuring that the equipment can still operate normally in extreme weather and complex environments, and provide stable monitoring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is the overall system connection block diagram of the utility model.

[0017] Figure 2 This is a schematic diagram of the actual structure of the utility model.

[0018] Figure 3 This is a flow chart for regularly collecting fire risk factors in the utility model. Specific implementation plan

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] refer to Figure 1The present invention provides a forest fire risk factor monitoring station system, which aims to achieve efficient monitoring and early warning of fire risk factors in forest areas. The system is mainly composed of a microprocessor, a fire risk factor sensing module, a power supply module, a communication module, a storage module, a display module and a protection module. The fire risk factor sensing module is responsible for real-time collection of key data such as soil moisture content, combustible moisture content, phenological status, location information and various meteorological elements, so as to achieve real-time monitoring of forest conditions and comprehensive assessment of fire risks. The fire risk factor data collected by various sensors is transmitted to the microprocessor for processing to achieve monitoring of forest fire risks. The microprocessor stores the collected data in the storage module, and analyzes historical data to perform trend monitoring and fire risk assessment, helping to formulate scientific prevention and emergency measures, and improving fire prediction models. After the collected data is processed, the microprocessor sends the data to the cloud platform through the communication module to achieve visual display and real-time monitoring of the data. The display module can display the latest fire risk factor data and provide the function of viewing historical data, which is convenient for on-site inspection of forest fire risk status. The protection module includes a lightning rod, surge protector, and waterproof and insect-proof housing, enhancing the device's anti-interference capabilities and environmental adaptability, ensuring long-term stable operation in complex forest environments and guaranteeing the continuity and accuracy of data collection. The power supply module, primarily composed of solar panels, a lithium battery pack for energy storage, and a power management module, ensures the device's power supply needs.

[0021] The fire risk factor perception module of the present invention includes a multi-meteorological element sensor, a soil moisture sensor, a combustible material moisture sensor, a phenological analyzer, and a Beidou / GPS dual positioning module. The multi-meteorological element sensor uses the MX-S60 micro-weather station provided by the China Meteorological Administration, which can accurately measure meteorological elements such as wind direction, wind speed, rainfall, temperature, humidity, and atmospheric pressure around the clock. The soil moisture sensor uses the widely used direct-plug soil temperature and humidity sensor, which has stable and reliable performance. The phenological analyzer uses the RR-8140 surface combustible material phenological state sensor, which uses visible light and near-infrared dual-band detection technology to identify the greening and snow accumulation status, and analyze the status and changes of surface combustibles. The positioning module uses the ATGM336H, which can obtain Beidou and GPS location information. The combustible material moisture content sensor measures the conductivity of a specific wooden stick, establishes a functional relationship with the moisture content, and then calculates the moisture content of dead combustibles at different hysteresis levels.

[0022] All kinds of sensors in the utility model are connected to the microprocessor, which adopts the STM32F103ZET6 single-chip microcomputer. The single-chip microcomputer is based on the ARM Cortex-M3 core, has an operating frequency of up to 72MHz, is equipped with 512KB flash memory and 64KB SRAM, and supports multiple communication interfaces (such as USART, SPI and I2C). The system is provided with an analog-to-digital conversion module for converting the analog voltage signal collected by the sensor into a digital signal, which is convenient for the microprocessor to perform subsequent data processing. The sensor serves as the signal input of the forest fire risk monitoring station and can sense various fire risk factor signals in the monitoring area. The microprocessor stores the collected data in the storage module. By analyzing the historical fire risk factor data, it can better perform data trend analysis, assist in formulating scientific prevention and emergency measures, and improve and verify the fire prediction model. The microprocessor can also package the data collected in real time and transmit it to the cloud platform through the communication module.

[0023] This utility model uses the BC260Y narrowband IoT chip as a communication module to achieve efficient data transmission. The BC260Y is a high-performance, low-power, multi-band NB-IoT module, making it particularly suitable for information transmission in complex forest environments. The microprocessor packages the fire risk factor data and transmits it to a cloud platform via the communication module, where it visualizes the received data. Furthermore, when the fire risk factor data exceeds a preset safety threshold, the system sends an alarm to the cloud platform via the communication module, enabling forest fire early warning and detection.

[0024] refer to Figure 2 The system's actual structural diagram illustrates the deployment of monitoring stations. Taking into account factors such as terrain, climate, and vegetation type, the optimal deployment location should possess the following characteristics: It should be away from human interference to reduce false alarms; and it should provide comprehensive coverage of forested areas, particularly high-risk areas with dense flammable vegetation, complex terrain, and a dry climate. Furthermore, to ensure the operation and maintenance of the equipment, the deployment site must have stable power supply and network communication conditions to ensure real-time and accurate data transmission to the monitoring center. During deployment, the phenological analyzer and multi-meteorological element sensor are installed on the top of the device, while the solar panel is installed in the middle for power supply. The protective chassis houses core components such as the microprocessor, communication module, lithium battery pack, and power management module. Combustible moisture content sensors and soil moisture sensors are located at the bottom of the device to monitor the corresponding fire risk factors. The monitoring station is also fenced to prevent wildlife from damaging the equipment.

[0025] refer to Figure 3The fluctuations of various fire risk factors are affected by seasons, but they are relatively stable in the short term. Therefore, periodically disabling the system's signal acquisition, data transmission, and wireless communication functions can significantly reduce redundant data and extend the system's operating life, especially in complex forest environments with limited energy. This system features a sleep function. After the system completes a data transmission to the cloud platform, the various sensors and microprocessors enter sleep mode in an orderly manner. The acquisition frequency is set based on the season, time of day, weather, and fire severity, and is controlled by a timer. When the timer triggers an interrupt, the system wakes up, and the sensors initialize and begin collecting the corresponding fire risk factor data. If a fire risk data item exceeds the set threshold, the system immediately sends an alarm to the cloud platform. After data collection is complete, the microprocessor first stores the data in the storage module and then transmits it to the cloud platform via the communication module. After data transmission is complete, the system resets the timer and enters sleep mode again, awaiting the next wakeup. To prevent the system from being able to detect unexpected events while in sleep mode, this system also features a triggered wakeup function. During timed sleep mode, the temperature and humidity sensors continue to operate, monitoring temperature and humidity changes in the environment in real time. When an environmental parameter exceeds the set threshold, the system immediately wakes up to perform signal acquisition and data transmission. The cloud platform will receive sudden fire risk information in a timely manner and alert the fire department or emergency management department to make decisions and deployments.

Claims

1. A forest fire risk monitoring station system, comprising: a fire risk factor sensing module, a microprocessor, a storage module, a power supply module, a communication module, a display module, a protection module, and a bracket; The fire risk factor perception module includes: Multiple meteorological element sensors, soil moisture sensor, combustible moisture sensor, phenological analyzer, temperature and humidity sensor, Beidou / GPS dual positioning module; The data collected by the fire risk factor sensing module is transmitted to the microprocessor, which transmits the received data to the storage module for storage; the power supply module supplies power to the remaining modules; the display module displays the parameter information of the fire risk factor sensing module; the microprocessor transmits the data stored in the storage module to the cloud platform wirelessly through the communication module; the protection module is used to protect the entire fire risk factor sensing module; The protection module includes a guardrail and a protective chassis. The guardrail surrounds the forest fire risk monitoring station. The microprocessor, storage module, power supply module, communication module, and display module are arranged in the protective chassis; the multi-meteorological element sensor, phenological analyzer, and protective chassis are arranged on a bracket, and the bracket is fixed to the ground.

2. A forest fire risk monitoring station system according to claim 1, characterized in that: The power supply module includes solar panels and batteries.

3. A forest fire risk monitoring station system according to claim 1, characterized in that: The soil moisture sensor is inserted into the soil, the combustible moisture sensor is arranged inside the combustibles around the fire risk monitoring station, and the soil moisture sensor and the combustible moisture sensor are connected to the microprocessor through a wiring harness.

4. A forest fire risk monitoring station system according to claim 1, characterized in that: The multi-meteorological element sensor uses the MX-S60 micro-weather station provided by the China Meteorological Administration, the phenological analyzer uses the RR-8140 surface combustible material phenological state sensor, and the positioning module uses the ATGM336H.

5. A forest fire risk monitoring station system according to claim 1, characterized in that: The microprocessor uses STM32F103ZET6 single chip microcomputer.

6. A forest fire risk monitoring station system according to claim 1, characterized in that: The communication module is the narrowband IoT chip BC260Y.

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