Fireproof monitoring system for forest farm in mountainous area

Abstract

The invention belongs to the technical field of forest fire monitoring and early warning, and relates to a mountain forest farm fire prevention monitoring system which comprises an environment monitoring module and a video monitoring module, the environment monitoring module comprises a sensor and a data acquisition unit, and the video monitoring module comprises a high-resolution camera and a processing module. The environment monitoring module and the video monitoring module are connected to the data transmission module, and the data transmission module is connected to the early warning response module. According to the invention, the forest environment change can be monitored in real time, the fire risk can be automatically identified, and the early warning information can be timely sent out, so that the scientificity and effectiveness of fire prevention and control are improved, and the monitoring, early warning and response capabilities of forest fire are improved to protect the ecological environment and human property safety.

Description

Technical Field

[0001] The invention belongs to the technical field of forest fire monitoring and early warning, and in particular relates to a forest fire prevention monitoring system in a mountainous area. Background Art

[0002] With global climate change and the increase of human activities, the frequency and intensity of forest fires are increasing year by year. Especially in mountainous areas, due to the complex terrain and dense vegetation, it is often difficult to detect and extinguish fires in time, causing serious ecological and economic losses. Therefore, it is particularly important to establish an efficient and accurate fire monitoring system.

[0003] At present, there are a variety of fire monitoring technologies and equipment, such as satellite remote sensing, drone monitoring, video surveillance, etc., which have improved the fire monitoring capabilities to a certain extent. However, when applied in complex mountainous terrain, there are still problems such as slow response speed, large monitoring blind spots, and frequent manual intervention.

[0004] Therefore, there is an urgent need for a new type of fire monitoring system suitable for mountain forests to achieve more comprehensive and timely fire warnings. Summary of the invention

[0005] The purpose of the present invention is to provide a forest fire monitoring system in a mountainous area, which has a forest fire monitoring function and solves the problems of slow response speed, large monitoring blind area and frequent manual intervention in the existing technology in complex terrain in mountainous areas.

[0006] In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is that the present invention provides a mountain forest fire prevention monitoring system, including an environmental monitoring module and a video monitoring module, the environmental monitoring module includes a sensor and a data acquisition unit, the video monitoring module includes a high-resolution camera and a processing module, the environmental monitoring module and the video monitoring module are connected to a data transmission module, and the data transmission module is connected to an early warning response module.

[0007] Specifically, a number of sensors are evenly distributed in a square array. The sensors are high-precision environmental sensors, including temperature and humidity sensors, wind speed and direction sensors, and light sensors. The temperature and humidity sensors, wind speed and direction sensors, and light sensors are combined as a detection point in the sensor array.

[0008] Specifically, the data acquisition unit is a microcontroller, specifically a single-chip microcomputer or a Raspberry Pi. The number of data acquisition units corresponds to the number of sensors, and each detection point is connected with a data acquisition unit.

[0009] Specifically, the high-resolution cameras are installed at key locations in the forest area to ensure real-time monitoring of key areas. The key locations in the forest area are mountain tops, forest edges, and flammable areas.

[0010] Specifically, the processing module has an image analysis function. The image analysis uses a deep learning framework to train a fire recognition model, analyzes images captured by the camera, and identifies smoke and open flames in real time. The learning framework is TensorFlow or PyTorch.

[0011] Specifically, the data transmission module adopts wireless communication, and the wireless communication technology uses a low-power wide area network, specifically LoRa or Zigbee.

[0012] Specifically, the data transmission module uses the AES encryption algorithm to encrypt the transmission data.

[0013] Specifically, the early warning response module includes a fire risk assessment model and an information release module.

[0014] Specifically, the fire risk assessment model is set based on environmental data and monitoring data, and automatically determines the fire risk level by setting thresholds for each detection data.

[0015] Specifically, the information release module automatically issues warning information after confirming the fire risk.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are:

[0017] 1. The present invention can monitor forest environmental changes in real time, automatically identify fire risks, and issue early warning information in a timely manner, thereby improving the scientificity and effectiveness of fire prevention and control, aiming to improve the monitoring, early warning and response capabilities of forest fires to protect the ecological environment and human property safety;

[0018] 2. The present invention has the function of forest fire prevention monitoring, which solves the problems of slow response speed, large monitoring blind area and frequent manual intervention in the existing technology in complex mountainous terrain. DETAILED DESCRIPTION

[0019] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described below in conjunction with embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways than those described herein. Therefore, the present invention is not limited to the specific embodiments of the following disclosure.

[0021] Embodiment 1, a mountain forest fire prevention monitoring system, includes an environmental monitoring module and a video monitoring module, the environmental monitoring module includes a sensor and a data acquisition unit, the video monitoring module includes a high-resolution camera and a processing module, the environmental monitoring module and the video monitoring module are connected to a data transmission module, and the data transmission module is connected to an early warning response module.

[0022] Sensors are used to monitor the environmental parameters of the forest area in real time, such as temperature and humidity, wind speed and direction, and light intensity. Each sensor has high precision and can sensitively capture environmental changes. The data acquisition unit is responsible for collecting and transmitting sensor data. High-resolution cameras are installed at key locations in the forest area to monitor images in key areas in real time. The processing module uses a fire recognition model trained by a deep learning framework to analyze images captured by the camera and identify smoke and open flames in real time.

[0023] The data transmission module is responsible for transmitting the data from the environmental monitoring module and the video surveillance module to the early warning response module. Based on the environmental monitoring and image data, the early warning response module automatically assesses the fire risk level by setting the threshold of each detection data. After confirming the fire risk, it automatically issues an early warning message to remind relevant personnel and institutions to take timely measures.

[0024] The following is a detailed description of the design of the above key components:

[0025] Specifically, a number of sensors are evenly distributed in a square array. The sensors are high-precision environmental sensors, including temperature and humidity sensors, wind speed and direction sensors, and light sensors. The temperature and humidity sensors, wind speed and direction sensors, and light sensors are combined as a detection point in the sensor array.

[0026] The sensors are evenly distributed in the forest area in the form of a square array. This layout ensures maximum monitoring coverage and avoids blind spots. Each detection point in the sensor array contains a combination of temperature and humidity sensors, wind speed and direction sensors, and light sensors.

[0027] Sensor Type:

[0028] Temperature and humidity sensor: monitors changes in air temperature and humidity to warn of potential risks of fire.

[0029] Wind speed and direction sensor: used to monitor wind speed and direction to assess the possibility and direction of fire spread.

[0030] Light sensors: Monitor changes in ambient light intensity, helping to detect light obstruction by fire or smoke.

[0031] Sensors are deployed at multiple key points within the monitoring area to ensure full coverage. The data from each sensor point is collected by connecting to a data acquisition unit.

[0032] Specifically, the data acquisition unit is a microcontroller, specifically a single-chip microcomputer or a Raspberry Pi. The number of data acquisition units corresponds to the number of sensors, and each detection point is connected with a data acquisition unit.

[0033] The choice of device is based on the balance between its processing power and resource consumption.

[0034] Each detection point is equipped with an independent data acquisition unit to collect and preliminarily process the sensor data from that point. The purpose is to reduce data transmission delays and improve real-time response capabilities.

[0035] The data acquisition unit is connected to the sensor via wired or wireless means, and the collected data is transmitted to the data transmission module in a timely manner for subsequent processing.

[0036] Specifically, the high-resolution cameras are installed at key locations in the forest area to ensure real-time monitoring of key areas. The key locations in the forest area are mountain tops, forest edges, and flammable areas.

[0037] The cameras are strategically placed at key locations in the forest, such as hilltops, forest edges and fire-prone areas. These locations provide a wide field of view and help detect fires early.

[0038] The cameras use high-resolution image sensors that can clearly capture details at long distances and in complex environments, which is critical for early detection of fires. The cameras are located in all key areas of the forest area and connected to the processing module to transmit images in real time for analysis.

[0039] Specifically, the processing module has an image analysis function. The image analysis uses a deep learning framework to train a fire recognition model, analyze the images captured by the camera, and identify smoke and open flames in real time. The learning framework is TensorFlow or PyTorch. It can analyze the images captured by the camera in real time and identify signs of fire such as smoke and open flames. The fire recognition model is trained based on a large amount of fire image data, and can quickly and accurately identify fires and distinguish them from normal phenomena to reduce false alarms. The image data is transmitted to the processing module wirelessly or wired, and the algorithm in the module will perform fire recognition.

[0040] Specifically, the data transmission module adopts wireless communication, and the wireless communication technology uses a low-power wide area network, specifically LoRa or Zigbee, which can provide wide-coverage wireless communication under complex terrain conditions to ensure stable data transmission.

[0041] Specifically, the data transmission module uses the AES encryption algorithm to encrypt the transmitted data, which can effectively prevent the data from being tampered with or leaked during the transmission process. The data is transmitted from the monitoring point to the central processing system via a wireless network, and the encryption algorithm performs real-time encryption processing on the data during the transmission process.

[0042] Specifically, the early warning response module includes a fire risk assessment model and an information release module.

[0043] The fire risk assessment model sets thresholds to judge the fire risk level based on the data obtained from the environmental monitoring module and the video surveillance module. The risk assessment process is automated, reducing reliance on manual judgment.

[0044] When the risk assessment model determines that the fire risk has reached a certain level, the information release module will automatically generate and send early warning information to notify relevant personnel and institutions to respond in a timely manner.

[0045] Specifically, the fire risk assessment model is set based on environmental data and monitoring data, and automatically determines the fire risk level by setting thresholds for each detection data.

[0046] The fire risk assessment model relies on a variety of data obtained from the environmental monitoring module and the video surveillance module, including temperature and humidity, wind speed and direction, light intensity, and real-time images captured by the camera. These data will serve as the basis for assessing fire risks.

[0047] Threshold setting:

[0048] Each monitoring data (such as temperature, humidity, wind speed, etc.) has a preset threshold. When a monitoring data exceeds its threshold, the system considers that there is a fire risk in the area. For example:

[0049] When the temperature is higher than a certain set value and the humidity is lower than a certain set value, it means that the conditions for fire are ripe. If there are signs of smoke or open flames in the images detected by the camera, risk alarms will also be triggered.

[0050] Automated judgment:

[0051] The fire risk assessment model will automatically determine the fire risk level by analyzing these environmental data and monitoring data in real time. The risk level is usually divided into multiple levels (such as low, medium, and high risk), and each level corresponds to a different early warning response strategy.

[0052] The data acquisition unit continuously collects environmental and image data and transmits them to the processing module, which analyzes the data using set thresholds to assess fire risks in real time.

[0053] When one or more data exceed their thresholds, the system will comprehensively judge the fire risk level based on preset rules and trigger the corresponding early warning mechanism.

[0054] Specifically, the information release module automatically issues warning information after confirming the fire risk.

[0055] When the fire risk assessment model determines that there is a fire risk in a certain area, the information release module will automatically generate early warning information. The early warning information usually includes the possibility of fire, risk level, affected area and recommended emergency measures.

[0056] Information release channels:

[0057] The information release module will release early warning information through multiple channels to ensure that relevant personnel can receive notifications in a timely manner. These channels include:

[0058] SMS notification: Send an alert SMS to a preset mobile phone number, usually to notify forest managers and fire departments as soon as possible.

[0059] Email: Send detailed fire warning reports to relevant agencies and persons in charge.

[0060] Broadcasting system: Notify people on site to evacuate urgently or take protective measures through the on-site broadcasting system.

[0061] Mobile application or website: Publish early warning information on a dedicated fire monitoring application or website for the public and relevant personnel to view at any time.

[0062] When the fire risk assessment model determines that an area reaches a certain risk level, the information release module is automatically activated to generate the corresponding warning information. The warning information will be sent out immediately through the above channels to ensure that all relevant parties can receive the alarm and take action in the shortest possible time.

[0063] The standard parts used in the present invention can all be purchased from the market, and special-shaped parts can be customized according to the description in the specification. The specific connection methods of each part adopt conventional means such as mature bolts, rivets, welding, etc. in the prior art. Machinery, parts and equipment all adopt conventional models in the prior art, and the circuit connection adopts the conventional connection method in the prior art. It will not be described in detail here. The content not described in detail in this specification belongs to the prior art known to professional and technical personnel in this field.

[0064] The above description is only a preferred embodiment of the present invention and does not limit the present invention in other forms. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A mountain forest fire monitoring system, characterized in that: It includes an environment monitoring module and a video monitoring module. The environment monitoring module includes a sensor and a data acquisition unit. The video monitoring module includes a high-resolution camera and a processing module. The environment monitoring module and the video monitoring module are connected to a data transmission module, and the data transmission module is connected to an early warning response module.

2. A mountain forest fire monitoring system according to claim 1, characterized in that: The number of sensors is evenly distributed in a square array. The sensors are high-precision environmental sensors, including temperature and humidity sensors, wind speed and direction sensors, and light sensors. The temperature and humidity sensors, wind speed and direction sensors, and light sensors are combined as a detection point in the sensor array.

3. A mountain forest fire monitoring system according to claim 2, characterized in that: The data acquisition unit is a microcontroller, specifically a single-chip microcomputer or a Raspberry Pi. The number of data acquisition units corresponds to the number of sensors, and each detection point is connected with a data acquisition unit.

4. A mountain forest fire monitoring system according to claim 1, characterized in that: The high-resolution cameras are installed at key locations in the forest area to ensure real-time monitoring of key areas. The key locations in the forest area are mountain tops, forest edges, and flammable areas.

5. A mountain forest fire monitoring system according to claim 1, characterized in that: The processing module has an image analysis function. The image analysis uses a deep learning framework to train a fire recognition model, analyzes images captured by the camera, and identifies smoke and open flames in real time. The learning framework is TensorFlow or PyTorch.

6. A mountain forest fire monitoring system according to claim 1, characterized in that: The data transmission module adopts wireless communication, and the wireless communication technology uses a low-power wide area network, specifically LoRa or Zigbee.

7. A mountain forest fire monitoring system according to claim 6, characterized in that: The data transmission module uses the AES encryption algorithm to encrypt the transmission data.

8. A mountain forest fire monitoring system according to claim 1, characterized in that: The early warning response module includes a fire risk assessment model and an information release module.

9. A mountain forest fire monitoring system according to claim 8, characterized in that: The fire risk assessment model is set based on environmental data and monitoring data, and automatically determines the fire risk level by setting thresholds for each detection data.

10. A mountain forest fire monitoring system according to claim 8, characterized in that: The information release module automatically issues early warning information after confirming the fire risk.