Forest disaster early warning method and system

Abstract

The invention discloses a forest disaster early warning method. Monitoring equipment is deployed to monitor a disaster in real time; carrying out analysis and risk assessment on the disaster situation; triggering early warning and notification according to the disaster situation; according to the invention, the monitoring and sensing equipment is arranged at the key position of the forest region site, so that the disaster situation can be conveniently monitored in time at the key position of the forest region, and the thermal imaging camera is arranged at the key position of the forest region, so that the thermal imaging camera can realize more reliable monitoring. Visible light cameras are arranged at multiple angles in a forest region, fire behavior can be conveniently found in time, a water level sensor can provide data support for a fire extinguishing water source, the meteorological stations arranged at different positions can comprehensively collect environmental data information in the forest region, and the fire risk level can be conveniently evaluated; by integrating and storing the data acquired by the sensing devices, the utilization of the data is facilitated, and the accurate judgment of the disaster situation is facilitated.

Description

Technical Field

[0001] This invention relates to the field of smart IoT technology, specifically to a method and system for early warning of forest disasters. Background Technology

[0002] Forest disaster early warning is a key measure to ensure the safety of forest resources and reduce disaster losses. Currently, forest disaster early warning usually refers to forest fire early warning. In order to effectively prevent and control forest fires, relevant departments will issue forest fire risk level forecasts and forest fire early warning information based on factors such as weather and forest fire risk level. These early warning information helps to reduce the occurrence of forest fires and protect forest resources and people's lives and property. When a forest fire early warning is issued in a region, the following measures need to be taken: reduce outdoor fire use, avoid using fire sources in and around forest areas, strengthen fire prevention awareness, learn and master basic knowledge of forest fires and self-rescue and mutual rescue skills, pay attention to weather changes and official forest fire risk early warning information, and make corresponding preparations. Currently, forest disaster early warning systems rely on a single method of disaster information acquisition. However, forest disasters manifest differently in their early, middle, and late stages. This single method fails to provide a comprehensive and accurate picture of the disaster situation, delaying early warning and rescue efforts and ultimately exacerbating the disaster. Summary of the Invention

[0003] This invention provides a forest disaster early warning method and system, which can effectively solve the problem mentioned in the background art that the current forest disaster early warning process only has a single disaster information acquisition method. However, the manifestation of forest disasters in the early, middle and late stages are different. The single acquisition method makes it impossible to comprehensively and accurately acquire forest disaster information, thereby delaying the time for disaster early warning and rescue, and further leading to the expansion of forest disasters.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a forest disaster early warning method, comprising the following steps: Step 1: Deploy monitoring equipment to monitor the disaster situation in real time; Step two: Analyze the disaster situation and conduct a risk assessment; Step 3: Trigger early warnings and notifications based on the disaster situation; Step 4: Initiate emergency response and on-site handling measures.

[0005] According to the above technical solution, in step one, when deploying monitoring equipment, the monitoring and sensing equipment is arranged at key locations in the forest area. Specifically, this includes installing thermal imaging cameras and visible light cameras, and simultaneously arranging water level sensors and meteorological monitoring stations at different locations in the forest area. During the arrangement and installation of each monitoring and sensing device, it is necessary to associate each monitoring and sensing device with a GIS map to realize the visual distribution of each monitoring and sensing device. Thermal imaging cameras are deployed in key locations in the forest area, specifically at high points and firebreaks. The cameras are used to monitor abnormal surface temperatures through their real-time temperature measurement function. Meanwhile, thermal imaging cameras can also detect high-temperature areas in forest areas, and can be configured with thermal image analysis tools to generate temperature distribution maps, thereby quickly identifying high-temperature areas and monitoring forest temperature anomalies in real time. Visible light cameras are deployed at multiple angles in the forest area. They use video patrol and intelligent analysis to detect smoke and fire to identify fires. Video patrol can automatically detect smoke and open flames. During intelligent analysis, smoke recognition algorithms are used in combination with image recognition technology to identify the shape and spread trend of smoke. At the same time, by linking thermal imaging with visible light video, the coordinate information of the fire source can be accurately located. Water level sensors are deployed within river water sources to monitor river and reservoir water levels, providing data support for firefighting water sources; Meteorological stations are set up in various locations within the forest area to collect environmental data, including temperature, humidity, wind speed, wind direction, and rainfall, to facilitate the assessment of fire risk levels.

[0006] According to the above technical solution, in step one, after acquiring the sensing data of the forest area based on the monitoring equipment, it is necessary to integrate and store the data collected by each sensing device. The platform connects various monitoring data through the IoT protocol and stores them uniformly in the database. During the data storage process, it supports data cleaning and standardization to ensure the quality of multi-source data.

[0007] According to the above technical solution, in step two, the analysis of the disaster situation and risk assessment are mainly carried out through an intelligent analysis engine, while the risk is assessed through a dynamic scoring method. The analysis process using the intelligent analysis engine specifically includes fire detection, risk prediction, and map linkage. Fire detection uses real-time temperature measurement in thermal imaging and smoke recognition in video to automatically mark suspected fire points. Suspected fire points are identified when the temperature is >60℃ and the smoke concentration exceeds PM2.5 >150μg / m³. During thermal imaging analysis, a temperature distribution map is generated using thermal image analysis tools, and high-temperature areas are automatically marked. During video analysis, intelligent algorithms are used to detect smoke patterns and trigger alarms. Smoke patterns include diffusion speed and smoke concentration. Risk prediction combines meteorological data and water level information, and predicts the fire spread trend and direction by setting thresholds. The fire spread trend is predicted by combining wind speed and humidity data: with wind speed equal to 15m / s and humidity equal to 30% as the critical point, if the wind speed is greater than 15m / s and the humidity is less than 30%, it indicates that the fire spread risk level is increased; if the wind speed is less than 15m / s and the humidity is greater than 30%, it indicates that the fire spread risk level is decreased. Map linkage involves marking fire locations, weather station data, water source distribution, and road distribution on a GIS electronic map to generate a disaster risk heat map. At the same time, it analyzes the distance between water sources and the fire site and assesses the availability of firefighting resources to assist in firefighting decision-making.

[0008] According to the above technical solution, in step two, when assessing the risk, a risk index is calculated based on temperature, smoke concentration and wind speed using a dynamic scoring method, and the risk level is divided according to the risk index. Specifically, the disaster is divided into low, medium, and high risk levels, and the corresponding level of emergency response plan is triggered when the disaster situation on site reaches the corresponding risk level. When the risk level is low, the disaster situation is monitored by visible light cameras on site. When the risk level is medium, forest area management personnel are dispatched to the site to observe the disaster situation. When the risk level is high, drones are launched to conduct on-site disaster reconnaissance.

[0009] According to the above technical solution, in step three, when triggering early warning and notification, alarm information is automatically triggered mainly based on the forest disaster situation, specifically including multi-level alarm and hierarchical push; The multi-level alarm includes temperature threshold trigger alarm and smoke recognition alarm. The temperature threshold trigger alarm is triggered when thermal imaging detects that the temperature is too high, and the temperature is >60℃. The smoke recognition alarm is automatically triggered when video analysis detects smoke and the smoke concentration exceeds PM2.5 >150μg / m³. Tiered push notifications send warning information to emergency management personnel via SMS and email after an alarm is triggered. At the same time, emergency broadcasts are activated to remotely announce warning information to people around the fire site, thus achieving verbal evacuation.

[0010] According to the above technical solution, in step three, during the process of graded push, it is also necessary to issue different color-level warning information according to the scale of the fire on the scene, specifically including blue warning and red warning. A blue alert indicates a small fire, which can be handled by local forest rangers. A red alert indicates a large-scale fire, which requires a multi-departmental joint response.

[0011] According to the above technical solution, step four, in the process of emergency response and on-site handling, includes resource scheduling and on-site handling; Resource scheduling is based on the visualization function of GIS map. It marks the location of fire points, fire boundaries, weather station data, water source distribution and road information on the map. At the same time, it also marks the location of fire brigade, fire equipment and material storage to automatically generate the optimal rescue route and help rescuers reach the forest fire site quickly. During the resource allocation process, it is also necessary to manage the equipment linkage. Specifically, this involves remotely opening the gates on forest roads to ensure the smooth flow of rescue channels, and launching drones to conduct fire reconnaissance and transmit images back to the command center in real time. The on-site response measures include: preventing the fire from spreading by remotely controlling forest fire doors and firebreaks; dispatching firefighters with firefighting equipment to designated locations via the platform; deploying fire pumps and firefighting drones to extinguish the fire; and monitoring the effectiveness of firefighting efforts in the forest area in real time via video surveillance.

[0012] A forest disaster early warning system includes a data acquisition and real-time monitoring module, a disaster analysis and risk assessment module, an early warning triggering and graded notification module, and an on-site emergency response and handling module.

[0013] According to the above technical solution, the data acquisition and real-time monitoring module involves deploying monitoring and sensing devices at key locations in the forest area. Specifically, this includes installing thermal imaging cameras and visible light cameras, while also deploying water level sensors and meteorological monitoring stations at different locations within the forest area. Furthermore, the module links each monitoring and sensing device with a GIS map to achieve a visualized distribution of each monitoring and sensing device. After acquiring the sensing data of the forest area based on the monitoring equipment, it is necessary to integrate and store the data collected by each sensing device, and connect the various monitoring data to the platform through the IoT protocol and store them in the database in a unified manner. The early warning triggering and graded notification module uses an intelligent analysis engine to analyze the disaster situation and assess the risk through dynamic scoring. The analysis process through the intelligent analysis engine includes fire detection, risk prediction, and map linkage. Fire detection uses real-time temperature measurement of thermal imaging and smoke recognition of video to automatically mark suspected fire points. Risk prediction combines meteorological data and water level information, and predicts the spread trend and direction of fire by setting thresholds. Map linkage marks the location of fire points, meteorological station data, water source distribution and road distribution on the GIS electronic map to generate a disaster risk heat map. When assessing risk through dynamic scoring, a risk index is calculated based on temperature, smoke concentration, and wind speed. The risk level is then divided into low, medium, and high levels based on the risk index. When the on-site disaster reaches the corresponding danger level, the corresponding level of emergency response plan is triggered. The early warning triggering and graded notification module automatically triggers alarm information based on the forest disaster situation. Specifically, it includes multi-level alarms and graded push notifications. The multi-level alarms include temperature threshold trigger alarms and smoke recognition alarms. The temperature threshold trigger alarm is triggered when thermal imaging detects excessively high temperatures. The smoke recognition alarm is automatically triggered when video analysis detects smoke and the smoke concentration is too high. The graded push notifications send early warning information to emergency management personnel via SMS and email after the alarm is triggered. At the same time, the emergency broadcast is invoked to broadcast the early warning information to people around the fire point through remote announcements, thereby achieving verbal evacuation. During the tiered notification process, different color-coded warning information needs to be issued based on the scale of the fire on the ground. Specifically, there are blue and red warnings. A blue warning indicates a small fire, which can be handled by local forest rangers. A red warning indicates a large-scale fire, which requires the activation of a multi-departmental joint response. The on-site emergency response and handling module includes resource scheduling and on-site handling during the emergency response and on-site handling process. Resource scheduling is based on the visualization function of GIS map, marking the location of fire points, fire boundaries, meteorological station data, water source distribution and road information on the map. At the same time, the location of fire brigade, fire equipment and material reserve are also marked to automatically generate the optimal rescue route. Equipment linkage management is also required. Specifically, it is necessary to remotely open the forest area road gate to ensure the smooth flow of rescue channels, and launch drones to conduct fire reconnaissance and transmit images back to the command center in real time. On-site response included remotely controlling forest fire doors and firebreaks to prevent the fire from spreading, dispatching firefighters with firefighting equipment to designated locations via the platform, and deploying fire pumps and firefighting drones to extinguish the fire.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. By deploying monitoring and sensing equipment at key locations in the forest area, it is convenient to monitor the disaster situation in a timely manner at key locations in the forest area. Thermal imaging cameras are deployed at high points in the forest area and in firebreaks, enabling thermal imaging cameras to measure the temperature at key locations. Visible light cameras are deployed at multiple angles in the forest area to facilitate the timely detection of fires. In addition, water level sensors can provide data support for fire extinguishing water sources. Meteorological stations deployed in multiple different locations can comprehensively collect environmental data information in the forest area, making it convenient to assess the fire risk level. Furthermore, after acquiring sensing data from various monitoring devices in the forest area, the data collected by each sensing device is integrated and stored to facilitate data conversion and utilization, enabling timely assessment of disaster information. Data cleaning and standardization processes ensure the quality of multi-source data.

[0015] 2. By leveraging real-time temperature measurement through thermal imaging and smoke recognition through video, suspected fire points can be automatically marked. Combined with meteorological data and water level information, and by setting thresholds, the spread trend and direction of fire can be accurately predicted. Through map linkage, the location of fire points, meteorological station data, water source distribution, and road distribution are marked on the GIS electronic map to generate a disaster risk heat map. At the same time, the distance between water sources and the fire site is analyzed to assess the availability of fire-fighting resources to assist in fire-fighting decision-making. When assessing risks, a dynamic scoring method is used to calculate the risk index based on temperature, smoke concentration, and wind speed. The risk level is then divided according to the risk index, which facilitates the triggering of the corresponding level of emergency response plan when the disaster situation on site reaches the corresponding danger level.

[0016] 3. The system automatically triggers alarm information based on forest fire conditions, enabling multi-level alarms and tiered push notifications. It can automatically trigger alarms when thermal imaging detects excessively high temperatures or smoke. After triggering an alarm, it sends warning information to emergency management personnel through different push notification methods. During the tiered push notification process, it can issue different color-coded warning information based on the scale of the fire on site, including blue and red warnings. This allows for different handling and response methods based on different color-coded warning information, thereby improving the ability to prevent and control forest fires.

[0017] 4. Through resource scheduling and on-site handling, the system can use the visualization function of GIS maps to mark the location of fire points, fire boundaries, weather station data, water source distribution, and road information on the map. It can also distinguish and mark the locations of fire brigades, fire equipment, and material reserves, making it easy to automatically generate the optimal rescue route and help rescuers quickly reach the forest fire site. Furthermore, it can remotely control forest fire doors and fire isolation facilities to prevent the spread of fire. The platform can also dispatch firefighters with fire extinguishing equipment to designated locations and call upon fire pumps and fire-fighting drones to extinguish fires, making it easier to more effectively fight forest fires. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0019] In the attached diagram: Figure 1 This is a flowchart of the early warning method of the present invention; Figure 2 This is an architecture diagram of the early warning system of the present invention. Detailed Implementation

[0020] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0021] Example: Figure 1 As shown, the present invention provides a technical solution, a method for early warning of forest disasters, comprising the following steps: Step 1: Deploy monitoring equipment to monitor the disaster situation in real time; Step two: Analyze the disaster situation and conduct a risk assessment; Step 3: Trigger early warnings and notifications based on the disaster situation; Step 4: Initiate emergency response and on-site handling measures.

[0022] Based on the above technical solution, in step one, when deploying monitoring equipment, the monitoring and sensing equipment will be arranged at key locations in the forest area. Specifically, this includes installing thermal imaging cameras and visible light cameras, and at the same time, water level sensors and meteorological monitoring stations will be arranged at different locations in the forest area. During the arrangement and installation of each monitoring and sensing device, it is necessary to associate each monitoring and sensing device with a GIS map to realize the visual distribution of each monitoring and sensing device. Thermal imaging cameras are deployed in key locations in the forest area, specifically at high points and firebreaks. The cameras monitor surface temperature anomalies through their real-time temperature measurement function, and detect abnormal temperatures when a fire occurs. Meanwhile, thermal imaging cameras can also detect high-temperature areas in forest areas, and can be configured with thermal image analysis tools to generate temperature distribution maps, thereby quickly identifying high-temperature areas and monitoring forest temperature anomalies in real time. Visible light cameras are deployed at multiple angles in the forest area. They use video patrol and intelligent analysis to detect smoke and fire to identify fires. Video patrol can automatically detect smoke and open flames. During intelligent analysis, smoke recognition algorithms are used in combination with image recognition technology to identify the shape and spread trend of smoke. At the same time, by linking thermal imaging with visible light video, the coordinate information of the fire source can be accurately located. Water level sensors are deployed within river water sources to monitor river and reservoir water levels, providing data support for firefighting water sources; Meteorological stations are set up in various locations within the forest area to collect environmental data, including temperature, humidity, wind speed, wind direction, and rainfall, to facilitate the assessment of fire risk levels.

[0023] Based on the above technical solution, in step one, after acquiring the sensing data of the forest area based on the monitoring equipment, it is necessary to integrate and store the data collected by each sensing device. Through the IoT protocol, specifically the 1400 protocol, various monitoring data are connected to the platform and stored uniformly in the database. At the same time, during the data storage process, data cleaning and standardization are supported to ensure the quality of multi-source data.

[0024] Based on the above technical solution, in step two, the analysis of the disaster situation and risk assessment are mainly carried out through an intelligent analysis engine, while the risk is assessed through dynamic scoring. The analysis process using the intelligent analysis engine specifically includes fire detection, risk prediction, and map linkage. Fire detection uses real-time temperature measurement in thermal imaging and smoke recognition in video to automatically mark suspected fire points. Suspected fire points are identified when the temperature is >60℃ and the smoke concentration exceeds PM2.5 >150μg / m³. During thermal imaging analysis, a temperature distribution map is generated using thermal image analysis tools, and high-temperature areas are automatically marked. During video analysis, intelligent algorithms are used to detect smoke patterns and trigger alarms. Smoke patterns include diffusion speed and smoke concentration. Risk prediction combines meteorological data and water level information, and predicts the fire spread trend and direction by setting thresholds. The fire spread trend is predicted by combining wind speed and humidity data: with wind speed equal to 15m / s and humidity equal to 30% as the critical point, if the wind speed is greater than 15m / s and the humidity is less than 30%, it indicates that the fire spread risk level is increased; if the wind speed is less than 15m / s and the humidity is greater than 30%, it indicates that the fire spread risk level is decreased. Map linkage involves marking fire locations, weather station data, water source distribution, and road distribution on a GIS electronic map to generate a disaster risk heat map. At the same time, it analyzes the distance between water sources and the fire site and assesses the availability of firefighting resources to assist in firefighting decision-making.

[0025] Based on the above technical solution, in step two, when assessing the risk, a risk index is calculated using a dynamic scoring method based on temperature, smoke concentration, and wind speed, and the risk level is divided according to the risk index. Specifically, the disaster is divided into low, medium, and high risk levels, and the corresponding level of emergency response plan is triggered when the disaster situation on site reaches the corresponding risk level. When the risk level is low, the disaster situation is monitored by visible light cameras on site. When the risk level is medium, forest area management personnel are dispatched to the site to observe the disaster situation. When the risk level is high, drones are launched to conduct on-site disaster reconnaissance.

[0026] Based on the above technical solution, in step three, when triggering early warning and notification, alarm information is automatically triggered mainly based on the forest disaster situation, specifically including multi-level alarms and hierarchical push notifications. The multi-level alarm includes temperature threshold trigger alarm and smoke recognition alarm. The temperature threshold trigger alarm is triggered when thermal imaging detects that the temperature is too high, and the temperature is >60℃. The smoke recognition alarm is automatically triggered when video analysis detects smoke and the smoke concentration exceeds PM2.5 >150μg / m³. Tiered push notifications send warning information to emergency management personnel via SMS and email after an alarm is triggered. At the same time, emergency broadcasts are activated to remotely announce warning information to people around the fire site, thus achieving verbal evacuation.

[0027] Based on the above technical solution, in step three, during the tiered push process, it is also necessary to issue different color-level warning information according to the scale of the fire on the scene, specifically including blue warning and red warning. A blue alert indicates a small fire, which can be handled by local forest rangers. A red alert indicates a large-scale fire, which requires a multi-departmental joint response.

[0028] Based on the above technical solution, step four includes resource allocation and on-site handling during emergency response and on-site disposal. Resource scheduling is based on the visualization function of GIS map. It marks the location of fire points, fire boundaries, weather station data, water source distribution and road information on the map. At the same time, it also marks the location of fire brigade, fire equipment and material storage to automatically generate the optimal rescue route and help rescuers reach the forest fire site quickly. During the resource allocation process, it is also necessary to manage the equipment linkage. Specifically, this involves remotely opening the gates on forest roads to ensure the smooth flow of rescue channels, and launching drones to conduct fire reconnaissance and transmit images back to the command center in real time. The on-site response measures include: preventing the fire from spreading by remotely controlling forest fire doors and firebreaks; dispatching firefighters with firefighting equipment to designated locations via the platform; deploying fire pumps and firefighting drones to extinguish the fire; and monitoring the effectiveness of firefighting efforts in the forest area in real time via video surveillance.

[0029] like Figure 2 As shown, a forest disaster early warning system includes a data acquisition and real-time monitoring module, a disaster analysis and risk assessment module, an early warning triggering and graded notification module, and an on-site emergency response and handling module.

[0030] Based on the above technical solution, the data acquisition and real-time monitoring module involves deploying monitoring and sensing devices at key locations in the forest area. Specifically, this includes installing thermal imaging cameras and visible light cameras, while also deploying water level sensors and meteorological monitoring stations at different locations within the forest area. Furthermore, it links each monitoring and sensing device with a GIS map to achieve a visualized distribution of each monitoring and sensing device. After acquiring the sensing data of the forest area based on the monitoring equipment, it is necessary to integrate and store the data collected by each sensing device, and connect the various monitoring data to the platform through the IoT protocol and store them in the database in a unified manner. The early warning triggering and graded notification module uses an intelligent analysis engine to analyze the disaster situation and assess the risk through dynamic scoring. The analysis process through the intelligent analysis engine includes fire detection, risk prediction, and map linkage. Fire detection uses real-time temperature measurement of thermal imaging and smoke recognition of video to automatically mark suspected fire points. Risk prediction combines meteorological data and water level information, and predicts the spread trend and direction of fire by setting thresholds. Map linkage marks the location of fire points, meteorological station data, water source distribution and road distribution on the GIS electronic map to generate a disaster risk heat map. When assessing risk through dynamic scoring, a risk index is calculated based on temperature, smoke concentration, and wind speed. The risk level is then divided into low, medium, and high levels based on the risk index. When the on-site disaster reaches the corresponding danger level, the corresponding level of emergency response plan is triggered. The early warning triggering and graded notification module automatically triggers alarm information based on the forest disaster situation. Specifically, it includes multi-level alarms and graded push notifications. Multi-level alarms include temperature threshold trigger alarms and smoke recognition alarms. Temperature threshold trigger alarms are triggered when thermal imaging detects excessively high temperatures. Smoke recognition alarms are automatically triggered when smoke is detected in video analysis and the smoke concentration is too high. Graded push notifications are sent to emergency management personnel via SMS and email after the alarm is triggered. At the same time, emergency broadcasting is activated to broadcast the early warning information to people around the fire point remotely, realizing verbal evacuation. During the tiered notification process, different color-coded warning information needs to be issued based on the scale of the fire on the ground. Specifically, there are blue and red warnings. A blue warning indicates a small fire, which can be handled by local forest rangers. A red warning indicates a large-scale fire, which requires the activation of a multi-departmental joint response. The on-site emergency response and handling module includes resource scheduling and on-site handling during the emergency response and on-site handling process. Resource scheduling is based on the visualization function of GIS map, marking the location of fire points, fire boundaries, weather station data, water source distribution and road information on the map. At the same time, the location of fire brigade, fire equipment and material reserve are also marked to automatically generate the optimal rescue route. Equipment linkage management is also required. Specifically, it ensures the smooth flow of rescue channels by remotely opening the forest road gate and launching drones to conduct fire reconnaissance and transmit images back to the command center in real time. On-site response included remotely controlling forest fire doors and firebreaks to prevent the fire from spreading, dispatching firefighters with firefighting equipment to designated locations via the platform, and deploying fire pumps and firefighting drones to extinguish the fire.

[0031] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for early warning of forest disasters, characterized in that: Includes the following steps: Step 1: Deploy monitoring equipment to monitor the disaster situation in real time; Step two: Analyze the disaster situation and conduct a risk assessment; Step 3: Trigger early warnings and notifications based on the disaster situation; Step 4: Initiate emergency response and on-site handling measures.

2. The forest disaster early warning method according to claim 1, characterized in that: In step one, when deploying monitoring equipment, the monitoring and sensing equipment is arranged at key locations in the forest area. Specifically, this includes installing thermal imaging cameras and visible light cameras, and simultaneously deploying water level sensors and meteorological monitoring stations at different locations in the forest area. During the deployment and installation of each monitoring and sensing device, it is necessary to associate each monitoring and sensing device with a GIS map to achieve a visual distribution of each monitoring and sensing device. Thermal imaging cameras are deployed in key locations in the forest area, specifically at high points and firebreaks. The cameras are used to monitor abnormal surface temperatures through their real-time temperature measurement function. Meanwhile, thermal imaging cameras can also detect high-temperature areas in forest areas, and can be configured with thermal image analysis tools to generate temperature distribution maps, thereby quickly identifying high-temperature areas and monitoring forest temperature anomalies in real time. Visible light cameras are deployed at multiple angles in the forest area. They use video patrol and intelligent analysis to detect smoke and fire to identify fires. Video patrol can automatically detect smoke and open flames. During intelligent analysis, smoke recognition algorithms are used in combination with image recognition technology to identify the shape and spread trend of smoke. At the same time, by linking thermal imaging with visible light video, the coordinate information of the fire source can be accurately located. Water level sensors are deployed within river water sources to monitor river and reservoir water levels, providing data support for firefighting water sources; Meteorological stations are set up in various locations within the forest area to collect environmental data, including temperature, humidity, wind speed, wind direction, and rainfall, to facilitate the assessment of fire risk levels.

3. The forest disaster early warning method according to claim 2, characterized in that: In step one, after acquiring the sensing data of the forest area based on the monitoring equipment, it is necessary to integrate and store the data collected by each sensing device. The platform connects various monitoring data through the IoT protocol and stores them uniformly in the database. During the data storage process, it supports data cleaning and standardization to ensure the quality of multi-source data.

4. The forest disaster early warning method according to claim 1, characterized in that: In step two, the analysis of the disaster situation and risk assessment are mainly carried out through an intelligent analysis engine, while the risk is assessed through dynamic scoring. The analysis process using the intelligent analysis engine specifically includes fire detection, risk prediction, and map linkage. Fire detection uses real-time temperature measurement in thermal imaging and smoke recognition in video to automatically mark suspected fire points. Suspected fire points are identified when the temperature is >60℃ and the smoke concentration exceeds PM2.5 >150μg / m³. During thermal imaging analysis, a temperature distribution map is generated using thermal image analysis tools, and high-temperature areas are automatically marked. During video analysis, intelligent algorithms are used to detect smoke patterns and trigger alarms. Smoke patterns include diffusion speed and smoke concentration. Risk prediction combines meteorological data and water level information, and uses thresholds to predict the fire spread trend and direction. The fire spread trend is predicted by combining wind speed and humidity data: with a wind speed of 15 m / s and humidity of 30% as the critical point, if the wind speed is greater than 15 m / s and the humidity is less than 30%, it indicates that the fire spread risk level is increased; if the wind speed is less than 15 m / s and the humidity is greater than 30%, it indicates that the fire spread risk level is decreased. Map linkage involves marking fire locations, weather station data, water source distribution, and road distribution on a GIS electronic map to generate a disaster risk heat map. At the same time, it analyzes the distance between water sources and the fire site and assesses the availability of firefighting resources to assist in firefighting decision-making.

5. A forest disaster early warning method according to claim 4, characterized in that: In step two, when assessing the risk, a risk index is calculated based on temperature, smoke concentration, and wind speed using a dynamic scoring method, and the risk level is divided according to the risk index. Specifically, the disaster is divided into low, medium, and high risk levels, and the corresponding level of emergency response plan is triggered when the disaster situation on site reaches the corresponding risk level. When the risk level is low, the disaster situation is monitored by visible light cameras on site. When the risk level is medium, forest area management personnel are dispatched to the site to observe the disaster situation. When the risk level is high, drones are launched to conduct on-site disaster reconnaissance.

6. The forest disaster early warning method according to claim 1, characterized in that: In step three, when triggering early warnings and notifications, alarm information is automatically triggered based on the disaster situation in the forest area, specifically including multi-level alarms and graded push notifications. The multi-level alarm includes temperature threshold trigger alarm and smoke recognition alarm. The temperature threshold trigger alarm is triggered when thermal imaging detects that the temperature is too high, and the temperature is >60℃. The smoke recognition alarm is automatically triggered when video analysis detects smoke and the smoke concentration exceeds PM2.5 >150μg / m³. Tiered push notifications send warning information to emergency management personnel via SMS and email after an alarm is triggered. At the same time, emergency broadcasts are activated to remotely announce warning information to people around the fire site, thus achieving verbal evacuation.

7. A forest disaster early warning method according to claim 6, characterized in that: In step three, during the tiered push process, it is also necessary to issue different color-level warning information based on the scale of the fire on the ground, specifically including blue warning and red warning. A blue alert indicates a small fire, which can be handled by local forest rangers. A red alert indicates a large-scale fire, which requires a multi-departmental joint response.

8. A forest disaster early warning method according to claim 1, characterized in that: Step four, in the process of emergency response and on-site handling, includes resource allocation and on-site handling; Resource scheduling is based on the visualization function of GIS map. It marks the location of fire points, fire boundaries, weather station data, water source distribution and road information on the map. At the same time, it also marks the location of fire brigade, fire equipment and material storage to automatically generate the optimal rescue route and help rescuers reach the forest fire site quickly. During the resource allocation process, it is also necessary to manage the equipment linkage. Specifically, this involves remotely opening the gates on forest roads to ensure the smooth flow of rescue channels, and launching drones to conduct fire reconnaissance and transmit images back to the command center in real time. The on-site response measures include: preventing the fire from spreading by remotely controlling forest fire doors and firebreaks; dispatching firefighters with firefighting equipment to designated locations via the platform; deploying fire pumps and firefighting drones to extinguish the fire; and monitoring the effectiveness of firefighting efforts in the forest area in real time via video surveillance.

9. A forest disaster early warning system, characterized in that: It includes modules for data acquisition and real-time monitoring, disaster analysis and risk assessment, early warning triggering and graded notification, and on-site emergency response and handling.

10. A forest disaster early warning system according to claim 9, characterized in that: The data acquisition and real-time monitoring module involves deploying monitoring and sensing devices at key locations in the forest area. Specifically, this includes installing thermal imaging cameras and visible light cameras, while also deploying water level sensors and meteorological monitoring stations at different locations within the forest area. Furthermore, it links each monitoring and sensing device with a GIS map to achieve a visualized distribution of the monitoring and sensing devices. After acquiring the sensing data of the forest area based on the monitoring equipment, it is necessary to integrate and store the data collected by each sensing device, and connect the various monitoring data to the platform through the IoT protocol and store them in the database in a unified manner. The early warning triggering and graded notification module uses an intelligent analysis engine to analyze the disaster situation and assess the risk through dynamic scoring. The analysis process through the intelligent analysis engine includes fire detection, risk prediction, and map linkage. Fire detection uses real-time temperature measurement of thermal imaging and smoke recognition of video to automatically mark suspected fire points. Risk prediction combines meteorological data and water level information, and predicts the spread trend and direction of fire by setting thresholds. Map linkage marks the location of fire points, meteorological station data, water source distribution and road distribution on the GIS electronic map to generate a disaster risk heat map. When assessing risk through dynamic scoring, a risk index is calculated based on temperature, smoke concentration, and wind speed. The risk level is then divided into low, medium, and high levels based on the risk index. When the on-site disaster reaches the corresponding danger level, the corresponding level of emergency response plan is triggered. The early warning triggering and graded notification module automatically triggers alarm information based on the forest disaster situation. Specifically, it includes multi-level alarms and graded push notifications. The multi-level alarms include temperature threshold trigger alarms and smoke recognition alarms. The temperature threshold trigger alarm is triggered when thermal imaging detects excessively high temperatures. The smoke recognition alarm is automatically triggered when video analysis detects smoke and the smoke concentration is too high. The graded push notifications send early warning information to emergency management personnel via SMS and email after the alarm is triggered. At the same time, the emergency broadcast is invoked to broadcast the early warning information to people around the fire point through remote announcements, thereby achieving verbal evacuation. During the tiered notification process, different color-coded warning information needs to be issued based on the scale of the fire on the ground. Specifically, there are blue and red warnings. A blue warning indicates a small fire, which can be handled by local forest rangers. A red warning indicates a large-scale fire, which requires the activation of a multi-departmental joint response. The on-site emergency response and handling module includes resource scheduling and on-site handling during the emergency response and on-site handling process. Resource scheduling is based on the visualization function of GIS map, marking the location of fire points, fire boundaries, meteorological station data, water source distribution and road information on the map. At the same time, the location of fire brigade, fire equipment and material reserve are also marked to automatically generate the optimal rescue route. Equipment linkage management is also required. Specifically, it is necessary to remotely open the forest area road gate to ensure the smooth flow of rescue channels, and launch drones to conduct fire reconnaissance and transmit images back to the command center in real time. On-site response included remotely controlling forest fire doors and firebreaks to prevent the fire from spreading, dispatching firefighters with firefighting equipment to designated locations via the platform, and deploying fire pumps and firefighting drones to extinguish the fire.

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