Study defense method and system based on digital twinborn and intelligent sensor

Abstract

The invention discloses a forest defense method and system based on digital twin and intelligent sensors, and the method comprises the steps: collecting and monitoring environment data, and carrying out the integration processing through data fusion; a big data analysis technology is adopted to analyze a potential risk area where a fire occurs, a high-risk area is identified by evaluating a risk level, and a fire spreading trend is predicted; constructing a three-dimensional virtual model of the forest region by adopting a digital twinborn technology; the spreading conditions of the fire at different time and spatial positions are simulated; according to the predicted fire spreading trend, when a potential fire risk is detected, an early warning mechanism is started; automatic generation and screening of the emergency plans are realized, and the emergency plans are generated. The invention provides an emergency protection system integrating the functions of multi-source sensing data fusion, intelligent early warning analysis, dynamic emergency plan generation and the like. In addition, the problem that a traditional emergency plan is static and cannot be dynamically adjusted according to actual conditions is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of forest fire prevention, and in particular relates to a forest fire prevention method and system based on digital twins and intelligent sensors. Background Art

[0002] With global warming and the intensification of human activities, the frequency and intensity of forest fires are increasing, posing a serious threat to the ecological environment and human life. Especially in areas with rich resources of ancient and famous trees, forest fires not only cause direct economic losses, but also lead to irreversible losses of cultural and natural heritage. In recent years, many major accidents caused by forest fires have occurred at home and abroad, highlighting the inadequacy of existing forest fire prevention technologies. A more efficient monitoring and emergency response system is urgently needed to ensure the safety of forest resources.

[0003] Traditional forest fire prevention measures mainly include ground patrols, observation towers, and satellite remote sensing monitoring. However, these methods generally have defects such as slow response speed, limited monitoring range, and inability to provide real-time warnings, making it difficult to effectively respond to sudden forest fires. With the development of information technology, especially the advancement of technologies such as the Internet of Things, big data analysis, and artificial intelligence, new solutions have been provided for forest fire prevention. By deploying multi-source sensing equipment in forest areas, collecting environmental data in real time, and using cloud computing platforms for data fusion and intelligent analysis, early warning and rapid response to forest fires can be achieved.

[0004] Despite this, the existing forest fire prevention systems on the market still have some shortcomings. Most systems lack a unified digital platform to integrate various monitoring data and cannot form a complete emergency protection system. In addition, traditional emergency plans are often static and cannot be dynamically adjusted according to actual conditions, which also limits the efficiency and effectiveness of emergency response. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a forest fire prevention method and system based on digital twins and intelligent sensors, aiming to overcome the shortcomings of the existing forest fire prevention system, such as slow response speed, limited monitoring range, and inability to provide real-time warning, and provide an emergency protection system that integrates multi-source sensor data fusion, intelligent warning analysis, and dynamic emergency plan generation. In addition, the present invention also solves the problem that traditional emergency plans are static and cannot be dynamically adjusted according to actual conditions. By building a unified digital platform to integrate various monitoring data, a complete emergency protection system is formed.

[0006] To achieve the above object, the technical solution adopted by the present invention is: a forest protection method based on digital twins and intelligent sensors, comprising the steps of: S10, using multiple sets of sensors and video image acquisition equipment to collect and monitor environmental data; integrating and processing the data and image information from different sensors through data fusion to complete data collection; S20, based on the collected data, uses big data analysis technology to analyze potential risk areas for fire, identifies high-risk areas by assessing risk levels, and predicts fire spread trends; S30, based on data processing, a three-dimensional virtual model of the forest area is constructed using digital twin technology to form a dynamically updated digital twin model of the forest area; based on the digital twin model of the forest area, the collected data is received in real time, and the spread of the fire at different time and space locations is simulated through the fire dynamics model, combined with the geographical environment, meteorological conditions and combustible distribution factors; S40, based on the predicted fire spread trend, when a potential fire risk is detected, the early warning mechanism is activated; S50 uses big data analysis and artificial intelligence technology to automatically generate emergency plans. Based on the route analysis algorithm and all escape routes uploaded by users, it comprehensively considers distance, traffic capacity, and current crowd density factors to screen and generate emergency plans.

[0007] Furthermore, the collection and monitoring of environmental data include: Sensor deployment and data collection: Various sensors are deployed in the forest area to collect real-time data on the growth environment of ancient trees; the sensors include soil moisture sensors, temperature sensors, light sensors and wind speed sensors to monitor environmental parameters around the clock; all collected data will be transmitted to the central management system via wireless networks; Video image data acquisition: Deploy high-point monitoring camera terminals and drone equipment in forest areas to collect image information in real time; use video analysis technology to further process image data and combine it with sensor data to form comprehensive environmental monitoring data.

[0008] Furthermore, through machine learning algorithms, historical environmental data are compared and analyzed with current data to evaluate the possibility of fire under different environmental conditions. High-risk areas are identified by assessing risk levels and predicting fire spread trends.

[0009] Furthermore, in step S30, a dynamically updated digital twin model of the forest area is formed by mapping sensor data, image information and environmental parameters to the virtual space; the model shows the geomorphic features, vegetation coverage and climate change information of the forest, and reflects the environmental changes of the forest area in real time.

[0010] Furthermore, in step S30, based on the forest area digital twin model, the collected data is received in real time, and the spread of fire at different time and space locations is simulated through the fire dynamics model in combination with the geographical environment, meteorological conditions and combustible distribution factors, including the steps of: Update the fire spread in real time. According to the geographical environment, meteorological conditions and combustible characteristics, the model obtains the fire spread speed and fire intensity based on the convolutional long short-term memory network and self-attention mechanism, and updates the simulation results in real time; Based on the fire spread simulation results, the natural fire risk rating rule evaluates the risk levels of different areas and highlights high-risk areas on the map through color coding.

[0011] Furthermore, in step S40, when a potential fire risk is detected, the system immediately starts the alarm program, adjusts the alarm priority and response method according to the risk level, and sends early warning information to relevant personnel through sound and light alarms, text message notifications and telephone calls.

[0012] Furthermore, in the step S50: obtaining real-time collected environmental data and image information, obtaining pre-planned escape route data uploaded by the user, the escape route data including path coordinates, road width and traffic capacity information; using GIS technology to calculate the actual distance of each escape route, and evaluating the traffic capacity of each route based on road width and obstacle distribution factors; Real-time monitoring of crowd density in scenic areas through video analysis and sensor data; Use Dijkstra path optimization algorithm or A* path optimization algorithm, combined with actual distance, traffic capacity and crowd density data, to screen out the optimal escape route; Based on the optimized escape route, an emergency plan is obtained.

[0013] On the other hand, the present invention also proposes a forest fire prevention method based on digital twins and intelligent sensors, comprising: The acquisition device uses multiple sets of sensors and video image acquisition equipment to collect and monitor environmental data; the data and image information from different sensors are integrated and processed through data fusion to complete data collection; The fire potential risk area analysis module uses big data analysis technology to analyze the potential risk areas for fire based on the collected data, identifies high-risk areas by assessing the risk level, and predicts the fire spread trend; The forest area digital twin model construction module uses digital twin technology to build a three-dimensional virtual model of the forest area, forming a dynamically updated forest area digital twin model; based on the forest area digital twin model, it receives the collected data in real time, and simulates the spread of fire in different time and space locations through the fire dynamics model, combined with the geographical environment, meteorological conditions and combustible distribution factors; The early warning module activates the early warning mechanism when a potential fire risk is detected based on the predicted fire spread trend; The emergency plan generation module uses big data analysis and artificial intelligence technology to automatically generate emergency plans. Based on the route analysis algorithm and all escape routes uploaded by users, it comprehensively considers distance, traffic capacity, and current crowd density factors to screen and generate emergency plans.

[0014] The beneficial effects of adopting this technical solution are: The present invention realizes early warning and rapid response to forest fires by real-time monitoring of forest environmental changes and combining intelligent algorithms for data analysis, thereby improving the efficiency and effectiveness of emergency response.

[0015] The present invention integrates the functions of multi-source sensor data fusion, intelligent early warning analysis and dynamic emergency plan generation into an emergency protection system; by constructing such a system, the early warning and emergency response capabilities of forest fires can be effectively improved, thereby promoting the development of forest resource protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a schematic diagram of a forest fire prevention method and system flow based on digital twins and intelligent sensors of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described below with reference to the accompanying drawings.

[0018] In this embodiment, see Figure 1 As shown, the present invention proposes a forest protection method based on digital twins and intelligent sensors, comprising the steps of: S10, using multiple sets of sensors and video image acquisition equipment to collect and monitor environmental data; integrating and processing the data and image information from different sensors through data fusion to complete data collection.

[0019] Through data fusion technology, the system integrates data from different sensors (such as temperature, humidity, light, wind speed, etc.) and video image information. Through fusion technology, the accuracy and completeness of the data are improved, ensuring that the environmental data of each monitoring point is highly consistent, providing an accurate basis for subsequent decision-making.

[0020] Specifically, the collection and monitoring of environmental data include: Sensor deployment and data collection: Various sensors are deployed in the forest area to collect real-time data on the growth environment of ancient trees; the sensors include soil moisture sensors, temperature sensors, light sensors and wind speed sensors to monitor environmental parameters around the clock; all collected data will be transmitted to the central management system via the wireless network; providing raw data support for subsequent data analysis and processing.

[0021] Video image data acquisition: In order to improve the diversity and accuracy of data collection, high-point monitoring camera terminals and drone equipment are deployed in forest areas to collect image information in real time; through video analysis technology, the image data is further processed and combined with sensor data to form comprehensive environmental monitoring data.

[0022] S20, based on the collected data, uses big data analysis technology to analyze potential risk areas for fire, identifies high-risk areas by assessing risk levels, and predicts the trend of fire spread.

[0023] Specifically, through machine learning algorithms, historical environmental data and current data are compared and analyzed to evaluate the possibility of fire under different environmental conditions. By assessing the risk level and predicting the fire spread trend, high-risk areas are identified.

[0024] Using machine learning and deep learning algorithms, we build a fire prediction model. The model combines historical fire data to analyze the patterns and trends of fires and identify key factors that affect fires. The model supports continuous optimization and updating to adapt to the ever-changing fire risk environment.

[0025] S30, based on data processing, uses digital twin technology to construct a three-dimensional virtual model of the forest area to form a dynamically updated digital twin model of the forest area; based on the digital twin model of the forest area, receives the collected data in real time, and uses the fire dynamics model, combined with the geographical environment, meteorological conditions and combustible distribution factors, to simulate the spread of fire in different time and space locations.

[0026] Specifically, based on data processing, the system uses digital twin technology to build a three-dimensional virtual model of the forest area. By mapping sensor data, image information and environmental parameters into virtual space, a dynamically updated digital twin model of the forest area is formed; the model shows the geomorphic features, vegetation coverage and climate change information of the forest, and reflects the environmental changes of the forest area in real time.

[0027] Based on the forest area digital twin model, the collected data is received in real time, and the fire dynamics model is used to simulate the spread of fire in different time and space locations in combination with the geographical environment, meteorological conditions and combustible distribution factors, including the following steps: The fire spread situation is updated in real time. According to the geographical environment, meteorological conditions and combustible characteristics, the model obtains the fire spread speed and fire intensity based on the convolutional long short-term memory network and self-attention mechanism, and updates the simulation results in real time. Through fire spread simulation, it helps decision makers understand the trend of fire spread and potential affected areas, and provides a scientific basis for formulating emergency response plans.

[0028] According to the fire spread simulation results, the natural fire risk rating rules evaluate the risk levels of different areas and highlight high-risk areas on the map through color coding. By prompting that high-risk areas will be treated first, the rational allocation of resources is ensured. The natural fire risk rating rules adopt existing rules. According to the flame spread trend obtained and the corresponding risk response measures of the risk assessment level of the national emergency management department, specific prevention and control suggestions are provided, including the activation of specific fire-fighting facilities, the deployment of rescue forces, etc., to help emergency personnel quickly formulate response measures.

[0029] The forest fire risk areas are displayed in the form of a 3D virtual model. Different levels of fire risk will be highlighted on the map, helping the emergency response team to monitor the fire situation in real time and adjust resource deployment. This function makes fire risk information more intuitive and easy to understand.

[0030] S40, based on the predicted fire spread trend, when a potential fire risk is detected, the early warning mechanism is activated.

[0031] Specifically, in step S40, when a potential fire risk is detected, the system immediately starts the alarm program, adjusts the alarm priority and response method according to the risk level, and sends early warning information to relevant personnel through sound and light alarms, text message notifications and telephone calls.

[0032] Ensure that relevant personnel can take countermeasures quickly. The fire warning module supports linkage control with other fire-fighting equipment, such as automatic sprinkler systems, smoke exhaust systems, etc. When the system detects a fire, it can automatically trigger related equipment to achieve timely control and fire fighting. The fire warning module has data recording and query functions, which can record changes in environmental parameters before and after the fire, warning information sending records, and other information. Users can query historical data at any time through the system interface or mobile application to provide support for the investigation and analysis of fire accidents.

[0033] S50 uses big data analysis and artificial intelligence technology to automatically generate emergency plans. Based on the route analysis algorithm and all escape routes uploaded by users, it comprehensively considers distance, traffic capacity, and current crowd density factors to screen and generate emergency plans.

[0034] Acquire real-time environmental data and image information, and obtain pre-planned escape route data uploaded by users. The escape route data includes path coordinates, road width, and capacity information. Use GIS technology to calculate the actual distance of each escape route to ensure the shortest or most reasonable path is selected, and evaluate the capacity of each route based on road width and obstacle distribution factors.

[0035] Through video analysis and sensor data, the density of people in the scenic area can be monitored in real time to avoid choosing routes with heavy traffic.

[0036] The Dijkstra path optimization algorithm or the A* path optimization algorithm is used to combine the actual distance, traffic capacity and crowd density data to screen out the optimal escape route.

[0037] Based on the optimized escape route, an emergency plan is obtained.

[0038] The present invention can dynamically adjust the plan according to real-time monitoring data and changes in fire conditions to ensure the real-time and effectiveness of the plan. When an alarm is issued, the relevant emergency management departments and personnel are automatically notified, and the various units are coordinated for rescue through intelligent scheduling algorithms. At the same time, firefighting drones are dispatched to the fire point to extinguish the fire. Through a user-friendly operation interface, the generated emergency plan and real-time updated fire information are displayed, which is convenient for managers to take timely actions. Provide a basic framework for the emergency response team to adjust and optimize according to actual conditions. With the help of big data technology and artificial intelligence algorithms, the system dynamically adjusts and provides targeted fire handling measures based on real-time simulation analysis of the fire (such as fire spread speed, wind direction changes), such as enabling specific firefighting facilities, deploying rescue forces, etc., thereby forming a dynamic response decision-making support system to effectively support the rapid decision-making of on-site commanders. The system establishes a plan library for storing and managing historical plan data. The plan library supports the query, modification and reuse of plans, so that the emergency response team can quickly find and apply suitable plans in similar situations. The plan library can also provide data support for the continuous improvement of emergency plans, helping the team to continuously optimize plans and improve the ability to respond to emergencies.

[0039] Based on the same principle as the above-mentioned forest protection method based on digital twins and intelligent sensors, the present invention also proposes a forest protection system based on digital twins and intelligent sensors, including: The acquisition device uses multiple sets of sensors and video image acquisition equipment to collect and monitor environmental data; the data and image information from different sensors are integrated and processed through data fusion to complete data collection; The fire potential risk area analysis module uses big data analysis technology to analyze the potential risk areas for fire based on the collected data, identifies high-risk areas by assessing the risk level, and predicts the fire spread trend; The forest area digital twin model construction module uses digital twin technology to build a three-dimensional virtual model of the forest area, forming a dynamically updated forest area digital twin model; based on the forest area digital twin model, it receives the collected data in real time, and simulates the spread of fire in different time and space locations through the fire dynamics model, combined with the geographical environment, meteorological conditions and combustible distribution factors; The early warning module activates the early warning mechanism when a potential fire risk is detected based on the predicted fire spread trend; The emergency plan generation module uses big data analysis and artificial intelligence technology to automatically generate emergency plans. Based on the route analysis algorithm and all escape routes uploaded by users, it comprehensively considers distance, traffic capacity, and current crowd density factors to screen and generate emergency plans.

[0040] Example 1: Forest environment monitoring and fire warning.

[0041] Hardware equipment: 1. Multi-dimensional ancient tree sensor terminal - deploy a variety of sensors (such as soil moisture sensors, temperature sensors, light sensors, wind speed sensors, etc.) around the ancient trees to monitor the growth environment of the ancient trees.

[0042] 2. High-point surveillance camera terminal - Use forest communication towers or newly built surveillance towers to install dual-spectrum thermal imaging cameras to monitor the forest area 24 hours a day.

[0043] Software system: 1. Data fusion and intelligent analysis - Collect data from various sensors, integrate the information through data fusion technology, and use machine learning algorithms to analyze the data to achieve intelligent early warning of fire conditions.

[0044] 2. Forest area visualization - Combining high-resolution satellite images and drone aerial images, a three-dimensional terrain model of the forest area is constructed to display the landform features and vegetation coverage of the forest area.

[0045] Operation process 1. Install sensor equipment and ensure data transmission to the central management system.

[0046] 2. Use video analysis technology to obtain forest area image information.

[0047] 3. Integrate various monitoring data through data fusion technology.

[0048] 4. Use intelligent algorithms to analyze data and identify potential fire risks. The system uses big data analysis and machine learning algorithms to perform real-time analysis on data collected from sensors and video surveillance equipment. First, the system preprocesses the data, including data cleaning, missing value filling, and outlier detection to ensure data quality and integrity. Then, the system uses fire prediction models, combined with historical fire data, meteorological data, and geographic information, to assess the fire risk in the current environment. The model dynamically adjusts the fire risk level based on changes in parameters such as temperature, humidity, wind speed, and smoke concentration. When a potential fire risk is detected, the system automatically triggers an alarm mechanism and notifies relevant personnel through a variety of methods (such as text messages, phone calls, broadcasts, etc.).

[0049] 5. When an abnormal situation is detected, the system automatically triggers the alarm mechanism and provides countermeasures.

[0050] Example 2: UAV cruising and intelligent control.

[0051] Hardware equipment: 1. Multi-purpose UAV - equipped with high-resolution camera, thermal imager, laser rangefinder, automatic obstacle avoidance system, etc., supports real-time transmission of high-definition video, and has automatic cruising and fire-extinguishing bomb launching functions.

[0052] 2. Drone Cabin - Designed with functions such as automated charging, material replenishment, equipment replacement and status monitoring, it provides comprehensive support for drones.

[0053] Software system: 1. Drone patrol and control - preset patrol paths and mission plans to achieve full coverage patrol of forest areas.

[0054] 2. Intelligent Analysis - Analyze the data collected by the drone, automatically identify abnormal situations, and generate corresponding countermeasures. The system uses image processing and computer vision technology to perform real-time analysis through high-definition video, thermal imaging images and laser ranging data collected by drones. First, the system pre-processes the collected images, including denoising, enhancement and segmentation to ensure image quality. Then, the system uses deep learning algorithms to automatically identify abnormal situations in the image, such as smoke, flames, high-temperature areas, etc. Once an abnormal situation is identified, the system will immediately generate an alarm and push relevant information (such as location, time, image fragments, etc.) to relevant personnel. At the same time, the system will automatically generate corresponding countermeasures according to the severity of the abnormal situation, such as dispatching ground firefighting teams, launching firefighting drones, etc. The system will also record the handling process of all abnormal situations for subsequent analysis and optimization.

[0055] Operation process 1. Preset the drone’s cruise path and mission plan.

[0056] 2. The drone takes off automatically and patrols along the predetermined route.

[0057] 3. Analyze the data collected by drones through intelligent algorithms.

[0058] 4. After identifying an abnormal situation, the system generates an alert and pushes it to relevant personnel.

[0059] 5. Users can remotely control drones through the system to conduct fixed-point monitoring or perform emergency tasks.

[0060] Example 3: Intelligent dispatch of emergency plans Software System 1. Plan library management - stores historical plan data and supports query, modification and reuse of plans.

[0061] 2. Automatic plan generation - Generate emergency plans based on real-time environmental data using big data analysis and artificial intelligence technology. The system uses big data analysis and machine learning algorithms to analyze environmental data in forest areas (such as meteorological data, fire data, terrain information, etc.) in real time to generate scientific and reasonable emergency plans. First, the system obtains real-time environmental data from multiple data sources and preprocesses it to ensure the quality and integrity of the data. Then, the system uses historical fire data and expert knowledge to build a fire risk assessment model. The model will dynamically assess the fire risk based on the current environmental data and generate corresponding emergency plans. The plan content includes detailed action steps, resource allocation plans and response measures. The system will automatically adjust the priority and response method of the plan according to the level of fire risk to ensure the timeliness and effectiveness of the emergency plan. The system also supports simulation drills of the plan, and improves the response ability and coordination level of emergency personnel by simulating different scenarios. The plan library will record all generated plans and their execution status, providing data support for subsequent emergency management work.

[0062] Operation process 1. Collect historical emergency event data, expert experience, and real-time environmental data.

[0063] 2. Use intelligent algorithms to analyze data and automatically generate draft emergency plans.

[0064] 3. Adjust and optimize the plan according to actual conditions.

[0065] 4. The system supports simulation drills of emergency plans, evaluates the feasibility of the plans through drills, and provides suggestions for improvement.

[0066] The above embodiments specifically demonstrate the application details of the present invention in forest environment monitoring and fire warning, drone cruising and intelligent control, ancient tree multi-dimensional perception terminal access, and emergency plan intelligent scheduling, ensuring that technicians in this field can implement the present invention without creative work.

[0067] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A forest fire prevention method based on digital twins and intelligent sensors, characterized in that: Includes steps: S10, using multiple sets of sensors and video image acquisition equipment to collect and monitor environmental data; integrating and processing the data and image information from different sensors through data fusion to complete data collection; S20, based on the collected data, uses big data analysis technology to analyze potential risk areas for fire, identifies high-risk areas by assessing risk levels, and predicts fire spread trends; S30, based on data processing, a three-dimensional virtual model of the forest area is constructed using digital twin technology to form a dynamically updated digital twin model of the forest area; based on the digital twin model of the forest area, the collected data is received in real time, and the spread of the fire at different time and space locations is simulated through the fire dynamics model, combined with the geographical environment, meteorological conditions and combustible distribution factors; S40, based on the predicted fire spread trend, when a potential fire risk is detected, the early warning mechanism is activated; S50 uses big data analysis and artificial intelligence technology to automatically generate emergency plans. Based on the route analysis algorithm and all escape routes uploaded by users, it comprehensively considers distance, traffic capacity, and current crowd density factors to screen and generate emergency plans.

2. A forest fire prevention method based on digital twins and intelligent sensors according to claim 1, characterized in that: Environmental data collection and monitoring include: Sensor deployment and data collection: Various sensors are deployed in the forest area to collect real-time data on the growth environment of ancient trees; the sensors include soil moisture sensors, temperature sensors, light sensors and wind speed sensors to monitor environmental parameters around the clock; all collected data will be transmitted to the central management system via wireless networks; Video image data acquisition: Deploy high-point monitoring camera terminals and drone equipment in forest areas to collect image information in real time; use video analysis technology to further process image data and combine it with sensor data to form comprehensive environmental monitoring data.

3. The forest fire prevention method based on digital twin and intelligent sensor according to claim 1 is characterized in that: Through machine learning algorithms, historical environmental data is compared and analyzed with current data to evaluate the possibility of fire under different environmental conditions. High-risk areas are identified by assessing risk levels and predicting fire spread trends.

4. A forest fire prevention method based on digital twins and intelligent sensors according to claim 1, characterized in that: In step S30, a dynamically updated digital twin model of the forest area is formed by mapping sensor data, image information and environmental parameters to the virtual space; the model shows the geomorphic features, vegetation coverage and climate change information of the forest, and reflects the environmental changes of the forest area in real time.

5. The forest fire prevention method based on digital twin and intelligent sensor according to claim 1 is characterized in that: In step S30, based on the forest area digital twin model, the collected data is received in real time, and the spread of fire at different time and space locations is simulated through the fire dynamics model in combination with the geographical environment, meteorological conditions and combustible distribution factors, including the steps of: Update the fire spread in real time. According to the geographical environment, meteorological conditions and combustible characteristics, the model obtains the fire spread speed and fire intensity based on the convolutional long short-term memory network and self-attention mechanism, and updates the simulation results in real time; Based on the fire spread simulation results, the natural fire risk rating rule evaluates the risk levels of different areas and highlights high-risk areas on the map through color coding.

6. The forest fire prevention method based on digital twin and intelligent sensor according to claim 1 is characterized in that: In step S40, when a potential fire risk is detected, the system immediately starts the alarm program, adjusts the alarm priority and response method according to the risk level, and sends early warning information to relevant personnel through sound and light alarms, text message notifications and telephone calls.

7. The forest fire prevention method based on digital twin and intelligent sensor according to claim 1 is characterized in that: In step S50: Acquire real-time environmental data and image information, and obtain pre-planned escape route data uploaded by users, including path coordinates, road width, and capacity information; use GIS technology to calculate the actual distance of each escape route, and evaluate the capacity of each route based on road width and obstacle distribution factors; Real-time monitoring of crowd density in scenic areas through video analysis and sensor data; Use Dijkstra path optimization algorithm or A* path optimization algorithm, combined with actual distance, traffic capacity and crowd density data, to screen out the optimal escape route; Based on the optimized escape route, an emergency plan is obtained.

8. A forest fire prevention system based on digital twins and intelligent sensors, characterized in that: include: A collection device, which uses multiple sets of sensors and video image acquisition equipment to collect and monitor environmental data; Integrate and process data and image information from different sensors through data fusion to complete data collection; The fire potential risk area analysis module uses big data analysis technology to analyze the potential risk areas for fire based on the collected data, identifies high-risk areas by assessing the risk level, and predicts the fire spread trend; The forest area digital twin model construction module uses digital twin technology to build a three-dimensional virtual model of the forest area, forming a dynamically updated forest area digital twin model; based on the forest area digital twin model, it receives the collected data in real time, and simulates the spread of fire in different time and space locations through the fire dynamics model, combined with the geographical environment, meteorological conditions and combustible distribution factors; The early warning module activates the early warning mechanism when a potential fire risk is detected based on the predicted fire spread trend; The emergency plan generation module uses big data analysis and artificial intelligence technology to automatically generate emergency plans. Based on the route analysis algorithm and all escape routes uploaded by users, it comprehensively considers distance, traffic capacity, and current crowd density factors to screen and generate emergency plans.

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