Digital intelligent control system for forest fire

Abstract

The invention, which relates to the technical field of forest fire prevention, discloses a forest fire digital intelligent prevention and control system comprising a sensing layer, a data transmission layer, a decision control layer, an execution layer and an energy guarantee layer. The sensing layer uses an AI image fire detector to identify flames, smog and a heat source, and cooperates with a humiture and surface temperature monitor to collect environmental data; a central cloud platform of the decision control layer performs fire danger evaluation and fire behavior positioning prediction on the data, and generates instructions to control fire extinguishing nozzle control boxes which are dispersedly deployed; the execution layer comprises an automatic fire extinguishing device, a pipe network electric butterfly valve, a hydroelectric pump linkage system and an assembly type reservoir, and is responsible for water resource scheduling, pressurization and fire extinguishing execution; the energy guarantee layer provides uninterruptible power, integrates commercial power, a diesel generator and a UPS, and is regulated and controlled by an intelligent power management module. According to the system, all-weather digital intelligent monitoring, early warning and disposal of forest fire are achieved, the forest fire prevention and disposal level is remarkably improved, and the safety of forest resources and the ecological environment is effectively guaranteed.

Description

Technical Field

[0001] This invention belongs to the field of forest fire prevention technology, and more specifically, relates to a digital intelligent forest fire prevention and control system. Background Technology

[0002] Forests, as a vital component of terrestrial ecosystems, play an irreplaceable role in global ecological balance, biodiversity conservation, and socio-economic development. However, forest fires, as a natural disaster characterized by their suddenness, destructiveness, and difficulty in control, pose a serious threat to forest resources and the ecological environment, often accompanied by significant economic losses and casualties. Given the increasing frequency of extreme weather events due to global warming, coupled with the growing amount of combustible material in forests due to the construction of ecological public welfare forests and the increased efforts in forest closure and reforestation, as well as the emergence of new risk points such as open-field fire source management and ultra-high voltage power transmission lines, the probability and intensity of forest fires are both on the rise. This presents unprecedented challenges to forest fire prevention efforts, urgently requiring more advanced and efficient technological support.

[0003] In my country's current forest fire prevention practices, the main approach relies on a combination of manual patrols and traditional monitoring methods. Specifically, manual patrols refer to forest rangers conducting ground patrols deep into forest areas. Their core tasks include disseminating fire prevention knowledge to local residents, strictly controlling human-caused fire sources, and supplementing patrols in blind spots of lookout towers. While this method is effective in controlling human-caused fire sources in specific periods and localized areas, its inherent limitations are becoming increasingly apparent in practical applications. These limitations include limited patrol area, narrow field of vision, and difficulty in accurately locating fire points in rugged terrain or densely forested areas. This is particularly challenging in remote mountainous regions with poor transportation, where implementation is difficult and labor and transportation costs are high. Correspondingly, lookout tower monitoring, as a fixed-point observation method, uses lookout towers positioned at high points to visually observe forest fires in order to detect them at an early stage. However, its observation effectiveness is significantly limited by terrain, often resulting in blind spots and dead zones. Furthermore, it struggles to effectively identify concealed fires such as light smoke, embers, or underground fires. In addition, its operation heavily relies on the observer's experience and judgment, leading to low accuracy, and the observer's safety is also at risk during severe weather conditions such as thunderstorms. Further, aerial patrols utilize forest patrol aircraft for large-scale forest fire detection, possessing the ability to quickly cover vast areas. However, this method is susceptible to being affected by weather conditions such as nighttime, strong winds, or low visibility, preventing takeoff. Patrols are strictly limited by flight routes and time, and in most cases, only one patrol of a specific forest area can be conducted per day. Missing the optimal observation window can easily lead to an initial fire escalating into a major disaster. Moreover, its rental and flight costs are extremely high, making it difficult to achieve routine, high-frequency, comprehensive coverage.

[0004] While the aforementioned traditional forest fire prevention methods have played a positive role in their respective historical stages, their deep-seated technical contradictions and inherent limitations have become increasingly prominent in the context of today's increasingly complex and volatile fire risks. The underlying reason lies in the fact that these methods are essentially based on discrete, intermittent information collection patterns and heavy reliance on human intervention. With the continuous increase in forest canopy density and the rapid rise in combustible material load within forests, visibility within forest areas has significantly decreased, directly exacerbating blind spots and errors in traditional visual observation. Simultaneously, extreme weather caused by global climate change not only directly increases forest fire risk levels but also severely hinders aerial patrols and manual outdoor operations that rely on specific weather conditions, limiting fire monitoring when it is most needed. Crucially, forest fires are characterized by extreme uncertainty and rapid spread, requiring monitoring systems to possess all-weather, real-time, high-precision, and rapid-response capabilities. Traditional methods, limited by physical constraints on manpower, the limited field of view of fixed facilities, and the time intervals of aircraft inspections, struggle to achieve continuous and uninterrupted monitoring of vast forest areas, and are unable to accurately locate and quickly confirm fires in their early stages. This lag and limitation allow initial fire points to spread rapidly before they are detected, missing the optimal time for firefighting and greatly increasing the risk of major disasters. Furthermore, existing systems fail to effectively integrate multi-source information, lack intelligent and automated linkage mechanisms to achieve seamless coordination between early warning, fire prevention, and firefighting, and do not adequately consider the challenges posed by the increasing risk of power transmission line fires in modern society, rendering them inadequate when facing emerging and complex fire sources.

[0005] Therefore, how to build an integrated solution that can identify forest fires comprehensively, around the clock, in real time, and with high precision, and has intelligent linkage early warning, proactive prevention, and efficient and rapid response capabilities, in addition to the inherent discreteness, lag, and limitations of traditional manual monitoring modes, has become a key challenge and an urgent technical problem for those skilled in the art. Summary of the Invention

[0006] To achieve the above-mentioned objectives, this invention provides a digital and intelligent forest fire prevention and control system, which aims to address the limitations of existing forest fire prevention methods in terms of real-time performance, coverage, accuracy, automation level, and all-weather adaptability. The system aims to construct an integrated solution that can identify forest fires comprehensively, around the clock, in real-time, and with high accuracy, and possess intelligent linkage early warning, proactive prevention, and efficient and rapid response capabilities.

[0007] The present invention proposes a digital intelligent forest fire prevention and control system, comprising: a perception layer, a data transmission layer, a decision control layer, an execution layer, and an energy security layer.

[0008] The perception layer includes multiple AI image fire detector groups and multiple temperature and humidity detector groups deployed within the forest fire prevention area. The AI ​​image fire detector groups are used to acquire visible light and infrared thermal images of the forest area in real time, and analyze the acquired image data in real time through a built-in dedicated artificial intelligence processing unit to identify the characteristics of flames, smoke, and abnormal heat sources. The internal structure of the AI ​​image fire detector group includes: a high-resolution visible light imaging module, an uncooled infrared thermal imaging module, an embedded artificial intelligence chipset, a data preprocessing module, and a communication interface module. The high-resolution visible light imaging module uses a CMOS image sensor with a pixel resolution of 3840×2160 and a frame rate of 30 frames / second, and integrates wide dynamic range and low-light enhancement functions to adapt to image acquisition under complex lighting conditions. The uncooled infrared thermal imaging module uses a vanadium oxide (VOx) microbolometer array with a pixel size of 17μm, a resolution of 640×512, a thermal sensitivity (NETD) of less than 40mK, and a spectral response range of 7.5μm to 13.5μm, used to detect the temperature distribution and abnormal hotspots on the forest surface. The embedded AI chipset boasts a computing power exceeding 20 TOPS (trillion operations per second) and integrates a Neural Processing Unit (NPU) for running pre-trained deep learning models. These deep learning models are based on a hybrid architecture of Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM), specifically trained for the flame and smoke characteristics of forest fires. This allows them to effectively distinguish between real fires and environmental interference (such as sunlight reflection, fog, and hot rocks), significantly reducing the false alarm rate. The data preprocessing module performs noise reduction, image enhancement, and distortion correction on the raw image data to optimize the quality of the data input to the AI ​​chipset. The communication interface module includes fiber optic and wireless communication interfaces for transmitting the processed fire identification results, image segments, and real-time video streams to the data transmission layer. Each AI image fire detector unit integrates a ruggedized housing with an IP68 dustproof and waterproof rating and is equipped with an automatic defogging and cleaning device to ensure stable operation in harsh environments.

[0009] The temperature and humidity monitoring unit is used to monitor air temperature, relative humidity, and surface temperature data in the forest area in real time. The unit employs high-precision digital MEMS (Micro-Electro-Mechanical Systems) sensors, with the temperature sensor having a measurement accuracy of ±0.3℃ and the humidity sensor an accuracy of ±2%RH, and a response time of less than 2 seconds. The temperature and humidity monitoring unit can be deployed co-located with or independently of the AI ​​image fire detector unit, and transmits data to the nearest AI image fire detector unit or independent gateway device via a short-range wireless communication module (such as a LoRa module).

[0010] The data transmission layer is responsible for the aggregation, transmission, and routing of data collected by the sensing layer. This layer employs a multimodal wireless communication network architecture, including a LoRaWAN network, a 5G cellular network, and a satellite communication link. The LoRaWAN network transmits low-rate, periodic sensor data and control commands generated by the AI ​​image fire detector group and temperature and humidity detector group. Its features include low power consumption and long-range coverage; each LoRaWAN gateway can cover a range with a diameter of up to 15 kilometers. The 5G cellular network transmits high-definition image streams, video streams, and large-capacity alarm data. Its high bandwidth and low latency characteristics ensure real-time transmission and analysis of fire video. The satellite communication link, as a supplement to 5G network coverage blind spots, uses low Earth orbit (LEO) satellite communication technology to ensure uninterrupted data transmission in remote, network-free areas, such as Iridium or Starlink terminals, with data transmission rates reaching up to 50 Mbps. The data transmission layer also includes multiple edge computing gateways, deployed at the edge of forest areas or communication hub nodes. These gateways perform preliminary filtering, compression, encryption, and protocol conversion on the received raw data to reduce the processing burden on the core cloud platform and improve data transmission efficiency. Each edge computing gateway has a built-in ARM architecture industrial-grade processor, 8GB of RAM, and 64GB of eMMC storage, and supports IoT communication protocols such as MQTT / CoAP / HTTPS.

[0011] The decision control layer is the core intelligent unit of this system, which includes multiple fire extinguishing nozzle control boxes and a central cloud platform.

[0012] The fire sprinkler control boxes are distributed at various nodes of the forest fire protection network and are tightly integrated with the intelligent tracking jet device. The internal structure of the fire sprinkler control box includes: an industrial-grade embedded controller, a dedicated communication module, a digital / analog I / O module, a power management module, and an edge computing module. The industrial-grade embedded controller uses a high-performance ARM Cortex-A53 processor with a main frequency of 1.2GHz and supports a real-time operating system (RTOS) to ensure low-latency execution of control commands. The dedicated communication module supports LoRaWAN and 5G / 4G multi-mode communication for bidirectional data exchange with the AI ​​image fire detector group, temperature and humidity detector group, and central cloud platform. The digital / analog I / O module receives sensor signals (such as water pressure, water level, and valve status) and outputs control signals to the electric butterfly valve, water pump starter, and actuators of the intelligent tracking jet device. The edge computing module has a built-in streamlined machine learning inference engine for secondary confirmation and local decision-making of the received fire data, such as calculating the optimal spray angle and flow rate of the intelligent tracking jet device in real time based on the fire point coordinates provided by the AI ​​image detector.

[0013] The central cloud platform serves as the system's management and decision-making center, deployed on cloud computing infrastructure (e.g., a private cloud environment built on OpenStack or Kubernetes clusters). The central cloud platform includes: data access services, big data storage and processing modules, an artificial intelligence analysis and decision-making engine, a geographic information system (GIS) module, a visual management interface, and user interface services.

[0014] The data access service adopts a message queue (such as Kafka) architecture to receive data streams from edge computing gateways, fire sprinkler control boxes, and other monitoring devices at high concurrency.

[0015] The big data storage and processing module employs a distributed file system (such as HDFS) and a distributed database (such as Cassandra or Elasticsearch) to store massive amounts of historical monitoring data, fire images, videos, and system logs. The processing module uses big data processing frameworks such as Apache Spark to perform batch processing analysis on historical data, uncovering fire occurrence patterns, risk area distribution, and optimizing prediction models.

[0016] The AI ​​analysis and decision-making engine is a core intelligent component of the central cloud platform. This engine receives and integrates fire alarm information from all AI image fire detector groups, environmental data from temperature and humidity sensors, and historical data. It employs multimodal fusion deep learning algorithms (e.g., a combination of a Transformer-based time-series analysis model and a graph neural network (GNN)) to comprehensively assess forest fire risk levels and provide high-precision location and trend prediction of fires. The prediction model considers meteorological parameters including wind speed, wind direction, temperature, humidity, rainfall, forest combustible load, and historical fire data. When a fire is detected or the fire risk level reaches a preset threshold, the engine automatically generates a fire alarm event and accurately displays the fire location through a Geographic Information System (GIS) module.

[0017] The Geographic Information System (GIS) module integrates high-resolution satellite imagery, topographic elevation maps, forest road maps, and forest vegetation distribution maps. It can overlay fire information, sensor distribution, fire duct network paths, and the coverage area of ​​the digital tracking jet device in real time, providing intuitive visualization support for command and dispatch.

[0018] The visual management interface is implemented using web front-end technologies (such as React or Vue.js frameworks), providing functions such as system status overview, real-time alarm display, historical data query, video preview, equipment management, policy configuration (such as timed water spraying rules, temperature and humidity thresholds, fire hazard level thresholds), and manual intervention operations (such as remote system start / stop, remote water spraying, and remote control of diesel generators).

[0019] The user interface service provides a RESTful API interface, supporting data interaction and function integration between mobile applications (e.g., native apps based on iOS and Android platforms) and third-party systems (such as emergency command platforms).

[0020] The execution layer is the physical execution mechanism for fire prevention and suppression in this system, including multiple intelligent tracking jet devices, multiple electric butterfly valves, a water and electricity pump linkage system, and a prefabricated water storage tank.

[0021] The intelligent tracking jet device is installed at various nodes of the forest fire fighting network, and its core function is to achieve precise tracking of the fire point. The internal structure of the intelligent tracking jet device includes: a high-flow-rate fire monitor, an electrically driven omnidirectional rotating mechanism, a variable-flow and spray mode nozzle, and an integrated local control unit. The fire monitor is made of high-strength stainless steel (SUS304 or SUS316), with a designed flow rate of 20 liters / second to 50 liters / second, an operating pressure range of 0.8 MPa to 1.6 MPa, and a maximum range of 80 to 100 meters. The electrically driven omnidirectional rotating mechanism is driven by a high-performance brushless DC motor, equipped with a high-precision reducer and an absolute encoder, achieving continuous horizontal rotation of 0-360° and vertical pitch adjustment of -30° to +90°. Its positioning accuracy is better than 0.1 degrees, and the rotation speed can reach 30 degrees / second, ensuring that the water monitor can quickly and accurately target moving fire points. The variable flow and spray mode nozzles are controlled by electrically adjustable valves, enabling stepless switching between various modes such as direct spray, fan-shaped spray (water curtain), and atomized spray to adapt to different fire conditions and prevention needs. The integrated local control unit receives commands from the fire extinguishing nozzle control box and drives the electric omnidirectional rotation mechanism and the variable flow and spray mode nozzles in real time.

[0022] The electric butterfly valve is installed on a branch of the fire protection pipeline to control the flow of water. The electric butterfly valve is driven by an intelligent electric actuator with an opening and closing time of less than 5 seconds and has a valve position feedback function, which can transmit the valve status back to the fire sprinkler control box in real time.

[0023] The water-electricity-pump linkage system is responsible for water resource scheduling and pressurization, and includes: high-pressure water pumps, a pump control box, a water pipeline network, and a bypass water supply line. The high-pressure water pumps consist of at least two (one operational, one standby) diesel-powered centrifugal fire pumps, each with a rated flow rate of 200 cubic meters per hour and a head of 120 meters. The pump control box is used for remote or automatic control of pump start-up and shutdown, monitoring pump operating status (such as speed, pressure, temperature), and oil level. The water pipeline network is laid with high-strength ductile iron or HDPE pipes, with a pressure resistance rating of PN16, ensuring safety and reliability under high-pressure water supply. The bypass water supply line allows water to flow for non-firefighting purposes, such as for periodic maintenance or testing, during non-emergency fire situations or system maintenance.

[0024] The prefabricated water storage tanks, serving as fire-fighting water reserves, are strategically located and dispersed throughout the forest area. Each tank utilizes a modular prefabricated steel plate structure combined with an HDPE impermeable lining, and its individual volume can be customized as needed, ranging from 500 to 2000 cubic meters. Equipped with a level sensor, the tanks monitor the water level in real time. When the water level falls below a preset threshold, an automatic water replenishment system (including an electric water replenishment valve and pump, connected to the municipal water supply network) automatically draws water from the municipal water supply network or nearby natural water sources to ensure the tanks maintain a consistently sufficient water level.

[0025] The energy security layer provides a stable and reliable power supply for the entire system, including: an uninterruptible power supply (UPS) module, a mains power access module, a diesel generator set, and an intelligent power management module. The UPS module adopts an online double-conversion topology, has a rated power of 50kVA, and is equipped with a lithium iron phosphate battery pack, providing at least 2 hours of backup power during mains power outages to ensure the continuous operation of critical system components. The mains power access module is used to introduce municipal power into the system. The diesel generator set, with a power output of 100kW, is equipped with an ATS (Automatic Transfer Switch). When the mains power is interrupted or the system detects a fire signal, it automatically starts through the intelligent power management module in the pump control box, completing grid connection and power generation within 15 seconds, interrupting mains power supply and switching to diesel generator power. This design ensures that even if a fire causes a potential mains power outage, the system can still obtain an independent and stable power supply, maintaining the normal operation of the fire pumps and the intelligent tracking jet device. The diesel generator set can also be remotely or manually started and stopped via a central cloud platform or on-site control panel. The intelligent power management module is also responsible for power distribution and power consumption monitoring of all power-consuming nodes, optimizing energy efficiency.

[0026] The core technical solution of this invention also includes the following digital and intelligent functions: Firstly, this system has digital and intelligent prevention functions.

[0027] In a preferred embodiment of the present invention, the intelligent prevention function includes a timed automatic water spraying mode. This system can be programmed with automatic activation rules via a central cloud platform, for example, automatically activating the system at specific intervals (e.g., every hour) to control the intelligent tracking jet device in a specific area to spray water in a fan-shaped spray pattern. This achieves comprehensive humidification and cooling within the protected area, effectively reducing the moisture content of combustibles in the forest and the surface temperature, thus reducing the probability of fire at its source. The spraying time, spraying intensity, and spray range can all be finely configured according to the actual conditions of the forest area, seasonal changes, and weather forecasts.

[0028] In a preferred embodiment of the present invention, the intelligent fire prevention function further includes an automatic water spraying mode based on environmental parameters. This system continuously collects data from temperature and humidity sensors. When the ambient temperature and relative humidity reach a preset fire hazard warning threshold (e.g., temperature above 30°C and relative humidity below 40%RH), the artificial intelligence analysis and decision-making engine of the central cloud platform will automatically trigger an early warning and activate the intelligent tracking jet device in the corresponding area to humidify and cool the protected area. The threshold can be dynamically adjusted according to the historical fire hazard level of the forest area, vegetation type, and seasonal characteristics.

[0029] In a preferred embodiment of the present invention, the intelligent prevention function also supports a remote manual activation mode. During periods of high fire risk, such as concentrated agricultural activities or frequent entry and exit of people in or near forests, managers can remotely activate the system in designated areas via a central cloud platform or mobile application to actively humidify and cool the fire-prevention areas to prevent fires.

[0030] Secondly, this system has a digital and intelligent early warning function.

[0031] In a preferred embodiment of the present invention, the core of the intelligent early warning function lies in the use of an AI image fire detector group to achieve all-weather, visualized, and automatic early fire warning. The AI ​​image fire detector group captures images of the forest area through a high-resolution visible light imaging module and obtains temperature distribution maps through an uncooled infrared thermal imaging module. The embedded artificial intelligence chip group runs a deep learning algorithm integrated within it in real time. This algorithm can perform pixel-level flame and smoke feature recognition on the acquired images and, combined with time series analysis, effectively distinguish between real fires and false alarms caused by sunlight, temperature changes, wind disturbances, etc. The deep learning algorithm is particularly optimized for recognizing weak smoke and concealed flames in the early stages of a fire, ensuring accurate early warnings. For example, when the algorithm detects flame or smoke features, it generates a confidence score. An alarm is triggered only when the confidence score is higher than a preset threshold and the duration exceeds a set time. The AI ​​image fire detector group also has security monitoring functions, replacing traditional surveillance cameras to achieve real-time monitoring and video recording of personnel activities and abnormal situations in the forest area.

[0032] Thirdly, this system has digital and intelligent fire extinguishing capabilities.

[0033] In a preferred embodiment of the present invention, when the AI ​​image fire detector group detects a fire and it is confirmed by the central cloud platform, the central cloud platform sends the precise location information of the fire point to the nearest fire extinguishing nozzle control box. Upon receiving the instruction, the fire extinguishing nozzle control box immediately controls the opening of the electric butterfly valve at the corresponding location in the fire extinguishing pipeline, connecting the corresponding fire extinguishing pipeline, and sends the instruction to the intelligent tracking jet device. Simultaneously, the water pump control box receives the pump start instruction from the central cloud platform, first starting the diesel generator set (if the mains power is interrupted or the system is set to prioritize diesel generators) to ensure power supply, and then, after a delay (e.g., a 5-second delay), starts the high-pressure water pump to pressurize the connected fire extinguishing pipeline. After receiving the fire coordinates and extinguishing command, the intelligent tracking jet device's integrated local control unit adjusts the horizontal and vertical angles of the water cannons in real time based on parameters such as fire location, wind direction, wind speed (from the temperature and humidity sensor group or meteorological data interface), and forest density. It also adjusts the variable flow rate and spray mode nozzles to the optimal extinguishing mode (e.g., direct spray mode for ignition and extinguishing, or fan-shaped water curtain mode for preventing spread), achieving precise tracking of the fire. The system has a rapid response capability; the time from fire detection to the activation of the intelligent tracking jet device for extinguishing can be controlled within 30 seconds. After extinguishing the fire, the system automatically detects whether the fire has been extinguished (e.g., confirming the disappearance of flame and smoke signals through the AI ​​image fire detector group and confirming that the temperature has returned to normal through the infrared thermal imaging module). Once confirmed, the system automatically shuts off the electric butterfly valve, stops the water pump and diesel generator set, and restores all equipment to its initial standby state.

[0034] Fourthly, this system possesses highly integrated and intelligent management capabilities.

[0035] In a preferred embodiment of the present invention, the high degree of integration of the system is reflected in the seamless connection between sensing, transmission, decision-making, and execution. All devices are connected to the central cloud platform via IoT interfaces, enabling data interconnection and precise command issuance. The central cloud platform provides comprehensive management functions, including but not limited to: Cloud platform management functions: Supports multi-user and multi-permission management, and provides remote management functions such as device registration, configuration, status monitoring, and firmware upgrade.

[0036] Fire alarm mobile viewing function: Fire alarm information is instantly pushed to the mobile phones of designated managers via a mobile application, and alarm details, fire location and real-time images / videos can be viewed.

[0037] On-site image preview function: Users can view high-definition on-site images and video streams collected by the AI ​​image fire detector group in real time through the central cloud platform or mobile application.

[0038] Fully automatic control function: The system achieves fully automatic operation of fire prevention and fire extinguishing based on preset strategies and real-time data.

[0039] Wireless remote control function: Supports wireless remote control of digital tracking jet device, water pump and diesel generator set through cloud platform or mobile application.

[0040] Historical record query function: All system operation logs, alarm events, water spray records, equipment status changes, etc. are recorded and can be queried for post-event analysis and traceability.

[0041] Video recording and storage function: The video stream of the AI ​​image fire detector group can be stored on demand to cloud storage or local storage devices for fire evidence collection and analysis.

[0042] Fire alarm output function: Supports outputting fire alarm signals to third-party fire protection systems or emergency command centers through various interfaces (such as relay contacts, Modbus TCP / IP, SNMP).

[0043] Pump start-up control function: The central cloud platform can directly control the start and stop of water pumps in the water-electricity pump linkage system.

[0044] Equipment status monitoring function: Real-time monitoring of the operating status, fault information, communication status, power status, etc. of all equipment.

[0045] IoT Interface: Provides standardized API interfaces to facilitate data sharing and collaborative management with government, fire departments, or other smart city platforms.

[0046] Electronic map function: Visually display the geographical location, coverage area, fire location, pipeline route and system operation status of all equipment on the GIS map.

[0047] Log function: Records all system operations, events and alarms in detail, facilitating fault diagnosis and auditing.

[0048] The intelligent forest fire prevention and control system proposed in this invention deeply integrates advanced artificial intelligence, Internet of Things, cloud computing technologies with traditional fire protection technologies to build a comprehensive solution with proactive prevention, real-time accurate early warning, rapid intelligent response and efficient fire extinguishing capabilities. It significantly improves the level of forest fire prevention and control, effectively overcomes the inherent limitations of existing technologies, and provides a strong guarantee for the safety of forest resources and the ecological environment. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a schematic diagram of the system architecture of the present invention; Figure 2 This is a schematic diagram of the internal structure of the AI ​​image fire detector group in this invention; Figure 3 This is a schematic diagram of the internal structure of the fire extinguishing nozzle control box in this invention; Figure 4 This is a schematic diagram of the digital intelligent fire extinguishing function process of the present invention; Figure 5 This is a schematic diagram of the system deployment of the present invention.

[0051] The attached diagram is labeled as follows: 1. Perception Layer; 2. Data Transmission Layer; 3. Decision Control Layer; 4. Execution Layer; 5. Energy Security Layer; 10. AI Image Fire Detector Group; 11. Temperature and Humidity Detector Group; 100. High-Resolution Visible Light Imaging Module; 101. Uncooled Infrared Thermal Imaging Module; 102. Embedded Artificial Intelligence Chipset; 103. Data Preprocessing Module; 104. Communication Interface Module; 20. LoRaWAN Network; 21. 5G Cellular Network; 22. Satellite Communication Link; 23. Edge Computing Gateway; 30. Fire sprinkler head control box; 31. Central cloud platform; 300. Industrial-grade embedded controller; 301. Dedicated communication module; 302. Digital / analog I / O module; 303. Power management module; 304. Edge computing module; 40. Digital tracking jet device; 41. Electric butterfly valve; 42. Water-electric pump linkage system; 43. Prefabricated water storage tank; 420. High-pressure water pump; 421. Water pump control box; 50. Uninterruptible power supply (UPS) module; 51. Diesel generator set. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0053] This invention provides a digital and intelligent forest fire prevention and control system. It aims to deeply integrate advanced artificial intelligence, the Internet of Things, and cloud computing technologies with traditional fire engineering practices to construct an integrated solution capable of comprehensive, all-weather, real-time, and high-precision identification of forest fires, with intelligent linkage early warning, proactive prevention, and efficient rapid response capabilities. The specific implementation of this system will be described in detail from its core components, the functional details of each module, the cooperation mechanisms between them, and the operation process and performance in actual application scenarios, to ensure that those skilled in the art can fully understand and replicate the technical solution of this invention.

[0054] Reference Figures 1-5 As shown, this system is generally divided into a perception layer, a data transmission layer, a decision control layer, an execution layer, and an energy security layer. Each layer exchanges data and transmits instructions through standardized interfaces, forming a collaborative and intelligent fire prevention system.

[0055] First, the perception layer of this system is described in detail. Its main function is to acquire environmental data and potential fire information in the forest area in real time. The core of the perception layer includes multiple AI image fire detector groups 10 and multiple temperature and humidity detector groups 11.

[0056] The AI ​​image fire detector array is the key front-end device for achieving accurate early fire identification in this invention. Each detector array adopts an integrated design with a sophisticated internal structure, such as... Figure 2 As shown, the system specifically includes a high-resolution visible light imaging module 100, an uncooled infrared thermal imaging module 101, an embedded artificial intelligence chipset 102, a data preprocessing module 103, and a communication interface module 104. The high-resolution visible light imaging module utilizes a CMOS image sensor with a resolution of 3840×2160 pixels, and its excellent frame rate of 30 frames per second ensures smooth capture of dynamic scenes. To cope with the complex and variable natural lighting conditions in forest areas, this module integrates a wide dynamic range function, capable of simultaneously presenting rich details in both bright and shadow areas. It also features low-light enhancement to ensure clear color images are acquired even at dawn, dusk, night, or in overcast or rainy weather, which is particularly important for early smoke detection. The uncooled infrared thermal imaging module is equipped with an advanced vanadium oxide (VOx) microbolometer array with a pixel size as fine as 17μm and a resolution of 640×512. Its thermal sensitivity (NETD) is less than 40mK, which can identify abnormal heat sources under extremely weak temperature differences. The detection spectral response range covers 7.5μm to 13.5μm. This band performs well in penetrating smoke and haze, and can effectively capture hidden fire points, embers or abnormal surface temperature areas in the forest, making up for the shortcomings of visible light at night or when visibility is obstructed.

[0057] The embedded AI chipset serves as the "brain" of the AI ​​image fire detector, boasting a computing power exceeding 20 TOPS (trillion operations per second) and integrating a high-performance neural network processor (NPU). This chipset pre-loads and runs a deep learning model trained on massive amounts of forest fire images and video data. The model employs a hybrid architecture of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). The CNN portion extracts spatial features of flames and smoke from images, such as shape, color, texture, and dynamic change patterns; the LSTM portion analyzes the evolution of these spatial features over time to identify dynamically changing smoke spread trajectories and flame flickering characteristics. This hybrid architecture significantly enhances the model's ability to distinguish between real fire conditions and environmental disturbances (such as sunlight reflection on water or leaves, natural fog, hot rocks in summer, and small amounts of smoke from burning agricultural and forestry waste), thereby drastically reducing false alarm rates and ensuring the accuracy and reliability of alarms. The data preprocessing module performs key optimization tasks before the image data enters the AI ​​chipset. These include adaptive noise reduction algorithms to eliminate random noise in the image, image enhancement algorithms to improve contrast and clarity, and distortion correction algorithms to eliminate geometric distortion caused by wide-angle lenses. All these processes aim to provide higher-quality input data for the deep learning model, thereby improving the accuracy and efficiency of fire detection. The communication interface module provides multiple connection methods. The fiber optic interface is suitable for long-distance, high-bandwidth, and interference-resistant data transmission, while the wireless communication interface (such as a 5G / 4G module) provides deployment flexibility. Together, they stably and reliably transmit the processed fire detection results, key image segments, and high-resolution real-time video streams to the data transmission layer. To ensure long-term stable operation in harsh forest environments, each AI image fire detector unit adopts an IP68-rated dustproof and waterproof reinforced shell design, which can completely resist dust intrusion and prolonged immersion in water. It is also equipped with an automatic defogging and cleaning device, which regularly removes condensation, dust, or rainwater from the lens through built-in heating elements and a spray mechanism, ensuring that the optical window is always clean, thereby guaranteeing the quality of image acquisition.

[0058] The temperature and humidity monitoring system is used to monitor key meteorological parameters such as air temperature, relative humidity, and surface temperature in forest areas in real time. This system employs high-precision digital MEMS (Micro-Electro-Mechanical Systems) sensors, with the temperature sensor achieving a measurement accuracy of ±0.3℃ and the humidity sensor ±2%RH, both with a response time of less than 2 seconds, ensuring the timeliness and accuracy of environmental data. This data is crucial for fire hazard assessment and the development of preventative spraying strategies. The temperature and humidity monitoring system can be deployed co-located with the AI ​​image fire detector system, utilizing its communication capabilities; alternatively, it can be deployed independently at key meteorological observation points within the forest area, transmitting data to the nearest AI image fire detector system or a standalone LoRaWAN gateway device via a low-power, long-range short-range wireless communication module (such as a LoRa module), achieving a flexible and convenient networking approach.

[0059] Secondly, we delve into the data transmission layer 2 of this system, whose core responsibility is to efficiently and stably aggregate, transmit, and route various types of data collected by the sensing layer. To address the complex terrain, wide coverage, and diverse data types in forest areas, this data transmission layer adopts a multimodal wireless communication network architecture, including a LoRaWAN network 20, a 5G cellular network 21, and a satellite communication link 22. Due to its low power consumption and wide coverage, the LoRaWAN network is prioritized for transmitting low-rate, periodic sensor data generated by the AI ​​image fire detector group and temperature and humidity detector group, such as environmental parameters, device status, and preliminary fire identification confidence scores. A single LoRaWAN gateway can theoretically cover a range with a diameter of up to 15 kilometers, enabling large-area data backhaul with a smaller number of gateways in vast forests, significantly reducing deployment costs and maintenance complexity. The 5G cellular network, with its high bandwidth and low latency, primarily undertakes the real-time transmission of high-definition image streams, video streams, and large-capacity alarm data. When the AI-powered image fire detector group identifies a potential fire, the 5G network ensures that high-definition fire video is transmitted back to the central cloud platform of the decision-making and control layer within seconds, providing real-time and visual evidence for subsequent intelligent analysis and decision-making. Furthermore, the low latency of the 5G network provides a reliable guarantee for the rapid issuance of remote control commands to execution-level devices. Given the potential 5G network coverage blind spots in some remote or mountainous areas, this system innovatively introduces a satellite communication link as a crucial supplementary means. This link employs low Earth orbit (LEO) satellite communication technology, such as by integrating Iridium or Starlink terminals, ensuring uninterrupted data transmission in any geographical location, even in extreme situations with no terrestrial network coverage. Its data transmission rate can reach 50Mbps, sufficient to meet the transmission needs of emergency alarm information and critical image fragments, thus achieving full-area coverage communication assurance. The data transmission layer also strategically deploys multiple edge computing gateways 23. These gateways are typically deployed at the edge of forest areas or major communication hub nodes, and their main function is to perform preliminary filtering, compression, encryption, and protocol conversion on the received raw data. By preprocessing near the data source, edge computing gateways can effectively reduce the processing burden on the core cloud platform and significantly improve data transmission efficiency, especially in scenarios with limited network bandwidth or high latency. Each edge computing gateway has a built-in industrial-grade ARM architecture processor, equipped with 8GB of RAM and 64GB of eMMC storage, providing powerful edge computing capabilities and supporting mainstream IoT communication protocols such as MQTT, CoAP, and HTTPS, ensuring broad compatibility with different types of devices and secure data transmission.

[0060] Furthermore, the decision control layer 3 of this system serves as the intelligent hub of the entire system. It is responsible for in-depth analysis of the data gathered by the perception layer, fire hazard assessment, fire location and prediction, and generating precise control commands, which are then transmitted to the execution layer through the data transmission layer. The decision control layer mainly consists of multiple fire extinguishing nozzle control boxes 30 and a central cloud platform 31.

[0061] The fire sprinkler control box 30 is a key node for distributed decision-making and execution control. It is distributed across various nodes of the forest fire fighting network and tightly integrated with the intelligent tracking jet device 40. For example... Figure 3 As shown, its internal structure includes an industrial-grade embedded controller 300, a dedicated communication module 301, a digital / analog I / O module 302, a power management module 303, and an edge computing module 304. The industrial-grade embedded controller uses a high-performance ARM Cortex-A53 processor with a main frequency of up to 1.2GHz and supports a real-time operating system (RTOS). This ensures low-latency response and precise execution of control commands issued by the central cloud platform, such as calculating valve opening or water cannon angle adjustment within milliseconds. The dedicated communication module supports both LoRaWAN and 5G / 4G multi-mode communication, enabling local data exchange with the AI ​​image fire detector group and temperature and humidity detector group, as well as reliable bidirectional data communication with the central cloud platform, receiving commands and transmitting device status. The digital / analog I / O module receives signals from field sensors (such as water pressure sensors, water level sensors, and valve status sensors) and outputs control signals to the electric butterfly valve 41, the water pump starter, and the electric actuator of the intelligent tracking jet device, achieving precise control of the physical equipment. The edge computing module has a built-in streamlined machine learning inference engine, which enables the fire nozzle control box to perform real-time secondary confirmation and local decision-making after receiving fire point information from the AI ​​image detector group. For example, based on environmental parameters such as fire point coordinates, wind speed and direction provided by the AI ​​image detector, combined with preset parameters such as forest density and terrain slope, the optimal spray angle, range and flow rate of the digital tracking jet device can be calculated in real time without waiting for the complete decision of the central cloud platform, thereby greatly shortening the response time and improving the fire extinguishing efficiency.

[0062] The central cloud platform is the core management and decision-making center of this system. It is deployed on a powerful cloud computing infrastructure, such as a private cloud environment built on OpenStack or Kubernetes clusters, ensuring high availability, scalability, and data security. The central cloud platform encompasses multiple collaborative service modules: data access service, big data storage and processing module, artificial intelligence analysis and decision engine, geographic information system (GIS) module, visual management interface, and user interface service.

[0063] The data access service employs a high-performance message queue (such as Apache Kafka) architecture, capable of handling high-concurrency, massive data streams from edge computing gateways, fire sprinkler control boxes, and other monitoring devices, ensuring that all real-time data reliably and systematically enters the cloud platform. The big data storage and processing module utilizes distributed file systems (such as HDFS) and distributed databases (such as Apache Cassandra or Elasticsearch) to construct a robust storage system capable of storing massive amounts of historical monitoring data, fire images, videos, and system logs. The processing module, for example, based on big data processing frameworks such as Apache Spark, can perform efficient batch processing and real-time stream processing analysis on this historical data, deeply exploring the occurrence patterns of forest fires, the distribution characteristics of risk areas, and the dynamic changes in combustible materials, and continuously optimizing fire risk prediction models and fire suppression strategies.

[0064] The AI ​​analysis and decision-making engine, as the core intelligent component of the central cloud platform, is the concentrated embodiment of the entire system's intelligence. This engine receives and integrates fire alarm information from all AI image fire detector groups, environmental data from temperature and humidity monitoring devices, and historical fire data. It employs advanced multimodal fusion deep learning algorithms, such as an algorithm combining a Transformer-based time-series analysis model with a graph neural network (GNN), to perform a high-precision and comprehensive assessment of forest fire risk levels. The Transformer architecture is used to capture long-term dependencies and trends in time-series data such as meteorological data and combustible material status, while the graph neural network is used to analyze the spatial correlations between nodes in the sensor network (such as adjacent detectors and temperature and humidity stations), thereby more accurately assessing fire spread risk and the optimal fire suppression response point. The predictive model comprehensively considers multiple key meteorological parameters, including real-time wind speed, wind direction, temperature, humidity, and rainfall, as well as forest combustible material load (assessed through historical and remote sensing data) and regional historical fire data, to generate accurate fire risk indices and fire development trend predictions. When a fire is detected or the fire risk level reaches a preset threshold, the engine automatically generates a fire alarm event and accurately displays the location of the fire point and the direction of fire spread through the Geographic Information System (GIS) module, and suggests the best fire extinguishing strategy.

[0065] The Geographic Information System (GIS) module integrates high-resolution satellite imagery, detailed topographic elevation maps, forest road maps, and comprehensive forest vegetation distribution maps. The GIS module can overlay real-time fire information, precise geographic distribution of various sensors, fire hydrant network layout paths, and the coverage and range of digitally tracked jet spray devices, providing intuitive and comprehensive visualization support for the command and dispatch center. This allows managers to quickly grasp the overall fire situation and resource deployment. The visual management interface is implemented using web front-end technologies (such as React or Vue.js frameworks), providing an intuitive and user-friendly interface. This interface is feature-rich, including system status overview, real-time alarm display, historical data query, video preview, equipment management, and policy configuration (such as timed sprinkler rules, temperature and humidity thresholds, and fire hazard level thresholds). It also supports manual intervention, such as remotely starting and stopping the entire system, remotely controlling sprinklers in specific areas, and remotely starting and stopping diesel generator sets, greatly improving the system's manageability and flexibility. The user interface service provides a standardized RESTful API interface, supporting data interaction and function integration between mobile applications (e.g., native apps based on iOS and Android platforms) and third-party systems (such as emergency command platforms at all levels and meteorological department platforms), ensuring that this system can be seamlessly connected to a wider range of smart city or emergency management systems.

[0066] Next, the key execution layer 4 of this system consists of physical actuators for fire prevention and suppression. The execution layer includes multiple intelligent tracking jet devices 40, multiple electric butterfly valves 41, a water-electric pump linkage system 42, and a prefabricated water storage tank 43.

[0067] The intelligent tracking jet device 40 is the core physical equipment for precise fire suppression in this system. Installed at strategic nodes throughout the forest fire fighting network, it accurately tracks and jets fire from fire points. The device's internal structure includes a fire monitor, an electrically operated omnidirectional rotating mechanism, variable flow and spray pattern nozzles, and an integrated local control unit. The fire monitor is made of high-strength stainless steel (SUS304 or SUS316) to ensure structural stability and corrosion resistance under harsh environments and high-pressure water flow. It has a wide design flow range, from 20 liters / second to 50 liters / second, an operating pressure range of 0.8 MPa to 1.6 MPa, and a maximum range of 80 to 100 meters, effectively covering large areas of fire. The electrically operated omnidirectional rotating mechanism is key to achieving precise tracking; it is driven by a high-performance brushless DC motor, supplemented by a high-precision reducer and an absolute encoder. This mechanism enables continuous horizontal rotation from 0-360° and vertical pitch adjustment from -30° to +90°, with a positioning accuracy better than 0.1 degrees and a rotation speed of up to 30 degrees / second, ensuring that the water cannon can quickly and accurately adjust its direction according to the movement or spread of the fire. The variable flow and spray mode nozzles are precisely controlled via integrated electric regulating valves, allowing for stepless switching between various modes such as direct spray, fan-shaped spray (water curtain), and atomized spray. The direct spray mode is suitable for precisely extinguishing point fires or initial flames; the fan-shaped spray mode can form a water curtain isolation zone to prevent the fire from spreading; and the atomized spray is suitable for cooling and humidifying or extinguishing small areas of embers, thus adapting to different fire situations and prevention needs. The integrated local control unit receives control commands from the fire extinguishing nozzle control box and drives the electric omnidirectional rotation mechanism of the water cannon and the variable flow and spray mode nozzles in real time, ensuring immediate response and precise execution of commands.

[0068] Electric butterfly valves are installed on various branches of the fire protection pipeline network for precise control of water flow on / off and diversion. These valves are driven by intelligent electric actuators with an opening / closing time of less than 5 seconds, providing rapid response. They also feature valve position feedback, accurately transmitting the valve's real-time status (fully open, fully closed, partially open, etc.) back to the fire sprinkler control box and central cloud platform, ensuring precise control of the water flow path. The water-electric pump linkage system is responsible for the scheduling and pressurization of the entire fire protection water supply network, ensuring that the intelligent tracking jet device receives a sufficient and continuous high-pressure water source. This system includes a high-pressure water pump 420, a pump control box 421, a water supply pipeline network, and bypass water supply lines. The high-pressure water pump consists of at least two (one operating and one standby configuration to ensure system redundancy and reliability) diesel-powered centrifugal fire pumps, each with a rated flow rate of 200 cubic meters per hour and a head of 120 meters, providing powerful water pressure and flow. The pump control box is used for remote or automatic control of the pump's start and stop, and to monitor the pump's operating status in real time, such as key parameters like speed, outlet pressure, motor temperature, and diesel engine oil level, ensuring the pump's safe and stable operation. The water supply pipeline network is laid with high-strength ductile iron or HDPE pipes, with a pressure resistance rating of PN16, capable of safely and reliably withstanding the impact and abrasion under high-pressure water supply. The bypass water supply line design allows water to flow through non-firefighting paths during non-emergency fire situations or system maintenance, such as for periodic network flushing, testing, or replenishing the reservoir, providing flexibility for system operation.

[0069] Prefabricated water storage tanks, serving as crucial fire-fighting water reserve units in this system, are strategically located within forest areas, such as at higher elevations or in regions with readily available water sources. These tanks utilize a modular prefabricated steel plate structure combined with an HDPE impermeable lining, offering advantages such as short construction time, small footprint, and strong environmental adaptability. Individual tank volumes can be customized to meet the specific needs of the protected area, ranging from 500 to 2000 cubic meters, ensuring ample water reserves for continuous firefighting operations. Each tank is equipped with a high-precision level sensor to monitor water levels in real time and upload data to a central cloud platform. When the water level falls below a preset warning threshold, an automatic water replenishment system (including an electric water replenishment valve and a small water pump connected to the municipal water supply network or nearby natural water sources) automatically draws water from external sources to replenish the tanks, ensuring a consistently sufficient water level and preventing firefighting capabilities from being affected by insufficient water supply.

[0070] Finally, the energy security layer 5 of this system provides a stable and reliable power supply for the entire intelligent forest fire prevention and control system, ensuring continuous operation under any circumstances. The energy security layer includes an uninterruptible power supply (UPS) module 50, a mains power access module, a diesel generator set 51, and an intelligent power management module. The UPS module adopts an online double-conversion topology, and its rated power is typically configured to 50kVA or higher to meet the system's peak power consumption requirements. This module is equipped with a high-performance lithium iron phosphate battery pack, which can provide at least 2 hours of backup power in the event of an unexpected mains power outage, ensuring the continuous operation of the AI ​​image fire detector group, edge computing gateway, fire extinguishing nozzle control box, and key servers and communication equipment of the central cloud platform, guaranteeing continuous perception and transmission of fire information, and the normal issuance of decision-making commands. The mains power access module is responsible for safely and stably introducing municipal power into the system. The diesel generator set has a power of 100kW and is equipped with an ATS (Automatic Transfer Switch) as the main backup power source. When the mains power is interrupted or the AI ​​analysis and decision-making engine of the central cloud platform detects a fire signal, the diesel generator set receives instructions through the intelligent power management module in the water pump control box. It automatically starts and connects to the grid within 15 seconds, replacing the mains power supply. This design is crucial, ensuring that the system still receives an independent and stable power supply even if a fire causes a potential interruption to municipal power (e.g., burned utility poles or damaged substations). This is particularly important for maintaining the normal operation of high-pressure fire pumps and intelligent tracking jet devices, thus not affecting firefighting operations. The diesel generator set can also be remotely or manually started and stopped via the central cloud platform or on-site control panel, enhancing its management flexibility. The intelligent power management module is responsible for real-time power distribution and fine-grained power consumption monitoring of all power-consuming nodes. By dynamically adjusting the power supply strategies of each device, it optimizes overall energy efficiency, extends the backup power supply's runtime, and promptly detects potential energy consumption anomalies.

[0071] The core technical solution of the intelligent forest fire prevention and control system proposed in this invention is also reflected in the deep integration and realization of intelligent digital functions in the following aspects.

[0072] Firstly, this system possesses comprehensive intelligent digital prevention functions. In a preferred embodiment of the invention, these functions include a timed automatic water spraying mode. The system allows managers to preset flexible automatic activation rules via a central cloud platform. For example, during daily high-temperature periods (e.g., 12 PM to 4 PM) or at specific times (e.g., every hour on the hour), the system automatically activates, controlling the intelligent tracking jet devices within a designated area to spray water in a fan-shaped spray pattern. This achieves comprehensive and uniform humidification and cooling within the protected area, effectively increasing the moisture content of forest combustibles (e.g., fallen leaves and humus), reducing the rate of fire spread during a fire, and simultaneously lowering surface temperature, thus reducing the probability of fires at their source. The spraying time, spraying intensity (by adjusting the flow rate and spray angle), and spray range can all be finely configured and dynamically adjusted according to the actual conditions of the forest area, seasonal changes (e.g., dry and windy springs, high temperatures in summer), and weather forecasts to achieve the best preventative effect.

[0073] In a preferred embodiment of the present invention, the intelligent fire prevention function further includes an automatic water spraying mode based on environmental parameters. This system continuously collects high-precision data from temperature and humidity sensors. When key parameters such as ambient temperature and relative humidity reach preset fire hazard warning thresholds (for example, when the forest temperature consistently exceeds 30°C and the relative humidity is below 40%RH, while the wind speed exceeds a specific threshold), the artificial intelligence analysis and decision-making engine of the central cloud platform automatically triggers an early warning and activates the intelligent tracking jet device in the corresponding area to automatically humidify and cool the protected area. This mode achieves real-time response to fire hazard conditions, transforming traditional passive fire prevention into proactive prevention. The thresholds are not fixed but can be dynamically adjusted based on historical fire hazard level data of the forest area, vegetation type (such as flammable coniferous or broadleaf forests), and seasonal characteristics to ensure the scientific validity and effectiveness of the prevention strategy.

[0074] As a preferred embodiment of the present invention, the intelligent prevention function also supports a remote manual activation mode. During periods of high fire risk, such as concentrated agricultural activities (where there may be a risk of burning), frequent entry and exit of people in or near forests, or during major national holidays, managers can conveniently and remotely activate the system in a designated area through the visual management interface of the central cloud platform or its dedicated mobile application to actively humidify and cool the fire-prevention area, thereby effectively preventing fires caused by human factors or potential risk sources.

[0075] Secondly, this system possesses advanced intelligent early warning capabilities. In a preferred embodiment of this invention, the core of these capabilities lies in utilizing an AI image fire detector array to achieve all-weather, visualized, and automatic early fire warnings. This AI image fire detector array captures high-definition images of the forest area through its high-resolution visible light imaging module, while its uncooled infrared thermal imaging module obtains accurate temperature distribution maps. Its built-in embedded artificial intelligence chipset runs in real-time the integrated deep learning algorithm. This algorithm goes beyond simple image recognition; it can perform pixel-level flame and smoke feature recognition on the acquired visible light images, such as identifying the color, brightness, and flicker frequency of flames, as well as the shape, color, and diffusion speed of smoke. More importantly, this algorithm combines time-series analysis technology, effectively distinguishing between real fires and non-fire-related disturbances caused by changes in sunlight angle, temperature variations (such as heat dissipation from rocks after sunlight exposure), and wind-induced leaf swaying, thereby achieving an extremely low false alarm rate and extremely high accuracy. The deep learning algorithm is specifically optimized for recognizing early-stage, weak smoke and concealed flames. Even early-stage smoke that is far away, small in area, and similar in color to the environment, or concealed flames obscured by tree canopies, can be accurately identified and warnings issued in the early stages of a fire. For example, when the algorithm detects flame or smoke features, it first generates a real-time confidence score. Only when this confidence score remains consistently above a preset decision threshold (e.g., confidence > 0.85) for a duration exceeding a set time (e.g., more than 5 consecutive seconds) will the system trigger a formal alarm event and immediately push the alarm information, screenshots, and video clips to the central cloud platform. Furthermore, the AI ​​image fire detector group also has security monitoring functions, completely replacing traditional forest area surveillance cameras. It enables real-time monitoring and high-definition video recording and storage of personnel activities, illegal intrusions, theft, or other abnormal situations in forest areas, greatly improving the overall management efficiency of forest areas.

[0076] Thirdly, this system possesses highly efficient digital and intelligent fire suppression capabilities. As a preferred embodiment of the present invention, such as... Figure 4As shown, when the AI ​​image fire detector group successfully detects a fire in the forest area, it transmits the information to the central cloud platform 31 through the data transmission layer. After comprehensive confirmation and precise fire point location by the artificial intelligence analysis and decision engine of the central cloud platform 31, the central cloud platform sends the precise coordinate information of the fire point and the fire extinguishing command to the fire extinguishing nozzle control box 30 closest to the fire point within seconds. After receiving the command, the fire extinguishing nozzle control box 30 will immediately execute a series of linked operations: First, it will control the electric butterfly valve 41 at the corresponding location in the fire hydrant network to open quickly, thereby connecting the fire extinguishing pipeline of the corresponding intelligent tracking jet device; at the same time, the command will also be immediately sent to the target intelligent tracking jet device 40, putting it into standby mode. Almost at the same time, the central cloud platform 31 will also send a pump start command to the water pump control box. After receiving the command, the water pump control box will first determine the mains power supply status. If the mains power is interrupted or the system is set to diesel generator priority mode (this is the default strategy in extreme forest fire prevention situations), it will immediately start the diesel generator set 51 to ensure a stable power supply. After the diesel generator set is successfully connected to the grid and generates electricity stably, the water pump control box will start the high-pressure water pump 420 after a delay (for example, a delay of 5 seconds) to pressurize the already connected fire extinguishing pipeline and ensure that the water cannon has sufficient working pressure.

[0077] Meanwhile, upon receiving the fire coordinates and extinguishing command, the intelligent tracking jet device's integrated local control unit performs millisecond-level high-speed calculations based on real-time wind speed and direction data from the temperature and humidity detector group or the central cloud platform's meteorological data interface, combined with preset parameters such as fire location, forest density, and terrain. This allows for real-time adjustment of the horizontal and vertical angles of the fire monitors and the optimization of the variable flow rate and spray mode nozzles to the optimal extinguishing mode. For example, for initial point fires, a high-pressure direct jet mode is used for precise targeting; for fires that have begun to spread, a fan-shaped water curtain mode is switched to form an effective hydraulic isolation zone, preventing further fire spread; and for small-scale ember clearing, a mist spray mode is used for cooling. This precise tracking jet capability ensures the most efficient use of water resources and maximizes fire extinguishing efficiency. The system boasts extremely rapid response capabilities; from the initial detection of a fire signal by the AI ​​image fire detector group to the activation of the intelligent tracking jet device for extinguishing spray, the entire time can be controlled within 30 seconds, which is crucial for controlling fires in their initial stages. After the firefighting operation is completed, the system does not shut down immediately. Instead, it continuously monitors the fire area using AI image fire detectors to confirm that the flames and smoke signals have completely disappeared. The infrared thermal imaging module then confirms that the temperature in the area has returned to normal environmental levels (e.g., the temperature drops to within 5°C of ambient temperature). Once confirmed, the central cloud platform issues a command, and the system automatically closes the electric butterfly valve, stops the high-pressure water pump and diesel generator set, and restores all relevant equipment to its initial standby state, preparing for the next potential fire. This achieves a fully automated and intelligent closed-loop process.

[0078] Fourthly, this system possesses highly integrated and intelligent management capabilities. As a preferred embodiment of the invention, the system's high integration is reflected in the seamless connection and close collaboration between its sensing, transmission, decision-making, and execution stages. All equipment deployed in the forest area, regardless of type, securely connects to the central cloud platform through standardized IoT interfaces, achieving interconnection of all data and precise, efficient issuance of control commands. The central cloud platform, as a unified management hub, provides comprehensive and powerful management functions, including but not limited to: It supports multi-user, multi-permission management, allowing access and operation permissions to be set according to the responsibilities of different administrators, ensuring system security. It also provides one-stop remote management functions such as device registration, configuration, status monitoring, and remote firmware upgrades, significantly reducing on-site maintenance costs. Once a fire alarm is generated, it will be instantly pushed to the mobile terminal of the designated administrator via a mobile application, allowing them to view detailed alarm information, precise fire location information, and real-time on-site images / video streams, achieving immediate transmission of fire information. Users can preview high-definition images and video streams collected by the AI ​​image fire detector group in real time through the central cloud platform's visual management interface or the mobile application for remote visual confirmation. The system can achieve fully automatic operation of fire prevention and extinguishing based on preset strategies and real-time data, without manual intervention. Furthermore, the central cloud platform or mobile application also supports wireless remote control of the intelligent tracking jet device, water pumps in the water-electric pump linkage system, and diesel generator sets, greatly improving flexibility in emergency situations. All system operation logs, alarm events, sprinkler records, equipment status changes, maintenance records, etc., are meticulously recorded and readily available for query, providing data support for post-event analysis, fire source tracing, performance evaluation, and system optimization. Video streams from the AI ​​image fire detector group can be stored on cloud or local storage devices according to time periods or events, for fire evidence collection and in-depth analysis. The system supports seamless output of fire alarm signals to third-party fire protection systems or emergency command centers at all levels via various standard interfaces (such as relay contacts, Modbus TCP / IP, SNMP), enabling coordinated response. The central cloud platform can directly control the start and stop of high-pressure water pumps in the water-electricity pump linkage system, achieving precise water resource scheduling. Real-time monitoring of the operating status, fault information, communication status, and power status of all equipment is conducted, and potential problems are detected early through an early warning mechanism. Standardized API interfaces are provided to facilitate data sharing and collaborative management with government departments, fire departments, or other smart city platforms, building a broader smart ecosystem. The precise geographical location, coverage area, fire location, fire pipeline route, and real-time system operating status of all equipment are intuitively displayed on a GIS map, providing an intuitive global view for command and decision-making. The system has a detailed logging function that records all system operations, events, and alarms, facilitating fault diagnosis and auditing, and ensuring the system's transparency and traceability.

[0079] Through the organic integration and synergy of the above-mentioned levels and functions, the forest fire digitalization and intelligent prevention system proposed in this invention realizes comprehensive digitalization and intelligent management of forest fires, constructing an efficient, intelligent and reliable closed-loop system from prevention to early warning to fire suppression.

[0080] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A digital intelligent forest fire prevention and control system, characterized in that, It includes the perception layer, data transmission layer, decision control layer, execution layer, and energy security layer; The perception layer includes: an AI image fire detector group, used to collect visible light and infrared thermal images of the forest area in real time, and to identify the characteristics of flames, smoke and abnormal heat sources; and a temperature and humidity detector group, used to monitor the air temperature, relative humidity and surface temperature data of the forest area in real time. The data transmission layer includes: LoRaWAN network, 5G cellular network and satellite communication link, as well as edge computing gateways for filtering, compressing, encrypting and converting the received raw data. The decision control layer is used to assess fire risk, locate and predict fires based on the data collected by the perception layer, and generate control commands, including a central cloud platform and several fire extinguishing nozzle control boxes distributed at various nodes of the fire extinguishing network. The execution layer is used for the physical execution of fire prevention and suppression, including several intelligent tracking jet devices installed at each node of the fire hydrant network, electric butterfly valves installed on the branches of the fire hydrant network, a water-electric pump linkage system responsible for water resource scheduling and pressurization, and a prefabricated water storage tank as a reserve of fire extinguishing water source. The water-electric pump linkage system includes a high-pressure water pump and a water pump control box. The energy security layer is used to provide a stable and reliable power supply for the system, including uninterruptible power supply modules, mains power access modules, diesel generator sets, and intelligent power management modules.

2. The intelligent forest fire prevention and control system according to claim 1, characterized in that, The internal structure of the AI ​​image fire detector array includes: The visible light imaging module uses a CMOS image sensor with a pixel resolution of 3840×2160 and a frame rate of 30 frames per second; The uncooled infrared thermal imaging module uses a vanadium oxide microbolometer array with a pixel size of 17μm, a resolution of 640×512, a thermal sensitivity of less than 40mK, and a spectral response range of 7.5μm to 13.5μm. The embedded artificial intelligence chipset integrates a neural network processor to run pre-trained deep learning models based on a hybrid architecture of convolutional neural networks and long short-term memory networks, so as to effectively distinguish between real fires and environmental interference. The data preprocessing module is used to perform noise reduction, image enhancement, and distortion correction on the raw image data; Communication interface module, including fiber optic interface and wireless communication interface; The AI ​​image fire detector array also integrates a dustproof and waterproof housing with an IP68 rating, and is equipped with an automatic defogging and cleaning device.

3. The intelligent forest fire prevention and control system according to claim 2, characterized in that, The central cloud platform includes: The data access service adopts a message queue architecture for high-concurrency reception of data streams from edge computing gateways and fire sprinkler control boxes; The big data storage and processing module uses a distributed file system and a distributed database to store historical monitoring data, fire images, videos, and system logs, and uses a big data processing framework to perform batch processing analysis on historical data. The AI ​​analysis and decision engine receives and integrates fire alarm information from all AI image fire detector groups, environmental data from temperature and humidity detector groups, and historical data. It uses a multimodal fusion deep learning algorithm to comprehensively assess the forest fire risk level, locate the fire, and predict its development trend. The prediction model considers meteorological parameters including wind speed, wind direction, temperature, humidity, rainfall, forest combustible load, and historical fire data. When a fire is detected or the fire risk level reaches a preset threshold, a fire alarm event is automatically generated. The geographic information system module integrates high-resolution satellite imagery, topographic elevation maps, forest road maps, and forest vegetation distribution maps. It can overlay fire information, sensor distribution, fire duct network paths, and the coverage area of ​​the digital tracking jet device in real time. The visual management interface provides a system status overview, real-time alarm display, historical data query, video preview, device management, policy configuration, and manual intervention functions; The user interface service provides a RESTful API interface to support data interaction and function integration between mobile applications and third-party systems.

4. The intelligent forest fire prevention and control system according to claim 3, characterized in that, The system has intelligent digital prevention functions, including: In the timed automatic water spraying mode, the system sets automatic start rules through the central cloud platform and controls the digital tracking jet device in a specific area to spray water in a fan-shaped spray pattern, achieving all-round humidification and cooling, and reducing the moisture content of combustibles in the forest and the surface temperature. Based on environmental parameters, the automatic water spraying mode continuously collects data from temperature and humidity detectors. When the ambient temperature and relative humidity reach the preset fire hazard warning threshold, the artificial intelligence analysis and decision engine of the central cloud platform will automatically trigger an early warning and activate the digital tracking jet device in the corresponding area for humidification and cooling. In the remote manual start mode, managers can remotely start the system in a designated area through the central cloud platform or mobile application to actively humidify and cool the fire-prevention area.

5. The intelligent forest fire prevention and control system according to claim 4, characterized in that, The system has intelligent early warning capabilities, using an AI image fire detector group to achieve all-weather, visualized early fire automatic warning; the AI ​​image fire detector group captures forest area images through a visible light imaging module and obtains temperature distribution maps through an uncooled infrared thermal imaging module. The embedded AI chipset runs the integrated deep learning algorithm in real time. This algorithm can perform pixel-level flame and smoke feature recognition on the acquired images and, combined with time series analysis, distinguish between real fires and environmental interference. When the deep learning algorithm detects flame or smoke features, it generates a confidence score. An alarm is triggered only when the confidence score is higher than a preset threshold and the duration exceeds a set time.

6. The intelligent forest fire prevention and control system according to claim 5, characterized in that, The system is equipped with intelligent fire suppression capabilities. When the AI ​​image fire detector group detects a fire and it is confirmed by the central cloud platform: the central cloud platform sends the precise location information of the fire point to the control box of the fire suppression nozzle closest to the fire point; the control box of the fire suppression nozzle controls the opening of the electric butterfly valve at the corresponding position in the fire protection pipeline, connecting the corresponding fire suppression pipeline, and sends the fire suppression command to the intelligent tracking jet device; the water pump control box receives the pump start command from the central cloud platform, starts the diesel generator set and starts the high-pressure water pump after a delay, pressurizing the connected fire suppression pipeline; the intelligent tracking jet device adjusts the horizontal and vertical angles of the jet in real time according to the location of the fire point, wind direction, wind speed, and forest density.

7. The intelligent forest fire prevention and control system according to claim 6, characterized in that, After the fire is extinguished, the system automatically detects whether the fire has been put out. It confirms the disappearance of flame and smoke signals through the AI ​​image fire detector group and confirms that the temperature has returned to normal through the uncooled infrared thermal imaging module. After confirmation, the system will automatically close the electric butterfly valve, stop the high-pressure water pump and diesel generator set, and restore all equipment to the initial standby state. The internal structure of the fire extinguishing nozzle control box also includes an edge computing module. The edge computing module has a built-in machine learning inference engine for secondary confirmation and local decision-making of the received fire data.

8. The intelligent forest fire prevention and control system according to claim 7, characterized in that, The central cloud platform provides multi-user, multi-permission management, supporting device registration, configuration, status monitoring, and firmware upgrades. Fire alarm information is instantly pushed to designated administrators' mobile phones via a mobile application, allowing them to view alarm details, fire location, and real-time images / videos. Users can view high-definition images and video streams collected by the AI ​​image fire detector group in real time through the central cloud platform or mobile application. The system supports wireless remote control of the intelligent tracking jet device, high-pressure water pump, and diesel generator set via the central cloud platform or mobile application. The system records all system operation logs, alarm events, sprinkler records, and equipment status changes, which are available for querying. The video streams from the AI ​​image fire detector group can be stored on demand in the cloud or on local storage devices for fire evidence collection and analysis. The system supports outputting fire alarm signals to third-party fire protection systems or emergency command centers through various interfaces. The central cloud platform can directly control the start and stop of the high-pressure water pump. The geographic information system module intuitively displays the geographical location, coverage area, fire location, pipeline route, and system operating status of all devices on a GIS map.

9. The intelligent forest fire prevention and control system according to claim 1, characterized in that, The uninterruptible power supply (UPS) module adopts an online double-conversion topology with a rated power of 50kVA and is equipped with a lithium iron phosphate battery pack. The diesel generator set has a power of 100kW and is equipped with an automatic transfer switch. When the mains power is interrupted or the system detects a fire signal, it will automatically start through the intelligent power management module and complete grid connection within 15 seconds. The intelligent power management module is responsible for power distribution and power consumption monitoring of all power consumption nodes.

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