A forest fire intelligent prediction and early warning system integrating machine learning and ecological behavior recognition

Abstract

This invention proposes an intelligent forest fire prediction and early warning system, integrating technologies such as satellite remote sensing, drone patrols, and ground monitoring, and employing deep learning algorithms to intelligently identify ecological behaviors. The system consists of four core modules: multi-source data acquisition, ecological behavior identification, machine learning prediction models, and intelligent early warning and emergency response. By comprehensively monitoring the forest environment and analyzing ecological data, it accurately predicts fire risks and automatically generates early warning information, closely linking with the emergency response system. This system effectively solves the problems of traditional early warning methods being singular, lacking timeliness, and insufficient accuracy, significantly improving the accuracy and response speed of forest fire early warnings, and providing strong technical support for forest resource protection and ecological environment security.

Description

I. Technical Field

[0001] This invention relates to the field of forest fire prevention technology, specifically a forest fire intelligent prediction and early warning system that integrates multiple monitoring methods and intelligent analysis technologies, deeply combining ecological behavior recognition with integrated machine learning models. By comprehensively utilizing various monitoring methods such as satellite remote sensing, drone patrols, and ground video surveillance, combined with real-time monitoring and analysis of forest ecological behavior, this system achieves comprehensive perception, accurate prediction, and efficient early warning of forest fire risks, providing an innovative technical solution for improving forest fire prevention capabilities and protecting forest resources and ecological environment security. II. Technical Background

[0002] With global climate change and the increasing preciousness of forest resources, forest fire prevention and early warning have become particularly important. However, traditional forest fire early warning methods are gradually revealing their limitations in the face of complex and ever-changing forest environments and frequent fire challenges. These methods often rely on single monitoring means, such as satellite remote sensing, ground monitoring stations, and manual patrols. While each has its advantages, they are difficult to comprehensively and in real-time capture all signs of a fire when used alone, thus limiting the timeliness and accuracy of forecasts and early warnings.

[0003] While satellite remote sensing technology can cover large areas of forest, its monitoring frequency and resolution are limited, making it difficult to capture subtle changes in the early stages of a fire. Manual patrols are constrained by manpower costs, patrol range, and time efficiency, making it difficult to promptly detect hidden fire sources. Ground monitoring stations, although capable of monitoring environmental parameters in specific areas in real time, are sparsely distributed and cannot form a comprehensive monitoring network. This limitation and simplistic approach to monitoring methods renders traditional early warning systems inadequate in the face of complex and ever-changing forest fires.

[0004] Furthermore, while some existing fire early warning systems have begun to incorporate machine learning techniques to predict fire risk through analysis of historical fire data and environmental parameters, most of these systems overlook the crucial role of ecological behavior in the fire process. Ecological behaviors, such as abnormal vegetation growth patterns, changes in soil moisture, and alterations in animal behavior, can all potentially foreshadow fires. Effective monitoring and analysis of this ecological behavioral information would significantly improve the sensitivity and accuracy of fire early warning systems. However, current technologies have not yet fully explored and utilized this ecological behavioral information.

[0005] Therefore, developing a highly innovative forest fire early warning and prevention system is particularly important. Such a system not only needs to comprehensively utilize multiple monitoring methods (including satellite remote sensing, drone patrols, and ground video surveillance) to form a comprehensive, real-time forest environment monitoring network, but also needs to creatively introduce the monitoring and analysis of ecological behavior information. Through intelligent analysis technology, the system can identify ecological behavior patterns that are precursors to fires in real time, providing comprehensive and detailed data support for fire risk assessment.

[0006] Meanwhile, to further improve the system's prediction accuracy and timeliness, we will creatively introduce an integrated machine learning model. This model can automatically learn the patterns and characteristics of fire occurrence and continuously optimize and adjust the prediction algorithm to adapt to changes in different forest environments and ecological behaviors. This adaptive learning capability enables the system to react quickly to new fire challenges and provide accurate and timely early warning information. III. Summary of the Invention

[0007] This invention aims to address the problems of existing forest fire early warning systems, such as limited monitoring methods, poor timeliness of early warning, and insufficient prediction accuracy. By integrating multiple advanced monitoring methods and intelligent analysis technologies, and deeply combining ecological behavior recognition with integrated machine learning models, a novel intelligent prediction and early warning system for forest fires is proposed.

[0008] The system of the present invention includes:

[0009] 1. Multi-source monitoring data acquisition module: By comprehensively utilizing various monitoring methods such as satellite remote sensing, drone patrols, and ground video surveillance, this module achieves all-round, multi-level monitoring of the forest environment, thereby serving fire early warning. These monitoring methods can cover data acquisition needs at different spatial scales and temporal resolutions: The satellite remote sensing submodule, with its high-resolution multispectral satellite imagery technology, regularly captures subtle temperature changes and vegetation growth status in forest-covered areas, providing a macroscopic perspective for rapid screening of large-scale fire hazards; the drone patrol submodule, with its flexibility and mobility, is equipped with high-definition cameras, infrared thermal imagers, and high-precision gas sensors to accurately locate and dynamically track initial signs of fire sources in key areas, achieving real-time response to fire risks from an aerial perspective; the ground video surveillance submodule innovatively combines intelligent cameras with advanced image recognition technology, not only building a tight monitoring network at key entrances and fire-prone areas, but also automatically identifying and analyzing ground fire conditions and abnormal ecological behavior, providing a more intuitive and richer source of information for fire early warning. Building upon this foundation, the multi-source monitoring data acquisition module innovatively incorporates spatiotemporal big data analysis technology. This groundbreaking approach completely overcomes the temporal and spatial limitations of traditional monitoring methods. By deeply integrating and analyzing the temporal series and spatial distribution characteristics of multi-source data, the system can accurately capture the dynamic evolution of fire hazards and predict potential fire trends, thereby significantly improving the continuity and accuracy of monitoring. We designed a fire hazard prediction algorithm based on spatiotemporal big data (ST-FHP). The core of this algorithm lies in using a spatiotemporal weighting function (STWF) to weight multi-source monitoring data, and then using deep learning technology to mine spatiotemporal correlation patterns in the data to achieve accurate prediction of fire hazards.

[0010] First, we define a specific formula for the Spatiotemporal Weighting Function (STWF) to quantify the impact of data from different times and spatial locations on the dynamic evolution of fire hazards:

[0011] STWF(t, x, y)=α·TimeSeriesWeigh t(t)+β·SpatialDistributionWeigh t(x, y)

[0012] Where t represents time, (x, y) represents spatial location, and α and β are weighting coefficients used to balance the influence of time series and spatial distribution, respectively. The deep learning model used in the ST-FHP algorithm is a hybrid neural network architecture that combines the advantages of convolutional neural networks (CNN) and long short-term memory networks (LSTM). Specifically, the model includes the following key layers:

[0013] a. Input layer: This layer is responsible for receiving preprocessed multi-source monitoring data, which has been organized according to time series and spatial distribution.

[0014] b. CNN Layer: The Convolutional Neural Network (CNN) layer is used to extract spatial features. Data is first fed into the CNN layer, which captures spatial patterns in image or video frames through convolution operations, such as the texture of vegetation or the shape of flames. This layer can be represented as:

[0015] CNN_Output = σ(W) cnn Input+b cnn )

[0016] Where σ is the activation function, W cnn and b cnn These are the weights and biases of the CNN layer, respectively.

[0017] c. LSTM Layer: The Long Short-Term Memory (LSTM) network layer is responsible for processing time-series data. The spatial feature sequence output from the CNN layer is fed into the LSTM layer to learn the changing patterns of fire hazards over time. The update equation for the LSTM layer can be expressed as:

[0018] h t =LSTM_Cell(CNN_Output) t h t-1 )

[0019] Among them, h t It is the hidden state of the LSTM layer at time t, CNN_Output t It is the output of the CNN layer at time t.

[0020] d. Fully Connected Layer: Following the LSTM layer is the fully connected (Dense Layer), which integrates the time-series features output by the LSTM layer for final fire hazard prediction. This layer can be represented as:

[0021] Dense_Output=σ(W dense ·LSTM_Output+b dense )

[0022] Where σ is the activation function, W dense and b dense These are the weights and biases of the fully connected layer, respectively.

[0023] e. Output layer: The last layer of the model is the output layer, which outputs the predicted probability or classification result of fire hazards based on the processing results of the fully connected layers.

[0024] The data processing flow is as follows:

[0025] (a) First, the multi-source monitoring data (including satellite remote sensing images, drone patrol videos and ground video surveillance data) will be cleaned, denoised and normalized to ensure the quality and consistency of the data.

[0026] (b) The processed data will be organized according to time series and spatial distribution, and then input into the STWF function for weighted extraction of spatiotemporal features.

[0027] (c) Finally, the weighted spatiotemporal features are fed into the deep learning model for training and prediction. During training, we adopted the following strategies:

[0028] We used a large amount of historical fire data and multi-source monitoring data as training sets to fully train the deep learning model.

[0029] During training, we calculate the model's prediction error based on either the cross-entropy loss function (for classification tasks) or the mean squared error loss function (for regression tasks). The loss function can be expressed as:

[0030]

[0031] Where N is the number of samples, y i It's a real tag, p i It is the probability predicted by the model.

[0032] By continuously adjusting the model's parameters using the backpropagation algorithm and gradient descent optimizer, the loss function is minimized, thereby improving the model's prediction accuracy. In summary, our ST-FHP algorithm and its deep learning model have innovated in several aspects, including algorithm design, model architecture, data processing, training process, and the application of specific formulas. These innovations collectively constitute our unique fire hazard prediction technology solution.

[0033] 2. Intelligent Ecological Behavior Recognition Module: This module utilizes cutting-edge algorithms such as deep learning to intelligently analyze monitored forest ecological behavior data, identifying abnormal behavioral patterns that may foreshadow fires. This ecological behavior data includes vegetation growth status, soil moisture changes, and animal behavior patterns. In-depth mining and analysis of this data can reveal potential fire risks. As the core highlight of this invention, the intelligent ecological behavior recognition module deeply explores the potential value of three key ecological elements—vegetation growth status, soil moisture, and animal behavior patterns—in fire early warning. This module innovatively integrates cutting-edge deep learning technology with ecological principles, achieving intelligent and precise recognition of forest ecological behavior. Specifically, through deep analysis of vegetation images using convolutional neural networks (CNNs), the system can keenly capture subtle changes in vegetation color and texture, accurately identifying abnormal yellowing and wilting phenomena under fire risk, providing intuitive and powerful ecological visual evidence for fire early warning. In the vegetation image analysis task, we used convolutional neural networks (CNNs) for deep analysis of vegetation images. CNNs automatically learn hierarchical feature representations from raw images through structures such as convolutional layers, pooling layers, and fully connected layers. The core of the convolutional layer lies in the convolution operation, the specific formula of which is:

[0034]

[0035] Among them, y ij Let x represent the value of the element in the i-th row and i-th column of the output feature map, x represent the input image, k represent the convolution kernel, b is the bias term, and M and N are the height and width of the convolution kernel, respectively.

[0036] In the soil moisture prediction task, we combined ground sensor data and satellite remote sensing data, and used time series analysis techniques to construct a prediction model. Specifically, we used an autoregressive moving average (ARMA) model for time series prediction. The general form of the ARMA model is:

[0037] y t =φ1y t-1 +φ2y t-2 +…+φ p y t-p +∈ t -θ1∈ t-1 -θ2∈ t-2 -…-θ q ∈ t-q

[0038] Among them, y t φ is the soil moisture observation value at time t. i and θ j These are the coefficients of the autoregressive and moving average methods, respectively. t It's white noise.

[0039] For animal behavior pattern analysis, we employed a Long Short-Term Memory (LSTM) network. LSTM is a special type of Recurrent Neural Network (RNN) capable of processing long sequences of data and capturing their temporal dependencies. The core of LSTM consists of cell states and three gating structures (input gate, forget gate, and output gate). The update equation of LSTM involves multiple formulas, including the calculation of activation values ​​for the forget gate, input gate, candidate cell states, cell states, output gate, and hidden states.

[0040] In addition, we also employed a deep learning model for noise recognition. This model includes an input layer, convolutional layers, pooling layers, recurrent layers (such as LSTM), and fully connected layers. Data processing involves preprocessing, feature extraction, sequence modeling, and classification. During training, we used the cross-entropy loss function to adjust the noise recognition model parameters; the specific formula is as follows:

[0041]

[0042] Where N is the number of samples, y i It's a real label. It is a predicted probability.

[0043] 3. Integrated Machine Learning Prediction Model: This system aims to build a highly adaptive, optimized, and innovative forest fire risk prediction model. This model integrates data from multi-source monitoring data acquisition modules and ecological behavior intelligent identification modules. Through in-depth analysis and processing of this data, the model not only achieves accurate prediction of forest fire risk but also has the ability to automatically adjust parameters and structure to adapt to changes in different forest environments and ecological behaviors.

[0044] To achieve this goal, the system not only employs a multi-dimensional algorithm fusion strategy but also combines various advanced machine learning algorithms, including Gradient Boosting Tree (GBDT), Random Forest (RF), and Deep Learning Convolutional Neural Network (CNN). GBDT constructs a strong predictive model by iteratively combining multiple weak predictive models (such as decision trees), and its formula is expressed as:

[0045] F m (x)=F m -1(x)+γ m h m (x)

[0046] Among them, F m (x) represents the model at step m, γ m h is the step size, used to control the degree of influence of the weak prediction model on the final model. m(x) represents the m-th weak prediction model (such as a decision tree). The advantage of GBDT lies in its ability to capture non-linear relationships between features and deeply uncover hidden patterns in the data. RF, on the other hand, is an ensemble learning method that makes predictions by constructing multiple decision trees and voting or averaging them. Its formula is:

[0047]

[0048] Among them, T i (x) represents the prediction result of the i-th decision tree, n tree This represents the total number of decision trees. RF (Regressive Randomization) can easily handle high-dimensional data and effectively reduce the risk of overfitting. Deep learning CNNs are particularly suitable for processing image and time-series data. Through structures such as convolutional layers, pooling layers, and fully connected layers, CNNs can automatically extract high-level features from the data. The formula for its convolution operation is expressed as:

[0049] Y = W * X + b

[0050] Where W is the convolution kernel used to extract features; X is the input data; and b is the bias term used to adjust the output. In addition to algorithm fusion, the prediction model also possesses the ability to adaptively adjust parameters and structure. Through an online learning mechanism, the model can update its parameters and structure in real time to adapt to the dynamic changes in the forest environment. During the optimization process, we use a variant of the gradient descent algorithm—Adam (Adaptive Moment Estimation)—to minimize the model's loss function. The update rule of the Adam algorithm is as follows:

[0051] a. Calculate the gradient:

[0052]

[0053] b. Update the first-order moment estimate:

[0054] m t =β1·m t-1 +(1-β1)·g t

[0055] c. Update the second-order moment estimate:

[0056]

[0057] d. Correcting the bias in the first-order moment estimation:

[0058]

[0059] e. Correcting the bias in the second-order moment estimation:

[0060]

[0061] f. Update parameters:

[0062]

[0063] Where θ is the model parameter, L t Here, is the loss function at step t, β1 and β2 are decay rates used to control the update speed of the first and second moment estimates, η is the learning rate, which determines the step size of the parameter updates, and ∈ is a very small number used to prevent the denominator from being zero. To further improve the model's generalization and adaptive capabilities, the system creatively introduces meta-learning techniques. Meta-learning aims to train the model to quickly adapt to new tasks or environments. We use a popular meta-learning algorithm—MAML (Model-Agnostic Meta-Learning)—to train the model. The update rules of MAML are as follows:

[0064] a. Inner loop: For each task τ i Update the model parameters using the gradient descent algorithm:

[0065]

[0066] Where θ is the initial value of the model parameters, and α is the learning rate. It is the loss function for the i-th task.

[0067] b. Outer loop: Updates the initial values ​​of the model parameters to minimize the loss across all tasks:

[0068]

[0069] Here, β is the learning rate, used to control the step size for updating the model parameter initialization values. In terms of feature engineering, the system creatively employs feature selection techniques (such as chi-square test and mutual information) and dimensionality reduction techniques (such as PCA and LDA) to extract key features and improve computational efficiency. Simultaneously, we introduce an attention mechanism to intelligently allocate feature weights, enhancing the effectiveness of features. The formula for the attention mechanism is expressed as:

[0070]

[0071] Where Q, K, and V are the query, key, and value vectors, respectively, used to calculate attention weights; d k This refers to the dimension of the key vector, used to scale the dot product result. In addition, we creatively introduced innovative data augmentation techniques and model fusion strategies. Data augmentation techniques transform and expand the original data, increasing the model's generalization ability. The model fusion strategy combines the prediction results of multiple different algorithms, obtaining more accurate and stable prediction results through methods such as voting and weighted averaging.

[0072] By integrating multiple machine learning algorithms, the system performs deep learning and analysis on historical fire data, environmental parameters, and ecological behavior information to predict the probability of fire occurrence and potential risk areas. Once the prediction model outputs a fire warning signal, the system automatically generates warning information according to preset rules and algorithms, and personalizes it based on the recipient's role and needs. In terms of dissemination, the module adopts a multi-channel strategy, including traditional and emerging channels, and dynamically adjusts based on real-time environmental data, recipient feedback, and historical warning effects to ensure that warning information can quickly reach a wider audience and improve reception effectiveness. Regarding emergency response, the module achieves deep linkage control with emergency response systems such as fire extinguishing equipment and personnel dispatch. Once a fire warning is received, the system can immediately trigger the emergency response mechanism, automatically dispatching nearby fire extinguishing equipment to the fire scene for firefighting, and intelligently planning personnel evacuation routes and rescue routes based on the fire's scale and spread trend. Furthermore, the system supports real-time communication and data sharing with external emergency agencies such as fire departments and medical institutions, forming a cross-departmental and cross-domain emergency response network. The application of these technologies has not only improved the accuracy and timeliness of fire early warning, but also enhanced the efficiency and effectiveness of emergency response, providing strong technical support for forest fire prevention.

[0073] This invention has the following advantages compared to existing methods:

[0074] This invention overcomes the limitations of traditional single-method monitoring by integrating multiple monitoring methods, including satellite remote sensing, drone patrols, and ground video surveillance, to construct a comprehensive, multi-layered forest environment monitoring network. More importantly, the system deeply integrates ecological behavior recognition technology, utilizing deep learning algorithms to intelligently analyze ecological indicators such as vegetation growth status, soil moisture changes, and animal behavior patterns, enabling proactive insights into fire risks. This deep integration of multi-source monitoring and ecological behavior provides richer and more accurate data support for fire early warning, significantly improving prediction accuracy.

[0075] This system integrates a variety of advanced machine learning algorithms, which can automatically learn the complex rules and characteristics of fire occurrence, and automatically adjust the model parameters and structure according to real-time data feedback. This adaptive optimization ability enables the model to continuously adapt to the changes in different forest environments and ecological conditions, maintaining long-term stable prediction performance. At the same time, the model also supports cross-domain knowledge transfer, and can quickly adapt to new monitoring scenarios and fire types, providing strong technical support for forest fire prevention work. This integrated machine learning engine also has the ability of cross-domain knowledge transfer. This means that the model can not only make accurate fire predictions within a specific forest area, but also transfer the learned knowledge and experience to other similar or different monitoring scenarios. For example, when a model is fully trained in a certain forest area, the fire occurrence rules and characteristics it has learned can be transferred to other forest areas with similar ecological conditions, thus enabling rapid deployment and efficient prediction. This ability of cross-domain knowledge transfer greatly expands the application scope of the model, enabling it to adapt to more diverse forest fire prevention needs. It can not only improve the accuracy and timeliness of fire warnings, but also reduce the costs and risks of manual intervention, enhancing the overall fire prevention efficiency. In practical applications, this engine can serve as the core component of the forest fire prevention command system, providing scientific basis and intelligent decision-making support for decision-makers, and helping to achieve the sustainable protection of forest resources and the safety and stability of the ecological environment.

[0076] The entire system has achieved intelligent and automated fire warning and prevention. Through multi-modal data fusion technology, the system can comprehensively evaluate fire risks; combined with a flexible warning release mechanism, it ensures that warning information is conveyed to relevant personnel in a timely and accurate manner; at the same time, the intelligent features of the system can continuously learn and optimize model parameters, further improving the accuracy and reliability of warnings. This process greatly improves the response speed and warning efficiency of the system, providing strong support for the prevention and response of forest fires. Once the system detects a fire risk or discovers a fire, it will immediately trigger the warning release module, sending out alarms to relevant personnel through various means such as sound, light, text messages, and emails. At the same time, the system supports the linkage control with emergency response systems such as fire extinguishing equipment and personnel dispatch, automatically dispatching resources and optimizing fire extinguishing strategies to achieve rapid fire suppression. This mechanism of instant response and intelligent linkage not only improves the efficiency of emergency response, but also reduces the risks of human misjudgment and delay, providing a more reliable guarantee for forest fire prevention work. IV. Description of the Drawings

[0077] Figure 1 Show the structure of the hybrid integrated machine learning model, including different integrated machine learning algorithms, the adjustment mechanism of model parameters, and the optimization process of the model. V. Specific Embodiments:

[0078] 1. System Initialization: After the system starts, each module performs initialization settings, including device self-test, network connection test, and model parameter loading.

[0079] 2. Multi-source data acquisition: The satellite remote sensing module, UAV monitoring module, video surveillance module, and animal behavior monitoring module begin collecting data, including images and videos of the forest environment, temperature, humidity, changes in plant status, and animal behavior.

[0080] 3. Data transmission and preprocessing: The collected data is transmitted to the data processing center through a secure and stable network, where it undergoes preliminary data cleaning, format conversion, and other preprocessing.

[0081] 4. Data Fusion and Intelligent Analysis: In the data processing center, a hybrid integrated machine learning model is used to fuse and intelligently analyze data from multiple modules to assess fire risks and predict the probability of fires occurring.

[0082] 5. Early Warning Judgment and Issuance: When the probability of a fire exceeds a preset threshold, the system triggers an early warning mechanism. The early warning issuance module selects appropriate warning methods and content based on the risk level and recipient needs, delivering the warning information promptly and accurately to relevant personnel via sound, light, SMS, email, or other means.

[0083] 6. System Optimization and Updates: With the continuous accumulation of data and ongoing model learning, the system can continuously optimize model parameters and algorithm structure, improving the accuracy and reliability of early warnings. Simultaneously, the system also supports regular updates and upgrades to adapt to the ever-changing forest environment and fire risks.

Claims

1. A forest fire intelligent prediction and early warning system integrating machine learning and ecological behavior recognition, characterized in that, The system realizes the early, accurate and efficient warning of forest fire risk by integrating machine learning technology, ecological behavior intelligent recognition and multi-dimensional data fusion analysis, and the system comprises: a. Ecological behavior deep analysis unit: This unit integrates high-definition cameras, infrared detectors, unmanned aerial vehicles and satellite remote sensing technology, and introduces computer vision and natural language processing algorithms to conduct deep analysis of forest ecological behavior, identify subtle changes in fire precursors, including abnormal vegetation growth patterns and animal behavior changes, and provide information for fire warning; b. Environmental parameter multi-dimensional perception module: This module is equipped with a high-precision sensor array to monitor the temperature, humidity, wind speed and air pressure parameters of the forest environment in real time, combined with meteorological data and historical fire records to build a dynamic model of the forest environment and provide data support for fire risk assessment; c. Intelligent monitoring and assessment module of combustible materials: This module uses gas sensors, spectral analysis technology and machine learning algorithms to monitor the types, concentrations and distribution of combustible materials in the forest in real time, assess their flammability and fire risk, and provide a basis for developing fire suppression strategies; d. Sky-ground integrated intelligent fire monitoring and emergency response network: Integrating satellite remote sensing, unmanned aerial vehicle patrol, ground intelligent video monitoring and Internet of Things technology, a fire monitoring system is formed, and once a fire is detected, an emergency response mechanism is triggered to automatically dispatch fire suppression resources, achieving rapid fire suppression; e. Self-adaptive evolution and multi-modal fusion machine learning decision engine: This engine uses multi-modal data fusion technology to integrate ecological behavior, environmental parameter and combustible material monitoring data, and through the integration of multiple machine learning algorithms and deep learning models, it realizes fire risk prediction and has self-adaptive evolution capability, which can automatically adjust model parameters and structure according to real-time data feedback to optimize prediction performance; f. Personalized, intelligent and multi-channel warning release and response platform: This platform generates personalized warning information based on fire risk assessment results and recipient characteristics, and pushes it through sound, light, mobile notification, email and social media channels, supports interactive feedback and dynamic adjustment of warning information to ensure the timeliness and effectiveness of the warning information, and has emergency response guidance function to provide real-time strategy support and resource scheduling optimization for fire suppression operations.

2. The system of claim 1, wherein, The ecological behavior deep analysis unit has cross-modal learning capability, which can combine multi-modal data such as sound and smell to improve the recognition accuracy and comprehensiveness of fire precursor ecological behavior.

3. The system of claim 1 or 2, wherein, The environmental parameter multi-dimensional perception module introduces edge computing technology to realize real-time data processing and analysis, reduce data transmission delay and improve system response speed.

4. The system of any one of claims 1 to 2, wherein, The sky-ground integrated intelligent fire monitoring and emergency response network supports the linkage control of other forest fire prevention facilities, including automatic triggering of forest fire prevention isolation belts and optimization of personnel evacuation routes, forming a three-dimensional forest fire prevention system.

5. A forest fire prevention intelligent ecological system construction method based on the system of any one of claims 1 or 2, characterized in that, The method comprises system deployment, data integration, model training and optimization, emergency response plan making and drilling, and through the collaborative work and data sharing between systems, the forest fire prevention and control ability and emergency response efficiency are improved, the method supports the integration with other ecological monitoring systems, including wild animal and plant protection, water resource management, and jointly constructs a smart forestry ecological system.

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