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

Abstract

The invention provides an intelligent forest fire prediction and early warning system which integrates satellite remote sensing, unmanned aerial vehicle patrol, ground monitoring and other technologies and intelligently identifies ecological behaviors by using a deep learning algorithm. The system is composed of four core modules: a multi-source data acquisition module, an ecological behavior identification module, a machine learning prediction model module and an intelligent early warning and emergency response module. The forest environment is comprehensively monitored, ecological data is analyzed, the fire risk is accurately predicted, early warning information is automatically generated, and the system is closely linked with an emergency system. The system effectively solves the problems of single traditional early warning means, poor timeliness and insufficient precision, significantly improves the accuracy and response speed of forest fire early warning, and provides powerful technical support for forest resource protection and ecological environment safety.

Description

1. Technical Field

[0001] The present invention relates to the field of forest fire prevention technology, specifically, an intelligent forest fire prediction and early warning system that integrates multiple monitoring methods and intelligent analysis technologies, deeply integrates ecological behavior recognition and integrated machine learning models. The system uses a variety of monitoring methods such as satellite remote sensing, drone inspections, and ground video monitoring, combined with real-time monitoring and analysis of forest ecological behavior, to achieve comprehensive perception, accurate prediction, and efficient early warning of forest fire risks, providing innovative technical solutions for improving forest fire prevention capabilities, protecting forest resources, and protecting ecological environment safety. 2. Technical Background

[0002] With global climate change and the increasing preciousness of forest resources, forest fire prevention and early warning work has become particularly important. However, traditional forest fire early warning methods have gradually revealed their limitations in the face of complex and changing forest environments and frequent fire challenges. These methods often rely on a single monitoring method, such as satellite remote sensing, ground monitoring stations, and manual inspections. Although each has certain advantages, it is difficult to capture all signs of fire in a comprehensive and real-time manner when used alone, resulting in limited timeliness and accuracy of prediction and early warning.

[0003] Although satellite remote sensing technology can cover a large range of forest areas, its monitoring frequency and resolution are limited, making it difficult to capture subtle changes in the early stages of a fire; manual inspections are limited by labor costs, inspection scope, and time efficiency, making it difficult to find hidden fire sources in a timely manner; although ground monitoring stations can monitor environmental parameters in a specific area in real time, they are sparsely distributed and cannot form a comprehensive monitoring network. The singleness and limitations of this monitoring method make traditional early warning systems incapable of dealing with complex and ever-changing forest fires.

[0004] In addition, although some existing fire warning systems have begun to try to introduce machine learning technology to predict fire risks by analyzing historical fire data and environmental parameters, most of these systems ignore the key role of ecological behavior in the occurrence of fires. Ecological behavior, such as abnormal growth patterns of vegetation, changes in soil moisture, changes in animal behavior, etc., may indicate the occurrence of fires. If this ecological behavior information can be effectively monitored and analyzed, it will greatly improve the sensitivity and accuracy of fire warnings. However, current technical means have not been able to fully explore and utilize this ecological behavior information.

[0005] Therefore, it is particularly important to develop a highly creative forest fire early warning and prevention system. Such a system not only needs to make comprehensive use of multiple monitoring methods (including satellite remote sensing, drone inspections, ground video monitoring, etc.) to form a comprehensive and 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 the ecological behavior patterns of fire precursors in real time, providing comprehensive and detailed data support for fire risk assessment.

[0006] At the same time, in order to further improve the prediction accuracy and timeliness of the system, we will creatively introduce an integrated machine learning model. This model can automatically learn the laws 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 ability enables the system to respond quickly to new fire challenges and provide accurate and timely warning information. III. Summary of the invention

[0007] The present invention aims to solve the problems existing in the existing forest fire early warning system, such as single monitoring means, poor warning timeliness, and insufficient prediction accuracy. By integrating a variety of advanced monitoring means and intelligent analysis technologies, deeply integrating ecological behavior recognition and integrated machine learning models, a new forest fire intelligent prediction and warning system is proposed.

[0008] The system of the present invention comprises:

[0009] 1. Multi-source monitoring data acquisition module: Through the comprehensive use of satellite remote sensing, drone inspections, ground video monitoring and other monitoring methods, all-round and multi-level monitoring of the forest environment is achieved, which in turn serves the fire warning. These monitoring methods can cover the data collection needs of different spatial scales and temporal resolutions: the satellite remote sensing submodule, with its high-resolution multispectral satellite image technology, regularly captures subtle temperature changes and vegetation growth status in forest-covered areas, providing a macro perspective for rapid screening of large-scale fire hazards; the drone inspection submodule, with its flexible and maneuverable advantages, is equipped with high-definition cameras, infrared thermal imagers and high-precision gas sensors, accurately locking and dynamically tracking the initial signs of fire sources in key areas, and realizing an immediate response to fire risks from an aerial perspective; the ground video monitoring submodule innovatively combines smart cameras with advanced image recognition technology, not only building a strict monitoring network at key entrances and fire-prone areas, but also automatically identifying and analyzing ground fire conditions and abnormal ecological behaviors, providing a more intuitive and rich source of information for fire warning. On this basis, the multi-source monitoring data acquisition module innovatively introduced spatiotemporal big data analysis technology. This breakthrough completely broke the time and space limitations of traditional monitoring methods. By deeply integrating and analyzing the time series and spatial distribution characteristics of multi-source data, the system can accurately capture the dynamic evolution of fire hazards and predict the potential trend of fire occurrence, thereby greatly improving the continuity and accuracy of monitoring. We designed a fire hazard prediction algorithm based on spatiotemporal big data (ST-FHP). The core of the algorithm is to use the spatiotemporal weight function (STWF) to weight the multi-source monitoring data, and then use deep learning technology to mine the spatiotemporal correlation patterns in the data to achieve accurate prediction of fire hazards.

[0010] First, we defined the specific formula of the spatiotemporal weight function (STWF) to quantify the impact of data at different time 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] Among them, t represents time, (x, y) represents spatial position, and α and β are weight coefficients, which are used to balance the influence of time series and spatial distribution respectively. The deep learning model used by the ST-FHP algorithm is a hybrid neural network architecture, which combines the advantages of convolutional neural network (CNN) and long short-term memory network (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 layer is used to extract spatial features. The data is first input to the CNN layer, which captures the spatial patterns in the image or video frame through convolution operations, such as the texture of vegetation, the shape of the flame, etc. This layer can be expressed as:

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

[0016] Among them, σ is the activation function, W cnn and b cnn are the weights and biases of the CNN layers respectively.

[0017] c. LSTM layer: The long short-term memory network layer is responsible for processing time series data. The spatial feature sequence output by the CNN layer will be sent to the LSTM layer to learn the change pattern of fire hazards over time. The update equation of the LSTM layer can be expressed as:

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

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

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

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

[0022] Among them, σ is the activation function, W dense and b dense 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 layer.

[0024] The data processing process is as follows:

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

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

[0027] (c) Finally, the weighted spatiotemporal features will be fed into the deep learning model for training and prediction. During the training process, 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 prediction error of the model based on 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 is the true label, p i is the probability predicted by the model.

[0032] Through the back-propagation algorithm and gradient descent optimizer, the model parameters are continuously adjusted to minimize the loss function, thereby improving the prediction accuracy of the model. In summary, our ST-FHP algorithm and its deep learning model have made innovations in many aspects, including algorithm design, model architecture, data processing, training process, and application of specific formulas. These innovations together constitute our unique fire hazard prediction technology solution.

[0033] 2. Ecological behavior intelligent identification module: This module uses cutting-edge algorithms such as deep learning to intelligently analyze the monitored forest ecological behavior data and identify abnormal behavior patterns that may indicate the occurrence of fire. These ecological behavior data include vegetation growth status, soil moisture changes, animal behavior patterns, etc. Through in-depth mining and analysis of these data, the potential risk of fire can be revealed. The ecological behavior intelligent identification module, as the core highlight of the present invention, deeply explores the potential value of the three key ecological elements of vegetation growth status, soil moisture and animal behavior patterns in fire warning. This module innovatively integrates the cutting-edge technology of deep learning with ecological principles to achieve intelligent and precise identification of forest ecological behavior. Specifically, through the deep analysis of vegetation images by convolutional neural networks (CNN), the system can keenly capture subtle changes in vegetation color, texture, etc., accurately identify abnormal yellowing and withering under fire risks, and provide intuitive and powerful ecological visual evidence for fire warning. In the vegetation image analysis task, we used convolutional neural networks (CNN) to deeply analyze vegetation images. CNN automatically learns hierarchical feature representation from the original image through structures such as convolutional layers, pooling layers, and fully connected layers. The core of the convolutional layer is the convolution operation, and its specific formula is:

[0034]

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

[0036] Soil moisture prediction In the soil moisture prediction task, we combined ground sensor data and satellite remote sensing data and used time series analysis technology to build a prediction model. Specifically, we used the 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 is the observed soil moisture at time t, φ i and θ j are the coefficients of autoregression and moving average, ∈ t It's white noise.

[0039] Animal Behavior Pattern Analysis For the analysis of animal behavior patterns, we used a long short-term memory network (LSTM). LSTM is a special recurrent neural network (RNN) that can process long sequence data and capture the temporal dependencies therein. The core of LSTM is the cell state and three gate structures (input gate, forget gate, output gate). The update equation of LSTM involves multiple formulas, including forget gate, input gate, candidate cell state, cell state, output gate, and activation value calculation of hidden state.

[0040] Noise Recognition Model In addition, we also used a deep learning model in the noise recognition task. The model includes an input layer, a convolutional layer, a pooling layer, a recurrent layer (such as LSTM), and a fully connected layer. The data processing process involves steps such as preprocessing, feature extraction, sequence modeling, and classification. During training, we use the cross entropy loss function calculation results to adjust the noise recognition model parameters. The specific formula is:

[0041]

[0042] Where N is the number of samples, y i is the true label, is the predicted probability.

[0043] 3. Integrated machine learning prediction model: This system is committed to building a highly adaptive, optimized and creative forest fire risk prediction model that can integrate data from multi-source monitoring data collection modules and ecological behavior intelligent identification modules. Through in-depth analysis and processing of these data, the model not only achieves accurate prediction of forest fire risks, but also has the ability to automatically adjust parameters and structures to adapt to different forest environments and changes in ecological behavior.

[0044] To achieve this goal, the system not only adopts a multi-dimensional algorithm fusion strategy, but also combines a variety of advanced machine learning algorithms, including gradient boosting tree (GBDT), random forest (RF) and deep learning convolutional neural network (CNN). GBDT builds a strong prediction model by iteratively combining multiple weak prediction 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 of the mth step, γ m is the step length, which is used to control the influence of the weak prediction model on the final model. m(x) represents the mth weak prediction model (such as a decision tree). The advantage of GBDT is that it can capture the nonlinear relationship between features and deeply explore the hidden patterns in the data. RF is an integrated learning method that makes predictions by building multiple decision trees for voting or averaging. Its formula is:

[0047]

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

[0049] Y=W*X+b

[0050] Among them, W is the convolution kernel, which is used to extract features; X is the input data; b is the bias term, which is used to adjust the output. In addition to algorithm fusion, the prediction model also has the ability to adjust adaptive parameters and structures. Through the online learning mechanism, the model can update parameters and structures in real time to adapt to the dynamic changes of the forest environment. In the optimization process, we use a variant of the gradient descent algorithm, Adam (Adaptive Moment Estimation), to minimize the loss function of the model. The update rules of the Adam algorithm are 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. Correct the bias of the first-order moment estimate:

[0058]

[0059] e. Correct the bias of the second-order moment estimate:

[0060]

[0061] f. Update parameters:

[0062]

[0063] Among them, θ is the model parameter, L t is the loss function of the tth step, β1 and β2 are the decay rates used to control the update speed of the first-order and second-order moment estimates, η is the learning rate, which determines the step size of the parameter update, and ∈ is a very small number used to prevent the denominator from being zero. In order to further improve the generalization and adaptability of the model, the system creatively introduces meta-learning technology. Meta-learning aims to train models to quickly adapt to new tasks or new 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 , use the gradient descent algorithm to update the model parameters:

[0065]

[0066] Among them, θ is the initialization value of the model parameters, α is the learning rate, is the loss function on the i-th task.

[0067] b. Outer loop: Update the initialization values ​​of the model parameters to minimize the loss on all tasks:

[0068]

[0069] Among them, β is the learning rate, which is used to control the update step size of the model parameter initialization value. In terms of feature engineering, the system creatively uses feature selection techniques (such as chi-square test, mutual information) and dimensionality reduction techniques (such as PCA, LDA) to extract key features and improve computational efficiency. At the same time, we introduce the attention mechanism to intelligently assign feature weights and improve the effectiveness of features. The formula of the attention mechanism is expressed as:

[0070]

[0071] Among them, Q, K, and V are query, key, and value vectors, respectively, which are used to calculate the attention weights; d k is the dimension of the key vector, which is used to scale the dot product result. In addition, we have creatively introduced innovative data enhancement technology and model fusion strategy. Through data enhancement technology, we can transform and expand the original data to increase the generalization ability of the model. The model fusion strategy combines the prediction results of multiple different algorithms and obtains more accurate and stable prediction results through voting, weighted averaging, etc.

[0072] By integrating multiple machine learning algorithms, the system conducts deep learning and analysis of historical fire data, environmental parameters, and ecological behavior information to predict the probability of fire and potential risk areas. Once the prediction model outputs a fire warning signal, the system will automatically generate warning information according to the preset rules and algorithms, and customize it according to the role and needs of the recipient. In terms of publishing methods, the module adopts a multi-channel publishing strategy, including traditional channels and emerging channels, and dynamically adjusts according to real-time environmental data, receiver feedback, and historical warning effects to ensure that the warning information can quickly cover a wider audience and improve the reception effect. In terms of emergency response, the module realizes deep linkage control with emergency response systems such as fire extinguishing equipment and personnel dispatch. Once a fire warning information is received, the system can immediately trigger the emergency response mechanism, automatically dispatch nearby fire extinguishing equipment to the fire scene for fire fighting, and intelligently plan personnel evacuation paths and rescue routes according to the scale and spread of the fire. In addition, the system also supports real-time communication and data sharing with external emergency agencies such as fire departments and medical institutions to form a cross-departmental and cross-field emergency response network. The application of these technologies not only improves the accuracy and timeliness of fire warnings, but also enhances the efficiency and effectiveness of emergency response, providing strong technical support for forest fire prevention.

[0073] Compared with the existing method, this invention has the following advantages:

[0074] This invention breaks through the limitations of traditional single monitoring methods, and builds a comprehensive, multi-level forest environment monitoring network by integrating multiple monitoring methods such as satellite remote sensing, drone inspections, and ground video monitoring. More importantly, the system deeply integrates ecological behavior recognition technology, and uses deep learning algorithms to intelligently analyze ecological indicators such as vegetation growth status, soil moisture changes, and animal behavior patterns, achieving advanced insight into fire risks. This deep integration of multi-source monitoring and ecological behavior provides richer and more accurate data support for fire warnings, significantly improving the accuracy of predictions.

[0075] The system integrates a variety of advanced machine learning algorithms, which can automatically learn the complex laws and characteristics of fire occurrence, and automatically adjust the model parameters and structure according to real-time data feedback. This adaptive optimization capability enables the model to continuously adapt to changes in different forest environments and ecological conditions, and maintain long-term stable prediction performance. At the same time, the model also supports cross-domain knowledge transfer, can quickly adapt to new monitoring scenarios and fire types, and provides strong technical support for forest fire prevention. The integrated machine learning engine also has the ability to transfer cross-domain knowledge. This means that the model can not only make accurate fire predictions in 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 learned laws and characteristics of fire occurrence can be transferred to other forest areas with similar ecological conditions, thereby achieving rapid deployment and efficient prediction. This ability to transfer cross-domain knowledge greatly expands the scope of application 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 cost and risk of manual intervention and improve overall fire prevention efficiency. In practical applications, the engine can serve as a core component of the forest fire prevention command system, providing scientific basis and intelligent decision-making support for decision makers, and helping to achieve sustainable protection of forest resources and the safety and stability of the ecological environment.

[0076] The entire system realizes intelligent and automated fire early warning and prevention. Through multimodal data fusion technology, the system can comprehensively assess fire risks; combined with a flexible early warning release mechanism, it ensures that early warning information is conveyed to relevant personnel in a timely and accurate manner; at the same time, the intelligent characteristics of the system can also continuously learn and optimize model parameters to further improve the accuracy and reliability of early warnings. This process greatly improves the response speed and early warning efficiency of the system, providing strong support for the prevention and response of forest fires. Once the system detects a fire risk or finds a fire, it will immediately trigger the early warning release module to send an alarm to relevant personnel through various means such as sound, light, text messages, and emails. At the same time, the system supports linkage control with emergency response systems such as fire extinguishing equipment and personnel dispatch, automatically dispatches resources, optimizes fire extinguishing strategies, and realizes rapid fire fighting. This mechanism of instant response and intelligent linkage not only improves the efficiency of emergency response, but also reduces the risk of human misjudgment and delay, providing a more reliable guarantee for forest fire prevention. IV. Description of the drawings

[0077] Figure 1 Demonstrate the structure of the hybrid ensemble machine learning model, including the different integrated machine learning algorithms, the adjustment mechanism of model parameters, and the optimization process of the model. V. Specific implementation methods

[0078] 1. System initialization: After the system is started, each module is initialized, including device self-test, network connection test, and model parameter loading.

[0079] 2. Multi-source data collection: The satellite remote sensing module, drone monitoring module, video surveillance module and animal behavior monitoring module start to collect data respectively, including images, videos, temperature, humidity, changes in plant status, animal behavior, etc. of the forest environment.

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

[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 fire.

[0082] 5. Warning judgment and release: When the probability of fire exceeds the preset threshold, the system triggers the warning mechanism. The warning release module selects the appropriate warning method and content according to the risk level and recipient needs, and conveys the warning information to relevant personnel in a timely and accurate manner through sound, light, text message, email, etc.

[0083] 6. System optimization and update: With the continuous accumulation of data and continuous learning of the model, the system can continuously optimize the model parameters and algorithm structure to improve the accuracy and reliability of early warning. At the same time, the system also supports regular updates and upgrades to adapt to the ever-changing forest environment and fire risks.

Claims

1. An intelligent forest fire prediction and warning system that integrates machine learning and ecological behavior recognition, characterized by The system integrates the most cutting-edge machine learning technology, intelligent identification of ecological behavior, and multi-dimensional data fusion analysis to achieve advanced, accurate and efficient early warning of forest fire risks. Specifically, it includes the following innovative modules and functions: a. Ecological behavior deep analysis unit: This unit not only integrates high-definition cameras, infrared detectors, drones, and satellite remote sensing technology, but also introduces computer vision and natural language processing algorithms to conduct in-depth analysis of forest ecological behavior and identify subtle changes in fire precursors, such as abnormal vegetation growth patterns and changes in animal behavior, providing a unique perspective and rich information for fire warning. b. Multi-dimensional perception module of environmental parameters: This module is equipped with a high-precision and high-stability sensor array to monitor multi-dimensional parameters of the forest environment in real time, such as temperature, humidity, wind speed, and air pressure. It combines meteorological data with historical fire records to build a dynamic model of the forest environment, providing comprehensive and detailed data support for fire risk assessment. c. Intelligent monitoring and assessment module for combustible materials: This module uses highly sensitive 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 risks, and provide a scientific basis for the formulation of fire-fighting strategies. d. Integrated sky-ground intelligent fire monitoring and emergency response network: Integrate satellite remote sensing, autonomous drone inspections, ground intelligent video surveillance and Internet of Things technology to form a multi-level, all-round fire monitoring system. Once a fire is discovered, the emergency response mechanism is immediately triggered, and fire-fighting resources are automatically dispatched to achieve rapid fire fighting. e. Adaptive evolution and multimodal fusion machine learning decision engine: This engine uses multimodal data fusion technology to deeply integrate ecological behavior, environmental parameters and combustible monitoring data, and integrates multiple machine learning algorithms and deep learning models to achieve accurate prediction of fire risks. At the same time, the engine has adaptive evolution capabilities, which can automatically adjust model parameters and structures according to real-time data feedback and continuously optimize prediction performance. f. Personalized, intelligent and multi-channel early warning release and response platform: The platform can intelligently generate personalized early warning information based on the fire risk assessment results and the characteristics of the recipients, and accurately push it to relevant personnel through multiple channels such as sound, light, mobile notifications, email, social media, etc. At the same time, the platform supports interactive feedback and dynamic adjustment of early warning information to ensure the timeliness, effectiveness and pertinence of early warning information. In addition, the platform also has emergency response guidance functions to provide real-time strategy support and resource scheduling optimization for firefighting operations.

2. The system according to claim 1, characterized in that The ecological behavior deep analysis unit also has cross-modal learning capabilities and can combine multimodal data such as sound and smell to further improve the accuracy and comprehensiveness of identifying fire precursor ecological behaviors.

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

4. The system according to any one of claims 1 to 4, characterized in that The integrated sky-ground intelligent fire monitoring and emergency response network supports linkage control with other forest fire prevention facilities, such as automatically triggering forest fire isolation belts, optimizing personnel evacuation routes, etc., to form an all-round and three-dimensional forest fire prevention system.

5. A method for constructing a forest fire prevention intelligent ecological system based on the system described in any one of claims 1 to 5, characterized in that This method includes multiple links such as system deployment, data integration, model training and optimization, emergency response plan formulation and drills, etc. It aims to comprehensively improve forest fire prevention and control capabilities and emergency response efficiency through collaborative work and data sharing among systems. At the same time, this method also supports integration with other ecological monitoring systems, such as wildlife and plant protection, water resources management, etc., to jointly build a smart forestry ecological system.

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