Real-time lightning fire prediction system based on adaptive multiple models and intelligent optimization

Abstract

The invention provides a real-time lightning fire prediction system based on adaptive multiple models and intelligent optimization, and relates to the technical field of natural disaster prediction. The real-time lightning fire prediction system based on adaptive multiple models and intelligent optimization comprises data acquisition and preprocessing: collecting multi-source data related to lightning fire through a meteorological station, satellite remote sensing or an unmanned aerial vehicle; built-in data cleaning, de-noising, feature extraction and standardization processing mechanisms are utilized to pre-process the acquired data, so that the data quality is ensured; an Internet of Things architecture is established, and instant transmission and updating of data are ensured; and the data preprocessing engine based on deep learning is utilized to automatically identify and process abnormal data, missing values and noise, so that the data cleaning efficiency and accuracy are improved. By integrating multiple prediction models and utilizing a self-adaptive selection and intelligent fusion mechanism, the optimal model or model combination can be dynamically selected according to different conditions, and the prediction precision is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of natural disaster prediction, and in particular to a real-time lightning fire prediction system based on adaptive multi-model and intelligent optimization. Background Art

[0002] Lightning fire, also known as fire caused by lightning, refers to fire caused by lightning hitting the ground or objects (such as trees, buildings, etc.). Lightning is a natural phenomenon. When the electric charge in the atmosphere accumulates to a certain level, it will be released in the form of lightning, generating huge energy. Lightning fire usually occurs in thunderstorms, especially in arid and forested areas, because these places are prone to accumulate combustible materials. Once a lightning strike occurs, it may quickly cause a fire.

[0003] Traditional lightning fire prediction methods mainly rely on a single model or a simple model combination, and have problems such as low prediction accuracy, poor real-time performance, and weak adaptability. With the rapid development of technologies such as big data and artificial intelligence, multi-model fusion and intelligent optimization technologies have been widely used in the field of natural disaster prediction. However, how to effectively integrate multiple prediction models and achieve adaptive selection and intelligent optimization is still a technical problem that needs to be solved in the current field of lightning fire prediction. Therefore, technicians in this field provide a real-time lightning fire prediction system based on adaptive multi-models and intelligent optimization to solve the problems raised in the above background technology. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides a real-time lightning fire prediction system based on adaptive multi-model and intelligent optimization, which solves the problems of low prediction accuracy, poor real-time performance and weak adaptability of the existing prediction methods.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a real-time lightning fire prediction system based on adaptive multi-model and intelligent optimization, comprising:

[0008] Data collection and preprocessing: Collect multi-source data related to lightning fires through weather stations, satellite remote sensing or drones; use built-in data cleaning, denoising, feature extraction and standardization processing mechanisms to preprocess the collected data to ensure data quality; establish an Internet of Things architecture to seamlessly connect data sources between weather stations, lightning monitoring stations, satellites and drones to ensure instant data transmission and updating; use a data preprocessing engine based on deep learning to automatically identify and process abnormal data, missing values ​​and noise to improve data cleaning efficiency and accuracy; generate additional features by using the generative adversarial network method to supplement the lack of information in the original data to enhance the performance of the prediction model;

[0009] Model selection and fusion: According to the current environmental conditions and prediction task requirements, the adaptive selection and fusion module uses machine learning algorithms to evaluate and select models from multiple models; selects the most suitable prediction model or model combination for prediction, and organically integrates the prediction results of different models through intelligent fusion algorithms; builds a variety of model libraries including physical models, statistical models, machine learning models and hybrid models, and optimizes each model for different prediction needs; introduces SHAP analysis value and LIME analysis value method interpretability enhancement technology to improve the transparency of model prediction results and user trust; the adaptive hybrid model based on meta-learning can dynamically adjust the model weights and fusion strategies according to environmental conditions and task requirements to ensure the efficiency and accuracy of prediction;

[0010] Intelligent deep optimization: The intelligent deep optimization module performs deep parameter tuning and structural optimization on the selected model. Through deep reinforcement learning or neural network optimization algorithms, it automatically finds the optimal configuration of model parameters to improve prediction accuracy and generalization ability. At the same time, it continuously optimizes based on new prediction data and evaluation results, introduces AdamW and RMSprop algorithms, and automatically adjusts the learning rate according to gradient changes to stabilize the model training process and avoid falling into local optimal solutions.

[0011] Forecast result display and evaluation, an interactive forecast dashboard is designed to provide an intuitive and easy-to-understand information display interface with lightning fire risk distribution maps, forecast trend maps, and historical comparative analysis; a built-in comprehensive performance evaluation mechanism automatically analyzes the differences between historical forecast data and actual observation results, and generates detailed performance reports and improvement suggestions to help users understand and optimize forecast results.

[0012] Preferably, the data preprocessing engine based on deep learning adopts an autoencoder, which is an unsupervised learning model for learning an effective representation of input data and then reconstructing the input data, with the goal of minimizing the difference between input and output. The specific formula is:

[0013]

[0014] Where X is the input data, is the reconstructed data, L is the loss function, and n is the number of samples.

[0015] Preferably, the generative adversarial network includes two networks, a generator and a discriminator, the generator learns to generate data, and the discriminator learns to distinguish between real data and generated data. During the training process, the two networks compete with each other to improve the quality of generated data.

[0016] Preferably, the intelligent fusion algorithm calculates the fusion result by using a weighted average fusion algorithm and assigning a weight to each data source. The specific steps are:

[0017] S1. Determine the weight and assign a weight ω to each data source i , where i represents the i-th data source, and

[0018] S2. Calculate the weighted sum, calculate the weighted sum of each data source where x i is the value of the i-th data source;

[0019] S3. Output the fusion result, which is the weighted S.

[0020] Preferably, the LIME analysis value provides an explanation for the prediction of a complex model by approximating the behavior of the complex model with a simple and interpretable model in a local area. The specific steps and calculation formula are as follows:

[0021] S1. Select an instance. First, select an instance x that needs to be explained.

[0022] S2. Generate a set of perturbed samples in the neighborhood of instance x, which are created by adding small random perturbations to the features of x;

[0023] S3. Use complex models to predict these;

[0024] S4. Use these perturbation samples and their corresponding model predictions as training data to train a local interpretable model;

[0025] S5. Explain the parameters of this local interpretable model, which can provide insights into the predictive behavior of the complex model in the vicinity of instance x;

[0026] S6. Visualize explanation results to help users understand the predictions of complex models;

[0027]

[0028] Where f is a complex model, g is a local interpretable model, z' is a perturbed sample, z is the original sample corresponding to the perturbed sample, and π(z) is a weight function used to emphasize samples that are closer to instance x;

[0029] The weight function π(z) is usually inversely proportional to the distance of sample z to instance x, ensuring that samples closer to x have greater weight in the explanation.

[0030] (III) Beneficial effects

[0031] The present invention provides a real-time lightning fire prediction system based on adaptive multi-model and intelligent optimization. It has the following beneficial effects:

[0032] 1. In the present invention, by integrating multiple prediction models and utilizing adaptive selection and intelligent fusion mechanisms, the optimal model or model combination can be dynamically selected according to different situations, thereby significantly improving the prediction accuracy. At the same time, the intelligent deep optimization module uses advanced algorithms to deeply tune the model to ensure that the system always maintains high prediction accuracy and generalization capabilities. In addition, the system's self-learning ability enables it to continuously optimize the model based on new data, further improving the level of intelligence.

[0033] 2. In the present invention, the system has an efficient data processing and model execution process, which can quickly collect and process multi-source data, including meteorology, lightning, terrain, vegetation, etc., and generate prediction results in real time to provide timely decision-making support for disaster prevention and mitigation work; the application of multi-source data fusion technology enables the prediction model to more comprehensively consider the various factors affecting the occurrence of lightning fires, thereby enhancing the comprehensiveness and accuracy of the prediction.

[0034] 3. In the present invention, the prediction result visualization and evaluation module not only provides users with an intuitive and interactive interface, making complex prediction data easy to understand and analyze, but also greatly improves the user experience; this design allows users to quickly grasp the prediction trend, identify potential risks, and make decisions more confidently; in addition, the system's built-in automated performance evaluation function provides users with detailed performance reports and key indicators, such as accuracy, recall rate, F1 score, etc., by continuously tracking and comparing historical prediction data with actual observation results; these data not only help users to fully understand the system performance, but also reveal potential optimization space, providing users with data-driven decision support; users can adjust the prediction strategy or optimize the system configuration in a targeted manner based on these evaluation results, thereby continuously improving the prediction effect and the overall performance of the system; in summary, the enhanced user experience and data-driven decision support make this invention have higher practical value and market competitiveness in the field of lightning fire prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the overall system architecture of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] Embodiment 1:

[0038] like Figure 1 As shown, the embodiment of the present invention provides a real-time lightning fire prediction system based on adaptive multi-model and intelligent optimization, including:

[0039] Data collection and preprocessing: Collect multi-source data related to lightning fires through weather stations, satellite remote sensing or drones; use built-in data cleaning, denoising, feature extraction and standardization processing mechanisms to preprocess the collected data to ensure data quality; establish an Internet of Things architecture to seamlessly connect data sources between weather stations, lightning monitoring stations, satellites and drones to ensure instant data transmission and updating; use a data preprocessing engine based on deep learning to automatically identify and process abnormal data, missing values ​​and noise to improve data cleaning efficiency and accuracy; generate additional features by using the generative adversarial network method to supplement the lack of information in the original data to enhance the performance of the prediction model;

[0040] Model selection and fusion: According to the current environmental conditions and prediction task requirements, the adaptive selection and fusion module uses machine learning algorithms to evaluate and select models from multiple models; selects the most suitable prediction model or model combination for prediction, and organically integrates the prediction results of different models through intelligent fusion algorithms; builds a variety of model libraries including physical models, statistical models, machine learning models and hybrid models, and optimizes each model for different prediction needs; introduces SHAP analysis value and LIME analysis value method interpretability enhancement technology to improve the transparency of model prediction results and user trust; the adaptive hybrid model based on meta-learning can dynamically adjust the model weights and fusion strategies according to environmental conditions and task requirements to ensure the efficiency and accuracy of prediction;

[0041] Intelligent deep optimization: The intelligent deep optimization module performs deep parameter tuning and structural optimization on the selected model to improve prediction accuracy and generalization ability. Through continuous iterative learning, it automatically discovers the optimal configuration of model parameters to improve prediction accuracy and generalization ability. At the same time, the module also has self-learning and evolution capabilities, and can continuously optimize model performance based on new prediction data and evaluation results; through deep reinforcement learning or neural network optimization algorithms, it automatically discovers the optimal configuration of model parameters to improve prediction accuracy and generalization ability; at the same time, it continuously optimizes based on new prediction data and evaluation results, introduces AdamW and RMSprop algorithms, and automatically adjusts learning according to gradient changes rate, stabilize the model training process and avoid falling into the local optimal solution. These algorithms can automatically adjust the learning rate size according to the gradient change to avoid the model falling into the local optimal solution or instability during the training process; use neural architecture search technology to automatically explore the optimal neural network structure; by defining the search space and search strategy, the system can automatically find the neural network architecture suitable for the current prediction task, thereby further improving the prediction performance; apply meta-learning technology to the optimization process, and guide the optimization process of the current task by learning the performance of different optimization strategies; this method can find the optimization strategy combination suitable for the current prediction task more quickly, improving the optimization efficiency and effect;

[0042] Forecast result display and evaluation, design interactive forecast dashboard, provide lightning fire risk distribution map, forecast trend map, historical comparative analysis intuitive and easy-to-understand information display interface; built-in comprehensive performance evaluation mechanism, can automatically analyze the difference between historical forecast data and actual observation results, calculate and display key performance indicators, such as accuracy, recall rate, F1 score, etc., to provide strong support for the continuous optimization of system performance, automatically analyze the difference between historical forecast data and actual observation results, and generate detailed performance reports and improvement suggestions to help users understand and optimize forecast results, build intelligent evaluation and suggestion system, automatically generate evaluation reports and suggestions based on the evaluation results, the system can identify the source of prediction errors and put forward targeted improvement measures, help users better understand and use the forecast results, combine augmented reality technology to superimpose the forecast results on the actual geographical environment for display, users can use mobile phones or AR glasses and other devices to intuitively see the distribution of lightning fire risks on the map and its relationship with the surrounding environment, so as to more accurately assess risks and take countermeasures.

[0043] The deep learning-based data preprocessing engine uses an autoencoder, which is an unsupervised learning model used to learn an effective representation of input data and then reconstruct the input data. Its goal is to minimize the difference between input and output. The specific formula is:

[0044]

[0045] Where X is the input data, is the reconstructed data, L is the loss function, and n is the number of samples.

[0046] The generative adversarial network consists of two networks, the generator and the discriminator. The generator learns to generate data, while the discriminator learns to distinguish between real data and generated data. During the training process, the two networks compete with each other to improve the quality of generated data.

[0047] The intelligent fusion algorithm uses a weighted average fusion algorithm to calculate the fusion result by assigning a weight to each data source. The specific steps are:

[0048] S1. Determine the weight and assign a weight ω to each data source i , where i represents the i-th data source, and

[0049] S2. Calculate the weighted sum, calculate the weighted sum of each data source where x i is the value of the i-th data source;

[0050] S3. Output the fusion result, which is the weighted S.

[0051] LIME analysis provides an explanation for the predictions of complex models by approximating the behavior of complex models with a simple and interpretable model in a local area. The specific steps and calculation formula are as follows:

[0052] S1. Select an instance. First, select an instance x that needs to be explained.

[0053] S2. Generate a set of perturbed samples in the neighborhood of instance x, which are created by adding small random perturbations to the features of x;

[0054] S3. Use complex models to predict these;

[0055] S4. Use these perturbation samples and their corresponding model predictions as training data to train a local interpretable model;

[0056] S5. Explain the parameters of this local interpretable model, which can provide insights into the predictive behavior of the complex model in the vicinity of instance x;

[0057] S6. Visualize explanation results to help users understand the predictions of complex models;

[0058]

[0059] Where f is a complex model, g is a local interpretable model, z' is a perturbed sample, z is the original sample corresponding to the perturbed sample, and π(z) is a weight function used to emphasize samples that are closer to instance x;

[0060] The weight function π(z) is usually inversely proportional to the distance of sample z to instance x, ensuring that samples closer to x have greater weight in the explanation.

[0061] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Real-time lightning fire prediction system based on adaptive multi-model and intelligent optimization, characterized by: include: Data acquisition and preprocessing: collecting multi-source data related to lightning fires through weather stations, satellite remote sensing or drones; Use built-in data cleaning, denoising, feature extraction and standardization processing mechanisms to pre-process the collected data to ensure data quality; Establish an IoT architecture to seamlessly connect data sources between weather stations, lightning monitoring stations, satellites, and drones to ensure real-time data transmission and updating; use a deep learning-based data preprocessing engine to automatically identify and process abnormal data, missing values, and noise to improve data cleaning efficiency and accuracy; generate additional features using a generative adversarial network approach to supplement the lack of information in the original data to enhance the performance of the prediction model; Model selection and fusion: According to the current environmental conditions and prediction task requirements, the adaptive selection and fusion module uses machine learning algorithms to evaluate and select models from multiple models; selects the most suitable prediction model or model combination for prediction, and organically integrates the prediction results of different models through intelligent fusion algorithms; builds a variety of model libraries including physical models, statistical models, machine learning models and hybrid models, and optimizes each model for different prediction needs; introduces SHAP analysis value and LIME analysis value method interpretability enhancement technology to improve the transparency of model prediction results and user trust; the adaptive hybrid model based on meta-learning can dynamically adjust the model weights and fusion strategies according to environmental conditions and task requirements to ensure the efficiency and accuracy of prediction; Intelligent deep optimization: The intelligent deep optimization module performs deep parameter tuning and structural optimization on the selected model. Through deep reinforcement learning or neural network optimization algorithms, it automatically finds the optimal configuration of model parameters to improve prediction accuracy and generalization ability. At the same time, it continuously optimizes based on new prediction data and evaluation results, introduces AdamW and RMSprop algorithms, and automatically adjusts the learning rate according to gradient changes to stabilize the model training process and avoid falling into local optimal solutions. Forecast result display and evaluation, an interactive forecast dashboard is designed to provide an intuitive and easy-to-understand information display interface with lightning fire risk distribution maps, forecast trend maps, and historical comparative analysis; a built-in comprehensive performance evaluation mechanism automatically analyzes the differences between historical forecast data and actual observation results, and generates detailed performance reports and improvement suggestions to help users understand and optimize forecast results.

2. The real-time lightning fire prediction system based on adaptive multi-model and intelligent optimization according to claim 1 is characterized in that: The deep learning-based data preprocessing engine uses an autoencoder, which is an unsupervised learning model used to learn an effective representation of input data and then reconstruct the input data. Its goal is to minimize the difference between input and output. The specific formula is: Where X is the input data, is the reconstructed data, L is the loss function, and n is the number of samples.

3. The real-time lightning fire prediction system based on adaptive multi-model and intelligent optimization according to claim 1 is characterized in that: The generative adversarial network includes two networks, a generator and a discriminator. The generator learns to generate data, while the discriminator learns to distinguish between real data and generated data. During the training process, the two networks compete with each other to improve the quality of generated data.

4. The real-time lightning fire prediction system based on adaptive multi-model and intelligent optimization according to claim 1 is characterized in that: The intelligent fusion algorithm adopts a weighted average fusion algorithm to calculate the fusion result by assigning a weight to each data source. The specific steps are as follows: S1. Determine the weight and assign a weight ω to each data source i , where i represents the i-th data source, and S2. Calculate the weighted sum, calculate the weighted sum of each data source where x i is the value of the i-th data source; S3. Output the fusion result, which is the weighted S.

5. The real-time lightning fire prediction system based on adaptive multi-model and intelligent optimization according to claim 1 is characterized in that: The LIME analysis value provides an explanation for the prediction of a complex model by approximating the behavior of the complex model with a simple and interpretable model in a local area. The specific steps and calculation formula are as follows: S1. Select an instance. First, select an instance x that needs to be explained. S2. Generate a set of perturbed samples in the neighborhood of instance x, which are created by adding small random perturbations to the features of x; S3. Use complex models to predict these; S4. Use these perturbation samples and their corresponding model predictions as training data to train a local interpretable model; S5. Explain the parameters of this local interpretable model, which can provide insights into the predictive behavior of the complex model in the vicinity of instance x; S6. Visualize explanation results to help users understand the predictions of complex models; Where f is a complex model, g is a local interpretable model, z' is a perturbed sample, z is the original sample corresponding to the perturbed sample, and π(z) is a weight function used to emphasize samples that are closer to instance x; The weight function π(z) is usually inversely proportional to the distance of sample z to instance x, ensuring that samples closer to x have greater weight in the explanation.

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