Natural disaster intelligent early warning system based on image processing

Abstract

The invention relates to the technical field of natural disaster early warning, in particular to a natural disaster intelligent early warning system based on image processing. Comprising a multi-modal sensor data acquisition module, an image and data preprocessing module, a deep learning and confrontation generative network identification module, a virtual reality disaster situation simulation module, a disaster prediction and trend analysis module, an alarm and notification module, a decision support and emergency response module, and a data storage, management and feedback optimization module. The multi-modal sensor data acquisition module is used for acquiring and fusing various sensor data; the image and data preprocessing module is used for processing multi-source data and aligning and fusing multi-modal data. Through fusion of a multi-modal sensor and a deep learning algorithm, the system improves the recognition capability in a complex environment, can maintain high-precision disaster monitoring under severe conditions, reduces false alarm and missing alarm, and remarkably improves the reliability of an early warning system.

Description

Technical Field

[0001] The present invention relates to the technical field of natural disaster early warning, and in particular to an intelligent natural disaster early warning system based on image processing. Background Art

[0002] Natural disaster early warning systems have important application value worldwide. When facing various sudden natural disasters such as floods, earthquakes, fires, and typhoons, fast and accurate early warning systems can significantly reduce casualties and property losses. With the advancement of science and technology, traditional natural disaster early warning systems have gradually evolved from a model based on simple meteorological data and sensor monitoring to an intelligent and multimodal data fusion model. Existing technologies have introduced some image processing and artificial intelligence technologies for disaster detection, but they still face several challenges:

[0003] The existing early warning system cannot guarantee the accuracy of data processing methods of single sensors in complex or extreme environments, and is easily affected by the environment, resulting in false alarms or missed alarms, and cannot provide accurate early warning information in the early stages of disasters.

[0004] The current disaster warning system is based on simple trend analysis and cannot dynamically predict the evolution of disasters. In addition, emergency responders lack effective simulation tools for drills or testing when facing complex disasters, which leads to insufficient response when real disasters occur.

[0005] When a disaster occurs, the timeliness and accuracy of information transmission are crucial. However, the existing system's alarm release method is relatively simple and cannot cover all potential disaster victims. In addition, during on-site rescue, there is a lack of real-time disaster information support, making it difficult for rescue workers to accurately grasp the development of the disaster, which in turn affects rescue efficiency.

[0006] Therefore, those skilled in the art provide a natural disaster intelligent early warning system based on image processing to solve the problems raised in the above background technology. Summary of the invention

[0007] In view of the deficiencies of the prior art, the present invention provides a natural disaster intelligent early warning system based on image processing to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent early warning system for natural disasters based on image processing, including a multimodal sensor data acquisition module, an image and data preprocessing module, a deep learning and adversarial generative network recognition module, a virtual reality disaster scenario simulation module, a disaster prediction and trend analysis module, an alarm and notification module, a decision support and emergency response module, and a data storage, management and feedback optimization module;

[0009] The multimodal sensor data acquisition module is used to collect and fuse multiple sensor data; the image and data preprocessing module is used to process multi-source data and align and fuse multimodal data; the deep learning and adversarial generative network recognition module uses deep convolutional neural networks and adversarial generative networks to extract image features, identify disasters and complete images; the virtual reality disaster scenario simulation module is used to generate virtual disaster scenes, simulate the disaster development process, and help emergency management and rescue personnel prepare in advance; the disaster prediction and trend analysis module predicts the future development of disasters based on multimodal data and historical trends and generates relevant alarms; the alarm and notification module warns relevant departments and the public when a disaster is identified or a high-risk event is predicted in the future; the decision support and emergency response module provides decision support for emergency response based on real-time data and prediction results; the data storage, management and feedback optimization module is used to store the collected image data, sensor data and processing results.

[0010] Preferably, the multimodal sensor data acquisition module includes a visible light camera unit, an infrared camera unit, a radar sensor unit, a laser radar unit, and a meteorological data acquisition unit; the visible light camera unit acquires standard visible light images and provides basic image data for preliminary disaster monitoring and identification; the infrared camera unit is used to work under low light, smoke, and night conditions, and to identify disaster scenes by capturing thermal radiation from objects; the radar sensor unit is used to penetrate obstructions caused by smoke and rainfall to obtain surface changes or other important environmental information; the laser radar unit generates a three-dimensional terrain map through laser scanning to help monitor terrain changes; the meteorological data acquisition unit is used to collect environmental data to assist in disaster warning;

[0011] The image and data preprocessing module includes an image denoising and enhancement unit, a multimodal data alignment unit, and a format conversion unit; the image denoising and enhancement unit performs denoising and enhancement processing on the collected image; the multimodal data alignment unit aligns and fuses data from different sensors; the format conversion unit converts the collected data into a unified processing format;

[0012] The deep learning and adversarial generative network recognition module includes a feature extraction unit, an adversarial generative network unit, and a disaster recognition unit; the feature extraction unit extracts key features from the image through a deep convolutional neural network; the adversarial generative network unit generates or completes disaster images and recognizes disasters using the adversarial generative network when the image quality is insufficient or the information is missing; the disaster recognition unit recognizes the specific disaster type in the image based on the deep learning algorithm;

[0013] The virtual reality disaster scenario simulation module includes a three-dimensional scene generation unit, a disaster scenario simulation unit, and a virtual interaction unit; the three-dimensional scene generation unit uses image data to generate a three-dimensional model of the disaster area to simulate the real-time dynamic changes of the disaster; the disaster scenario simulation unit simulates the occurrence and evolution process of the disaster in virtual reality; the virtual interaction unit allows emergency management personnel to interact with the disaster scene in a virtual reality environment, adjust simulation conditions or test different emergency plans.

[0014] Preferably, the disaster prediction and trend analysis module includes a historical data analysis unit, a real-time data monitoring unit, and a prediction model unit; the historical data analysis unit analyzes historical disaster data to establish a disaster pattern or trend model; the real-time data monitoring unit monitors sensor data and image changes, and analyzes the dynamic development of disasters in real time; the prediction model unit analyzes data based on a machine learning algorithm to predict the future development direction, speed, and impact range of disasters;

[0015] The alarm and notification module includes a disaster level assessment unit, a multi-channel alarm unit, and an alarm threshold setting unit; the disaster level assessment unit assesses the severity of the disaster based on the scale, impact range and development trend of the disaster to determine the warning level; the multi-channel alarm unit issues warnings to relevant departments and the public through multiple channels; the alarm threshold setting unit sets warning thresholds for different disaster types;

[0016] The decision support and emergency response module includes an emergency evacuation route planning unit, a material dispatching unit, and an emergency plan suggestion unit; the emergency evacuation route planning unit formulates an evacuation route for the disaster site based on real-time disaster data and geographic information system; the material dispatching unit automatically plans the deployment plan of emergency materials according to the impact scope and severity of the disaster; the emergency plan suggestion unit provides disaster emergency response suggestions to assist decision makers in responding;

[0017] The data storage, management and feedback optimization module includes a data storage unit, a model feedback optimization unit, and a data retrieval unit; the data storage unit classifies and stores sensor data, images, prediction results, and historical disaster records; the model feedback optimization unit automatically adjusts and optimizes deep learning models and recognition algorithms based on the system's early warning accuracy feedback; the data retrieval unit allows users to retrieve historical data and analyze the causes and patterns of disasters and the effectiveness of rescue strategies.

[0018] Preferably, the image captured by the visible light camera unit contains noise, and a non-local means algorithm is used to perform image denoising:

[0019] Among them, I denoisedI(x) is the value of the denoised image at pixel x, I(y) is the value of the original image at pixel y, The color difference between pixel x and y, h is the parameter controlling the smoothness, and Z(x) is the normalization coefficient;

[0020] The infrared camera unit estimates the temperature of the object based on the temperature radiation of the object using the Stefan-Boltzmann law: P = σ·∈·A·T 4 ,

[0021] Among them, P is the infrared radiation power, ∈ is the emissivity of the object, A is the surface area of ​​the object, and T is the absolute temperature of the object.

[0022] Preferably, the radar sensor detects objects in the environment by emitting electromagnetic waves, and uses fast Fourier transform to perform frequency domain analysis on the echo signal to determine the position and speed of the target, and the formula is as follows:

[0023] Where S(f) is the representation of the signal in the frequency domain, s(t) is the time domain signal received by the radar, f is the frequency, t is the time, and j is the imaginary unit;

[0024] The laser radar unit generates a three-dimensional point cloud by calculating the return time of the laser pulse. According to the flight time method of the laser pulse, the distance calculation formula is as follows:

[0025] Where d is the distance between the object and the lidar, c is the speed of light, and Δt is the time difference between the laser emission and return.

[0026] The meteorological data acquisition unit captures wind speed, temperature and humidity data in the environment through sensors, and uses a model based on the Navier-Stokes equations to predict changes in the wind field:

[0027]

[0028] Among them, u is velocity field, p is air density, t is time, p is air pressure, μ is dynamic viscosity, and f is external force.

[0029] Preferably, the total variation denoising in the image denoising and enhancement unit is a denoising algorithm in the field of image processing, which can retain image edge details, and its optimization goal is to smooth the noise by minimizing the total variation of the image gradient. The formula is as follows:

[0030]

[0031] Among them, I(x,y) is the denoised image, I0(x,y) is the original noisy image, The gradient of the image, λ is the weight parameter that balances denoising and preserving image details, and Ω is the domain of the image.

[0032] Preferably, the mutual information in the multimodal data alignment unit is a method for image registration, and the images are aligned by maximizing the mutual information of the joint histogram, and the formula is as follows:

[0033]

[0034] Among them, I1 and I2 are images to be aligned. The probability distribution of gray levels i and j in images I1 and I2, Joint probability distribution of images I1 and I2.

[0035] Preferably, the disaster identification unit classifies the features extracted by the convolutional neural network to judge the natural disasters in the image, and the classifier uses the Softmax function to map the features output by the network into a probability distribution. The Softmax function:

[0036] Among them, z i The original output of the network for category i, P(y=i|x) is the probability that the image belongs to category i;

[0037] The classification loss function uses cross entropy loss to measure the difference between the true label and the predicted probability distribution:

[0038] Among them, y i Unique encoding of the true category label, P(y=i|x) The network predicts the probability that the image belongs to category i;

[0039] By minimizing the cross entropy loss function, the disaster identification unit can optimize the convolutional neural network model to enable it to accurately classify disasters. The attention mechanism can improve the model's attention to important features in the disaster identification task. The formula is:

[0040] e ij =f(h i ,s j ),

[0041] Among them, α ij The attention weight at j, e ij represents the correlation between positions i and j, h i 、s j Representation of input features.

[0042] Preferably, the real-time data monitoring unit is used to dynamically track and predict the real-time data provided by the sensor, satellite or monitoring station, and optimize the estimation of the state by using the data updated in real time by the Kalman filter. The formula of the Kalman filter is:

[0043] Status prediction:

[0044] P t|t-1 =AP t-1|t-1 A T +Q,

[0045] Kalman gain:

[0046] K t =P t|t-1 H T (HP t|t-1 H T +R) -1 ,

[0047] Status Update:

[0048] P t|t =(IK t H)P t|t-1 ,

[0049] in, The predicted state vector at time t, P t|t-1 The predicted state covariance matrix, A state transition matrix, B control matrix, for external control variables u t , Q process noise covariance matrix, K t Kalman gain, z t Observation data, H observation matrix, R observation noise covariance matrix.

[0050] Preferably, the alarm threshold setting unit dynamically adjusts the alarm triggering threshold based on a variety of different parameters. To handle the complex relationship between variables, multivariate logistic regression can be used to estimate the alarm triggering probability under different variable combinations;

[0051] Logistic Regression Model:

[0052]

[0053] Among them, P(Y=1) is the probability of alarm triggering, X1, X2,…, X n Input variables, β0 bias term, β1, β2, …, β n Regression coefficients;

[0054] Threshold setting: The alarm threshold is determined based on the probability output by the logistic regression model, such as:

[0055] Alarm trigger ifP(Y=1|X1,X2,…,X n )>θ,

[0056] Among them, θ is the set trigger threshold, which can be adjusted dynamically to optimize the threshold based on historical data and real-time environmental conditions.

[0057] The present invention provides a natural disaster intelligent early warning system based on image processing. It has the following beneficial effects:

[0058] 1. Through the fusion of multimodal sensors and deep learning algorithms, the present invention improves the system's recognition ability in complex environments, can maintain high-precision disaster monitoring under harsh conditions, reduce false alarms and missed alarms, and significantly improve the reliability of the early warning system.

[0059] 2. By combining deep learning and virtual reality technology, the system of the present invention can dynamically predict the development trend of disasters and simulate disaster scenarios in a virtual environment. Emergency response personnel can conduct drills and tests in advance, optimize the decision-making process, and effectively reduce the impact of disasters.

[0060] 3. By using augmented reality technology and multi-channel alarm dispatch, the system of the present invention can quickly and widely transmit disaster information and provide real-time key data for on-site rescue, thereby improving the efficiency of emergency response and ensuring public safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is a system diagram of the present invention;

[0062] Figure 2 It is a schematic diagram of a multi-modal sensor data acquisition module of the present invention;

[0063] Figure 3 It is a schematic diagram of the image and data preprocessing module of the present invention;

[0064] Figure 4 A schematic diagram of a deep learning and adversarial generation network recognition module of the present invention;

[0065] Figure 5 A schematic diagram of a virtual reality disaster scenario simulation module of the present invention;

[0066] Figure 6 This is a schematic diagram of the disaster prediction and trend analysis module of the present invention;

[0067] Figure 7 It is a schematic diagram of the alarm and notification module of the present invention;

[0068] Figure 8 It is a schematic diagram of the decision support and emergency response module of the present invention;

[0069] Fig. 9 It is a schematic diagram of the data storage, management and feedback optimization module of the present invention. DETAILED DESCRIPTION

[0070] In order to make the technical personnel in the technical field understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in combination with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a partial embodiment of the present invention, not a complete embodiment. Based on the embodiment of the present invention, other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0071] The present invention is described in detail below in conjunction with the accompanying drawings:

[0072] Example:

[0073] Please see attached Figure 1 -Attached Fig. 9 The embodiment of the present invention provides a natural disaster intelligent early warning system based on image processing, including a multimodal sensor data acquisition module, an image and data preprocessing module, a deep learning and adversarial generation network recognition module, a virtual reality disaster scenario simulation module, a disaster prediction and trend analysis module, an alarm and notification module, a decision support and emergency response module, and a data storage, management, and feedback optimization module; the multimodal sensor data acquisition module is used to collect and fuse multiple sensor data; the image and data preprocessing module is used to process multi-source data and align and fuse multimodal data; the deep learning and adversarial generation network recognition module uses a deep convolutional neural network and adversarial generation network to identify natural disasters. The virtual reality disaster scenario simulation module is used to generate virtual disaster scenes, simulate the disaster development process, and help emergency management and rescue personnel prepare in advance; the disaster prediction and trend analysis module predicts the future development of disasters and generates relevant alarms based on multimodal data and historical trends; the alarm and notification module warns relevant departments and the public when a disaster is identified or a high-risk event is predicted in the future; the decision support and emergency response module provides decision support for emergency response based on real-time data and prediction results; the data storage, management and feedback optimization module is used to store collected image data, sensor data and processing results.

[0074] The benefits of the multimodal sensor data acquisition module are to improve the stability and reliability of disaster identification and provide accurate data input in low visibility or extreme weather conditions; the benefits of the image and data preprocessing module are to improve the image analysis and processing capabilities of the overall system. In addition, the standardized processing of data provides more consistent input for subsequent deep learning algorithms, which helps to improve recognition accuracy; the benefits of the deep learning and adversarial generative network recognition module are to improve the accuracy and robustness of disaster identification, so that the system can effectively respond in complex scenarios; the benefits of the virtual reality disaster scenario simulation module are to improve the disaster response capabilities of decision makers, help formulate more complete emergency plans before disasters occur, and effectively reduce disasters. Losses; The benefits of the disaster prediction and trend analysis module are that it can identify potential risks of disasters in advance, and issue warnings based on trend changes, giving relevant departments more time to prepare and improving the timeliness and accuracy of disaster warnings; the benefits of the alarm and notification module are that it reduces the threat of sudden disasters to public safety while ensuring the accuracy of information transmission; the benefits of the decision support and emergency response module are that it improves the efficiency and accuracy of emergency response, helps decision makers make quick and effective response measures in complex disaster environments, and minimizes the losses caused by disasters; the benefits of the data storage, management and feedback optimization module are that it improves the system's recognition and prediction capabilities, making it more accurate in future disaster warnings.

[0075] The multimodal sensor data acquisition module includes a visible light camera unit, an infrared camera unit, a radar sensor unit, a lidar unit, and a meteorological data acquisition unit; the visible light camera unit collects standard visible light images and provides basic image data for preliminary disaster monitoring and identification; the infrared camera unit is used to work in low light, smoke, and night conditions, and identifies disaster scenes by capturing the thermal radiation of objects; the radar sensor unit is used to penetrate obstructions such as smoke and rain to obtain surface changes or other important environmental information; the lidar unit generates a three-dimensional terrain map through laser scanning to help monitor terrain changes; the meteorological data acquisition unit is used to collect environmental data and assist in disaster warning; the image and data preprocessing module includes an image denoising and enhancement unit, a multi-modal sensor data acquisition module ... image and data preprocessing module includes an image denoising and enhancement unit, a multi-modal sensor data acquisition module, a lidar unit, and a meteorological data acquisition unit; the image and data preprocessing module Modal data alignment unit, format conversion unit; image denoising and enhancement unit denoises and enhances the collected images; multimodal data alignment unit aligns and fuses data from different sensors; format conversion unit converts the collected data into a unified processing format; deep learning and adversarial generative network recognition module includes feature extraction unit, adversarial generative network unit, and disaster recognition unit; feature extraction unit extracts key features from images through deep convolutional neural networks; adversarial generative network unit generates or completes disaster images and identifies disasters using adversarial generative networks when image quality is insufficient or information is missing; disaster recognition unit identifies specific disaster types in images based on deep learning algorithms; virtual reality disaster scenario simulation module It includes a 3D scene generation unit, a disaster scenario simulation unit, and a virtual interaction unit; the 3D scene generation unit uses image data to generate a 3D model of the disaster area to simulate the real-time dynamic changes of the disaster; the disaster scenario simulation unit simulates the occurrence and evolution of disasters in virtual reality; the virtual interaction unit allows emergency management personnel to interact with disaster scenes in a virtual reality environment, adjust simulation conditions or test different emergency plans; the disaster prediction and trend analysis module includes a historical data analysis unit, a real-time data monitoring unit, and a prediction model unit; the historical data analysis unit analyzes historical disaster data to establish a disaster pattern or trend model; the real-time data monitoring unit monitors sensor data and image changes, and analyzes the dynamic development of disasters in real time; The prediction model unit analyzes data based on machine learning algorithms to predict the future development direction, speed and impact range of disasters; the alarm and notification module includes a disaster level assessment unit, a multi-channel alarm unit, and an alarm threshold setting unit; the disaster level assessment unit assesses the severity of the disaster based on the scale, impact range and development trend of the disaster to determine the warning level; the multi-channel alarm unit issues warnings to relevant departments and the public through multiple channels; the alarm threshold setting unit sets warning thresholds for different types of disasters; the decision support and emergency response module includes an emergency evacuation route planning unit, a material dispatch unit, and an emergency plan recommendation unit; the emergency evacuation route planning unit formulates evacuation routes for the disaster site based on real-time disaster data and geographic information systems;The material dispatch unit automatically plans the allocation of emergency materials according to the scope and severity of the disaster; the emergency plan suggestion unit provides disaster emergency response suggestions to assist decision makers in responding; the data storage, management and feedback optimization module includes a data storage unit, a model feedback optimization unit, and a data retrieval unit; the data storage unit classifies and stores sensor data, images, prediction results, and historical disaster records; the model feedback optimization unit automatically adjusts and optimizes the deep learning model and recognition algorithm based on the system's early warning accuracy feedback; the data retrieval unit allows users to retrieve historical data to analyze the causes and patterns of disasters and the effectiveness of rescue strategies. ;

[0076] The benefits of the visible light camera unit are that it helps with preliminary disaster monitoring and identification, ensuring that the system can accurately capture the specific conditions of the disaster scene under normal light; the benefits of the infrared camera unit ensure that the system can conduct effective monitoring in low-light environments; the benefits of the radar sensor unit make up for the shortcomings of optical sensors under adverse weather conditions and ensure the stability of the system in complex environments; the benefits of the lidar unit improve the system's perception of geological disasters; the benefits of the meteorological data acquisition unit assist the system in multi-level disaster warnings; the benefits of the image denoising and enhancement unit improve image quality, reduce noise interference, and enhance key features; the multimodal data alignment unit improves the accuracy and consistency of the overall data; the format conversion unit avoids recognition problems caused by data inconsistency; the benefits of the feature extraction unit improve the system's intelligent recognition capabilities; the benefits of the adversarial generative network unit improve the robustness of the system; the benefits of the disaster identification unit provide rapid response capabilities and reduce false alarms and missed alarms; the benefits of the three-dimensional scene generation unit enhance the scene understanding capabilities and decision-making basis of emergency management personnel; the benefits of the disaster scenario simulation unit improve their Disaster preparedness and emergency response capabilities; the benefits of the virtual interaction unit optimize the decision-making process and improve response efficiency; the benefits of the historical data analysis unit provide historical basis for disaster prediction and help identify long-term potential risks; the benefits of the real-time data monitoring unit ensure that the system can capture the latest status of the disaster in a timely manner; the benefits of the prediction model unit improve the accuracy and advance time of the warning, so that relevant departments can respond early; the benefits of the disaster level assessment unit ensure that the system can issue accurate warning information according to different disaster situations; the benefits of the multi-channel alarm unit improve the efficiency of disaster response; the alarm threshold setting unit avoids false alarms or missed alarms; the emergency evacuation route planning unit ensures life safety; the benefits of the material dispatch unit ensure efficient dispatch and reasonable allocation of rescue resources; the benefits of the emergency plan recommendation unit assist decision makers in making rapid and accurate adjustments to response strategies to reduce losses caused by disasters; the benefits of the data storage unit ensure the long-term preservation of data, which is convenient for subsequent analysis and model optimization; the benefits of the model feedback optimization unit improve the accuracy of disaster identification and prediction; the benefits of the data retrieval unit provide valuable experience for future disaster response and research.

[0077] The images collected by the visible light camera unit contain noise, and the non-local means algorithm is used to denoise the images:

[0078] Among them, I denoised I(x) is the value of the denoised image at pixel x, I(y) is the value of the original image at pixel y, The color difference between pixel x and y, h is the parameter controlling the smoothness, and Z(x) is the normalization coefficient;

[0079] The infrared camera unit estimates the temperature of an object based on the temperature radiation of the object using the Stefan-Boltzmann law: P = σ·∈·A·T 4 ,

[0080] Among them, P is the infrared radiation power, ∈ is the emissivity of the object, A is the surface area of ​​the object, and T is the absolute temperature of the object.

[0081] The non-local means algorithm is used for denoising visible light images. It reduces noise by weighted calculation of the average value of similar pixels while retaining image details. This method plays a key role in the initial stage of image processing. When the image quality obtained in harsh environments is poor, denoising can improve the usability of the image.

[0082] The Stefan-Boltzmann law is used to estimate the temperature of an object based on an infrared camera unit. The temperature information is inferred by measuring the thermal radiation power of the object. This method plays a key role in temperature detection in infrared imaging technology, especially in detecting disasters such as fires through temperature detection at night or in smoky environments.

[0083] The radar sensor detects objects in the environment by emitting electromagnetic waves and uses fast Fourier transform to perform frequency domain analysis on the echo signal to determine the position and speed of the target. The formula is as follows:

[0084] Where S(f) is the representation of the signal in the frequency domain, s(t) is the time domain signal received by the radar, f is the frequency, t is the time, and j is the imaginary unit;

[0085] The LiDAR unit generates a 3D point cloud by calculating the return time of the laser pulse. According to the time-of-flight method of the laser pulse, the distance calculation formula is as follows:

[0086] Where d is the distance between the object and the lidar, c is the speed of light, and Δt is the time difference between the laser emission and return.

[0087] The meteorological data acquisition unit captures wind speed, temperature and humidity data in the environment through sensors, and uses a model based on the Navier-Stokes equations to predict changes in the wind field:

[0088]

[0089] Among them, u is velocity field, p is air density, t is time, p is air pressure, μ is dynamic viscosity, and f is external force.

[0090] Fast Fourier transform is used for signal processing of radar sensors. It detects the speed and position of the target by converting the time domain signal into the frequency domain signal. Fast Fourier transform can accurately locate the target and calculate its speed by analyzing the frequency change and the Doppler effect.

[0091] The time-of-flight method of LiDAR calculates the time difference between the emission and return of laser pulses to accurately measure the distance between the object and the LiDAR and generate a three-dimensional point cloud map. This algorithm is widely used in terrain mapping and object recognition, helping the system monitor terrain and environmental changes;

[0092] The Navier-Stokes equations are used to predict and simulate the dynamic changes of wind fields. Based on the principles of fluid mechanics, they comprehensively consider factors such as air density, air pressure, and air viscosity to provide accurate calculations of wind field information such as wind speed and wind direction. This model has important applications in wind disaster prediction in meteorological forecasting and disaster warning.

[0093] The total variation denoising in the image denoising and enhancement unit is a denoising algorithm in the field of image processing. It can retain the edge details of the image. Its optimization goal is to smooth the noise by minimizing the total variation of the image gradient. The formula is as follows:

[0094]

[0095] Among them, I(x,y) is the denoised image, I0(x,y) is the original noisy image, The gradient of the image, λ is the weight parameter that balances denoising and preserving image details, and Ω is the domain of the image.

[0096] The total variation denoising algorithm effectively avoids excessive blurring of the image during denoising by smoothing the noisy areas in the image and retaining the edge details. Its optimization goal is to reduce the noise while maintaining the image's detailed features, especially in the edge areas with large image gradients.

[0097] Total variation denoising can significantly improve the quality of noisy images. It is used in scenes with obvious edges in the image to reduce edge blur caused by image smoothing. It is applied in medical image processing, remote sensing images, visual perception and other fields.

[0098] The mutual information in the multimodal data alignment unit is a method for image registration. The images are aligned by maximizing the mutual information of the joint histogram. The formula is as follows:

[0099]

[0100] Among them, I1 and I2 are images to be aligned. The probability distribution of gray levels i and j in images I1 and I2, Joint probability distribution of images I1 and I2.

[0101] Mutual information is a statistic used to measure the similarity between two images. In image registration, the role of mutual information is to achieve image alignment by maximizing the joint information of the two images. If the images are well aligned, their joint probability distribution will be more correlated, so the value of mutual information will be higher.

[0102] The disaster identification unit classifies the features extracted by the convolutional neural network to determine the natural disasters in the image, and the classifier uses the Softmax function to map the features output by the network to a probability distribution. The Softmax function:

[0103] Among them, z i The original output of the network for category i, P(y=i|x) is the probability that the image belongs to category i;

[0104] The classification loss function uses cross entropy loss to measure the difference between the true label and the predicted probability distribution:

[0105] Among them, y i Unique encoding of the true category label, P(y=i|x) The network predicts the probability that the image belongs to category i;

[0106] By minimizing the cross entropy loss function, the disaster identification unit can optimize the convolutional neural network model to enable it to accurately classify disasters. The attention mechanism can improve the model's attention to important features in the disaster identification task. The formula is:

[0107] e ij =f(h i ,s j ),

[0108] Among them, α ij The attention weight at j, e ij represents the correlation between positions i and j, h i 、s j Representation of input features.

[0109] The Softmax function converts the output features of the convolutional neural network into a category probability distribution, which helps the network determine the type of disaster to which the image belongs in multi-classification tasks and is a key step in image classification.

[0110] The cross entropy loss function measures the difference between the network prediction and the true label, guiding the network to gradually optimize the parameters to achieve higher classification accuracy. In the disaster identification task, it ensures that the network can correctly classify various natural disaster scenarios.

[0111] The attention mechanism focuses on the most relevant areas in the image by calculating the correlation between features, improving the network's disaster recognition ability and being used to extract key details in complex images.

[0112] The real-time data monitoring unit is used to dynamically track and predict the real-time data provided by sensors, satellites or monitoring stations. It optimizes the state estimation by using the real-time updated data of the Kalman filter. The formula of the Kalman filter is:

[0113] Status prediction:

[0114] P t|t-1 =AP t-1|t-1 A T +Q,

[0115] Kalman gain:

[0116] K t =P t|t-1 H T (HP t|t-1 H T +R) -1 ,

[0117] Status Update:

[0118] P t|t =(IK t H)P t|t-1 ,

[0119] in, The predicted state vector at time t, P t|t-1 The predicted state covariance matrix, A state transition matrix, B control matrix, for external control variables u t , Q process noise covariance matrix, K t Kalman gain, z t Observation data, H observation matrix, R observation noise covariance matrix.

[0120] The Kalman filter optimizes the estimation of the system state by combining the system's prediction model and real-time observation data. Through state prediction, Kalman gain, and state update, the Kalman filter can update and track the system state in real time. This algorithm is used for real-time data processing in dynamic systems and can provide accurate state estimation in noisy environments.

[0121] The alarm threshold setting unit dynamically adjusts the alarm trigger threshold based on a variety of different parameters. To handle the complex relationship between variables, multivariate logistic regression can be used to estimate the alarm trigger probability under different variable combinations;

[0122] Logistic Regression Model:

[0123]

[0124] Among them, P(Y=1) is the probability of alarm triggering, X1, X2,…, X n Input variables, β0 bias term, β1, β2, …, β n Regression coefficients;

[0125] Threshold setting: The alarm threshold is determined based on the probability output by the logistic regression model, such as:

[0126] Alarm trigger ifP(Y=1|X1,X2,…,X n )>θ,

[0127] Among them, θ is the set trigger threshold, which can be adjusted dynamically to optimize the threshold based on historical data and real-time environmental conditions.

[0128] The role of the logistic regression model Multivariate logistic regression models multiple factors that affect alarm triggering and their impact on the probability of alarm triggering. The model combines the factors and outputs the probability of alarm triggering, helping the system make decisions under complex environmental conditions;

[0129] The role of alarm threshold setting The alarm trigger probability calculated by the logistic regression model is compared with the preset threshold θ to decide whether to trigger the alarm. The threshold θ is dynamically adjusted to adapt to different historical data and real-time conditions, making the system more flexible and intelligent in responding to disaster warning scenarios;

[0130] Through the logistic regression model and dynamically adjusted thresholds, the system can more accurately predict the occurrence of disasters under complex and changing environmental conditions, and reasonably adjust the alarm triggering criteria to avoid false alarms and missed alarms. The method combines the comprehensive effect of multiple input parameters to improve the accuracy and timeliness of alarm triggering.

[0131] 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. An intelligent early warning system for natural disasters based on image processing, characterized in that: It includes multimodal sensor data acquisition module, image and data preprocessing module, deep learning and adversarial generative network recognition module, virtual reality disaster scenario simulation module, disaster prediction and trend analysis module, alarm and notification module, decision support and emergency response module, data storage, management and feedback optimization module; The multimodal sensor data acquisition module is used to collect and fuse multiple sensor data; the image and data preprocessing module is used to process multi-source data, and align and fuse multimodal data; the deep learning and adversarial generative network recognition module uses deep convolutional neural networks and adversarial generative networks to perform image feature extraction, disaster identification and image completion; the virtual reality disaster scenario simulation module is used to generate virtual disaster scenes, simulate the disaster development process, and help emergency management and rescue personnel prepare in advance; the disaster prediction and trend analysis module predicts the future development of disasters based on multimodal data and historical trends, and generates relevant alarms; The alarm and notification module warns relevant departments and the public when a disaster is identified or a high-risk event is predicted in the future; The decision support and emergency response module provides decision support for emergency response based on real-time data and prediction results; the data storage, management and feedback optimization module is used to store collected image data, sensor data and processing results.

2. According to claim 1, a natural disaster intelligent early warning system based on image processing is characterized in that: The multimodal sensor data acquisition module includes a visible light camera unit, an infrared camera unit, a radar sensor unit, a laser radar unit, and a meteorological data acquisition unit; the visible light camera unit collects standard visible light images and provides basic image data for preliminary disaster monitoring and identification; the infrared camera unit is used to work in low light, smoke, and night conditions, and to identify disaster scenes by capturing the thermal radiation of objects; the radar sensor unit is used to penetrate obstructions caused by smoke and rain to obtain surface changes or other important environmental information; the laser radar unit generates a three-dimensional terrain map through laser scanning to help monitor terrain changes; the meteorological data acquisition unit is used to collect environmental data to assist in disaster warning; The image and data preprocessing module includes an image denoising and enhancement unit, a multimodal data alignment unit, and a format conversion unit; the image denoising and enhancement unit performs denoising and enhancement processing on the collected image; The multimodal data alignment unit aligns and fuses data from different sensors; The format conversion unit converts the collected data into a unified processing format; The deep learning and adversarial generative network recognition module includes a feature extraction unit, an adversarial generative network unit, and a disaster recognition unit; the feature extraction unit extracts key features from the image through a deep convolutional neural network; the adversarial generative network unit generates or completes disaster images and recognizes disasters using the adversarial generative network when the image quality is insufficient or the information is missing; the disaster recognition unit recognizes the specific disaster type in the image based on the deep learning algorithm; The virtual reality disaster scenario simulation module includes a three-dimensional scene generation unit, a disaster scenario simulation unit, and a virtual interaction unit; the three-dimensional scene generation unit uses image data to generate a three-dimensional model of the disaster area to simulate the real-time dynamic changes of the disaster; the disaster scenario simulation unit simulates the occurrence and evolution process of the disaster in virtual reality; the virtual interaction unit allows emergency management personnel to interact with the disaster scene in a virtual reality environment, adjust simulation conditions or test different emergency plans.

3. According to claim 1, a natural disaster intelligent early warning system based on image processing is characterized in that: The disaster prediction and trend analysis module includes a historical data analysis unit, a real-time data monitoring unit, and a prediction model unit; the historical data analysis unit analyzes historical disaster data to establish a disaster pattern or trend model; the real-time data monitoring unit monitors sensor data and image changes, and analyzes the dynamic development of disasters in real time; the prediction model unit analyzes data based on a machine learning algorithm to predict the future development direction, speed, and impact range of disasters; The alarm and notification module includes a disaster level assessment unit, a multi-channel alarm unit, and an alarm threshold setting unit; the disaster level assessment unit assesses the severity of the disaster based on the scale, impact range and development trend of the disaster to determine the warning level; the multi-channel alarm unit issues warnings to relevant departments and the public through multiple channels; the alarm threshold setting unit sets warning thresholds for different disaster types; The decision support and emergency response module includes an emergency evacuation route planning unit, a material dispatching unit, and an emergency plan suggestion unit; the emergency evacuation route planning unit formulates an evacuation route for the disaster site based on real-time disaster data and geographic information system; the material dispatching unit automatically plans the deployment plan of emergency materials according to the impact scope and severity of the disaster; the emergency plan suggestion unit provides disaster emergency response suggestions to assist decision makers in responding; The data storage, management and feedback optimization module includes a data storage unit, a model feedback optimization unit, and a data retrieval unit; the data storage unit classifies and stores sensor data, images, prediction results, and historical disaster records; the model feedback optimization unit automatically adjusts and optimizes deep learning models and recognition algorithms based on the system's early warning accuracy feedback; the data retrieval unit allows users to retrieve historical data and analyze the causes and patterns of disasters and the effectiveness of rescue strategies.

4. The natural disaster intelligent early warning system based on image processing according to claim 2 is characterized in that: The image collected by the visible light camera unit contains noise, and the non-local means algorithm is used to denoise the image: Among them, I denoised I(x) is the value of the denoised image at pixel x, I(y) is the value of the original image at pixel y, The color difference between pixel x and y, h is the parameter controlling the smoothness, and Z(x) is the normalization coefficient; The infrared camera unit estimates the temperature of the object based on the temperature radiation of the object using the Stefan-Boltzmann law: P = σ·∈·A·T 4 , Among them, P is the infrared radiation power, ∈ is the emissivity of the object, A is the surface area of ​​the object, and T is the absolute temperature of the object.

5. The natural disaster intelligent early warning system based on image processing according to claim 2 is characterized in that: The radar sensor detects objects in the environment by emitting electromagnetic waves and uses fast Fourier transform to perform frequency domain analysis on the echo signal to determine the position and speed of the target. The formula is as follows: Where S(f) is the representation of the signal in the frequency domain, s(t) is the time domain signal received by the radar, f is the frequency, t is the time, and j is the imaginary unit; The laser radar unit generates a three-dimensional point cloud by calculating the return time of the laser pulse. According to the flight time method of the laser pulse, the distance calculation formula is as follows: Where d is the distance between the object and the lidar, c is the speed of light, and Δt is the time difference between the laser emission and return. The meteorological data acquisition unit captures wind speed, temperature and humidity data in the environment through sensors, and uses a model based on the Navier-Stokes equations to predict changes in the wind field: Among them, u is velocity field, p is air density, t is time, p is air pressure, μ is dynamic viscosity, and f is external force.

6. The natural disaster intelligent early warning system based on image processing according to claim 2 is characterized in that: The total variation denoising in the image denoising and enhancement unit is a denoising algorithm in the field of image processing, which can retain image edge details. Its optimization goal is to smooth the noise by minimizing the total variation of the image gradient. The formula is as follows: Among them, I(x,y) is the denoised image, I0(x,y) is the original noisy image, The gradient of the image, λ is the weight parameter that balances denoising and preserving image details, and Ω is the domain of the image.

7. The natural disaster intelligent early warning system based on image processing according to claim 2 is characterized in that: The mutual information in the multimodal data alignment unit is a method for image registration, which aligns images by maximizing the mutual information of the joint histogram. The formula is as follows: Among them, I1 and I2 are images to be aligned. The probability distribution of gray levels i and j in images I1 and I2, Joint probability distribution of images I1 and I2.

8. The natural disaster intelligent early warning system based on image processing according to claim 2 is characterized in that: The disaster identification unit classifies the features extracted by the convolutional neural network to determine the natural disasters in the image, and the classifier uses the Softmax function to map the features output by the network into a probability distribution. The Softmax function: Among them, z i The original output of the network for category i, P(y=i|x) is the probability that the image belongs to category i; The classification loss function uses cross entropy loss to measure the difference between the true label and the predicted probability distribution: Among them, y i Unique encoding of the true category label, P(y=i|x) The network predicts the probability that the image belongs to category i; By minimizing the cross entropy loss function, the disaster identification unit can optimize the convolutional neural network model to enable it to accurately classify disasters. The attention mechanism can improve the model's attention to important features in the disaster identification task. The formula is: Among them, α ij The attention weight at j, e ij represents the correlation between positions i and j, h i 、s j Representation of input features.

9. The natural disaster intelligent early warning system based on image processing according to claim 3 is characterized in that: The real-time data monitoring unit is used to dynamically track and predict the real-time data provided by sensors, satellites or monitoring stations, and optimizes the state estimation by using the data updated in real time by the Kalman filter. The formula of the Kalman filter is: Status prediction: Kalman gain: K t =P t|t-1 H T (HP t|t-1 H T +R) -1 , Status Update: in, The predicted state vector at time t, P t|t-1 The predicted state covariance matrix, A state transition matrix, B control matrix, for external control variables u t , Q process noise covariance matrix, K t Kalman gain, z t Observation data, H observation matrix, R observation noise covariance matrix.

10. The natural disaster intelligent early warning system based on image processing according to claim 3 is characterized in that: The alarm threshold setting unit dynamically adjusts the alarm triggering threshold based on a variety of different parameters. To handle the complex relationship between variables, multivariate logistic regression can be used to estimate the alarm triggering probability under different variable combinations; Logistic Regression Model: Among them, P(Y=1) is the probability of alarm triggering, X1, X2,…, X n Input variables, β0 bias term, β1, β2, …, β n Regression coefficients; Threshold setting: The alarm threshold is determined based on the probability output by the logistic regression model, such as: Alarm trigger ifP(Y=1|X1,X2,…,X n )>θ, Among them, θ is the set trigger threshold, which can be adjusted dynamically to optimize the threshold based on historical data and real-time environmental conditions.

Images