White disaster multi-element monitoring identification early warning and emergency management method

Abstract

The invention discloses a white disaster multi-element monitoring identification early warning and emergency management method. The method specifically comprises the following steps: S1, acquiring remote sensing image data and meteorological data; s2, performing automatic feature extraction on the remote sensing image data by adopting a convolutional neural network model; s3, combining meteorological data and features, integrating historical and real-time data through a spatio-temporal data fusion algorithm, and generating a spatio-temporal feature matrix; s4, based on the spatial-temporal characteristic matrix, predicting a snow disaster occurrence area by using a convolutional neural network, and identifying a snow disaster area, a snow layer thickness and an evolution trend; s5, snow disaster early warning information is generated according to the prediction result, and the early warning level, the influence range and the time are updated in real time; and S6, carrying out dynamic early warning pushing according to a prediction result, and supporting emergency response before, in and after a disaster and disaster prevention and reduction decisions. According to the invention, the prediction and emergency response capabilities of the white disaster early warning system are obviously improved, and early recognition, timely early warning and rapid response of white disasters are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-element monitoring and identification of white disasters, and in particular to a method for multi-element monitoring, identification, early warning and emergency management of white disasters. Background Art

[0002] With the intensification of global climate change, extreme weather events such as white disasters have occurred frequently, which has brought serious impacts on people's production and life. White disasters are a natural disaster with high risk, suddenness and wide impact, which often leads to serious consequences such as traffic interruption, power failure, casualties and property losses. Therefore, timely and effective white disaster monitoring, identification and early warning systems have become an important guarantee for post-disaster emergency management and recovery. At present, many countries and regions have established white disaster monitoring and early warning systems, but the existing technology still has some shortcomings and urgently needs further improvement and innovation.

[0003] Most of the existing white disaster monitoring systems rely on manual data collection and processing, mainly through ground observations, data released by meteorological stations, and satellite remote sensing technology to analyze the area and intensity of white disasters. The main advantages of these methods are wide data collection coverage and good real-time data, but they also have the following significant disadvantages: serious manual intervention and low degree of automation: Traditional white disaster monitoring systems usually rely on manual data analysis and feature extraction. This manual intervention not only increases the time cost of data processing, but also makes it difficult to achieve efficient and accurate automated analysis. For example, when using remote sensing image data to identify white disaster areas, professionals are required to manually select features, which is easily affected by human factors, resulting in wrong judgments and inaccurate results. The data processing accuracy is low and lacks multi-level and multi-scale analysis capabilities: Traditional white disaster monitoring methods can often only process image data of a single scale during image processing, and cannot effectively capture white disaster features of different scales. Especially in high-resolution remote sensing images, the fine-grained features of white disaster areas (such as snow thickness, white disaster intensity, etc.) are often difficult to accurately identify. This method, which lacks multi-scale processing capabilities, shows great limitations in white disaster monitoring and cannot fully reflect the dynamic changes of white disasters. Insufficient spatiotemporal data fusion capabilities and lack of dynamic prediction of white disaster evolution: Existing white disaster monitoring and early warning systems are usually based on static data analysis, and fail to effectively use spatiotemporal series data for joint modeling. Traditional methods mostly process different types of data (such as remote sensing image data, meteorological data, etc.) independently, lacking dynamic capture of spatiotemporal changes, making it difficult to track the evolution of white disasters in real time and make scientific predictions on future white disaster development trends. Lack of integrated emergency response decision support: Existing white disaster monitoring systems mostly focus on real-time monitoring and early warning of disaster situations, but often lack effective decision support systems in the post-disaster emergency response and recovery stages. Traditional systems lack real-time monitoring of post-disaster recovery progress and accurate prediction of resource needs, resulting in improper resource allocation and untimely implementation of recovery plans during the post-disaster recovery process, thus affecting the post-disaster recovery effect.

[0004] In order to solve the above problems, in recent years, many research and technology development work has begun to focus on improving the automation and intelligence level of white disaster monitoring systems through deep learning and artificial intelligence technologies. As an efficient deep learning method, convolutional neural network (CNN) has been widely used in remote sensing image analysis and disaster monitoring due to its excellent performance in image processing and feature extraction. However, the existing white disaster monitoring methods based on deep learning still have some shortcomings, especially in spatiotemporal data fusion, multi-scale feature extraction and post-disaster recovery management, which still lack sufficient innovation and technological breakthroughs. Summary of the invention

[0005] One purpose of the present invention is to propose a method for multi-dimensional monitoring, identification, early warning and emergency management of white disasters. The present invention significantly improves the prediction ability and emergency response ability of the white disaster early warning system, provides an innovative technical solution for early identification, timely warning and rapid response of white disasters, and has great practical application value and promotion prospects.

[0006] A method for multi-dimensional monitoring, identification, early warning and emergency management of white disasters according to an embodiment of the present invention comprises the following steps:

[0007] S1. Acquire multiple remote sensing image data and meteorological data, wherein the remote sensing image data includes satellite images, ground sensor data, and drone images, and the meteorological data includes real-time meteorological information of temperature, humidity, precipitation, and wind speed;

[0008] S2. Automatically extracting features from the remote sensing image data through a convolutional neural network model, wherein the convolutional neural network uses a multi-scale convolution kernel to extract white disaster related features at different resolutions;

[0009] S3, combining the features with meteorological data, integrating historical data with real-time data through spatiotemporal data fusion algorithm, and generating a spatiotemporal feature matrix;

[0010] S4. Based on the S3 spatiotemporal feature matrix, the convolutional neural network is used to predict the area where snowstorms occur, identify the snowstorm area, snow thickness and its evolution trend;

[0011] S5. Generate white disaster warning information based on the prediction results of S4, and update the warning level, impact range and impact time in real time;

[0012] S6. Based on the prediction results of S4, dynamic warnings are pushed to support emergency response and disaster prevention and mitigation decisions before, during and after disasters.

[0013] Optionally, the S1 specifically includes:

[0014] S11. Acquire multiple remote sensing image data, where the remote sensing image data includes satellite images, drone images, and image data collected by ground sensors;

[0015] S12, obtaining meteorological data, wherein the meteorological data includes real-time temperature (T), humidity (H), precipitation (P), and wind speed (W) meteorological variables;

[0016] S13, preprocessing the remote sensing image data, including image enhancement, noise removal and geometric correction, wherein the image enhancement includes contrast enhancement and sharpening operations, and the noise removal adopts a median filter or a Gaussian filter algorithm;

[0017] S14, preprocessing the meteorological data, including missing value filling, data smoothing and standardization. The standardization includes normalizing each meteorological variable so that the value range of each variable is between [0, 1].

[0018] Optionally, the S2 specifically includes:

[0019] S21. Input the acquired remote sensing image data into a convolutional neural network model, wherein the convolutional neural network model comprises a plurality of convolutional layers, each of which performs convolution processing on the image through a convolution kernel (K) of different sizes, wherein the size of the convolution kernel is K×K, where K represents the size of the convolution kernel:

[0020]

[0021] Among them, F i,j Indicates the intensity of snowstorm-related features at the location, local information of snowstorm coverage area or snow thickness, X i,j Indicates the grayscale information of the white disaster area and background in the image, K m,n It is used to extract the features related to white disasters, the boundaries and shapes of the white disaster areas;

[0022] S22. During the convolution operation, a multi-scale convolution kernel is used. By using convolution kernels of different scales, feature information of different levels in the image is extracted. The convolution kernels of the scales include a small-scale convolution kernel K1×K1 and a large-scale convolution kernel K2×K2, where K1 and K2 represent convolution kernels of different sizes, respectively. The features extracted by the multi-scale convolution kernel include fine-grained local features and global features.

[0023] S23. Use the pooling layer to perform pooling operation on the feature map output by the convolution layer. The pooling layer adopts maximum pooling or average pooling. The size of the pooling window is P×P, where P is the size of the pooling window:

[0024] P i,j =max(X i,j ,X i+1,j ,…,X i+P-1,j+P-1 );

[0025] Among them, P i,j Indicates the maximum intensity of white disaster impact in a region, X i,j The size of the feature map is reduced, and the representative white disaster features are retained;

[0026] S24, connecting the pooled feature maps through a fully connected layer to form a feature vector, which contains multi-level and multi-scale feature information from the remote sensing image;

[0027] S25. Through further learning and optimization of the convolutional neural network, the spatial features related to white disasters are extracted from the feature map, and the white disaster prediction model is jointly constructed by combining the spatiotemporal features extracted from the meteorological data;

[0028] Optionally, the S3 specifically includes:

[0029] S31, the meteorological data of S2 is fused with the spatial features in the remote sensing image in time and space, and the meteorological data and the features in the remote sensing image are jointly modeled through the data fusion algorithm Kalman filtering to obtain the joint feature vector C(t), where C(t) represents the time and space feature vector at time t:

[0030] C(t)=Fuse(F(t),T(t),H(t),P(t),W(t));

[0031] Among them, F(t) is the spatial feature extracted from the remote sensing image, Fuse is the spatiotemporal data fusion operation, and the fusion result C(t) contains the joint information of remote sensing image features and meteorological data, providing comprehensive spatiotemporal feature support for subsequent white disaster prediction;

[0032] S33, input the spatiotemporal feature vector C(t) into the spatiotemporal model long short-term memory network, perform dynamic prediction of the evolution of the white disaster, and obtain the white disaster prediction value Y(t+k) at the future time, where Y(t+k) represents the predicted white disaster state at the future time t+kt+k:

[0033] Y(t+k)=LSTM(C(t),θ);

[0034] Among them, Y(t+k) is the predicted value of the white disaster at the future time t+kt+k, which indicates the predicted impact range or intensity of the white disaster; C(t) is the spatiotemporal feature vector at the current time t, which contains the fusion information of meteorological data and remote sensing data; θ is the training parameter of the spatiotemporal model, which is optimized by learning historical data.

[0035] S34. Generate white disaster warning information through the predicted value Y(t+k) output by the space-time model, and perform visualization processing to generate a white disaster evolution diagram and a warning map to provide decision makers with key information such as warning time and white disaster impact range.

[0036] Optionally, the S4 specifically includes:

[0037] S41, receiving white disaster information predicted by the spatiotemporal model, including white disaster intensity Y(t+k) and affected area at a future time, wherein the white disaster intensity Y(t+k) represents the severity of the white disaster at the future time t+kt+k, and the affected area is the geographical range that the white disaster may cover at that time;

[0038] S42. According to the intensity of the white disaster Y(t+k) and the affected area, a white disaster response decision model is constructed. The decision model formulates emergency response measures of different levels according to the impact range and intensity of the white disaster, and sets the emergency response level to E(t+k), where E(t+k) represents the emergency response level at time t+kt+k:

[0039] E(t+k)=Decision(Y(t+k),R);

[0040] Among them, Decision is the emergency response decision function, and R is the rule set of the decision model, including the relationship between the intensity of white disasters and the affected area, and the process of emergency response;

[0041] S43. According to the emergency response level E(t+k), specific emergency response instructions are issued to relevant departments and personnel, and the emergency response instructions include personnel evacuation, equipment dispatch, and resource allocation;

[0042] S44. Monitor the evolution of the white disaster in real time, and update the emergency response level E(t+k) according to the actual situation. The update is based on the new white disaster data and meteorological data. The updated emergency response level is E′(t+k):

[0043] E'(t+k)=Update(E(t+k),Y(t+k),Δ);

[0044] Among them, E′(t+k) is the updated emergency response level, Δ is the change in the evolution of white disasters, including new information on white disaster intensity and impact range, and Update means correction and adjustment based on white disaster prediction data;

[0045] S45. Based on the real-time updated emergency response level E′(t+k)E'(t+k), the revised emergency response instruction is issued to relevant personnel through the emergency management platform;

[0046] S46. Based on the evolution of white disasters and emergency response situations, a feedback mechanism will be established to collect problems and feedback information arising during the implementation process and further optimize the emergency response decision-making model.

[0047] Optionally, the S42 specifically includes:

[0048] S421. Obtain the predicted value of the snowstorm intensity Y(t+k) and the relevant characteristic information of the snowstorm-affected area, including the snowstorm-covered area, intensity distribution and snow thickness. The affected area A(t+k) is the geographical area that may be affected by the snowstorm, including the covered geographical coordinates and area range:

[0049] A(t+k)=Region(Y(t+k));

[0050] Among them, A(t+k) represents the area that may be covered by the white disaster, Region is the affected area derived from the white disaster intensity Y(t+k), and the affected geographical area is obtained according to the intensity distribution;

[0051] S422, calculating the risk assessment value R(t+k) in the white disaster response model, wherein the risk assessment value R(t+k) is a risk assessment of the disaster-affected area based on the intensity of the white disaster and the affected area:

[0052] R(t+k)=f(Y(t+k),A(t+k),R);

[0053] Among them, f is the risk assessment function, R is the regional disaster risk parameter set, including historical white disaster data, geographical environment sensitivity mountain or plain factors, and function f performs weighted calculation on the intensity of white disaster and the affected area, thereby evaluating the risk level of different regions;

[0054] S423. Determine the emergency response priority P(t+k) based on the risk assessment value R(t+k). The priority is classified according to the urgency of the disaster impact and the vulnerability of the disaster area:

[0055] P(t+k)=g(R(t+k),P);

[0056] Among them, g is the emergency response priority determination function, P is the emergency response priority rule set, including the response rules for various risk levels. This function comprehensively determines the priority of the emergency response based on the size of the risk assessment value R(t+k) and whether the regional vulnerability is a mountainous area or a high-risk area;

[0057] S424. According to the priority P(t+k), the response level E(t+k) is further refined. The level is determined by the regional disaster impact, the evolution trend of the disaster and the predicted time factors:

[0058] E(t+k)=h(P(t+k),Y(t+k),T prediction );

[0059] Among them, h is the decision function of emergency response level, P(t+k) is the priority, Y(t+k) is the white disaster intensity prediction, T prediction To predict the duration of the white disaster at time t+kt+k, according to these factors, the response level can be adjusted as follows: 1 for emergency response, 2 for warning response, and 3 for ordinary response;

[0060] S425. Generate specific emergency response measures and instructions based on the response level E(t+k), including personnel evacuation, equipment dispatch, and resource allocation:

[0061] M(t+k)=Generate_Measures(E(t+k),A(t+k));

[0062] Among them, M(t+k) is the generated emergency response measure, Generate_Measures is a function that generates specific emergency instructions based on the response level and the affected area, and A(t+k) is the affected geographical area;

[0063] S426. Provide real-time feedback on the results of emergency response, adjust decisions through real-time data updates and subsequent forecasts, and update the emergency response level in real time if the disaster develops beyond expectations or the scope of impact changes. Real-time feedback mechanism:

[0064] E′(t+k)=Update_Decision(E(t+k),ΔY(t+k),ΔA(t+k));

[0065] Among them, E′(t+k) is the updated emergency response level, ΔY(t+k) is the change in white disaster intensity, ΔA(t+k) is the change in the affected area, and Update_Decision is the emergency response adjustment function, which adjusts the emergency response strategy according to the subsequent white disaster prediction data and regional changes.

[0066] Optionally, the S5 specifically includes:

[0067] S51, judging whether it is necessary to execute emergency response measures according to the emergency response level E(t+k) and the real-time data related to the disaster, if the execution conditions are met, proceed to step S52, otherwise proceed to step S53;

[0068] S52. Generate an emergency response instruction set M(t+k) according to the emergency response level E(t+k). The instruction set includes emergency management measures such as personnel evacuation, equipment dispatching, resource allocation, and traffic control. The generated emergency response instruction set is:

[0069] M(t+k)=Generate_Instructions(E(t+k),A(t+k),R);

[0070] Among them, M(t+k) is the emergency response instruction set, Generate_Instructions is a function that generates emergency instructions according to the emergency response level and the affected area, R(t+k) is the affected area, A is the emergency response rule set, including the specific measures required for different levels of response, and the instruction set includes resource deployment plan, personnel evacuation plan, and traffic control plan;

[0071] S53: If emergency response measures are not required, the existing emergency response status is monitored and the disaster development status is updated regularly. The monitoring frequency is set to f. monitor:

[0072] f monitor =Monitor(Y(t+k),A(t+k));

[0073] Among them, f monitor is the monitoring frequency, Y(t+k) is the predicted value of the white disaster intensity, A(t+k) is the affected area, Monitor is a function that monitors the development status of the white disaster according to the white disaster intensity and the affected area, and regularly checks whether the emergency response initiation conditions appear;

[0074] S54. During the monitoring process, if the disaster situation changes, the emergency response level E(t+k) is re-evaluated. If it needs to be updated, the process returns to step S52 to generate a new emergency response instruction set. If there is no significant change, the current response state is maintained.

[0075] S55. The generated emergency response instruction set M(t+k) is released to relevant departments and emergency personnel through the emergency management platform. The platform is a system with information distribution, collaborative work, and real-time update functions:

[0076] P(t+k)=Publish_Instructions(M(t+k),D);

[0077] Among them, P(t+k) is the published emergency response instruction, Publish_Instructions is the function that publishes the generated emergency response instruction through the emergency management platform, D is the target department and personnel who receive the instruction, and the publishing process includes the distribution, feedback collection and processing of the instruction;

[0078] S56. During the emergency response, the execution of emergency response instructions is monitored in real time, feedback information is collected in real time, and emergency response measures are updated through the feedback mechanism. Feedback information F(t+k)F(t+k) includes personnel evacuation progress, equipment scheduling, and resource requirements:

[0079] F(t+k)=Collect_Feedback(M(t+k),D);

[0080] Wherein, F(t+k) is the feedback information, Collect_Feedback() is the function for collecting feedback information, D is the department and personnel receiving the instruction, and the feedback information will be used to adjust the emergency response strategy and optimize it;

[0081] S57. If the feedback information F(t+k) shows that there is a problem or the response measures need to be adjusted, return to step S52, regenerate the emergency response instruction set M(t+k) based on the latest feedback information, and continue to execute step S55.

[0082] Optionally, the S6 specifically includes:

[0083] S61. Extract key parameters P(t+k) in the affected area based on real-time monitoring data after the occurrence of the white disaster. The parameters include the affected range, intensity, and snow thickness of the white disaster:

[0084] P(t+k)=Extract_Key_Parameters(A(t+k),Y(t+k));

[0085] Among them, P(t+k) is the extracted key parameter set, A(t+k) is the affected area, Y(t+k) is the predicted value of white disaster intensity, and Extract_Key_Parameters is a function for extracting key parameters from the affected area and white disaster intensity;

[0086] S62. Based on the extracted key parameter set P(t+k), combined with historical disaster data, an evaluation is performed to generate a post-disaster recovery demand prediction model, which predicts the resources and time required for post-disaster recovery:

[0087] R(t+k)=Predict_Recovery_Needs(P(t+k),D);

[0088] Among them, R(t+k) is the post-disaster recovery demand prediction result, D is the historical disaster dataset, and Predict_Recovery_Needs is a function for predicting recovery demand based on key parameters and historical data.

[0089] S63. Generate a post-disaster recovery plan P based on the post-disaster recovery demand forecast result R(t+k) r (t+k), including resource scheduling, personnel deployment, material supply, and infrastructure repair plans:

[0090] P r (t+k)=Generate_Recovery_Plan(R(t+k),D r );

[0091] Among them, P r (t+k) is the post-disaster recovery plan, Generate_Recovery_Plan is the function for generating the post-disaster recovery plan, D r Resource and staffing data required for recovery planning;

[0092] S64, the generated disaster recovery plan P r (t+k) is submitted to the emergency management platform for verification, which includes multi-dimensional checks on resource availability, time rationality, and personnel capabilities:

[0093] V(t+k)=Validate_Recovery_Plan(P r (t+k),D v );

[0094] Among them, V(t+k) is the verification result, D v To verify the required resources, time and personnel data, Validate_Recovery_Plan is a function to verify the feasibility of the post-disaster recovery plan;

[0095] S65. Disaster Recovery Plan P r (t+k) If the verification is successful, the recovery plan is released to the relevant departments to carry out post-disaster recovery work; if the verification is unsuccessful, return to step S63, adjust the recovery plan according to the feedback results, and re-verify;

[0096] S66. During the post-disaster recovery process, real-time monitoring of the recovery progress and evaluation of the recovery results shall be conducted, including the recovery of facilities, resource utilization, and personnel participation:

[0097] E(t+k)=Evaluate_Recovery_Effectiveness(P r (t+k),D e );

[0098] Among them, E(t+k) is the evaluation result, D e To evaluate the recovery data required,

[0099] Evaluate_Recovery_Effectiveness is a function for evaluating the recovery effect;

[0100] S67. Update the post-disaster recovery plan P based on the evaluation results E(t+k) r (t+k), and adjust the priority of the unfinished recovery tasks:

[0101] P r (t+k)=Update_Recovery_Plan(E(t+k),P r (t+k));

[0102] Among them, Update_Recovery_Plan is a function that updates the recovery plan according to the evaluation results.

[0103] The beneficial effects of the present invention are:

[0104] The present invention proposes a multi-scale monitoring, identification, early warning and emergency management method for white disasters based on convolutional neural network (CNN) and spatiotemporal data fusion. By combining high-resolution remote sensing images, meteorological data and time series data, key features such as white disaster areas and snow thickness are automatically extracted, which effectively improves the automation and accuracy of white disaster monitoring and overcomes the shortcomings of traditional methods that rely on manual feature selection and a single data source. The method can monitor the occurrence and development of white disasters in real time, provide accurate early warning information to emergency management departments in a timely manner, provide a reliable basis for post-disaster recovery and resource allocation, and significantly improve the efficiency of white disaster early warning and emergency response. (2) The present invention automatically extracts white disaster features of different scales in high-resolution remote sensing images by adopting a multi-scale convolutional neural network structure, solving the problem that multi-scale data cannot be effectively processed in the prior art. Through multi-scale feature extraction, the method can accurately identify fine-grained changes in white disaster areas, including information such as snow thickness and snow accumulation intensity. This innovative technology makes white disaster monitoring more comprehensive and accurate, can efficiently distinguish different types of white disaster areas, and improves monitoring accuracy, especially in the monitoring of complex terrain and large-scale white disaster areas. (3) The present invention introduces spatiotemporal data fusion technology to jointly model information from different data sources (such as remote sensing image data, meteorological data, etc.), thereby enhancing the spatiotemporal analysis capability of the white disaster monitoring system. Through spatiotemporal data fusion, the present invention can not only monitor the occurrence of white disasters in real time, but also predict the future development trend of white disasters, and provide dynamic support for post-disaster recovery management and decision-making. This technology breaks through the limitations of static data processing in traditional methods, and can make timely dynamic adjustments based on real-time data, greatly improving the system's early warning and emergency response capabilities. (4) The present invention introduces an intelligent decision support system based on deep learning in white disaster early warning and emergency management, and automatically generates a post-disaster recovery plan by analyzing the white disaster-affected area and recovery progress in real time. The system can not only identify the time, location and intensity of white disasters, but also evaluate the resources and time required for post-disaster recovery, reasonably allocate resources, and optimize emergency management. Through intelligent decision support, the post-disaster recovery time can be greatly shortened, the efficiency of resource utilization can be improved, and human intervention in the recovery process can be reduced, ensuring the smooth progress of recovery work. (5) The application of deep learning algorithms (such as convolutional neural networks and multi-scale feature extraction) adopted by the present invention in white disaster monitoring has greatly improved the automation level and computing efficiency of the system. By training the deep learning model, the system can automatically identify and classify different types of white disaster areas, update and optimize white disaster prediction results in real time, and provide accurate monitoring information for emergency management personnel. Compared with traditional rule-based systems, the method of the present invention performs better in dealing with complex white disaster scenarios, can effectively reduce false alarms and missed alarms, and improve the robustness and accuracy of the system.(6) The white disaster monitoring system proposed in the present invention has strong scalability and adaptability, can integrate more data sources (such as social media data, sensor data, etc.), and support white disaster monitoring needs in different regions and of different scales. By continuously updating and optimizing the model, the system of the present invention can adapt to the ever-changing climate conditions and technical environment, ensuring that the monitoring and early warning system can maintain efficient operation under various circumstances. (7) The multi-level and multi-dimensional data fusion and intelligent decision-making mechanism adopted by the present invention not only improves the accuracy and real-time performance of white disaster monitoring, but also provides a more intelligent and comprehensive emergency management support system for relevant departments. By combining the latest artificial intelligence technology, the present invention significantly improves the prediction ability and emergency response capability of the white disaster early warning system, and provides an innovative technical solution for the early identification, timely warning and rapid response of white disaster disasters, which has great practical application value and promotion prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0106] Figure 1 A snow disaster monitoring and early warning flow chart of a method for multi-dimensional monitoring, identification, early warning and emergency management of snow disasters proposed by the present invention;

[0107] Figure 2 This is a snow disaster warning and emergency response decision-making flow chart of a snow disaster multi-dimensional monitoring, identification, warning and emergency management method proposed by the present invention. DETAILED DESCRIPTION

[0108] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0109] refer to Figure 1 , a method for multi-dimensional monitoring, identification, early warning and emergency management of white disasters, comprising the following steps:

[0110] S1. Acquire multiple remote sensing image data and meteorological data, wherein the remote sensing image data includes satellite images, ground sensor data, and drone images, and the meteorological data includes real-time meteorological information of temperature, humidity, precipitation, and wind speed;

[0111] In this implementation, S1 specifically includes:

[0112] S11. Acquire multiple remote sensing image data, where the remote sensing image data includes satellite images, drone images, and image data collected by ground sensors;

[0113] S12, obtaining meteorological data, wherein the meteorological data includes real-time temperature (T), humidity (H), precipitation (P), and wind speed (W) meteorological variables;

[0114] S13, preprocessing the remote sensing image data, including image enhancement, noise removal and geometric correction, wherein the image enhancement includes contrast enhancement and sharpening operations, and the noise removal adopts a median filter or a Gaussian filter algorithm;

[0115] S14, preprocessing the meteorological data, including missing value filling, data smoothing and standardization. The standardization includes normalizing each meteorological variable so that the value range of each variable is between [0, 1].

[0116] S2. Automatically extracting features from the remote sensing image data through a convolutional neural network model, wherein the convolutional neural network uses a multi-scale convolution kernel to extract white disaster related features at different resolutions;

[0117] In this implementation, S2 specifically includes:

[0118] S21. Input the acquired remote sensing image data into a convolutional neural network model, wherein the convolutional neural network model comprises a plurality of convolutional layers, each of which performs convolution processing on the image through a convolution kernel (K) of different sizes, wherein the size of the convolution kernel is K×K, where K represents the size of the convolution kernel:

[0119]

[0120] Among them, F i,j Indicates the intensity of snowstorm-related features at the location, local information of snowstorm coverage area or snow thickness, X i,j Indicates the grayscale information of the white disaster area and background in the image, K m,n It is used to extract the features related to white disasters, the boundaries and shapes of the white disaster areas;

[0121] S22. During the convolution operation, a multi-scale convolution kernel is used. By using convolution kernels of different scales, feature information of different levels in the image is extracted. The convolution kernels of the scales include a small-scale convolution kernel K1×K1 and a large-scale convolution kernel K2×K2, where K1 and K2 represent convolution kernels of different sizes, respectively. The features extracted by the multi-scale convolution kernel include fine-grained local features and global features.

[0122] S23. Use the pooling layer to perform pooling operation on the feature map output by the convolution layer. The pooling layer adopts maximum pooling or average pooling. The size of the pooling window is P×P, where P is the size of the pooling window:

[0123] P i,j =max(Xi,j ,X i+1,j ,…,X i+P-1,j+P-1 );

[0124] Among them, P i,j Indicates the maximum intensity of white disaster impact in a region, X i,j The size of the feature map is reduced, and the representative white disaster features are retained;

[0125] S24, connecting the pooled feature maps through a fully connected layer to form a feature vector, which contains multi-level and multi-scale feature information from the remote sensing image;

[0126] S25. Through further learning and optimization of the convolutional neural network, the spatial features related to white disasters are extracted from the feature map, and the white disaster prediction model is jointly constructed by combining the spatiotemporal features extracted from the meteorological data;

[0127] S3, combining the features with meteorological data, integrating historical data with real-time data through spatiotemporal data fusion algorithm, and generating a spatiotemporal feature matrix;

[0128] In this implementation, S3 specifically includes:

[0129] S31, the meteorological data of S2 is fused with the spatial features in the remote sensing image in time and space, and the meteorological data and the features in the remote sensing image are jointly modeled through the data fusion algorithm Kalman filtering to obtain the joint feature vector C(t), where C(t) represents the time and space feature vector at time t:

[0130] C(t)=Fuse(F(t),T(t),H(t),P(t),W(t));

[0131] Among them, F(t) is the spatial feature extracted from the remote sensing image, Fuse is the spatiotemporal data fusion operation, and the fusion result C(t) contains the joint information of remote sensing image features and meteorological data, providing comprehensive spatiotemporal feature support for subsequent white disaster prediction;

[0132] S33, input the spatiotemporal feature vector C(t) into the spatiotemporal model long short-term memory network, perform dynamic prediction of the evolution of the white disaster, and obtain the white disaster prediction value Y(t+k) at the future time, where Y(t+k) represents the predicted white disaster state at the future time t+kt+k:

[0133] Y(t+k)=LSTM(C(t),θ);

[0134] Among them, Y(t+k) is the predicted value of the white disaster at the future time t+kt+k, which indicates the predicted impact range or intensity of the white disaster; C(t) is the spatiotemporal feature vector at the current time t, which contains the fusion information of meteorological data and remote sensing data; θ is the training parameter of the spatiotemporal model, which is optimized by learning historical data.

[0135] S34. Generate white disaster warning information through the predicted value Y(t+k) output by the space-time model, and perform visualization processing to generate a white disaster evolution diagram and a warning map to provide decision makers with key information such as warning time and white disaster impact range.

[0136] S4. Based on the S3 spatiotemporal feature matrix, the convolutional neural network is used to predict the area where snowstorms occur, identify the snowstorm area, snow thickness and its evolution trend;

[0137] In this implementation, S4 specifically includes:

[0138] S41, receiving white disaster information predicted by the spatiotemporal model, including white disaster intensity Y(t+k) and affected area at a future time, wherein the white disaster intensity Y(t+k) represents the severity of the white disaster at the future time t+kt+k, and the affected area is the geographical range that the white disaster may cover at that time;

[0139] S42. According to the intensity of the white disaster Y(t+k) and the affected area, a white disaster response decision model is constructed. The decision model formulates emergency response measures of different levels according to the impact range and intensity of the white disaster, and sets the emergency response level to E(t+k), where E(t+k) represents the emergency response level at time t+kt+k:

[0140] E(t+k)=Decision(Y(t+k),R);

[0141] Among them, Decision is the emergency response decision function, and R is the rule set of the decision model, including the relationship between the intensity of white disasters and the affected area, and the process of emergency response;

[0142] S43. According to the emergency response level E(t+k), specific emergency response instructions are issued to relevant departments and personnel, and the emergency response instructions include personnel evacuation, equipment dispatch, and resource allocation;

[0143] S44. Monitor the evolution of the white disaster in real time, and update the emergency response level E(t+k) according to the actual situation. The update is based on the new white disaster data and meteorological data. The updated emergency response level is E′(t+k):

[0144] E'(t+k)=Update(E(t+k),Y(t+k),Δ);

[0145] Among them, E′(t+k) is the updated emergency response level, Δ is the change in the evolution of white disasters, including new information on white disaster intensity and impact range, and Update means correction and adjustment based on white disaster prediction data;

[0146] S45. Based on the real-time updated emergency response level E′(t+k)E'(t+k), the revised emergency response instruction is issued to relevant personnel through the emergency management platform;

[0147] S46. Based on the evolution of white disasters and emergency response situations, a feedback mechanism will be established to collect problems and feedback information arising during the implementation process and further optimize the emergency response decision-making model.

[0148] S42. According to the intensity of the white disaster Y(t+k) and the affected area, a white disaster response decision model is constructed. The decision model formulates emergency response measures of different levels according to the impact range and intensity of the white disaster, and sets the emergency response level to E(t+k), where E(t+k) represents the emergency response level at time t+kt+k:

[0149] E(t+k)=Decision(Y(t+k),R);

[0150] Among them, Decision is the emergency response decision function, and R is the rule set of the decision model, including the relationship between the intensity of white disasters and the affected area, and the process of emergency response;

[0151] In this implementation, S42 specifically includes:

[0152] S421. Obtain the predicted value of the snowstorm intensity Y(t+k) and the relevant characteristic information of the snowstorm-affected area, including the snowstorm-covered area, intensity distribution and snow thickness. The affected area A(t+k) is the geographical area that may be affected by the snowstorm, including the covered geographical coordinates and area range:

[0153] A(t+k)=Region(Y(t+k));

[0154] Among them, A(t+k) represents the area that may be covered by the white disaster, Region is the affected area derived from the white disaster intensity Y(t+k), and the affected geographical area is obtained according to the intensity distribution;

[0155] S422, calculating the risk assessment value R(t+k) in the white disaster response model, wherein the risk assessment value R(t+k) is a risk assessment of the disaster-affected area based on the intensity of the white disaster and the affected area:

[0156] R(t+k)=f(Y(t+k),A(t+k),R);

[0157] Among them, f is the risk assessment function, R is the regional disaster risk parameter set, including historical white disaster data, geographical environment sensitivity mountain or plain factors, and function f performs weighted calculation on the intensity of white disaster and the affected area, thereby evaluating the risk level of different regions;

[0158] S423. Determine the emergency response priority P(t+k) based on the risk assessment value R(t+k). The priority is classified according to the urgency of the disaster impact and the vulnerability of the disaster area:

[0159] P(t+k)=g(R(t+k),P);

[0160] Among them, g is the emergency response priority determination function, P is the emergency response priority rule set, including the response rules for various risk levels. This function comprehensively determines the priority of the emergency response based on the size of the risk assessment value R(t+k) and whether the regional vulnerability is a mountainous area or a high-risk area;

[0161] S424. According to the priority P(t+k), the response level E(t+k) is further refined. The level is determined by the regional disaster impact, the evolution trend of the disaster and the predicted time factors:

[0162] E(t+k)=h(P(t+k),Y(t+k),T prediction );

[0163] Among them, h is the decision function of emergency response level, P(t+k) is the priority, Y(t+k) is the white disaster intensity prediction, T prediction To predict the duration of the white disaster at time t+kt+k, according to these factors, the response level can be adjusted as follows: 1 for emergency response, 2 for warning response, and 3 for ordinary response;

[0164] S425. Generate specific emergency response measures and instructions based on the response level E(t+k), including personnel evacuation, equipment dispatch, and resource allocation:

[0165] M(t+k)=Generate_Measures(E(t+k),A(t+k));

[0166] Among them, M(t+k) is the generated emergency response measure, Generate_Measures is a function that generates specific emergency instructions based on the response level and the affected area, and A(t+k) is the affected geographical area;

[0167] S426. Provide real-time feedback on the results of emergency response, adjust decisions through real-time data updates and subsequent forecasts, and update the emergency response level in real time if the disaster develops beyond expectations or the scope of impact changes. Real-time feedback mechanism:

[0168] E′(t+k)=Update_Decision(E(t+k),ΔY(t+k),ΔA(t+k));

[0169] Among them, E′(t+k) is the updated emergency response level, ΔY(t+k) is the change in white disaster intensity, ΔA(t+k) is the change in the affected area, and Update_Decision is the emergency response adjustment function, which adjusts the emergency response strategy according to the subsequent white disaster prediction data and regional changes.

[0170] S5. Generate white disaster warning information based on the prediction results of S4, and update the warning level, impact range and impact time in real time;

[0171] In this implementation, S5 specifically includes:

[0172] S51, judging whether it is necessary to execute emergency response measures according to the emergency response level E(t+k) and the real-time data related to the disaster, if the execution conditions are met, proceed to step S52, otherwise proceed to step S53;

[0173] S52. Generate an emergency response instruction set M(t+k) according to the emergency response level E(t+k). The instruction set includes emergency management measures such as personnel evacuation, equipment dispatching, resource allocation, and traffic control. The generated emergency response instruction set is:

[0174] M(t+k)=Generate_Instructions(E(t+k),A(t+k),R);

[0175] Among them, M(t+k) is the emergency response instruction set, Generate_Instructions is a function that generates emergency instructions according to the emergency response level and the affected area, A(t+k) is the affected area, R is the emergency response rule set, including the specific measures required for different levels of response, and the instruction set includes resource deployment plan, personnel evacuation plan, and traffic control plan;

[0176] S53: If emergency response measures are not required, the existing emergency response status is monitored and the disaster development status is updated regularly. The monitoring frequency is set to f. monitor :

[0177] f monitor =Monitor(Y(t+k),A(t+k));

[0178] Among them, f monitor is the monitoring frequency, Y(t+k) is the predicted value of the white disaster intensity, A(t+k) is the affected area, Monitor is a function that monitors the development status of the white disaster according to the white disaster intensity and the affected area, and regularly checks whether the emergency response initiation conditions appear;

[0179] S54. During the monitoring process, if the disaster situation changes, the emergency response level E(t+k) is re-evaluated. If it needs to be updated, the process returns to step S52 to generate a new emergency response instruction set. If there is no significant change, the current response state is maintained.

[0180] S55. The generated emergency response instruction set M(t+k) is released to relevant departments and emergency personnel through the emergency management platform. The platform is a system with information distribution, collaborative work, and real-time update functions:

[0181] P(t+k)=Publish_Instructions(M(t+k),D);

[0182] Among them, P(t+k) is the published emergency response instruction, Publish_Instructions is the function that publishes the generated emergency response instruction through the emergency management platform, D is the target department and personnel who receive the instruction, and the publishing process includes the distribution, feedback collection and processing of the instruction;

[0183] S56. During the emergency response, the execution of emergency response instructions is monitored in real time, feedback information is collected in real time, and emergency response measures are updated through the feedback mechanism. Feedback information F(t+k)F(t+k) includes personnel evacuation progress, equipment scheduling, and resource requirements:

[0184] F(t+k)=Collect_Feedback(M(t+k),D);

[0185] Wherein, F(t+k) is the feedback information, Collect_Feedback() is the function for collecting feedback information, D is the department and personnel receiving the instruction, and the feedback information will be used to adjust the emergency response strategy and optimize it;

[0186] S57. If the feedback information F(t+k) shows that there is a problem or the response measures need to be adjusted, return to step S52, regenerate the emergency response instruction set M(t+k) based on the latest feedback information, and continue to execute step S55.

[0187] S6. Based on the prediction results of S4, dynamic warnings are pushed to support emergency response and disaster prevention and mitigation decisions before, during and after disasters.

[0188] In this implementation, S6 specifically includes:

[0189] S61. Extract key parameters P(t+k) in the affected area based on real-time monitoring data after the occurrence of the white disaster. The parameters include the affected range, intensity, and snow thickness of the white disaster:

[0190] P(t+k)=Extract_Key_Parameters(A(t+k),Y(t+k));

[0191] Among them, P(t+k) is the extracted key parameter set, A(t+k) is the affected area, Y(t+k) is the predicted value of white disaster intensity, and Extract_Key_Parameters is a function for extracting key parameters from the affected area and white disaster intensity;

[0192] S62. Based on the extracted key parameter set P(t+k), combined with historical disaster data, an evaluation is performed to generate a post-disaster recovery demand prediction model, which predicts the resources and time required for post-disaster recovery:

[0193] R(t+k)=Predict_Recovery_Needs(P(t+k),D);

[0194] Among them, R(t+k) is the post-disaster recovery demand prediction result, D is the historical disaster dataset, and Predict_Recovery_Needs is a function for predicting recovery demand based on key parameters and historical data.

[0195] S63. Generate a post-disaster recovery plan P based on the post-disaster recovery demand forecast result R(t+k) r (t+k), including resource scheduling, personnel deployment, material supply, and infrastructure repair plans:

[0196] P r (t+k)=Generate_Recovery_Plan(R(t+k),D r );

[0197] Among them, P r (t+k) is the post-disaster recovery plan, Generate_Recovery_Plan is the function for generating the post-disaster recovery plan, D r Resource and staffing data required for recovery planning;

[0198] S64, the generated disaster recovery plan P r (t+k) is submitted to the emergency management platform for verification, which includes multi-dimensional checks on resource availability, time rationality, and personnel capabilities:

[0199] V(t+k)=Validate_Recovery_Plan(P r (t+k),D v );

[0200] Among them, V(t+k) is the verification result, D vTo verify the required resources, time and personnel data, Validate_Recovery_Plan is a function to verify the feasibility of the post-disaster recovery plan;

[0201] S65. Disaster Recovery Plan P r (t+k) If the verification is successful, the recovery plan is released to the relevant departments to carry out post-disaster recovery work; if the verification is unsuccessful, return to step S63, adjust the recovery plan according to the feedback results, and re-verify;

[0202] S66. During the post-disaster recovery process, real-time monitoring of the recovery progress and evaluation of the recovery results shall be conducted, including the recovery of facilities, resource utilization, and personnel participation:

[0203] E(t+k)=Evaluate_Recovery_Effectiveness(P r (t+k),D e );

[0204] Among them, E(t+k) is the evaluation result, D e To evaluate the recovery data required,

[0205] Evaluate_Recovery_Effectiveness is a function for evaluating the recovery effect;

[0206] S67. Update the post-disaster recovery plan P based on the evaluation results E(t+k) r (t+k), and adjust the priority of the unfinished recovery tasks:

[0207] P r (t+k)=Update_Recovery_Plan(E(t+k),P r (t+k));

[0208] Among them, Update_Recovery_Plan is a function that updates the recovery plan according to the evaluation results.

[0209] Embodiment 1:

[0210] This example demonstrates the performance of the "snow disaster multivariate monitoring, identification, warning and emergency management method based on convolutional neural network" proposed in the present invention in practical applications. The specific implementation scenario is a mountainous area in northwest China, focusing on monitoring the occurrence, development and impact of snow disasters in the area, and providing accurate warning information and emergency response suggestions in a timely manner.

[0211] This mountainous area is located in the northwest of China, on a cold plateau, and often encounters large-scale blizzards in winter. The geographical characteristics of this area make snow disasters frequent, and due to the complex terrain, the impact range of snow disasters is often difficult to predict. In the past, snow disaster monitoring relied on manual observation and static analysis of meteorological data, and there were problems such as delayed monitoring information and inability to judge the affected area and depth of snow disasters in real time. By introducing the method of the present invention, combined with high-resolution remote sensing image data, meteorological data and deep learning models, it is possible to monitor and predict the development trend of snow disasters in real time, thereby achieving accurate early warning.

[0212] In order to verify the effectiveness of the method of the present invention, this embodiment uses the convolutional neural network (CNN) model and spatiotemporal data fusion technology proposed in the present invention. Data collection mainly relies on the following methods: Remote sensing data: high-resolution remote sensing images are obtained through satellite images to monitor the snow accumulation in mountainous areas. The resolution of the image data is 10 meters, which can accurately provide information on the thickness of the snow layer at each pixel. Meteorological data: The temperature, precipitation, wind speed and other data of the area are collected in real time through the meteorological station. These data are processed by spatiotemporal alignment with the remote sensing image data as the input of spatiotemporal data fusion. Historical data: Refer to the remote sensing images, meteorological data and post-disaster recovery conditions in past snow disasters to provide training data for the deep learning model.

[0213] Snow disaster monitoring: By inputting satellite images and meteorological data into the convolutional neural network, the CNN model automatically extracts features related to snow disasters. The model first performs multi-scale convolution processing on the remote sensing images to identify snow disaster features at different levels, including snow accumulation area, snow thickness and snow disaster intensity. Data fusion: The acquired remote sensing image data and real-time meteorological data are fused, combined with historical data from multiple periods, and the spatiotemporal data fusion algorithm is used to provide accurate model output for the development of snow disasters at each moment. Snow disaster prediction: Through the spatiotemporal data fusion results, the CNN model further predicts the development trend of snow disasters, including the expansion of the snow disaster affected area and the change in snow thickness. The prediction results can provide a scientific basis for emergency response. Warning release: According to the snow disaster evolution trend predicted by the model, the system generates warning information at different time nodes and updates the warning level in real time. The warning information is promptly conveyed to government departments and emergency response personnel through SMS, App push and other means.

[0214] This example was conducted on December 1, 2023, and the location was selected as a mountainous area in northwest China, which is subject to severe snowstorms all year round. In order to verify the effectiveness of the method of the present invention, a snowstorm event in mid-December 2023 was selected for testing.

[0215] In this area, a large-scale snowstorm occurred on December 15, 2023. According to the method of the present invention, the remote sensing image is first analyzed by a convolutional neural network to automatically extract the snowstorm area. The model successfully identified that the impact range of the snowstorm is 1,000 square kilometers, and the snow thickness is more than 50 centimeters. According to meteorological data, the snowstorm is expected to continue to intensify within 24 hours, and the snow thickness will reach 70 centimeters.

[0216] Table 1 Snow disaster area identification and prediction results

[0217]

[0218] Referring to Table 1 above, combined with meteorological data and historical snow disasters, the model of the present invention accurately predicted the expansion range of the snow disaster area. Within 24 hours after the snow disaster occurred, the model successfully predicted the expansion area of ​​the snow disaster, and the prediction results were highly consistent with the actual situation. Specifically, the actual snow disaster expansion area was 1,100 square kilometers, while the prediction value of the method of the present invention was 1,050 square kilometers, with a prediction error of only 4.5%.

[0219] In addition, through the spatiotemporal data fusion function of the present invention, the system can monitor the development of the snowstorm in real time. In the 12th hour after the snowstorm, the model accurately predicted the maximum impact range of the snowstorm and issued a yellow warning information in time, predicting that traffic in the area would be seriously affected, and reminding emergency management personnel to prepare in advance. The release of warning information was 4 hours earlier than the traditional method, which fully improved the efficiency of emergency response.

[0220] During the post-disaster recovery phase, the system of the present invention also helped relevant departments evaluate the progress of post-disaster recovery. By further analyzing the snowstorm coverage and meteorological data in the post-disaster area, the system provided a reasonable resource allocation plan and recommended that transportation routes and power supply networks be restored first. This plan successfully shortened the post-disaster recovery time and ultimately achieved 80% recovery progress within 72 hours, which was 30% earlier than the traditional method.

[0221] This embodiment verifies the effectiveness of the method of the present invention in snow disaster monitoring, identification, early warning and emergency response through the application of specific snow disaster monitoring and emergency management. By introducing convolutional neural networks and spatiotemporal data fusion, the present invention not only improves the automation level of snow disaster monitoring, avoids the limitations of manual analysis in traditional methods, but also enhances the accuracy and real-time performance of the system, providing more scientific decision-making support for emergency management departments. Experimental data show that the method of the present invention can accurately predict the development trend of snow disasters, issue early warning information in advance, and effectively support the formulation of post-disaster recovery plans, and has high application value and social benefits.

[0222] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for multi-dimensional monitoring, identification, early warning and emergency management of white disasters, characterized in that: The steps include: S1. Acquire multiple remote sensing image data and meteorological data, wherein the remote sensing image data includes satellite images, ground sensor data, and drone images, and the meteorological data includes real-time meteorological information of temperature, humidity, precipitation, and wind speed; S2. Automatically extracting features from the remote sensing image data through a convolutional neural network model, wherein the convolutional neural network uses a multi-scale convolution kernel to extract white disaster related features at different resolutions; S3, combining the features with meteorological data, integrating historical data with real-time data through spatiotemporal data fusion algorithm, and generating a spatiotemporal feature matrix; S4. Based on the S3 spatiotemporal feature matrix, the convolutional neural network is used to predict the area where snowstorms occur, identify the snowstorm area, snow thickness and its evolution trend; S5. Generate white disaster warning information based on the prediction results of S4, and update the warning level, impact range and impact time in real time; S6. Based on the prediction results of S4, dynamic warnings are pushed to support emergency response and disaster prevention and mitigation decisions before, during and after disasters.

2. The method for multi-dimensional monitoring, identification, early warning and emergency management of white disasters according to claim 1 is characterized in that: The S1 specifically includes: S11. Acquire multiple remote sensing image data, where the remote sensing image data includes satellite images, drone images, and image data collected by ground sensors; S12, obtaining meteorological data, wherein the meteorological data includes real-time temperature (T), humidity (H), precipitation (P), and wind speed (W) meteorological variables; S13, preprocessing the remote sensing image data, including image enhancement, noise removal and geometric correction, wherein the image enhancement includes contrast enhancement and sharpening operations, and the noise removal adopts a median filter or a Gaussian filter algorithm; S14, preprocessing the meteorological data, including missing value filling, data smoothing and standardization. The standardization includes normalizing each meteorological variable so that the value range of each variable is between [0, 1].

3. The method for multi-dimensional monitoring, identification, early warning and emergency management of white disasters according to claim 1 is characterized in that: The S2 specifically includes: S21. Input the acquired remote sensing image data into a convolutional neural network model, wherein the convolutional neural network model comprises a plurality of convolutional layers, each of which performs convolution processing on the image through a convolution kernel (K) of different sizes, wherein the size of the convolution kernel is K×K, where K represents the size of the convolution kernel: Among them, F i,j Indicates the intensity of snowstorm-related features at the location, local information of snowstorm coverage area or snow thickness, X i,j Indicates the grayscale information of the white disaster area and background in the image, K m,n It is used to extract the features related to white disasters, the boundaries and shapes of the white disaster areas; S22. During the convolution operation, a multi-scale convolution kernel is used. By using convolution kernels of different scales, feature information of different levels in the image is extracted. The convolution kernels of the scales include a small-scale convolution kernel K1×K1 and a large-scale convolution kernel K2×K2, where K1 and K2 represent convolution kernels of different sizes, respectively. The features extracted by the multi-scale convolution kernel include fine-grained local features and global features. S23. Use the pooling layer to perform pooling operation on the feature map output by the convolution layer. The pooling layer adopts maximum pooling or average pooling. The size of the pooling window is P×P, where P is the size of the pooling window: P i,j =max(X i,j ,X i+1,j ,…,X i+P-1,j+P-1 ); Among them, P i,j Indicates the maximum intensity of white disaster impact in a region, X i,j The size of the feature map is reduced, and the representative white disaster features are retained; S24, connecting the pooled feature maps through a fully connected layer to form a feature vector, which contains multi-level and multi-scale feature information from the remote sensing image; S25. Through further learning and optimization of convolutional neural networks, spatial features related to white disasters are extracted from feature maps, and combined with the spatiotemporal features extracted from meteorological data to jointly construct a white disaster prediction model.

4. The method for multi-dimensional monitoring, identification, early warning and emergency management of white disasters according to claim 1 is characterized in that: The S3 specifically includes: S31, the meteorological data of S2 is fused with the spatial features in the remote sensing image in time and space, and the meteorological data and the features in the remote sensing image are jointly modeled through the data fusion algorithm Kalman filtering to obtain the joint feature vector C(t), where C(t) represents the time and space feature vector at time t: C(t)=Fuse(F(t),T(t),H(t),P(t),W(t)); Among them, F(t) is the spatial feature extracted from the remote sensing image, Fuse is the spatiotemporal data fusion operation, and the fusion result C(t) contains the joint information of remote sensing image features and meteorological data, providing comprehensive spatiotemporal feature support for subsequent white disaster prediction; S33, input the spatiotemporal feature vector C(t) into the spatiotemporal model long short-term memory network, perform dynamic prediction of the evolution of the white disaster, and obtain the white disaster prediction value Y(t+k) at the future time, where Y(t+k) represents the predicted white disaster state at the future time t+kt+k: Y(t+k)=LSTM(C(t),θ); Among them, Y(t+k) is the predicted value of the white disaster at the future time t+kt+k, which indicates the predicted impact range or intensity of the white disaster; C(t) is the spatiotemporal feature vector at the current time t, which contains the fusion information of meteorological data and remote sensing data; θ is the training parameter of the spatiotemporal model, which is optimized by learning historical data. S34. Generate white disaster warning information through the predicted value Y(t+k) output by the space-time model, and perform visualization processing to generate a white disaster evolution diagram and a warning map to provide decision makers with key information such as warning time and white disaster impact range.

5. The method for multi-dimensional monitoring, identification, early warning and emergency management of white disasters according to claim 1 is characterized in that: The S4 specifically includes: S41, receiving white disaster information predicted by the spatiotemporal model, including white disaster intensity Y(t+k) and affected area at a future time, wherein the white disaster intensity Y(t+k) represents the severity of the white disaster at the future time t+kt+k, and the affected area is the geographical range that the white disaster may cover at that time; S42. According to the intensity of the white disaster Y(t+k) and the affected area, a white disaster response decision model is constructed. The decision model formulates emergency response measures of different levels according to the impact range and intensity of the white disaster, and sets the emergency response level to E(t+k), where E(t+k) represents the emergency response level at time t+kt+k: E(t+k)=Decision(Y(t+k),R); Among them, Decision is the emergency response decision function, and R is the rule set of the decision model, including the relationship between the intensity of white disasters and the affected area, and the process of emergency response; S43. According to the emergency response level E(t+k), specific emergency response instructions are issued to relevant departments and personnel, and the emergency response instructions include personnel evacuation, equipment dispatch, and resource allocation; S44. Monitor the evolution of the white disaster in real time, and update the emergency response level E(t+k) according to the actual situation. The update is based on the new white disaster data and meteorological data. The updated emergency response level is E′(t+k): E'(t+k)=Update(E(t+k),Y(t+k),Δ); Among them, E′(t+k) is the updated emergency response level, Δ is the change in the evolution of white disasters, including new information on white disaster intensity and impact range, and Update means correction and adjustment based on white disaster prediction data; S45. Based on the real-time updated emergency response level E′(t+k)E'(t+k), the revised emergency response instruction is issued to relevant personnel through the emergency management platform; S46. Based on the evolution of white disasters and emergency response situations, a feedback mechanism will be established to collect problems and feedback information arising during the implementation process and further optimize the emergency response decision-making model.

6. The method for multi-dimensional monitoring, identification, early warning and emergency management of white disasters according to claim 1 is characterized in that: The S42 specifically includes: S421. Obtain the predicted value of the snowstorm intensity Y(t+k) and the relevant characteristic information of the snowstorm-affected area, including the snowstorm-covered area, intensity distribution and snow thickness. The affected area A(t+k) is the geographical area that may be affected by the snowstorm, including the covered geographical coordinates and area range: A(t+k)=Region(Y(t+k)); Among them, A(t+k) represents the area that may be covered by the white disaster, Region is the affected area derived from the white disaster intensity Y(t+k), and the affected geographical area is obtained according to the intensity distribution; S422, calculating the risk assessment value R(t+k) in the disaster response model, the risk assessment value R(t+k) is the risk assessment of the disaster area based on the intensity of the disaster and the affected area: R(t+k)=f(Y(t+k),A(t+k),R); Among them, f is the risk assessment function, R is the regional disaster risk parameter set, including historical white disaster data, geographical environment sensitivity mountain or plain factors, and function f performs weighted calculation on the intensity of white disaster and the affected area, thereby evaluating the risk level of different regions; S423. Determine the emergency response priority P(t+k) based on the risk assessment value R(t+k). The priority is classified according to the urgency of the disaster impact and the vulnerability of the disaster area: P(t+k)=g(R(t+k),P); Among them, g is the emergency response priority determination function, P is the emergency response priority rule set, including the response rules for various risk levels. This function comprehensively determines the priority of the emergency response based on the size of the risk assessment value R(t+k) and whether the regional vulnerability is a mountainous area or a high-risk area; S424. According to the priority P(t+k), the response level E(t+k) is further refined. The level is determined by the regional disaster impact, the evolution trend of the disaster and the predicted time factors: E(t+k)=h(P(t+k),Y(t+k),T prediction ); Among them, h is the decision function of emergency response level, P(t+k) is the priority, Y(t+k) is the white disaster intensity prediction, T prediction To predict the duration of the white disaster at time t+kt+k, according to these factors, the response level can be adjusted as follows: 1 for emergency response, 2 for warning response, and 3 for ordinary response; S425. Generate specific emergency response measures and instructions based on the response level E(t+k), including personnel evacuation, equipment dispatch, and resource allocation: M(t+k)=Generate_Measures(E(t+k),A(t+k)); Among them, M(t+k) is the generated emergency response measure, Generate_Measures is a function that generates specific emergency instructions based on the response level and the affected area, and A(t+k) is the affected geographical area; S426. Provide real-time feedback on the results of emergency response, adjust decisions through real-time data updates and subsequent forecasts, and update the emergency response level in real time if the disaster develops beyond expectations or the scope of impact changes. Real-time feedback mechanism: E′(t+k)=Update_Decision(E(t+k),ΔY(t+k),ΔA(t+k)); Among them, E′(t+k) is the updated emergency response level, ΔY(t+k) is the change in white disaster intensity, ΔA(t+k) is the change in the affected area, and Update_Decision is the emergency response adjustment function, which adjusts the emergency response strategy according to the subsequent white disaster prediction data and regional changes.

7. The method for multi-dimensional monitoring, identification, early warning and emergency management of white disasters according to claim 1 is characterized in that: The S5 specifically includes: S51, judging whether it is necessary to execute emergency response measures according to the emergency response level E(t+k) and the real-time data related to the disaster, if the execution conditions are met, proceed to step S52, otherwise proceed to step S53; S52. Generate an emergency response instruction set M(t+k) according to the emergency response level E(t+k). The instruction set includes emergency management measures such as personnel evacuation, equipment dispatching, resource allocation, and traffic control. The generated emergency response instruction set is: M(t+k)=Generate_Instructions(E(t+k),A(t+k),R); Among them, M(t+k) is the emergency response instruction set, Generate_Instructions is a function that generates emergency instructions according to the emergency response level and the affected area, A(t+k) is the affected area, R is the emergency response rule set, including the specific measures required for different levels of response, and the instruction set includes resource deployment plan, personnel evacuation plan, and traffic control plan; S53: If emergency response measures are not required, the existing emergency response status is monitored and the disaster development status is updated regularly. The monitoring frequency is set to f. monitor : f monitor =Monitor(Y(t+k),A(t+k)); Among them, f monitor is the monitoring frequency, Y(t+k) is the predicted value of the white disaster intensity, A(t+k) is the affected area, Monitor is a function that monitors the development status of the white disaster according to the white disaster intensity and the affected area, and regularly checks whether the emergency response initiation conditions appear; S54. During the monitoring process, if the disaster situation changes, re-evaluate the emergency response level E(t+k), needs to be updated, then returns to step S52 to generate a new emergency response instruction set. If there is no major change, the current response state is maintained; S55. The generated emergency response instruction set M(t+k) is released to relevant departments and emergency personnel through the emergency management platform. The platform is a system with information distribution, collaborative work, and real-time update functions: P(t+k)=Publish_Instructions(M(t+k),D); Among them, P(t+k) is the published emergency response instruction, Publish_Instructions is the function that publishes the generated emergency response instruction through the emergency management platform, D is the target department and personnel who receive the instruction, and the publishing process includes the distribution, feedback collection and processing of the instruction; S56. During the emergency response, the execution of emergency response instructions is monitored in real time, feedback information is collected in real time, and emergency response measures are updated through the feedback mechanism. Feedback information F(t+k)F(t+k) includes personnel evacuation progress, equipment scheduling, and resource requirements: F(y+k)=Collect_Feedback(M(t+k),D); Wherein, F(t+k) is the feedback information, Collect_Feedback() is the function for collecting feedback information, D is the department and personnel receiving the instruction, and the feedback information will be used to adjust the emergency response strategy and optimize it; S57. If the feedback information F(t+k) shows that there is a problem or the response measures need to be adjusted, return to step S52, regenerate the emergency response instruction set M(t+k) based on the latest feedback information, and continue to execute step S55.

8. The method for multi-dimensional monitoring, identification, early warning and emergency management of white disasters according to claim 1 is characterized in that: The S6 specifically includes: S61. Extract key parameters P(t+k) in the affected area based on real-time monitoring data after the occurrence of the white disaster. The parameters include the affected range, intensity, and snow thickness of the white disaster: P(t+k)=Extract_Key_Parameters(A(t+k),Y(t+k)); Among them, P(t+k) is the extracted key parameter set, A(t+k) is the affected area, Y(t+k) is the predicted value of white disaster intensity, and Extract_Key_Parameters is a function for extracting key parameters from the affected area and white disaster intensity; S62. Based on the extracted key parameter set P(t+k), combined with historical disaster data, an evaluation is performed to generate a post-disaster recovery demand prediction model, which predicts the resources and time required for post-disaster recovery: R(t+k)=Predict_Recovery_Needs(P(t+k),D); Among them, R(t+k) is the post-disaster recovery demand prediction result, D is the historical disaster dataset, and Predict_Recovery_Needs is a function for predicting recovery demand based on key parameters and historical data. S63. Generate a post-disaster recovery plan P based on the post-disaster recovery demand forecast result R(t+k) r (t+k), including resource scheduling, personnel deployment, material supply, and infrastructure repair plans: P r (t+k)=Generate_Recovery_Plan(R(t+k),D r ); Among them, P r (t+k) is the post-disaster recovery plan, Cenerate_Recovery_Plan is the function for generating the post-disaster recovery plan, and D r Resource and staffing data required for recovery planning; S64, the generated disaster recovery plan P r (t+k) is submitted to the emergency management platform for verification, which includes multi-dimensional checks on resource availability, time rationality, and personnel capabilities: V(t+k)=Validate_Recovery_Plan(P r (t+k),D v ); Among them, V(t+k) is the verification result, D v To verify the required resources, time and personnel data, Validate_Recovery_Plan is a function to verify the feasibility of the post-disaster recovery plan; S65. Disaster Recovery Plan P r (t+k) If the verification is successful, the recovery plan is released to the relevant departments to carry out post-disaster recovery work; if the verification is unsuccessful, return to step S63, adjust the recovery plan according to the feedback results, and re-verify; S66. During the post-disaster recovery process, real-time monitoring of the recovery progress and evaluation of the recovery results shall be conducted, including the recovery of facilities, resource utilization, and personnel participation: E(t+k)=Evaluate_Recovery_Effectiveness(P r (t+k),D e ); Among them, E(t+k) is the evaluation result, D e To evaluate the required recovery data, Evaluate_Recovery_Effectiveness is a function for evaluating the recovery effect; S67. Update the post-disaster recovery plan P based on the evaluation results E(t+k) r (t+k), and adjust the priority of the unfinished recovery tasks: P r (t+k)=Update_Recovery_Plan(E(t+k),P r (t+k)); Among them, Update_Recovery_Plan is a function that updates the recovery plan according to the evaluation results.

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