Geological disaster monitoring, prediction and early warning method based on artificial intelligence

Abstract

This application relates to the field of geological disaster early warning technology, and discloses a method for monitoring, predicting and warning of geological disasters based on artificial intelligence, including the following steps: data collection: collecting geological disaster monitoring data; data preprocessing: time alignment and missing completion of the collected data; spatiotemporal correlation matrix construction: analyzing the preprocessed monitoring data; feature extraction: extracting the nonlinear spatiotemporal characteristics of geological disaster monitoring data; time series prediction: outputting the geological disaster risk level in the future period; feedback optimization: based on the error between the prediction result and the actual observation data; warning release: generating geological disaster warning information based on the predicted risk level. Through data collection, preprocessing, feature extraction, prediction optimization and warning release, a complete geological disaster monitoring and prediction method is constructed, which integrates multi-source data, extracts key features and dynamically predicts risks, so as to ensure the accuracy of the model and provide a scientific basis for disaster prevention and control.

Description

Technical Field

[0001] The present invention relates to the field of geological disaster early warning technology, and specifically to a geological disaster monitoring, prediction and early warning method based on artificial intelligence. Background Art

[0002] Geological disasters are geological environmental problems caused by natural factors or human activities, including landslides, debris flows, ground subsidence, collapse, etc., which are characterized by suddenness, complexity and great destructiveness. With the intensification of global climate change and the expansion of human activities, the frequency and scope of geological disasters are increasing, seriously threatening the safety of human life and property and the stability of the ecological environment. Therefore, how to achieve efficient monitoring, accurate prediction and timely warning of geological disasters has become an important research topic in the field of geological disaster prevention and control.

[0003] The existing geological disaster monitoring data sources are complex, including sensor data, remote sensing image data and historical disaster data, but the existing technology cannot efficiently integrate these data. The temporal resolution, spatial resolution and acquisition frequency of different data sources vary significantly, which often leads to low data processing efficiency and inaccurate fusion results, affecting the overall analysis effect; Most existing methods adopt isolated processing methods, modeling time and space characteristics separately, and fail to effectively capture the interaction of geological hazards in time dynamics and spatial expansion. In addition, existing methods are insufficient in the face of the nonlinear characteristics of geological hazard data, making it difficult to accurately describe the complex evolution of disasters; Traditional prediction methods have fixed parameters and cannot adjust the model according to real-time monitoring data, making it difficult to adapt to the dynamic evolution of disaster risks as environmental conditions change. In addition, there is a lack of effective feedback mechanism and the model cannot be optimized iteratively using the latest data, resulting in a decrease in prediction accuracy in the long run. Summary of the invention

[0004] In view of the shortcomings of the existing technology, the present invention provides a method for geological disaster monitoring, prediction and early warning based on artificial intelligence, which solves the problems in the existing technology, such as insufficient integration ability of multi-source monitoring data, isolated processing of time and space characteristic information leading to loss of important information, and difficulty in adapting the model to changes in the geological environment.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for monitoring, predicting and warning geological disasters based on artificial intelligence, comprising the following steps: Data collection: Collect geological disaster monitoring data, including sensor data, remote sensing image data and historical geological disaster data, to provide basic data input for subsequent processing; Data preprocessing: Time alignment and missing completion of collected data to ensure the temporal and spatial consistency and integrity of data, and provide high-quality data for subsequent feature extraction; Construction of spatiotemporal correlation matrix: Analyze the preprocessed monitoring data, calculate the spatiotemporal correlation degree between different monitoring points, and construct a spatiotemporal correlation matrix that reflects the correlation characteristics between monitoring points to provide constraints for feature extraction; Feature extraction: The preprocessed monitoring data is encoded and decoded using a variational autoencoder, and spatiotemporal correlation matrix constraints are introduced during the data reconstruction process to extract the nonlinear spatiotemporal characteristics of the geological disaster monitoring data; Time series prediction: The extracted spatiotemporal features are input into the long short-term memory network for time series prediction. The weights of different time steps are dynamically adjusted through the attention mechanism to output the geological disaster risk level in the future period. Feedback optimization: Based on the error between the prediction results and the actual observation data, the back propagation algorithm is used to optimize the parameters of the feature extraction and time series prediction model, so that the model can adapt to the changing geological conditions and improve the prediction accuracy; Warning release: Generate geological disaster warning information based on the predicted risk level, and display high-risk areas and their dynamic evolution trends through a visualization platform to support geological disaster prevention and control decision-making.

[0006] Preferably, the data preprocessing includes: Time-align data with different time resolutions to ensure consistency in the time dimension of the data; Generate adversarial networks to complete data and solve the problem of missing data.

[0007] Preferably, the step of constructing the spatiotemporal correlation matrix includes: By analyzing the changing trends and spatial distribution characteristics of each monitoring point in the time dimension, the spatiotemporal correlation degree between the monitoring points is calculated to form a correlation matrix reflecting the spatiotemporal characteristics.

[0008] Preferably, the feature extraction step comprises: Use variational autoencoders to encode and decode geological disaster monitoring data; The spatiotemporal correlation constraints are introduced in the reconstruction process to make the characteristics of the reconstructed data consistent with the spatiotemporal relationship between the monitoring points.

[0009] Preferably, the time series prediction step includes: Use long short-term memory network to extract time series features from reconstructed data; Based on the attention mechanism, the data weights of different time steps are dynamically adjusted to highlight the moments that have a greater impact on the prediction results.

[0010] Preferably, the feedback optimization step includes: Calculate the error between the predicted results and the actual observed data; The back propagation algorithm is used to update and optimize the model parameters to minimize the prediction error.

[0011] Preferably, the feedback optimization further comprises: The newly acquired geological monitoring data and prediction results are fed back into the training data set, the model is retrained and the parameters are iteratively optimized to improve the prediction accuracy of the model.

[0012] Preferably, the warning issuing step includes: Classify geohazard risks into low-, medium- and high-risk areas; Generate corresponding warning information based on the risk classification results.

[0013] Preferably, the visualization platform display includes: Display the distribution of geological disaster risk areas based on three-dimensional geographic information system; Displays dynamic trends of risk evolution and visual analysis results of monitoring point data.

[0014] Preferably, the spatiotemporal correlation constraints achieve dynamic optimization of reconstructed data features through parameter adjustment to ensure that the extracted spatiotemporal features conform to the actual physical laws of geological hazard data.

[0015] The present invention provides a method for monitoring, predicting and warning geological disasters based on artificial intelligence. It has the following beneficial effects: 1. The present invention realizes a full-chain geological disaster monitoring and prediction method from data input to warning output through the overall process design of data collection, data preprocessing, spatiotemporal correlation matrix construction, feature extraction, time series prediction, feedback optimization and warning release. This method integrates sensor data, remote sensing images and historical disaster data. Through full-process information processing and modeling, it can effectively extract key features in geological disaster monitoring data and dynamically predict future risk levels. The overall technical solution covers the complete links of data processing, ensures the comprehensiveness and accuracy of the model's prediction, and provides a scientific basis for geological disaster prevention and control.

[0016] 2. The present invention dynamically compares the prediction results with the actual observation data through the feedback optimization mechanism, and optimizes the model parameters through the back propagation algorithm, so that the model can be adjusted in real time according to the changes in geological conditions, improving the system's adaptability to dynamic changes in the geological environment. In addition, a dynamic update mechanism is used to feed back the newly acquired monitoring data into the training data set, and the model is iteratively optimized to ensure the continuous improvement of the prediction results, significantly improving the stability of the model to long-term complex environments.

[0017] 3. The present invention effectively extracts nonlinear features in geological disaster data through the variational autoencoder in the feature extraction step, combined with the spatiotemporal correlation constraints, so that the features of data reconstruction can truly reflect the temporal dynamic relationship and spatial distribution law between monitoring points. Compared with traditional feature extraction methods, the technical solution of the present invention can more accurately capture the complex characteristics of geological disasters and provide high-quality input data for subsequent time series prediction.

[0018] 4. The present invention visualizes the geological disaster risk area through a three-dimensional geographic information system (GIS), and presents the dynamic evolution trend of the risk in combination with the time dimension, which effectively improves the intuitiveness of risk display and the efficiency of information transmission. At the same time, combined with the visual analysis of monitoring point data, users can quickly obtain detailed information on the monitoring area, and support accurate query and analysis of risks in different regions, ultimately providing scientific decision-making support for disaster prevention and control and emergency management. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] In order to better understand the present invention, the above contents are described in detail below in conjunction with specific embodiments. Please see attached Figure 1 The embodiment of the present invention provides a method for monitoring, predicting and warning geological disasters based on artificial intelligence, comprising the following steps: Data collection: Collect geological disaster monitoring data, including sensor data, remote sensing image data and historical geological disaster data, to provide basic data input for subsequent processing; In this step, collecting geological disaster monitoring data is the basis of the entire prediction and early warning method. The data sources are extensive, mainly including the following categories: Sensor data: Through the deployment of multiple sensors in the monitoring area, key physical parameter data related to geological hazards are obtained in real time, such as surface displacement, pore water pressure, soil moisture, rainfall and ground temperature. Sensor data is real-time and accurate, and can reflect the dynamic changes of geological conditions.

[0022] Remote sensing image data: Surface image data is obtained through satellite remote sensing images or drone aerial photography technology to capture the changing trends of geological environment in a large area, such as potential signs of landslides, subsidence and mudslides. Remote sensing image data has the characteristics of wide spatial coverage and high resolution, and can provide information from an overall perspective for the monitoring area.

[0023] Historical geological disaster data: Collect historical geological disaster records, geological structure data and meteorological data related to the monitoring area. These historical data provide training and reference basis for the model, which can help identify the potential laws and influencing factors of disaster occurrence.

[0024] Through the collection of the above multi-source data, comprehensive and reliable basic data input is provided for subsequent data preprocessing, feature extraction and predictive analysis, laying a solid foundation for the monitoring and early warning of geological disasters.

[0025] Data preprocessing: Time alignment and missing completion of collected data to ensure the temporal and spatial consistency and integrity of data, and provide high-quality data for subsequent feature extraction; In this step, preprocessing the collected geological disaster monitoring data is an important step to ensure that the subsequent model can accurately extract data features and perform predictive analysis. It specifically includes the following two parts: Time alignment: Geological disaster monitoring data often comes from multiple data sources (such as sensors, remote sensing images, and historical data). Due to differences in acquisition time resolution and sampling frequency, the data may be inconsistent in the time dimension. To this end, time alignment technology is used to process data with different time resolutions so that all data can be synchronized on the same time axis. The purpose of time alignment is to ensure the consistency of multi-source data in the time dimension and provide accurate time series input for subsequent analysis. The dynamic time warping (DTW) method can effectively process data sources with non-uniform sampling and ensure that the time series characteristics of the data are preserved.

[0026] Missing completion: During the collection process, some monitoring data may be missing due to equipment failure, network interruption or external interference. In order to avoid the impact of incomplete data on subsequent analysis, data completion technology is used to complete the missing data. Use generative adversarial network (GAN) or interpolation algorithm to fill the missing values. GAN simulates the distribution of missing data through the generator, and the discriminator evaluates the completion results to generate a completed value consistent with the original data distribution. For small-scale data missing, simple methods such as linear interpolation or multivariate interpolation can also be used for rapid processing. Through time alignment and missing completion, the consistency and integrity of the data in time and space are guaranteed, providing high-quality basic data input for subsequent feature extraction and model analysis.

[0027] Construction of spatiotemporal correlation matrix: Analyze the preprocessed monitoring data, calculate the spatiotemporal correlation degree between different monitoring points, and construct a spatiotemporal correlation matrix that reflects the correlation characteristics between monitoring points to provide constraints for feature extraction; In this step, in geological disaster monitoring, there is usually a temporal and spatial correlation between the data of different monitoring points. For example, disasters such as landslides and subsidence may spread between multiple adjacent monitoring points. In order to capture these correlation characteristics, the preprocessed monitoring data is analyzed, the temporal and spatial correlation degree between each monitoring point is calculated, and the temporal and spatial correlation matrix is ​​constructed for modeling constraints in subsequent feature extraction. The specific process is as follows: Time dimension correlation analysis: Analyze the data change trend of each monitoring point in the time dimension. By calculating the dynamic consistency of time series data, the time correlation between different monitoring points is determined. The correlation of the time dimension can be compared with the change pattern of historical observation data to identify those monitoring points with similar behavior in time. For example, multiple sensors in a landslide area may show similar surface displacement changes in a similar time period.

[0028] Spatial dimension association analysis: Analyze the spatial distribution relationship between monitoring points, such as the distance between sensors, geological structure similarity, or geographical proximity. Use spatial association calculations (such as metrics based on Euclidean distance or geological similarity) to evaluate the spatial dependence between monitoring points. Adjacent areas or points with similar geological structures usually show strong spatial associations.

[0029] Comprehensive spatiotemporal correlation calculation: The correlation characteristics of time and space dimensions are integrated to calculate the degree of time and space correlation between monitoring points. By combining the dynamic changes of the time dimension with the distribution relationship of the space dimension, a complete time and space correlation is formed.

[0030] The spatiotemporal correlation can be calculated using correlation analysis methods (such as the Pearson correlation coefficient): in the time dimension, it measures whether the trend of the monitoring point data changing over time is consistent; in the spatial dimension, it measures the similarity between the geographical locations or physical characteristics of the monitoring points.

[0031] Constructing a spatiotemporal correlation matrix: The spatiotemporal correlation between monitoring points is expressed in matrix form, and a spatiotemporal correlation matrix that reflects the spatiotemporal correlation characteristics between monitoring points is constructed. Each element of the matrix represents the spatiotemporal correlation between monitoring points. The larger the value in the matrix, the stronger the correlation between the two monitoring points.

[0032] Constraints on feature extraction: The spatiotemporal correlation matrix is ​​used as a constraint in the subsequent feature extraction process to ensure that the features extracted from the data can reflect the correlation characteristics between monitoring points. For example, in the reconstruction process of the variational autoencoder, the accuracy of feature extraction is optimized by introducing the constraints of the spatiotemporal correlation matrix, so that the extracted features conform to the actual physical laws of geological hazards.

[0033] Feature extraction: The preprocessed monitoring data is encoded and decoded using a variational autoencoder, and spatiotemporal correlation matrix constraints are introduced during the data reconstruction process to extract the nonlinear spatiotemporal characteristics of the geological disaster monitoring data; In this step, feature extraction is the core step of nonlinear spatiotemporal modeling based on geological disaster monitoring data. Variational autoencoder (VAE) is used to encode and decode the preprocessed data. By introducing spatiotemporal correlation matrix constraints, the extracted features can reflect the complex spatiotemporal characteristics of geological disaster monitoring data and provide high-quality input for subsequent time series prediction.

[0034] Time series prediction: The extracted spatiotemporal features are input into the long short-term memory network for time series prediction. The weights of different time steps are dynamically adjusted through the attention mechanism to output the geological disaster risk level in the future period. In this step, time series prediction is a key step to predict the future geological disaster risk level based on the spatiotemporal characteristics extracted from geological disaster monitoring data. This step uses the long short-term memory network (LSTM) to model time series data, enhances the weight distribution of key time points through the attention mechanism, and finally outputs the geological disaster risk level in the future period.

[0035] Feedback optimization: Based on the error between the prediction results and the actual observation data, the back propagation algorithm is used to optimize the parameters of the feature extraction and time series prediction model, so that the model can adapt to the changing geological conditions and improve the prediction accuracy; In this step, feedback optimization is a key step to dynamically adjust model parameters by comparing the differences between model prediction results and actual observation data. By introducing the back propagation algorithm, the parameters of the feature extraction model (variational autoencoder) and the time series prediction model (LSTM-Attention) are optimized, so that the model can adapt to changes in geological conditions and continuously improve the accuracy and robustness of predictions.

[0036] Warning release: Generate geological disaster warning information based on the predicted risk level, and display high-risk areas and their dynamic evolution trends through a visualization platform to support geological disaster prevention and control decision-making.

[0037] In this step, warning release is a key link to provide timely warning and intuitive decision-making support for geological disaster prevention and control based on the risk level predicted by the model. By generating warning information of risk level and combining the visualization platform to display high-risk areas and their dynamic evolution trends, the ability to respond to geological disasters can be effectively improved.

[0038] Data preprocessing includes: Time-align data with different time resolutions to ensure consistency in the time dimension of the data; Generate adversarial networks to complete data and solve the problem of missing data.

[0039] In this embodiment, geological disaster monitoring data usually comes from multiple different data sources, such as sensors, remote sensing images, and historical geological disaster records. These data may have significant differences in temporal resolution and spatial coverage, and some data may be missing or incomplete due to equipment failure or transmission problems. Therefore, in order to ensure the accuracy of subsequent geological disaster predictions and the stability of model modeling, it is necessary to pre-process the collected raw data, including time alignment of data with different temporal resolutions and missing data completion through generative adversarial networks, so as to unify the temporal and spatial characteristics of the data and ensure the integrity of the data.

[0040] In order to solve the problem of inconsistent temporal resolution of geological disaster monitoring data, the Dynamic Time Warping (DTW) method is used to align multi-source time series data. The main goal of dynamic time warping is to dynamically adjust the time step of the time series, minimize the time deviation between the series, and align all time series on a unified time scale.

[0041] In dynamic time warping, the cumulative matching distance between monitoring points and time series is calculated, and the time step of the time series is dynamically adjusted so that the data can remain consistent in the same time dimension. The calculation formula is:

[0042] in, Represents the time step and The cumulative matching distance between ; Represents the Euclidean distance between two time step data; Indicates the selection of the best matching path among three possible paths.

[0043] Through the above process, the time series data of the monitoring points are dynamically adjusted to synchronize them in the time dimension. For example, if the sampling frequency of some sensor data is low (such as once an hour) and the frequency of other data is high (such as once a minute), the dynamic time warping method is used to align all data to a common time resolution, thereby ensuring the consistency of time series analysis.

[0044] In order to solve the problem of missing data in the collected data, a generative adversarial network (GAN) is used to complete the data to solve the problem of incomplete or interrupted sensor data. The generative adversarial network consists of a generator and a discriminator. The generator simulates the distribution of missing data and generates complementary data. At the same time, the discriminator judges the authenticity of the generated data to form an adversarial optimization process. Specifically, the input of the generator is the original monitoring data with missing data, and the output is the complementary data. The goal of the generator is to minimize the difference between the generated data and the actual data distribution. The optimization goal is:

[0045] in, Represents the output data of the generator; Represents the true probability of the discriminator for the data; Represents the distribution of real data; represents the distribution of input noise.

[0046] During the adversarial training process, the generator continuously learns and optimizes its output results, making it difficult for the discriminator to distinguish the generated data as false data. It should be noted that in order to further improve the accuracy of the completed data, a conditional generative adversarial network (Conditional GAN, CGAN) can be introduced into the generator to constrain the contextual characteristics of the generated data by taking conditional information such as time steps or sensor positions as additional inputs.

[0047] For example, if a sensor is at time If the data of adjacent time points are missing, and As a conditional input, the generator generates time under this condition The completion data of , the generation formula is as follows:

[0048] This method can ensure that the generated data is consistent with the changing trend of adjacent data in the time dimension.

[0049] In some embodiments, in order to improve the processing efficiency of dynamic time warping and generative adversarial networks, the data can also be processed by region. For example, the monitoring area is divided into several sub-regions, and the time alignment and missing completion methods are independently applied to the data in each sub-region to reduce the computational complexity. Specifically, for areas with high spatial correlation, the change pattern of their time series data may be more consistent. In this case, the same optimization goal can be set for the time alignment and completion process in the sub-region, thereby improving the overall preprocessing efficiency.

[0050] It should be noted that the data preprocessing step is crucial for subsequent model building. Through time alignment, the temporal consistency of different data sources can be ensured to avoid analysis bias caused by differences in temporal resolution; by generating adversarial networks to complete missing data, data gaps can be effectively filled and the integrity and coherence of monitoring data can be enhanced.

[0051] It is understandable that although the embodiments of the present invention mainly use dynamic time warping and generative adversarial networks for time alignment and data completion, for some special scenarios, other methods such as simple interpolation, spline fitting and other methods can also be used for partial replacement. However, these alternative methods are only applicable to situations where the amount of missing data is small or the time step changes relatively slowly. For complex dynamic environments, generative adversarial networks can better ensure the authenticity and distribution consistency of the completed data.

[0052] The steps to construct the spatiotemporal correlation matrix include: By analyzing the changing trends and spatial distribution characteristics of each monitoring point in the time dimension, the spatiotemporal correlation degree between the monitoring points is calculated to form a correlation matrix reflecting the spatiotemporal characteristics.

[0053] In this embodiment, by analyzing the change trend of the monitoring points in the time dimension, the correlation analysis method is used to calculate the time correlation degree between the monitoring points. Specifically, the Pearson correlation coefficient is used to analyze the time series data of each monitoring point, and its calculation formula is:

[0054] in, Indicates monitoring point and The temporal correlation between and Respectively indicate monitoring points and In time Observed value of and Respectively indicate monitoring points and The time series mean of ; Indicates the total length of the time series.

[0055] The above formula can be used to measure the degree of synchronization of the time series changes of each monitoring point. For example, if the time correlation of two monitoring points is A higher value indicates that the change trends of the two points in the time dimension are relatively consistent. For example, the regional displacement characteristics caused by rainfall may be reflected in the data of the two monitoring points at the same time.

[0056] By analyzing the distribution characteristics of monitoring points in the spatial dimension, the spatial correlation between each monitoring point is calculated. Specifically, the spatial correlation calculation method based on Euclidean distance is adopted, and the formula is:

[0057] in, Indicates monitoring point and The degree of spatial correlation between Indicates monitoring point and The Euclidean distance between them is calculated as:

[0058] in,( , , )and( , , ) represent monitoring points and The three-dimensional coordinates of .

[0059] In order to comprehensively reflect the spatiotemporal correlation characteristics between monitoring points, the temporal correlation and spatial correlation are combined to construct a complete spatiotemporal correlation matrix. , the formula is as follows:

[0060] in, and is the weight coefficient of temporal correlation and spatial correlation, satisfying , can be adjusted according to the actual geological disaster scenario; represents time correlation, Indicates the degree of spatial association.

[0061] As an option, when the spatiotemporal characteristics of geological hazards are more dependent on spatial proximity (e.g., landslides usually affect adjacent areas first), the value; for disasters that are greatly affected by the external environment (such as mudslides caused by rainfall), the The weight of .

[0062] Specifically, the final spatiotemporal correlation matrix can be expressed as:

[0063] Among them, the matrix elements represent the monitoring points and The spatiotemporal correlation between them, the dimension of the matrix is * . is the total number of monitoring points.

[0064] The spatiotemporal correlation matrix can not only be used for correlation analysis between geological disaster monitoring points, but also serve as an important constraint in the subsequent feature extraction process. For example, in the reconstruction process of the variational autoencoder, the spatiotemporal correlation matrix is ​​introduced as a constraint term into the objective function to ensure that the extracted features can reflect the actual correlation between the monitoring points.

[0065] It is understandable that although the embodiment of the present invention adopts a weighted combination of time correlation and spatial correlation to construct a spatiotemporal correlation matrix, for specific scenarios (such as geological disasters in a specific area that significantly rely on spatial characteristics), a correlation matrix can also be constructed based on only a single dimension. However, a matrix that integrates spatiotemporal characteristics is more suitable for complex disaster scenarios, thereby providing more reliable analysis support.

[0066] The feature extraction steps include: Use variational autoencoders to encode and decode geological disaster monitoring data; The spatiotemporal correlation constraints are introduced in the reconstruction process to make the characteristics of the reconstructed data consistent with the spatiotemporal relationship between the monitoring points.

[0067] In this embodiment, the geological disaster monitoring data is encoded and decoded by a variational autoencoder (VAE). Specifically, VAE includes two parts: an encoder and a decoder, which are used for feature extraction and data reconstruction respectively.

[0068] During the encoding process, the original monitoring data Mapping to latent variables space The output of the encoder is the latent variable , whose distribution is assumed to be normal , where the mean and variance The original input data Learned through neural networks.

[0069] The encoder calculation formula is as follows:

[0070] in, and Represent the functions used to calculate the mean and standard deviation in the encoder neural network respectively; is the encoded latent variable, which represents the potential features extracted from the monitoring data.

[0071] Decoder part During the decoding process, the hidden variable Decoded back to the original data space to reconstruct the monitoring data The decoder generates reconstructed data through a neural network , and its calculation formula is:

[0072] in, represents the decoder neural network, which is used to extract latent variables Restore the characteristics of the original data.

[0073] It should be noted that the encoder and decoder together constitute VAE, which realize the mapping from high-dimensional data to low-dimensional latent space and the reverse reconstruction from latent space to high-dimensional data through mutual cooperation.

[0074] Introducing spatiotemporal correlation constraints during reconstruction To ensure the reconstruction of the data The spatiotemporal characteristics of the original input data To keep consistent, in this embodiment, the spatiotemporal correlation matrix is ​​introduced as a constraint condition in the training process of VAE. Specifically, the spatiotemporal correlation constraint term is defined to measure whether the spatiotemporal correlation degree of the reconstructed data conforms to the correlation characteristics of the original monitoring data. The calculation formula is:

[0075] in, Indicates monitoring point and The temporal correlation between represents the spatiotemporal correlation of corresponding monitoring points in the reconstructed data; is the weight coefficient of the spatiotemporal correlation constraint, which is used to balance the impact between the reconstruction error and the correlation constraint.

[0076] Objective function: During the model optimization process, the following comprehensive loss function is defined to guide the training of VAE:

[0077] in, Reconstruction error term: measures the reconstruction of data and the original input data The difference ensures that the reconstructed data is as close to the original data as possible:

[0078] Among them, the KL divergence term : Constrained latent variable distribution Close to the prior distribution , ensuring that the latent space has good generative capabilities:

[0079] Among them, the spatiotemporal correlation constraint : Ensure that the spatiotemporal correlation characteristics of the reconstructed data conform to the physical laws of the original data.

[0080] In some embodiments, in order to adapt to the characteristics of different regions or different types of geological disasters, the weight coefficient of the spatiotemporal correlation matrix can be adjusted. Specifically: For disasters with more significant spatial characteristics (such as landslides), the The weight of the spatial correlation part in ; For disasters with more prominent temporal characteristics (such as mudslides caused by rainfall), the weight of the time-related part can be appropriately enhanced.

[0081] By introducing spatiotemporal correlation constraints in the reconstruction process, the method of the present invention can not only extract low-dimensional potential features from high-dimensional data, but also effectively retain the spatiotemporal relationship between monitoring points, so that the extracted features are more in line with the physical laws of geological disasters, providing high-quality input data for subsequent time series prediction.

[0082] It can be understood that the method of combining variational autoencoder and spatiotemporal correlation constraints adopted in the present invention has significant advantages in nonlinear and spatiotemporal consistency feature extraction compared to traditional dimensionality reduction methods (such as principal component analysis PCA), and is therefore more suitable for complex geological disaster data scenarios.

[0083] The time series forecasting steps include: Use long short-term memory network to extract time series features from reconstructed data; Based on the attention mechanism, the data weights of different time steps are dynamically adjusted to highlight the moments that have a greater impact on the prediction results.

[0084] In this embodiment, firstly, a long short-term memory network (LSTM) is used to model the time series features of the reconstructed data. LSTM is an improved recurrent neural network (RNN), which is characterized by being able to effectively capture dependencies in long time series and solve the gradient vanishing and gradient exploding problems in traditional RNN. Specifically, the structure of the LSTM unit includes the following core parts: forget gate: the forget gate determines whether to retain the memory content of the previous time step, input gate: the input gate determines whether the new information of the current time step needs to be written into the memory unit, update memory unit: the memory unit of the current time step is controlled by the forget gate and the input gate; output gate: the output gate controls the update of the hidden state of the current time step.

[0085] In this embodiment, based on the LSTM extraction of time series features, the attention mechanism is further introduced to dynamically adjust the weights of different time steps to highlight the time points that have a greater impact on the prediction results. The attention mechanism can assign a weight coefficient to each time step, and the size of the weight is determined by the feature importance of the current time step.

[0086] Specifically, the implementation of the attention mechanism includes the following steps: 1. Weight calculation, calculate the attention weight score for each time step. The score function is usually the degree of match between the current hidden state and the global context:

[0087] in, represents the hidden state of the current time step; is the global context vector; is the function used to compute the score, which can be a simple inner product, MLP, or other form.

[0088] 2. Normalize the weights and use the Softmax function to normalize the weight scores to get the attention weights:

[0089] in, satisfy .

[0090] 3. Context vector, calculate the weighted sum of the hidden states according to the attention weights to get the global context vector:

[0091] in, is the global context vector.

[0092] The attention mechanism can also combine external information of the time step, such as rainfall, displacement rate and other influencing factors of specific geological hazards, to further optimize the weight allocation strategy. Specifically, by introducing external information as an additional input into the scoring function, the sensitivity of the attention mechanism to the dynamic changes of geological hazards can be enhanced.

[0093] In some embodiments, different attention weight allocation strategies can also be designed for prediction tasks with different time spans (such as short-term prediction or long-term prediction). For example: For short-term predictions, the weights are more concentrated on the most recent time step; For long-term forecasts, the weights are distributed more evenly to capture long-term trends.

[0094] By combining the time series modeling capability of LSTM with the dynamic weight adjustment capability of the attention mechanism, the time series prediction step of the present invention can accurately capture the changing characteristics of key moments in complex geological disaster scenarios, thereby providing a scientific basis for future risk level predictions. It can be understood that compared with traditional time series prediction methods, the method of the present invention has significant advantages in dynamic weight allocation and time dependency modeling, and can more effectively deal with complex time series relationships in geological disaster monitoring data.

[0095] The feedback optimization steps include: Calculate the error between the predicted results and the actual observed data; The back propagation algorithm is used to update and optimize the model parameters to minimize the prediction error.

[0096] Feedback optimization further includes: The newly acquired geological monitoring data and prediction results are fed back into the training data set, the model is retrained and the parameters are iteratively optimized to improve the prediction accuracy of the model.

[0097] In this embodiment, feedback optimization is a key step in the geological disaster monitoring and prediction system. By calculating the error between the prediction results and the actual observation data, and combining the back propagation algorithm to update and optimize the model parameters, the prediction performance can be continuously improved. At the same time, by feeding back the newly acquired geological monitoring data and prediction results into the training data set, retraining the model and iteratively optimizing the parameters, the prediction accuracy and adaptive ability of the model are further improved. This step aims to dynamically optimize the model so that it can adapt to the ever-changing geological conditions and complex environment.

[0098] For the feedback optimization of the model, the error between the predicted result and the actual observed data is first calculated as an indicator to measure the performance of the model. The mean square error (MSE) is used as the error evaluation standard, and its calculation formula is:

[0099] in, represents the prediction error; is the total number of data samples; For the The actual observed value of samples; For the The model predicts the value of the sample.

[0100] It should be noted that the mean square error can intuitively reflect the degree to which the prediction results deviate from the actual observed values. The smaller the value, the closer the model prediction results are to the actual situation.

[0101] The model parameters are optimized through the back propagation algorithm to minimize the prediction error. The core of the back propagation algorithm is to use the gradient information of the error to update the model parameters, which specifically includes the following steps: 1. Calculate the error gradient The loss function for the model Derivatively obtain the gradient of the loss function with respect to the model parameters:

[0102] in, Represents all trainable parameters of the model, including parameters of feature extraction models (such as variational autoencoders) and time series prediction models (such as LSTM-Attention).

[0103] 2. Parameter Update Use gradient descent or other optimization algorithms (such as Adam optimizer) to iteratively update the parameters:

[0104] in, is the learning rate, which controls the magnitude of each update; is the gradient value, indicating the direction of parameter adjustment.

[0105] 3. Model optimization scope Feature extraction model optimization: Update the parameters of the encoder and decoder in the variational autoencoder to improve the extraction of spatiotemporal features; Time series prediction model optimization: Update the LSTM weight matrix, bias, and attention mechanism parameters to enhance the modeling capability of dynamic changes in time series.

[0106] Feedback optimization further includes: feeding back newly acquired geological monitoring data and prediction results into the training data set, retraining the model and iteratively optimizing parameters. The specific implementation includes the following: Acquisition and preprocessing of new data: Collect new monitoring data, such as new time step data from sensors, remote sensing image updates, or historical records of new geological disasters.

[0107] Preprocess the new data, including time alignment and missing data completion, to ensure that the data quality meets the training requirements.

[0108] Data feedback and expanded training set: Merge new data with existing training data to form an expanded training set; adjust the weight of training data according to the characteristics of the new data, such as giving higher weight to recent data to enhance the model's ability to respond to dynamic changes.

[0109] Model retraining: Retrain the model using an extended training set, and update the parameters of the feature extraction and time series prediction models based on the new data distribution; during the training process, the inheritance of historical data characteristics is maintained while optimizing the adaptability to new data.

[0110] Iterative optimization process: New data and the latest prediction results are introduced into the training loop, and the "training-feedback-update" process is continuously repeated; through multiple iterative training, the model maintains high prediction accuracy in complex geological environments.

[0111] In some embodiments, in order to improve the efficiency of feedback optimization, the model parameters can be updated in real time in combination with online learning methods, without the need to fully train the model every time new data is acquired. Specifically, by quickly training on small batches of new data, the weights of the model are dynamically updated, thereby achieving real-time optimization.

[0112] For example: after the sensor acquires new data, the model locally optimizes some parameters based on the current data; using the sliding window method, only the most recent monitoring data is trained to keep the model lightweight and efficient.

[0113] It should be noted that: through the feedback optimization step, the present invention can dynamically adjust the model to adapt to the changing geological conditions, making the prediction results more accurate and timely. Compared with the static training model, the feedback optimization method of the present invention can maintain the efficiency and stability of the model during long-term operation.

[0114] It is understandable that the feedback optimization method used in the present invention not only includes direct back propagation optimization of errors, but also strengthens the self-learning ability of the model through the feedback loop of data, thereby achieving continuous improvement in geological disaster monitoring and prediction. By introducing new data into the training set, the model can continuously update the understanding of the dynamic changes of geological disasters, providing more scientific prediction support for geological disaster prevention and control.

[0115] The steps for issuing warnings include: Classify geohazard risks into low-, medium- and high-risk areas; Generate corresponding warning information based on the risk classification results.

[0116] In this embodiment, warning release is the final link in the technical solution of the present invention. Its core is to classify the region according to the geological disaster risk level predicted by the model and generate corresponding warning information so as to be delivered to relevant departments or the public in a timely manner. This step ensures effective warning and scientific response support for geological disaster risks through the systematic design of risk classification and information generation, thereby achieving the purpose of disaster prevention and mitigation.

[0117] First, the geological disaster risk values ​​predicted by the model are classified, and the regional risks are divided into three levels: low risk, medium risk and high risk, in order to provide graded early warning services.

[0118] Risk classification rules: define risk level classification rules by setting thresholds for risk values ​​predicted by the model. Specific classification standards can be adjusted based on historical geological disaster data and distribution characteristics of monitoring point data. The general rules are as follows: Low risk: When the risk value , indicating that the area is generally safe and no immediate action is required; Medium risk: , indicating that there is a certain degree of disaster risk in the region, and it is recommended to strengthen monitoring and early warning preparation; High risk: When , indicating that the possibility of geological disasters in the region is high and emergency prevention and control measures need to be taken immediately.

[0119] The classification formula is expressed as:

[0120] in, and These are the upper and lower thresholds for risk classification, which can be adjusted based on different regions or different types of geological hazards.

[0121] Generate corresponding warning information based on risk classification results. Specific contents include: Basic warning information Risk level: clearly mark the risk level as "low risk", "medium risk" or "high risk"; Risk area: Give the scope of the area threatened by disasters and mark the specific location based on geographic information.

[0122] Dynamic risk information Time dimension: provides the time range for risk prediction, such as the risk level in the next hour, day or week; Dynamic change trend: describes the change of risk level over time, such as "risk level continues to rise" or "risk level remains stable".

[0123] Recommended prevention and control measures Low-risk areas: Regular monitoring and regular inspections are recommended; Medium-risk areas: It is recommended to take early warning and preparedness measures, such as strengthening the maintenance of monitoring equipment or deploying emergency personnel in advance; High-risk areas: It is recommended to immediately activate the emergency plan, including evacuation of personnel, closure of dangerous areas and related prevention and control measures.

[0124] In some embodiments, the generated warning information can be delivered to relevant departments or the public through multiple channels to ensure rapid delivery and efficient use of information. The delivery methods of warning information include: SMS notification: Send brief warning information to residents, monitoring personnel or emergency management departments, such as risk level and recommended actions; Mobile application push: push detailed warning information in real time through geological disaster monitoring and early warning APP; Government website announcements: Detailed risk assessment and warning information will be published on the local emergency management department or disaster prevention and control official website; Public broadcasting: For high-risk areas, emergency warnings are issued through the broadcasting system; Social media communication: Share real-time risk information through social platforms such as Weibo and WeChat to expand information coverage.

[0125] In order to enhance the scientificity and intuitiveness of early warning information, the high-risk areas can be visualized in combination with the Geographic Information System (GIS). The specific steps include: Map marking: Use GIS technology to mark risk areas on the map and use colors to distinguish risk levels, for example: green for low risk; yellow for medium risk; red for high risk.

[0126] Dynamic trend chart: Displays the risk change trend on a timeline, such as using a line chart or heat map to show the risk evolution at different time points.

[0127] User interaction: Users can click on the monitoring point on the map to view the risk level, change trend and recommended measures of the specific location.

[0128] The visualization platform display includes: Display the distribution of geological disaster risk areas based on three-dimensional geographic information system; Displays dynamic trends of risk evolution and visual analysis results of monitoring point data.

[0129] In this embodiment, the visualization platform display is an important display and decision support link in the technical solution of the present invention. It intuitively displays the distribution of geological disaster risk areas based on a three-dimensional geographic information system (GIS), and combines the time dynamic evolution trend and the visualization analysis of monitoring point data to provide relevant personnel with a comprehensive insight into geological disasters and a scientific basis for decision-making. This platform can not only display the spatial distribution of current risks, but also intuitively reflect the time-varying characteristics of risks through dynamic analysis.

[0130] Using three-dimensional geographic information system (3DGIS) as the core technology, the spatial distribution of geological disaster risk areas is displayed in the form of three-dimensional maps.

[0131] 1. 3D Geographic Information Display Risk area marking: Areas with different geological disaster risk levels are displayed on the 3D map by color differentiation. Example: Low-risk areas: marked in green; Medium-risk areas: marked in yellow; High-risk areas: marked in red.

[0132] Spatial coverage: For each risk area, the specific range is displayed in combination with geographic coordinate information (such as latitude and longitude, altitude, etc.), for example, "The high-risk area covers an area of ​​about 10 square kilometers and is located near the coordinates (102.35,34.12)" 2. Terrain information overlay Based on the capabilities of the three-dimensional geographic information system, the terrain features of the geological disaster monitoring area, such as slope, surface undulation, etc., are displayed.

[0133] By superimposing terrain information, the geographical characteristics of the area where geological disasters occur can be intuitively displayed, such as the steepness of the landslide area or the distribution direction of debris flow channels.

[0134] 3. Dynamic display of risk level Dynamically adjust the transparency or color depth of the risk level label to reflect the severity of the risk in different areas. For example, the color of the high-risk area can increase from light red to dark red to intuitively show the severity of the risk level.

[0135] The platform further combines the time dimension to display the dynamic evolution trend of geological disaster risks.

[0136] Risk change timeline: Add a timeline control to the visualization platform, allowing users to select a specific time point or time period to view changes in risk areas.

[0137] Important time nodes are marked on the timeline, such as "the moment when the landslide risk increases significantly" or "the high-risk period for mudslides".

[0138] Dynamic evolution of risk: Dynamically generate a heat map of risk areas changing over time, showing the changing trend of risk levels at different time points. For example: Time point 1: High-risk areas are concentrated on specific hillsides; Time point 2: The high-risk area expands to a wider area; Time point 3: The risk in some areas is reduced and the color changes from red to yellow.

[0139] The heat map animation displays the temporal evolution of risk levels in a smooth manner, helping users to intuitively understand the dynamic development process of disasters.

[0140] Risk trend chart: Provides a graphical display of risk changes in the platform, such as a line chart showing the change in risk value of a specific monitoring point over time.

[0141] You can select multiple monitoring points, overlay and compare their risk value change trends, and analyze the synchronization or asynchrony of risk changes in different areas.

[0142] At the same time, combined with monitoring point data, it further supports multi-dimensional visual analysis of monitoring data.

[0143] Monitoring point information interaction: In the 3D map, users can click on any monitoring point to view the real-time and historical data of the point, such as surface displacement rate, rainfall, soil moisture content, and pore water pressure.

[0144] All data are displayed in the form of line graphs, bar graphs or pie charts, allowing users to quickly understand the current status and changing trends of monitoring points.

[0145] Multi-dimensional data overlay: supports visual display of multiple monitoring data at the same time. For example, you can overlay a rainfall distribution map or a surface displacement rate map on the risk area layer to analyze the triggering factors of geological disasters from multiple angles.

[0146] Geological background information display: Provides auxiliary information of regional geological background, such as rock and soil types, fault distribution or historical disaster records, and displays it in superposition with risk area information through layer switching In some embodiments, the visualization platform supports users to customize display content to meet the analysis needs of different users. For example: Users can choose to display only high-risk areas, or only the risk changes around specific monitoring points; Users can adjust the time interval of the timeline (such as hours, days or weeks) to obtain more detailed or more summarized risk dynamics.

[0147] The platform also provides a comparative analysis function for prediction data. For example, compare the prediction results of different models, such as the difference in risk prediction in the same area between the current model and the model trained with historical data; Compare actual observation data with model prediction results to analyze the accuracy of the model and room for improvement It should be noted that: The visualization platform is not only a tool for risk display, but also an important means of decision support. Through the three-dimensional geographic information system, the spatial distribution and temporal evolution of geological disaster risks are intuitively displayed, providing users with a full range of disaster analysis capabilities. Combined with the dynamic update of real-time data and the multi-dimensional analysis of monitoring points, the platform can meet the early warning and response needs in complex geological disaster scenarios.

[0148] It is understandable that the present invention uses 3D GIS technology combined with dynamic analysis functions, which can better reflect the three-dimensional and time-dynamic characteristics of geological disasters than traditional 2D displays. This design enables users to locate high-risk areas more accurately and intuitively grasp the occurrence and spread trends of disasters, thereby providing a scientific decision-making basis for geological disaster prevention and control.

[0149] The spatiotemporal correlation constraints achieve dynamic optimization of the reconstructed data features through parameter adjustment to ensure that the extracted spatiotemporal features conform to the actual physical laws of geological hazard data.

[0150] In this embodiment, in order to ensure that the features extracted from the geological disaster monitoring data not only have data-driven statistical significance, but also reflect the physical regularity of actual geological disasters, spatiotemporal correlation constraints are introduced, and the optimization of the reconstructed data features is achieved through dynamic adjustment of the correlation parameters. This optimization enables the extracted features to not only capture the nonlinear characteristics of the monitoring data, but also accurately reflect the spatiotemporal correlation between the monitoring points, thereby improving the model's ability to predict disaster risks. Among them: 1. The core of spatiotemporal correlation constraints is to model the temporal dynamics and spatial interactions of monitoring data, and dynamically optimize the reconstructed features by adjusting the correlation weights during feature extraction and data reconstruction. This method has the following two advantages: Physical consistency: Through constraints, the reconstructed data not only conforms to the characteristics in a statistical sense, but also reflects the actual physical characteristics of the disaster data, such as the overall movement of the landslide body and multi-point synchronous response.

[0151] Adaptability: The constraint weights are dynamically adjusted according to the degree of dependence of different disaster scenarios on temporal or spatial characteristics, making the method widely applicable.

[0152] 2. Implementation method (1) Modeling of temporal and spatial characteristics: In the temporal and spatial correlation constraints, temporal characteristics are used to capture the dynamic consistency of monitoring points, while spatial characteristics reflect the geographical dependence between monitoring points. These correlation characteristics are not simply fixed, but need to be dynamically adjusted according to specific scenarios.

[0153] Time dependence: For example, debris flows caused by increased rainfall will make the time characteristics dominate; Spatial dependence: Landslides caused by fault sliding will enhance the spatial dependence characteristics.

[0154] (2) Dynamic parameter adjustment: By dynamically adjusting the weight parameters, the performance of spatiotemporal constraints at different stages of disasters can be made more realistic: Dynamic time weighting: For time-sensitive disasters, enhance the impact of time correlation in the optimization objective; Dynamic spatial weighting: For disasters with greater spatial expansibility, it enhances the role of spatial correlation.

[0155] Parameter tuning can be based on the following strategies: Data-driven dynamic weights: Dynamically set the time and space characteristic weights based on the distribution characteristics of monitoring data (such as temporal and spatial change trends, mutation point distribution); Verification and optimization: During the training process, adaptive adjustment of parameters is achieved by continuously optimizing the spatiotemporal weights (such as through the validation set test results).

[0156] 3. Specific optimization steps During the data reconstruction process, the spatiotemporal correlation constraints are dynamically optimized through the following steps: Construct initial spatiotemporal weights: Set initial time and space weights based on the disaster type (such as landslide, debris flow); Based on experience with historical data, determine the approximate importance ratio of time and space characteristics.

[0157] Real-time data-driven adjustments: In the real-time monitoring of disasters, the importance of dynamically evaluating temporal and spatial characteristics using newly collected data; Dynamically adjust weights to adapt to the latest disaster evolution trends. For example, in the disaster expansion stage, more emphasis may be placed on spatial characteristics, while in the early stages of a disaster, more emphasis may be placed on temporal characteristics.

[0158] Dynamic feedback of optimization objectives: In model optimization, the temporal and spatial feature weights are associated with the model error to ensure that the adjustment process can improve the model's predictive ability; For data in a specific period, priority is given to optimizing the constraints’ ability to respond dynamically, such as strengthening the synchronous change trends between monitoring points.

[0159] 4. Expansion of actual application scenarios Scenario 1: Landslide monitoring Landslide is a disaster dominated by spatial characteristics, which is often manifested as the synchronous displacement of multiple monitoring points in the landslide area. In this case, the spatiotemporal correlation constraints need to be: Increase the weight of spatial features to ensure that the spatial relationship between monitoring points is accurately modeled; Dynamically adjust the time characteristic weights to capture the cumulative effect of landslide mass movement.

[0160] Scenario 2: Debris flow monitoring Debris flow is significantly affected by time-driven factors such as rainfall, and often changes dramatically in a short period of time. At this time, the spatiotemporal correlation constraints should be: The weight of the time characteristics is strengthened to capture the dynamic trend of the monitoring points with rainfall changes; During periods of high disaster incidence, the spatial characteristic weights are appropriately adjusted to capture the spatial expansion characteristics.

[0161] Scenario 3: Compound disasters For complex scenarios with multiple disasters (such as landslides and debris flows occurring simultaneously), spatiotemporal correlation constraints require a dynamic balance between temporal and spatial characteristics: According to the primary and secondary relationships of disaster types, time and space weights are set respectively; The weight distribution is optimized separately for different regions and monitoring points to make the model adapt to complex environments.

[0162] 5. Parameter optimization and adjustment mechanism Weight initialization: Set initial values ​​based on experience and dynamically adjust for different scenarios and The numerical value of .

[0163] Real-time adjustment mechanism: Adjust parameters through dynamic feedback based on actual monitoring data changes.

[0164] Verification and optimization: Use the verification set to evaluate the effect of the weight combination and optimize the best parameters 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. A method for monitoring, predicting and warning geological disasters based on artificial intelligence, characterized in that: The following steps are involved: Data collection: Collect geological disaster monitoring data, including sensor data, remote sensing image data and historical geological disaster data, to provide basic data input for subsequent processing; Data preprocessing: Time alignment and missing completion of collected data to ensure the temporal and spatial consistency and integrity of data, and provide high-quality data for subsequent feature extraction; Construction of spatiotemporal correlation matrix: Analyze the preprocessed monitoring data, calculate the spatiotemporal correlation degree between different monitoring points, and construct a spatiotemporal correlation matrix that reflects the correlation characteristics between monitoring points to provide constraints for feature extraction; Feature extraction: The preprocessed monitoring data is encoded and decoded using a variational autoencoder, and spatiotemporal correlation matrix constraints are introduced during the data reconstruction process to extract the nonlinear spatiotemporal characteristics of the geological disaster monitoring data; Time series prediction: The extracted spatiotemporal features are input into the long short-term memory network for time series prediction. The weights of different time steps are dynamically adjusted through the attention mechanism to output the geological disaster risk level in the future period. Feedback optimization: Based on the error between the prediction results and the actual observation data, the back propagation algorithm is used to optimize the parameters of the feature extraction and time series prediction model, so that the model can adapt to the changing geological conditions and improve the prediction accuracy; Warning release: Generate geological disaster warning information based on the predicted risk level, and display high-risk areas and their dynamic evolution trends through a visualization platform to support geological disaster prevention and control decision-making.

2. The method for monitoring, predicting and warning geological disasters based on artificial intelligence according to claim 1 is characterized in that: The data preprocessing includes: Time-align data with different time resolutions to ensure consistency in the time dimension of the data; Generate adversarial networks to complete data and solve the problem of missing data.

3. The method for monitoring, predicting and warning geological disasters based on artificial intelligence according to claim 1 is characterized in that: The step of constructing the spatiotemporal correlation matrix comprises: By analyzing the changing trends and spatial distribution characteristics of each monitoring point in the time dimension, the spatiotemporal correlation degree between the monitoring points is calculated to form a correlation matrix reflecting the spatiotemporal characteristics.

4. The method for monitoring, predicting and warning geological disasters based on artificial intelligence according to claim 1 is characterized in that: The feature extraction step comprises: Use variational autoencoders to encode and decode geological disaster monitoring data; The spatiotemporal correlation constraints are introduced in the reconstruction process to make the characteristics of the reconstructed data consistent with the spatiotemporal relationship between the monitoring points.

5. The method for monitoring, predicting and warning geological disasters based on artificial intelligence according to claim 1 is characterized in that: The timing prediction step comprises: Use long short-term memory network to extract time series features from reconstructed data; Based on the attention mechanism, the data weights of different time steps are dynamically adjusted to highlight the moments that have a greater impact on the prediction results.

6. The method for monitoring, predicting and warning geological disasters based on artificial intelligence according to claim 1 is characterized in that: The feedback optimization step comprises: Calculate the error between the predicted results and the actual observed data; The back propagation algorithm is used to update and optimize the model parameters to minimize the prediction error.

7. The method for monitoring, predicting and warning geological disasters based on artificial intelligence according to claim 1 is characterized in that: The feedback optimization further comprises: The newly acquired geological monitoring data and prediction results are fed back into the training data set, the model is retrained and the parameters are iteratively optimized to improve the prediction accuracy of the model.

8. The method for monitoring, predicting and warning geological disasters based on artificial intelligence according to claim 1 is characterized in that: The warning release steps include: Classify geohazard risks into low-, medium- and high-risk areas; Generate corresponding warning information based on the risk classification results.

9. The method for monitoring, predicting and warning geological disasters based on artificial intelligence according to claim 1 is characterized in that: The visualization platform display includes: Display the distribution of geological disaster risk areas based on three-dimensional geographic information system; Displays dynamic trends of risk evolution and visual analysis results of monitoring point data.

10. The method for monitoring, predicting and warning geological disasters based on artificial intelligence according to claim 1 is characterized in that: The spatiotemporal correlation constraint realizes dynamic optimization of the reconstructed data features through parameter adjustment to ensure that the extracted spatiotemporal features conform to the actual physical laws of geological hazard data.

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