Debris flow formation mechanism analysis and risk assessment method based on multi-modal data fusion

Abstract

The invention discloses a debris flow formation mechanism analysis and risk assessment method based on multi-modal data fusion. The method comprises the steps that S1, debris flow multi-modal data are collected and preprocessed; s2, constructing a condition vector; s3, generating debris flow risk scene data according to the condition vector; s4, evaluating the authenticity probability of the generated data by using a discriminator in the conditional generative adversarial network; s5, performing hierarchical modeling on the generated debris flow risk scene data by adopting a multi-scale image convolution layer to obtain updated node feature representation; s6, calculating a global debris flow risk prediction result, and generating a debris flow risk assessment report; s7, finely adjusting the generator and the discriminator according to the actual debris flow event occurrence condition; and S8, performing real-time evaluation and early warning on the debris flow risk. According to the method, an efficient and scientific optimization scheme can be provided in debris flow formation mechanism analysis and risk assessment, and remarkable technical values and economic benefits are brought to practical application.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of debris flow risk assessment, and particularly relates to a debris flow formation mechanism analysis and risk assessment method based on multi-modal data fusion. BACKGROUND

[0002] In recent years, with the frequent occurrence of climate change and natural disasters, debris flow as a highly destructive natural disaster, seriously threatens the safety of people's life and property and the balance of environment and ecology. The formation of debris flow is usually closely related to heavy rainfall, geological conditions, topographic factors and other factors. Traditional debris flow risk assessment methods mainly rely on physical models, empirical formulas or statistical analysis. These methods can provide a preliminary judgment of the probability of debris flow occurrence to some extent, but they often fail to fully consider the complex interaction between different environmental factors, and have great limitations in real-time and accuracy. With the development of remote sensing technology, meteorological monitoring and geological survey technology, existing technologies gradually try to fuse multiple data sources to improve the accuracy of debris flow risk assessment.

[0003] However, traditional multi-modal data fusion technology still faces several obvious problems. First, the processing of multi-source data usually lacks a unified framework, making it difficult to effectively integrate data information from different sources. For example, meteorological data, geological data and remote sensing images have different characteristics and scales. Direct combination of these data often leads to data mismatch and information loss, affecting the accuracy of the evaluation results. Second, existing technologies mostly rely on rule-based models, lacking the ability of self-adaptation and dynamic adjustment, which means that traditional methods are difficult to update debris flow risk assessment in a timely manner according to changing environmental conditions, resulting in a lack of sufficient flexibility and real-time performance in practical applications.

[0004] In addition, although technologies such as graph neural networks and deep learning have made significant progress in some fields, their application in debris flow risk assessment still faces many challenges. Although graph neural networks can handle complex dependencies between nodes, they still face difficulties in efficiently fusing data and modeling spatial information when dealing with large-scale, heterogeneous multi-modal data. In particular, in debris flow risk assessment, different scales of spatial information and dynamically changing environmental factors must be considered, which puts higher requirements on traditional graph neural network models.

[0005] In addition, the generative adversarial network as a powerful generative model has achieved remarkable results in image generation, data enhancement and other fields, but its application in debris flow risk assessment is still preliminary. Although the existing generative adversarial network-based model can generate realistic data, how to generate data highly consistent with the actual debris flow risk scenario through the generative adversarial network and combine it with the graph neural network for effective risk assessment is still a problem to be solved. The traditional generative adversarial network model usually lacks deep fusion of conditional input, and it is difficult to make accurate risk prediction under dynamic environmental changes.

[0006] The defects of the prior art mainly lie in data processing, model adaptability and real-time performance. Although the fusion of multi-modal data can provide more comprehensive information, in actual operation, how to effectively integrate these heterogeneous data and how to capture complex spatial and temporal relationships through advanced model frameworks are still the main bottlenecks in current debris flow risk assessment technology.

[0007] Therefore, in view of the problems of insufficient data fusion, poor model adaptability and poor real-time performance in the prior art, there is an urgent need for a new method that can better handle multi-modal data, capture complex spatial and temporal dependencies and have dynamic adjustment capability to achieve more accurate debris flow risk prediction and timely warning. This is the original intention of the present application, which proposes a debris flow risk assessment method based on conditional generative adversarial network and graph neural network, aiming to solve the problems existing in the prior art by fusing advanced deep learning techniques to improve the accuracy, real-time performance and adaptability of debris flow risk assessment. SUMMARY

[0008] One object of the present application is to provide a multi-modal data fusion-based debris flow formation mechanism analysis and risk assessment method. The present application can provide an efficient and scientific optimization scheme in debris flow risk assessment, bringing significant technical value and economic benefits to practical applications.

[0009] According to an embodiment of the present application, a multi-modal data fusion-based debris flow formation mechanism analysis and risk assessment method comprises the following steps:

[0010] S1, collecting debris flow multi-modal data and performing preprocessing;

[0011] S2, constructing a conditional vector, wherein the conditional vector is spliced from meteorological data, geological data and remote sensing image data;

[0012] S3, generating debris flow risk scenario data according to the conditional vector by using the generator in the conditional generative adversarial network;

[0013] S4, the discriminator in the conditional generative adversarial network compares the generated debris flow risk scenario data with the real debris flow risk scenario data, and evaluates the authenticity probability of the generated data;

[0014] S5, using a graph neural network, a multi-scale graph convolutional layer is used to hierarchically model the generated debris flow risk scenario data, capture the spatial dependency between different geographic regions, and adjust the learning process of the graph convolutional layer through an adaptive weight mechanism to obtain updated node feature representations;

[0015] S6, based on the updated node feature representations obtained by the graph neural network, a global debris flow risk prediction result is calculated, and a final debris flow risk assessment report is generated;

[0016] S7, according to the actual debris flow event occurrence, the generator and the discriminator are fine-tuned;

[0017] S8, according to the change of real-time environmental conditions, dynamically adjusting the prediction result, realizing the real-time evaluation and early warning of debris flow risk.

[0018] Optionally, the S1 includes the following steps:

[0019] S11, collecting debris flow multi-modal data, the debris flow multi-modal data including meteorological data, geological data, remote sensing image data, the meteorological data including precipitation, temperature, humidity, wind speed, the geological data including slope, soil moisture, soil type, soil density, and the remote sensing image data being a vectorization representation of a remote sensing image corresponding to each geographic location;

[0020] S12, standardizing the collected debris flow multi-modal data.

[0021] Optionally, the S2 includes the following steps:

[0022] S21, performing feature extraction on the standardized debris flow multi-modal data to generate a meteorological condition vector c weather , a geological condition vector c geo , and an image condition vector c image .

[0023] S22, splicing and normalizing the meteorological condition vector c weather , the geological condition vector c geo , and the image condition vector c image to generate a condition vector c norm .

[0024] Optionally, the S3 includes the following steps:

[0025] S31, through the multi-layer self-attention layer in the generator, the condition vector cnorm feature extraction is performed to capture long-range dependencies between different features, and a high-dimensional intermediate feature representation z is generated;

[0026] Specifically, the conditional vector c norm The following calculation is performed:

[0027] Q = W Q c norm , K = W K c norm , V = W V c norm ;

[0028] where W Q , W K , and W V are weight matrices, Q is a query matrix, K is a key matrix, and V is a value matrix;

[0029] The weight matrix A of self-attention is calculated based on the query matrix and the key matrix:

[0030]

[0031] The self-attention output matrix is:

[0032] z = AV;

[0033] S32, input the intermediate feature representation z into a multi-layer Transformer network, and use a multi-head self-attention mechanism to weight process the features, enhance the context relationship between the features, and generate debris flow risk scenario data with richer feature expression;

[0034] S33, the generated debris flow risk scenario data is subjected to nonlinear transformation by an activation function to obtain the final debris flow risk scenario data

[0035] Optionally, the S4 includes the following steps:

[0036] S41, input the generated debris flow risk scenario data and the real debris flow risk scenario data Y and the conditional vector c norm into a discriminator network, which includes multiple convolutional layers and fully connected layers;

[0037] S42, local features of the input data are extracted by the convolutional layers to obtain an intermediate feature representation, and the intermediate feature representation is further processed by the fully connected layers to obtain a final feature representation h final , which is used to determine the authenticity of the generated data;

[0038] S43, the authenticity of the generated data is determined by comparing the final feature representation hfinal The Sigmoid activation function is used for transformation to calculate the authenticity probability of the generated data and the authenticity probability D(Y, c norm ) of the real data.

[0039] Optionally, the S5 comprises the following steps:

[0040] S51, the generated debris flow risk scenario data is constructed as graph structure data, each node represents a geographic location of debris flow risk characteristics, and the edge represents the geographic dependency relationship between nodes. The adjacency matrix A of the constructed graph is obtained;

[0041] S52, the normalized adjacency matrix

[0042] S53, the graph structure data is input into the graph neural network, different weights are given to the relationship between different nodes, and the attention weight α ij between each node is calculated to adjust the dependency degree between nodes:

[0043]

[0044] Wherein, a is the attention weight vector, || represents the vector splicing operation, W is the transformation matrix of node features, h i and h j are the feature representations of node i and node j respectively, is the neighbor node set of node i, and exp(.) is the natural exponential function;

[0045] Combined with the adaptive graph attention mechanism, the graph convolution layer is used to update the features of each node through multi-scale graph convolution:

[0046]

[0047] Wherein, H (l+1) is the node feature of the l+1 layer, σ is the Sigmoid activation function, H (l) is the node feature of the l layer, and H (0) is the generated debris flow risk scenario data K is the number of multi-scale, α ij is the attention weight matrix between each node, is the normalized adjacency matrix, is the weight matrix corresponding to the k-order adjacency matrix.

[0048] Optionally, the S51 comprises the following steps:

[0049] S511, each element A in the adjacency matrix A of the graph ij The calculation method is:

[0050]

[0051] Where, d ij is the geographical distance between node i and node j, σ is the standard deviation of the weight decay, which is used to control the influence of spatial distance, A ij is the weight between node i and node j, and exp(.) is the natural exponential function.

[0052] Optionally, the S6 comprises the following steps:

[0053] S61, updating the node feature representation H (L) based on the graph neural network, aggregating the feature representations of all nodes by weighted summation to obtain the global feature representation H global ;

[0054] S62, performing nonlinear transformation on the global feature representation H global , and obtaining the final global debris flow risk prediction value through the fully connected layer

[0055]

[0056] Where, W fc is the weight matrix, b fc is the bias term, and σ(·) is the Sigmoid activation function.

[0057] S63, generating the final debris flow risk assessment report according to the global debris flow risk prediction result .

[0058] Optionally, the S7 comprises the following steps:

[0059] S71, calculating the error between the generated debris flow risk scenario data and the actual debris flow event data Y actual . The error calculation formula is:

[0060]

[0061] Where, ||·|| 2 represents the Euclidean distance error, and m is the sample number.

[0062] S72, using the error to perform back propagation on the parameters θ G of the generator, and updating the generator through gradient descent:

[0063]

[0064] wherein η1 is a learning rate, denotes the gradient with respect to the generator parameters θ G .

[0065] S73, calculate the error between the authenticity probability of the generated data output by the discriminator and the authenticity probability of the real data D(Y, c norm ). The calculation formula is:

[0066]

[0067] wherein m is the number of samples;

[0068] S74, use the error to back propagate the parameters θ D of the discriminator, and update the discriminator through gradient descent:

[0069]

[0070] wherein η2 is a learning rate, denotes the gradient with respect to the discriminator parameters θ D .

[0071] Optionally, the S8 comprises the following steps:

[0072] S81, receive real-time environmental data and perform standardization processing on the real-time environmental data;

[0073] S82, update the condition vector according to the standardized real-time environmental data;

[0074] S83, input the updated condition vector into the generator to generate new debris flow risk scenario data;

[0075] S84, use the graph neural network to perform spatial modeling on the generated debris flow risk scenario data to obtain a new global debris flow risk prediction result, generate a debris flow risk early warning through a real-time early warning system, and update a debris flow risk assessment and early warning report in real time.

[0076] The beneficial effects of the present application are:

[0077] (1) The present application realizes the effective fusion of meteorological data, geological data, remote sensing images and historical debris flow event data by introducing conditional generative adversarial networks and graph neural networks. In traditional technology, the fusion of heterogeneous data often has problems such as data mismatch and information loss. However, by constructing a unified conditional vector, the present application enables data from different sources to be processed in a unified framework, which not only effectively improves the accuracy of data fusion, but also enhances the adaptability of the model to complex debris flow risk scenarios.

[0078] (2) The present application captures the complex spatial relationships between different geographical regions through graph neural networks. In debris flow risk assessment, the spatial dependence of geographical information is crucial. Traditional methods often ignore or simplify these relationships. However, through graph neural networks, especially based on multi-scale graph convolution, the present application can dynamically adjust the assessment of debris flow risk according to the characteristics and mutual relationships of different regions. This multi-scale modeling capability enables the model to more accurately identify and predict hotspots of debris flow occurrence and their risk levels.

[0079] (3) The present application combines the generation ability of generative adversarial networks with the spatial modeling ability of graph neural networks, enabling dynamic adjustment of debris flow risk prediction results according to changes in real-time environmental conditions. Compared to traditional static models, the present application can quickly update risk assessment based on real-time meteorological changes, geological conditions and environmental data, and make more accurate predictions by generating new risk scenario data. This enables the present application to provide more timely debris flow warnings, providing effective support for post-disaster rescue and disaster prevention decision-making. BRIEF DESCRIPTION OF DRAWINGS

[0080] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, together with the embodiments of the present application, to explain the present application, and do not constitute a limitation on the present application. In the drawings:

[0081] Fig. 1 A flowchart of a debris flow formation mechanism analysis and risk assessment method based on multi-modal data fusion according to the present application;

[0082] Fig. 2 A flowchart of a debris flow risk scenario data generation process using a conditional generative adversarial network in a debris flow formation mechanism analysis and risk assessment method based on multi-modal data fusion according to the present application;

[0083] Fig. 3 A flowchart of a graph neural network extracting node feature representation in a debris flow formation mechanism analysis and risk assessment method based on multi-modal data fusion according to the present application. DETAILED DESCRIPTION

[0084] The application will be described in further detail below with reference to the drawings. These drawings are simplified schematic diagrams and only show the basic structure of the application in a schematic manner, and thus only show the components relevant to the application.

[0085] Reference Figs. 1-3 A debris flow formation mechanism analysis and risk assessment method based on multi-modal data fusion, comprising the following steps:

[0086] S1, collecting debris flow multi-modal data and preprocessing;

[0087] S2, constructing a condition vector, the condition vector being spliced from meteorological data, geological data and remote sensing image data;

[0088] S3, using a generator in a conditional generative adversarial network to generate debris flow risk scenario data according to the condition vector;

[0089] S4, using a discriminator in a conditional generative adversarial network to compare the generated debris flow risk scenario data with real debris flow risk scenario data and evaluate the authenticity probability of the generated data;

[0090] S5, using a graph neural network to hierarchically model the generated debris flow risk scenario data by using a multi-scale graph convolution layer to capture the spatial dependency between different geographic regions, and simultaneously adjusting the learning process of the graph convolution layer through an adaptive weight mechanism to obtain updated node feature representations;

[0091] S6, calculating a global debris flow risk prediction result based on the updated node feature representations obtained by the graph neural network and generating a final debris flow risk assessment report;

[0092] S7, fine-tuning the generator and the discriminator according to the actual debris flow event occurrence;

[0093] S8, dynamically adjusting the prediction result according to the changes in real-time environmental conditions to realize real-time assessment and early warning of debris flow risk.

[0094] The present application can obtain key information required for debris flow risk assessment from multiple dimensions by collecting and standardizing multi-modal data, including meteorological data, geological data, remote sensing image data and historical debris flow event data. Standardized processing ensures consistency between different data sources, making subsequent processing more accurate and efficient. Compared with traditional single data source processing, the present application provides more comprehensive data support and can capture more potential influencing factors, thereby improving the accuracy of debris flow risk assessment.

[0095] In the present embodiment, S1 comprises the following steps:

[0096] S11, collect debris flow multi-modal data, the debris flow multi-modal data including meteorological data, geological data, remote sensing image data, the meteorological data including precipitation, temperature, humidity, wind speed, the geological data including slope, soil humidity, soil type, soil density, the remote sensing image data is the vectorization representation of the remote sensing image corresponding to each geographical position;

[0097] S12, standardize the collected debris flow multi-modal data.

[0098] The application can fuse multiple information into a unified input as the condition of the generator by splicing and constructing the condition vector of data from different sources, which makes the generated debris flow risk scenario dynamically adapt to different environmental changes, improves the flexibility and accuracy of the model, compared with the traditional method, the application can process and fuse multiple heterogeneous data, improves the overall understanding and prediction ability of the debris flow occurrence mechanism.

[0099] In this embodiment, S2 includes the following steps:

[0100] S21, feature extraction is carried out on the standardized debris flow multi-modal data, and meteorological condition vector c weather , geological condition vector c geo and image condition vector c image are generated.

[0101] S22, meteorological condition vector c weather , geological condition vector c geo and image condition vector c image are spliced and normalized to generate condition vector c norm .

[0102] The application can generate realistic and closely related risk scenarios by using the generator in the conditional generative adversarial network to generate debris flow risk scenario data according to the input condition vector, which not only enhances the data expansion ability of the model, but also improves the data quality through the generation of the adversarial manner, so that the generated simulation data is closer to the actual situation, compared with the traditional method, the application can generate more accurate debris flow risk scenarios in the case of data scarcity or large environmental changes, providing more reliable input for subsequent analysis and prediction.

[0103] In this embodiment, S3 includes the following steps:

[0104] S31, feature extraction is carried out on condition vector c norm by using the multi-layer self-attention layer in the generator, the long-range dependency relationship between different features is captured, and high-dimensional intermediate feature representation z is generated.

[0105] The specific process is that the conditional vector c is used for self-attention mechanism norm The following calculation is performed:

[0106] Q=W Q c norm ,K=W K c norm ,V=W V c norm ;

[0107] Wherein, W Q , W K , W V are weight matrices, Q is a query matrix, K is a key matrix, and V is a value matrix;

[0108] The weight matrix A of self-attention is calculated based on the query matrix and the key matrix:

[0109]

[0110] The self-attention output matrix is:

[0111] z=AV;

[0112] S32, input the intermediate feature representation z to the multi-layer Transformer network, and use the multi-head self-attention mechanism to weight process the features, enhance the context relationship between the features, and generate debris flow risk scene data with richer feature expression;

[0113] S33, the generated debris flow risk scene data is nonlinearly transformed through an activation function to obtain the final debris flow risk scene data

[0114] The present application compares the generated data with the real data by using the discriminator in the conditional generative adversarial network, and evaluates the authenticity of the generated data, so that the generator can continuously optimize the generated scene data in the training process. The present application realizes high matching degree of the generated data and the actual situation in this way, and improves the precision of the generated model. Through the real-time feedback of the discriminator, the model can be dynamically adjusted, avoiding the problem of inconsistent data quality in the traditional generated model, thereby ensuring the high authenticity and reliability of the finally output debris flow risk scene data.

[0115] In the embodiment, S4 includes the following steps:

[0116] S41, input the generated debris flow risk scene data and the real debris flow risk scene data Y and the conditional vector c norm to the discriminator network, wherein the discriminator network comprises a plurality of convolutional layers and a fully connected layer;

[0117] S42, local features of the input data are extracted through a convolutional layer to obtain an intermediate feature representation, and the intermediate feature representation is further processed through a fully connected layer to obtain a final feature representation h final , and the feature representation is used to determine the authenticity of the generated data;

[0118] S43, the final feature representation h final is transformed by using a Sigmoid activation function to calculate the authenticity probability of the generated data and the authenticity probability D(Y, c norm ) of the real data.

[0119] The present application can capture the complex spatial dependence relationship between different geographical regions by introducing a graph neural network to model the generated debris flow risk scenario data. The graph neural network can model the non-Euclidean relationship between nodes, significantly improving the understanding ability of the model for geographical features and spatial interaction relationship. Compared with traditional grid-based or statistical analysis methods, the present application is more flexible and accurate in processing debris flow risk assessment, and can dynamically adjust the spatial influence between different regions to generate more accurate risk assessment results.

[0120] In this embodiment, S5 includes the following steps:

[0121] S51, the generated debris flow risk scenario data is constructed as graph structure data, each node represents a geographical location debris flow risk feature, and the edge represents the geographical dependence relationship between nodes. The adjacency matrix A of the constructed graph is obtained.

[0122] S52, the adjacency matrix A is normalized to obtain the normalized adjacency matrix

[0123] S53, the graph structure data is input into the graph neural network, different weights are given to the relationship between different nodes, and the dependence degree between nodes is adjusted by calculating the attention weight α ij between each node:

[0124]

[0125] Wherein, a is the attention weight vector, || represents the vector splicing operation, W is the transformation matrix of the node feature, h i and h j are the feature representations of node i and node j respectively, is the neighbor node set of node i, and exp(.) is the natural exponential function.

[0126] Combined with the adaptive graph attention mechanism, the graph convolution layer is used to update the features of each node through multi-scale graph convolution:

[0127]

[0128] where H (l+1) is the node feature of the l+1th layer, and σ is a Sigmoid activation function, H (l) is the node feature of the lth layer, and H (0) is the generated debris flow risk scenario data K is the number of multi-scale, and α ij is the attention weight matrix between each node, is the normalized adjacency matrix, is the weight matrix corresponding to the kth-order adjacency matrix.

[0129] In this embodiment, S51 includes the following steps:

[0130] S511, each element A ij in the adjacency matrix A of the graph is calculated as follows:

[0131]

[0132] where d ij is the geographical distance between node i and node j, σ is the standard deviation for controlling the weight decay, and A ij is the weight between node i and node j, and exp(.) is the natural exponential function.

[0133] The present application outputs the debris flow risk assessment value of each node through the graph neural network, and finally generates the global debris flow risk prediction result. The present application can accurately assess the risk level of different regions in real time. This method not only can handle the complex relationship between nodes, but also can comprehensively assess the debris flow risk of different regions. Compared with traditional single data or model, the graph neural network provides more fine-grained risk assessment, which can identify and capture potential and difficult-to-directly-observe risk factors.

[0134] In this embodiment, S6 includes the following steps:

[0135] S61, based on the updated node feature representation H (L) of the graph neural network, the feature representations of all nodes are aggregated through weighted summation to obtain the global feature representation H global ;

[0136] S62, performing nonlinear transformation on the global feature representation H global , and obtaining the final global debris flow risk prediction value through the fully connected layer

[0137]

[0138] Among them, W fc is the weight matrix, b fc is the bias term, σ(·) is the Sigmoid activation function;

[0139] S63. Based on the global debris flow risk prediction results Generate a final debris flow risk assessment report.

[0140] The present invention fine-tunes the generator and discriminator according to the actual occurrence of debris flow events. The present invention can adaptively adjust the model according to the actual debris flow event data, thereby improving the model's ability to fit the actual situation. Through the back propagation and optimization process, the generator can continuously improve the accuracy of its generated data in a dynamic environment, while the discriminator can more accurately evaluate the difference between the generated data and the actual situation. Compared with traditional methods, the present invention can make rapid adjustments based on real-time feedback, significantly improving the adaptability and accuracy of the model.

[0141] In this embodiment, S7 includes the following steps:

[0142] S71. Calculated debris flow risk scenario data Compared with the actual debris flow event data Y actual The error between The error calculation formula is:

[0143]

[0144] Among them, ||·|| 2 represents the Euclidean distance error, m is the number of samples;

[0145] S72. Utilization Error The parameters θ of the generator G Perform backpropagation and update the generator through gradient descent:

[0146]

[0147] Among them, η1 is the learning rate, Represents the generator parameter θ G gradient;

[0148] S73. Calculate the authenticity probability of the generated data output by the discriminator and the authenticity probability of the real data D(Y,c norm ) The calculation formula is:

[0149]

[0150] wherein m is the number of samples;

[0151] S74, using the error The parameters of the discriminator D Back propagation is performed, and the discriminator is updated by gradient descent:

[0152]

[0153] wherein η2 is the learning rate, indicates the gradient with respect to the discriminator parameters D .

[0154] The present application dynamically adjusts the prediction results of the model according to the changes of real-time environmental conditions. The present application can quickly update the debris flow risk assessment and generate new early warning information when the real-time environmental conditions such as weather and geology change. This function makes the present application have high real-time and adaptability, and can provide timely and accurate decision support for disaster prevention and mitigation work. Compared with the traditional static model, the present application can adjust the risk assessment in real time according to the rapid changes of the environment, significantly improving the timeliness and early warning accuracy of the debris flow risk assessment.

[0155] In this embodiment, S8 includes the following steps:

[0156] S81, receiving real-time environmental data and performing standardization processing on the real-time environmental data;

[0157] S82, updating the condition vector according to the standardized real-time environmental data;

[0158] S83, inputting the updated condition vector into the generator to generate new debris flow risk scenario data;

[0159] S84, using a graph neural network to perform spatial modeling on the generated debris flow risk scenario data to obtain new global debris flow risk prediction results, and generating debris flow risk warning through a real-time warning system to update the debris flow risk assessment and warning report in real time.

[0160] Embodiment:

[0161] The embodiment is in a mountainous area in the southwest. Due to the steep terrain, heavy rainfall and high degree of land exposure, debris flow disasters occur frequently in this area. Debris flow not only poses a great threat to the lives and property of local people, but also seriously affects the ecological environment and transportation. The traditional debris flow risk assessment method faces many challenges in practical application, especially in how to efficiently and accurately combine meteorological, geological, remote sensing image and other multi-modal data for dynamic assessment. In order to more accurately assess and predict debris flow risk, the implementer decides to use the risk assessment method based on conditional generative adversarial network and graph neural network.

[0162] In order to conduct comprehensive debris flow risk assessment, the implementer first collects multi-modal data of the area, including meteorological data, geological data, remote sensing image data and historical debris flow event data. Meteorological data records the precipitation, temperature, wind speed and other information of the area in the past 5 years, collected every hour. Geological data includes soil moisture, soil type and slope, etc. These data come from multiple geological monitoring points in the area. Remote sensing image data is obtained through satellite images, reflecting the vegetation coverage, topographic changes and other information of the area. In addition, historical debris flow event data includes the time, location, intensity of debris flow occurrence, providing important historical reference for the model.

[0163] The implementer inputs the collected meteorological, geological, remote sensing image and other data into the conditional generative adversarial network. The generator generates debris flow risk scenario data according to the input condition vector. Each generated risk scenario data reflects the probability of debris flow occurrence and the potential impact area under specific meteorological conditions, soil moisture, geological characteristics and other factors. For example, when the precipitation exceeds 100mm, the system generates a risk scenario that predicts the high-risk area of debris flow occurrence in the area and provides specific risk assessment values. In this way, the implementer can generate a large number of diversified debris flow risk scenarios to provide rich data support for subsequent analysis.

[0164] The generated debris flow risk scenario data is input into the graph neural network for spatial modeling. Each node represents a geographic location. The features of the node include the debris flow risk value of the location, meteorological data, geological data, etc. The graph neural network can capture the spatial dependency between nodes, for example, areas with higher slope are more likely to have debris flow than areas with lower slope. Through multi-layer graph convolution operation, the features of the nodes are constantly updated to better express the complex relationships between geographical areas. The implementer effectively improves the model's understanding of spatial dependence through this process and can more accurately assess the debris flow risk of different areas.

[0165] The implementer also updates the debris flow risk assessment according to real-time environmental data through the dynamic adjustment function of the method of the present application. For example, when the precipitation reaches a certain threshold, the system can automatically recalculate the risk of debris flow occurrence based on the latest meteorological data. On June 3, 2024, the precipitation was 130 mm, and the implementer timely updated the debris flow risk assessment result using the method of the present application, successfully predicted the high-risk area, and issued a warning to the local relevant departments. Compared with the traditional method, the traditional method failed to provide timely warning when the precipitation was 130 mm, while the real-time performance of the method of the present application significantly improved the accuracy of risk assessment and the efficiency of emergency response.

[0166] To verify the effectiveness of the method of the present application, the implementer conducted a comparative test with the traditional debris flow risk assessment method. In the case of June 2, 2024, the traditional method predicted the debris flow risk in the area to be 55%, while the method of the present application improved the risk prediction value to 85% by generating debris flow risk scenario data and graph neural network modeling, successfully predicting the high-risk area. By comparing data on other dates, the implementer found that the method of the present application can significantly improve the accuracy of risk assessment under different precipitation and soil moisture conditions and can timely issue warnings.

[0167] Based on the prediction results of the method of the present application, the implementer successfully implemented protective measures for high-risk areas and completed 30 days of field monitoring on June 4, 2024. In the test area, the implementer found that the protective measures evaluated and implemented by the method of the present application effectively reduced the frequency of debris flow occurrence. Compared with the protective measures designed by the traditional method, the method of the present application significantly improved the accuracy and real-time performance of debris flow risk assessment, effectively reducing the loss of local debris flow disasters.

[0168] The following is a specific data comparison table before and after implementation:

[0169] Table 1 Comparison of results of the present application and traditional methods in debris flow risk assessment

[0170]

[0171]

[0172] From these data, it can be seen that the accuracy, real-time performance and adaptability of the method of the present application in debris flow risk assessment have been significantly improved. In the face of changing environmental conditions, the method of the present application can timely update the assessment results and issue warnings, providing precise support for the prevention and response to debris flow disasters.

[0173] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A debris flow formation mechanism analysis and risk assessment method based on multimodal data fusion, characterized by: The steps include: S1, collect debris flow multimodal data and perform preprocessing; S2. Constructing a conditional vector, wherein the conditional vector is formed by splicing meteorological data, geological data, and remote sensing image data; S3. Using a generator in a conditional generative adversarial network, generate debris flow risk scenario data according to the conditional vector; S4. Using the discriminator in the conditional generative adversarial network, the generated debris flow risk scenario data is compared with the real debris flow risk scenario data, and the authenticity probability of the generated data is evaluated; S5. Using a graph neural network, we hierarchically model the generated debris flow risk scenario data using a multi-scale graph convolutional layer to capture the spatial dependencies between different geographical regions. We also use an adaptive weight mechanism to adjust the learning process of the graph convolutional layer to obtain updated node feature representations. S6. Calculate the global debris flow risk prediction results based on the updated node feature representation obtained by the graph neural network and generate the final debris flow risk assessment report; S7. Fine-tune the generator and discriminator based on the actual occurrence of debris flow events; S8. Dynamically adjust the prediction results according to the changes in real-time environmental conditions to achieve real-time assessment and early warning of debris flow risks.

2. The debris flow formation mechanism analysis and risk assessment method based on multimodal data fusion according to claim 1 is characterized in that: Said S1 comprises the following steps: S11. Collect multimodal data of debris flow, wherein the multimodal data of debris flow includes meteorological data, geological data, and remote sensing image data. The meteorological data includes precipitation, temperature, humidity, and wind speed; the geological data includes slope, soil moisture, soil type, and soil density; and the remote sensing image data is a vectorized representation of a remote sensing image corresponding to each geographic location. S12. Standardize the collected debris flow multimodal data.

3. The debris flow formation mechanism analysis and risk assessment method based on multimodal data fusion according to claim 1 is characterized in that: The S2 comprises the following steps: S21. Extract features from standardized debris flow multimodal data to generate meteorological condition vector c weather , geological condition vector c geo , image condition vector c image ; S22, the meteorological condition vector c weather , geological condition vector c geo and image condition vector c image Perform splicing and normalization to generate the conditional vector c norm .

4. The debris flow formation mechanism analysis and risk assessment method based on multimodal data fusion according to claim 1 is characterized in that: The S3 includes the following steps: S31, through the multi-layer self-attention layer in the generator, the conditional vector c norm Perform feature extraction to capture the long-range dependencies between different features and generate a high-dimensional intermediate feature representation z; S32: Input the intermediate feature representation z into the multi-layer Transformer network, use the multi-head self-attention mechanism to perform weighted processing on the features, and generate debris flow risk scenario data; S33, the generated debris flow risk scenario data is nonlinearly transformed through the activation function to obtain the final debris flow risk scenario data 5. The debris flow formation mechanism analysis and risk assessment method based on multimodal data fusion according to claim 1 is characterized in that: The S4 comprises the following steps: S41, generate debris flow risk scenario data and the real debris flow risk scenario data Y and condition vector c norm Input to the discriminator network, which includes multiple convolutional layers and fully connected layers; S42, extract the local features of the input data through the convolution layer to obtain the intermediate feature representation, and further process the intermediate feature representation through the fully connected layer to obtain the final feature representation h final ; S43, by expressing the final feature h final Use Sigmoid activation function to transform and calculate the authenticity probability of generated data and the authenticity probability of the real data D(Y,c norm ).

6. The debris flow formation mechanism analysis and risk assessment method based on multimodal data fusion according to claim 1, characterized in that: The S5 comprises the following steps: S51, generate debris flow risk scenario data Constructed as graph-structured data, each node represents the debris flow risk characteristics of a geographical location, and the edges represent the geographical dependencies between nodes, constructing the graph adjacency matrix A; S52. Normalize the adjacency matrix A to obtain a normalized adjacency matrix S53. Input the graph structure data into the graph neural network, assign different weights to the relationships between different nodes, and calculate the attention weight α between each node. ij To adjust the dependency between nodes: Among them, a is the attention weight vector, || represents the vector splicing operation, W is the transformation matrix of the node feature, h i and h j are the feature representations of node i and node j respectively, is the set of neighbor nodes of node i, exp(.) is the natural exponential function; Combined with the adaptive graph attention mechanism, the graph convolution layer is used to update the features of each node through multi-scale graph convolution: Among them, H (l+1) is the node feature of the l+1 layer, σ is the Sigmoid activation function, H (l) is the node feature of the lth layer, H (0) Debris flow risk scenario data generated K is the number of multi-scales, α ij is the attention weight matrix between each node, is the normalized adjacency matrix, is the weight matrix corresponding to the k-th order adjacency matrix.

7. The debris flow formation mechanism analysis and risk assessment method based on multimodal data fusion according to claim 6, characterized in that: The S51 includes the following steps: S511, each element A in the adjacency matrix A of the graph ij The calculation method is: Among them, d ij is the geographical distance between node i and node j, σ is the standard deviation of the weight decay control, A ij is the weight between node i and node j, and exp(.) is the natural exponential function.

8. The debris flow formation mechanism analysis and risk assessment method based on multimodal data fusion according to claim 1, characterized in that: The S6 comprises the following steps: S61. Node feature representation H after updating based on graph neural network (L) , aggregate the feature representations of all nodes through weighted summation to obtain the global feature representation H global ; S62, global feature representation H global Perform nonlinear transformation and obtain the final global debris flow risk prediction value through the fully connected layer S63. Based on the global debris flow risk prediction results Generate a final debris flow risk assessment report.

9. The debris flow formation mechanism analysis and risk assessment method based on multimodal data fusion according to claim 1, characterized in that: The S7 comprises the following steps: S71. Calculated debris flow risk scenario data Compared with the actual debris flow event data Y actual The error between S72. Utilization Error The parameters θ of the generator G Perform backpropagation and update the generator through gradient descent; S73. Calculate the authenticity probability of the generated data output by the discriminator and the authenticity probability of the real data D(Y,c norm ) S74. Utilization Error The parameters θ of the discriminator D Perform back propagation and update the discriminator through gradient descent.

10. The debris flow formation mechanism analysis and risk assessment method based on multimodal data fusion according to claim 1, characterized in that: The S8 comprises the following steps: S81, receiving real-time environmental data and performing standardization processing on the real-time environmental data; S82. Update the condition vector according to the standardized real-time environmental data; S83: Input the updated condition vector into the generator to generate new debris flow risk scenario data; S84. Use graph neural networks to perform spatial modeling on the generated debris flow risk scenario data to obtain new global debris flow risk prediction results, generate debris flow risk warnings through a real-time warning system, and update debris flow risk assessments and warning reports in real time.

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