A geological disaster intelligent early warning method and system based on multi-source remote sensing data

Abstract

The present invention relates to the field of intelligent monitoring technology, specifically to an intelligent early warning method and system for geological disasters based on multi-source remote sensing data, comprising the following steps: extracting data related to geological disasters from multi-source remote sensing data, identifying location information and time tags, and converting location and time information into a coordinate system to obtain a data source feature set. In the present invention, by integrating multi-source remote sensing data, the monitoring accuracy of geological changes is effectively improved, the dimension and richness of data are enhanced, and the relationship between data sources is accurately mapped. The constructed graph model structure allows more accurate identification and prediction of potential risks of geological disasters through node and edge weight analysis, enhances the accuracy and response speed of disaster warnings, significantly improves the efficiency of geological disaster management, reduces casualties and economic losses, and enables the early warning system to effectively warn before a disaster occurs, effectively reducing the impact of the disaster.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and in particular to an intelligent early warning method and system for geological disasters based on multi-source remote sensing data. Background Art

[0002] The field of intelligent monitoring technology mainly involves the use of advanced information technology, sensor technology and data processing methods to monitor various environmental and industrial parameters in real time, with particular emphasis on data integration and analysis, and the use of machine learning, artificial intelligence and big data technologies to predict and diagnose potential problems. In geological disaster early warning, intelligent monitoring includes not only data collection, but also efficient data processing and analysis, so as to quickly and accurately predict the occurrence of natural disasters such as landslides, earthquakes and mudslides. The application of technology can greatly improve the speed and efficiency of disaster response, reduce economic losses and protect people's lives.

[0003] Among them, the intelligent early warning method of geological disasters based on multi-source remote sensing data refers to the use of data from multiple remote sensing technology sources (such as satellite images, aerial images, ground sensor data, etc.) to build a comprehensive early warning system, which improves the monitoring accuracy of geological changes and the ability to predict geological disasters by integrating multiple types of remote sensing data. The main purpose is to provide accurate early warnings before disasters occur, so that relevant agencies and the public can take necessary preventive measures to avoid or mitigate the impact of disasters. Intelligent early warning methods play a vital role in improving the efficiency of disaster management and reducing casualties.

[0004] Although the existing intelligent monitoring technology has made significant progress in the application of disaster warning, it still has shortcomings. Traditional monitoring systems rely on data input from a single source, which limits monitoring accuracy and disaster prediction capabilities. For example, relying solely on ground sensors or single satellite data cannot provide comprehensive disaster warning information, resulting in the early warning system being unable to provide sufficient risk assessment before a disaster occurs, affecting the timeliness and accuracy of the early warning. Existing data processing methods have limitations in integrating and analyzing multi-source data, and cannot effectively identify the relationship between cross-source data, resulting in missed judgments of key risk factors. In actual operations, this leads to delayed response to disasters and reduces the overall effectiveness of the early warning system. For example, without effective integration of satellite and ground data, the monitoring system cannot accurately predict the specific location of landslides or mudslides, and cannot promptly notify residents in dangerous areas to take evasive measures. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a geological disaster intelligent early warning method and system based on multi-source remote sensing data.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme, a geological disaster intelligent early warning method based on multi-source remote sensing data, comprising the following steps:

[0007] S1: Extract data related to geological hazards from multi-source remote sensing data, identify location information and time tags, and convert location and time information into a coordinate system to obtain a data source feature set;

[0008] S2: Based on the data source feature set, the relationship between data sources is mapped, nodes represent data points, edges represent data associations, and edge weights are analyzed to obtain a graph model structure;

[0009] S3: Apply the graph model structure to identify the interaction intensity of geological disaster data, calculate the influence of nodes, identify factors that affect node changes, and calibrate key nodes to obtain a key node list;

[0010] S4: By using the key node list, the nodes are divided into differentiated categories, the potential risks of geological disasters are identified and the risk areas are analyzed, early warning is carried out according to the risk level, and the risk area division result is obtained;

[0011] S5: Using the risk area division results, setting state monitoring points, evaluating the probability of potential geological disasters through dynamic data capture and state transition analysis, conducting intelligent early warning of geological disasters, and obtaining disaster probability analysis results;

[0012] S6: Based on the above-mentioned disaster occurrence probability analysis results, the geological disaster on-site response data is integrated, and geological disaster early warning tests are conducted by simulating real-time scenarios to optimize emergency strategies and early warning guidelines to obtain response strategy plans.

[0013] As a further solution of the present invention, the data source feature set includes standardized longitude and latitude coordinates and a unified time format, the graph model structure includes node characteristic analysis results, edge association strength assessment and network topology characteristics, the key node list includes risk area nodes, geological disaster occurrence nodes and monitoring data nodes, the risk area division results include the geographical boundaries and attribute characteristics of the risk area, the disaster occurrence probability analysis results include probability values, impact range and potential impact assessment, and the response strategy plan includes emergency response measures, resource allocation optimization and information dissemination strategy.

[0014] As a further solution of the present invention, the steps of extracting data related to geological disasters from multi-source remote sensing data, identifying location information and time tags, and converting the location and time information into a coordinate system to obtain a data source feature set are specifically as follows:

[0015] S101: Extract data related to geological disasters from multi-source remote sensing data, identify surface features of geological disaster locations, analyze time attributes and time series of labeled data, and generate standardized geological disaster data;

[0016] S102: extracting the geographical location and timestamp of the occurrence of the geological disaster through the standardized geological disaster data, converting the geographical location into coordinates, and synchronizing the timestamp to the standard time to obtain a location time tag set;

[0017] S103: Using the location time tag set, the location and time information of the geological disaster is converted into a unified coordinate system, and combined with the characteristics of the geological disaster data, a data source feature set is generated.

[0018] As a further solution of the present invention, based on the data source feature set, the relationship between data sources is mapped, nodes represent data points, edges represent data associations, and the weights of edges are analyzed to obtain the graph model structure in the following steps:

[0019] S201: Mapping geological disaster data points through the data source feature set, taking each data point as a node in a graph, wherein the node includes geographic and time information of the data point, and generating a node information set;

[0020] S202: Using the node information set, connecting related data points, constructing edges in the graph according to the fit between the data points and the distance between the geographical locations, and assigning an initialization weight based on the fit to each edge to obtain an initialization graph model;

[0021] S203: Using the initialized graph model, calculating edge weights, and sorting edge weights, referring to the impact of geological events and the degree of interaction between data points, to generate a graph model structure.

[0022] As a further solution of the present invention, the graph model structure is applied to identify the interaction intensity of geological hazard data, calculate the influence of nodes, identify factors affecting node changes, and calibrate key nodes. The steps of obtaining a key node list are specifically as follows:

[0023] S301: Analyze the interaction strength between multiple nodes according to the graph model structure, refer to the weight of the edge between the nodes and the connection distribution degree of the data points, evaluate the influence relationship between the nodes, and generate a node interaction degree analysis result;

[0024] S302: Based on the node interaction degree analysis result, the influence of each node is calculated, and the centrality of the node is evaluated according to the node weight and the node connectivity index to obtain the node influence score;

[0025] S303: Using the node influence score, identify key factors that affect node changes, including geological activity data and environmental change records, mark influential nodes as key nodes, and generate a key node list.

[0026] As a further solution of the present invention, the nodes are divided into differentiated categories through the key node list, the potential risks of geological disasters are identified and the risk areas are analyzed, and early warning is carried out according to the risk level. The steps of obtaining the risk area division result are specifically as follows:

[0027] S401: Based on the key node list, a cluster analysis method is used to classify the nodes according to their influence scores and geographical locations, and a node risk classification result is generated;

[0028] S402: using the node risk classification results, analyzing the potential risks of geological disasters around the nodes of different categories, and obtaining the initialization risk area analysis results with reference to the geological disaster data and the stability of the geological structure;

[0029] S403: Based on the initialization risk area analysis result, according to the risk level and geographic data of the node, the associated geological disaster intelligent warning level is set, including red alert, yellow alert and green alert, and the risk area division result is generated.

[0030] As a further solution of the present invention, the formula of the cluster analysis method is as follows:

[0031] in, For Node and nodes The weighted distance between and For Node and The impact score, and For Node and The geographic location coordinates of and For Node and The geological stability and For Node and The historical frequency of disasters, , , and is the weight coefficient.

[0032] As a further solution of the present invention, the risk area division results are used to set state monitoring points, and the probability of potential geological disasters is evaluated through dynamic data capture and state transition analysis, and intelligent early warning of geological disasters is performed. The specific steps for obtaining the disaster occurrence probability analysis results are as follows:

[0033] S501: Based on the risk area division result, a risk area is selected to set up geological status monitoring points, and a status monitoring point layout is generated by deploying seismometers and ground displacement sensors;

[0034] S502: According to the layout of the status monitoring points, dynamic data of geological changes, including earthquake activity frequency and soil moisture changes, are collected, and real-time analysis of the geological change data is performed to obtain dynamic data analysis results;

[0035] S503: Based on the dynamic data analysis results, evaluate the probability of geological disasters at differentiated monitoring points, integrate geological disaster data and real-time monitoring data, conduct intelligent early warning of geological disasters, and generate disaster probability analysis results.

[0036] As a further solution of the present invention, based on the disaster probability analysis results, the geological disaster on-site response data is integrated, the geological disaster early warning test is carried out by simulating real-time scenarios, and the emergency strategy and early warning guide are optimized. The specific steps of obtaining the response strategy scheme are as follows:

[0037] S601: using the disaster occurrence probability analysis results, integrating the response data at the geological disaster site, including rescue team feedback and disaster impact assessment, to generate a disaster response data set;

[0038] S602: Using the disaster response data set, simulating real-time scenarios of geological disasters of different levels, testing the response time and efficiency of intelligent early warning of geological disasters, and obtaining early warning test results through cyclic testing;

[0039] S603: Based on the warning test results, evaluate the real-time warning strategy and emergency response effect, adjust the emergency evacuation route and resource allocation plan, update the warning guide to optimize the geological disaster response efficiency, and generate a response strategy plan.

[0040] A geological disaster intelligent early warning system based on multi-source remote sensing data, the geological disaster intelligent early warning system based on multi-source remote sensing data is used to execute the geological disaster intelligent early warning method based on multi-source remote sensing data, the system comprises:

[0041] The data processing module identifies and records the surface features, time tags and location data related to geological hazards based on multi-source remote sensing data, and performs data standardization, correlation analysis with time tags, and generates a data source feature set;

[0042] The graph model construction module constructs nodes and edges representing the degree of association between data points and data according to the data source feature set, and generates a graph model structure by analyzing the weights of the edges;

[0043] The node analysis module uses the graph model structure to evaluate the interaction strength and influence of differentiated nodes, calibrates and classifies key nodes with reference to geological activities and environmental factors, and generates a key node list;

[0044] The risk warning module uses the key node list to analyze potential risks, divide regions according to risk levels, monitor the geological disasters in risk areas in real time, and generate risk area division results;

[0045] The early warning plan module sets up monitoring points based on the risk area division results, evaluates the probability of potential geological disasters through real-time data, and conducts intelligent early warning, optimizes emergency strategies and early warning guidelines, and generates response strategy plans.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are:

[0047] In the present invention, by integrating multi-source remote sensing data, including satellite images, aerial images and ground sensor data, the monitoring accuracy of geological changes is effectively improved, not only enhancing the dimension and richness of the data, but also accurately mapping the relationship between data sources through efficient data processing processes, such as geographic tagging and time synchronization. The constructed graph model structure allows more accurate identification and prediction of the potential risks of geological disasters through node and edge weight analysis. By comprehensively considering the influence of nodes and geological activity data, it can more effectively calibrate key nodes and divide risk categories, enhance the accuracy and response speed of disaster warnings, significantly improve the efficiency of geological disaster management, and reduce casualties and economic losses. The application of dynamic data capture and state transition analysis further improves the real-time and accuracy of monitoring data, enabling the early warning system to effectively warn before a disaster occurs and effectively reduce the impact of the disaster. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0049] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0050] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0051] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0052] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0053] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0054] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0055] Figure 8 The present invention is a flow chart of the intelligent early warning system for geological disasters based on multi-source remote sensing data. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0058] See also Figure 1 The present invention provides a technical solution, a geological disaster intelligent early warning method based on multi-source remote sensing data, comprising the following steps:

[0059] S1: Extract data related to geological hazards from multi-source remote sensing data, identify location information and time tags, convert location and time information into a coordinate system, establish an initialized data framework, and obtain a data source feature set;

[0060] S2: Based on the data source feature set, map the relationship between data sources. Nodes represent data points, edges represent data associations, analyze and record edge weights, and obtain the graph model structure.

[0061] S3: Apply the graph model structure to identify the interaction intensity of geological hazard data, calculate the influence of nodes, identify the factors that affect node changes, and calibrate key nodes to obtain a list of key nodes;

[0062] S4: Through the key node list, the nodes are divided into differentiated categories, each category represents a risk area, the risk area is analyzed, the potential risk of geological disasters is identified, and early warning is issued according to the risk level to obtain the risk area division result;

[0063] S5: Using the risk area division results, set up state monitoring points, evaluate the probability of potential geological disasters through dynamic data capture and state transition analysis, build a geological activity trend map, conduct intelligent geological disaster warning, and obtain disaster probability analysis results;

[0064] S6: Based on the results of disaster probability analysis, integrate the geological disaster on-site response data, conduct geological disaster early warning tests by simulating real-time scenarios, optimize emergency strategies and early warning guidelines, and obtain response strategy plans.

[0065] The data source feature set includes standardized longitude and latitude coordinates and a unified time format. The graph model structure includes node characteristic analysis results, edge association strength assessment and network topology characteristics. The key node list includes risk area nodes, geological disaster occurrence nodes and monitoring data nodes. The risk area division results include the geographical boundaries and attribute characteristics of the risk area. The disaster occurrence probability analysis results include probability values, impact range and potential impact assessment. The response strategy plan includes emergency response measures, resource allocation optimization and information dissemination strategy.

[0066] See also Figure 2 , extracting data related to geological disasters from multi-source remote sensing data, identifying location information and time tags, and converting location and time information into a coordinate system, the steps to obtain the data source feature set are as follows:

[0067] S101: Extracting data related to geological disasters from multi-source remote sensing data, identifying surface features of the geological disaster location, analyzing the time attributes and time series of the marked data, and generating standardized geological disaster data are performed as follows;

[0068] Substep S101 extracts data related to geological hazards from multi-source remote sensing data, including identifying and analyzing surface features from satellite images, topographic maps, and geographic information systems. Features indicate the occurrence of geological hazards, such as landslides, earthquake rupture zones, or flood zones. The key is to analyze and mark the temporal attributes of the data, such as changes before and after the disaster, and the time series related to the disaster event, standardize the data to ensure the accuracy and consistency of subsequent analysis, and generate standardized geological hazard data. The formula used is:

[0069] in, represents the standardized score of geological hazard data, It is The standardized weights of the features, It is for The function to process feature data. is the mean value of the feature, is the total number of features.

[0070] S102: By standardizing the geological disaster data, extracting the geographical location and timestamp of the geological disaster, converting the geographical location into coordinates, and synchronizing the timestamp to the standard time, the execution process of obtaining the location time tag set is as follows;

[0071] Sub-step S102 extracts the specific geographical location and time information of the occurrence of geological disasters by standardizing the geological disaster data, converting the geographical location from descriptive data into precise coordinates, such as longitude and latitude, to facilitate further geographic information system processing and analysis. The timestamp information is standardized to Coordinated Universal Time or any applicable standard time to eliminate the impact of time differences, and the location time tag set is obtained. The formula used is:

[0072] in, represents the validity score of the location time label set, is the transformation weight, is a function, and They are The coordinates and timestamps of the data points, is the total number of data points.

[0073] S103: Using the location time tag set, the location and time information of the geological disaster is converted into a unified coordinate system, and combined with the characteristics of the geological disaster data, the execution process of generating the data source feature set is as follows;

[0074] Sub-step S103 uses a location-time tag set to convert specific events of geological disasters into data points in a unified coordinate system, standardizes geographic and temporal information in differentiated data sources, and includes all key characteristics of geological disasters, such as disaster type, impact range, and duration, to form a data feature set using the formula:

[0075] in, represents the comprehensive score of the data source feature set, It is The weight of the feature, is a function, It is The geological hazard characteristics are the total number of characteristics.

[0076] See also Figure 3 ,Based on the data source feature set, the relationship between data sources is mapped. Nodes represent data points, edges represent data associations, and the weights of edges are analyzed. The specific steps to obtain the graph model structure are:

[0077] S201: Map geological disaster data points through data source feature sets, and use each data point as a node in the graph. The node includes geographic and time information of the data point. The execution process of generating a node information set is as follows;

[0078] In sub-step S201, each geological disaster data point is regarded as a node through the data source feature set. The node contains key geographical location and time information, as well as the type and scale of the disaster. Each node is mapped to a graph, which will be used to analyze the correlation and pattern between geological disasters. The creation of nodes can accurately represent the integrity and multidimensionality of disaster data, and generate a node information set using the formula:

[0079] in, Indicates the construction effect of the node information set. It is The importance weight of each node, is a function, For geographical location, It's time. is the total number of nodes.

[0080] S202: Using the node information set, connecting the associated data points, constructing the edges in the graph according to the fit between the data points and the distance of the geographical locations, and assigning an initialization weight based on the fit to each edge, the execution flow of the initialization graph model is as follows;

[0081] In sub-step S202, the edges of the graph model are created using the node information set. Each edge connects two nodes and represents the geological hazard relationship between them. The edges are created based on the geographical distance between the nodes and the data fit, which can ensure that the graph model reflects the actual relationship and interaction between the geological data points. The initial graph model is obtained using the formula:

[0082] in, Represents the initialization state of the edge in the graph model, It is a slave node arrive The weight of the edge, is a function, is the total number of edges.

[0083] S203: using the initialization graph model, calculating the weights of the edges, and sorting the weights of the edges, referring to the impact of geological events and the degree of interaction between data points, the execution flow of generating the graph model structure is as follows;

[0084] Sub-step S203 uses an initialized graph model to calculate and sort the weights of the edges, which involves comprehensively considering the influence of geological disasters, the data fit between nodes, and the geographical location distance to ensure that the relationship between geological disasters can be accurately reflected and generate a graph model structure. The formula used is:

[0085] in, Represents the graph model structure, It is the edge The adjustment weight of is a function, is the total number of edges.

[0086] See also Figure 4 ,Applying the graph model structure, identify the interaction intensity of geological hazard data, calculate the influence of nodes, identify the factors affecting node changes, and calibrate key nodes. The specific steps to obtain the key node list are:

[0087] S301: According to the graph model structure, the interaction strength between multiple nodes is analyzed, and the influence relationship between nodes is evaluated with reference to the weight of the edge between nodes and the connection distribution degree of the data points. The execution process of generating the node interaction degree analysis result is as follows;

[0088] Substep S301 analyzes the interaction intensity between multiple nodes based on the graph model structure. The process relies on evaluating the edge weights between nodes and the degree of connection distribution between nodes, and can comprehensively evaluate the mutual influence relationship between nodes. It helps reveal how nodes affect the entire network through mutual connections, which is crucial for understanding the interaction pattern of geological hazard data. The node interaction degree analysis results are generated using the formula:

[0089] in, represents the sum of the interaction strengths between nodes, Is a node and The edge weights between Representation Node and The connection distribution index of is the total number of nodes.

[0090] S302: Based on the node interaction degree analysis results, the influence of each node is calculated, and the centrality of the node is evaluated according to the node weight and node connectivity index, and the execution process of obtaining the node influence score is as follows;

[0091] Sub-step S302 calculates the influence of each node based on the node interaction degree analysis results, which involves evaluating the weight of the node and the connectivity index in the network, such as the degree or betweenness centrality of the node. The index helps determine the centrality and influence of the node in the network and obtains the node influence score. The formula used is:

[0092] in, Represents the total score of the node’s influence, Is a node The weight of Is a node The degree of Is a node and The number of paths between is the total number of nodes.

[0093] S303: Using the node influence score, identify the key factors that affect node changes, including geological activity data and environmental change records, and mark the influential nodes as key nodes. The execution process of generating a key node list is as follows;

[0094] Sub-step S303 uses node influence scores to identify key factors that affect node changes, including geological activity data and environmental change records. Nodes are evaluated to determine whether they become key nodes due to high influence, and are marked, classified and sorted to generate a key node list. The formula used is:

[0095] in, represents the set of key nodes, Is a node The impact score, is the total number of nodes, is the threshold of influence score, is a function, when Greater than Returns a node , otherwise nothing is returned.

[0096] See also Figure 5 ,Through the key node list, the nodes are divided into differentiated categories, the potential risks of geological hazards and the risk areas are identified, and early warnings are made according to the risk levels. The specific steps to obtain the risk area division results are:

[0097] S401: Based on the key node list, a cluster analysis method is used to classify the nodes according to their influence scores and geographical locations, and the execution process of generating node risk classification results is as follows;

[0098] Substep S401 performs cluster analysis based on the list of key nodes, allowing nodes to be grouped according to influence scores and geographic locations. Classification helps to better understand the geographical distribution and relative risk level of each node in the network. The clustering process uses the influence and geographic coordinates of the nodes as input, and adopts K-means clustering or clustering algorithm to minimize intra-class differences and maximize inter-class differences to generate node risk classification results.

[0099] The formula of the cluster analysis method is as follows:

[0100] in, For Node and nodes The weighted distance between and For Node and The impact score, and For Node and The geographic location coordinates of and For Node and The geological stability and For Node and The historical frequency of disasters, , , and is the weight coefficient.

[0101] The execution flow is as follows:

[0102] Weight coefficient , , and The weight coefficients are set through historical data analysis and expert review. Each weight coefficient represents the importance of the corresponding parameter in the total score. The setting of the weight coefficients includes normalization processing to ensure that the sum is 1. For each pair of nodes, the differences between the influence score, geographical location coordinates, geological stability and historical disaster frequency are calculated respectively, and the differences are weighted by the corresponding weight coefficients. The square root of the sum of the squares of all weighted difference values ​​is taken to obtain the distance between nodes, which is used for risk classification in cluster analysis.

[0103] S402: Using the node risk classification results, analyzing the potential risk of geological disasters around the differentiated category nodes, referring to the geological disaster data and the stability of the geological structure, the execution process of obtaining the initialization risk area analysis results is as follows;

[0104] Sub-step S402 uses the node risk classification results to analyze the potential risks of geological disasters around nodes of different categories, and combines historical data of geological disasters and the stability information of current geological structures to predict and evaluate potential risk areas. The analysis involves calculating the risk probability and potential disaster probability of nodes in different geographical areas, and obtaining the initial risk area analysis results, using the formula:

[0105] in, Indicates category The total risk, Is a node The risk score of Is a node Distance to known geological hazard areas, Is the risk category All nodes in .

[0106] S403: Based on the initialization risk area analysis results, according to the risk level and geographic data of the node, the associated geological disaster intelligent warning level is set, including red alert, yellow alert and green alert, and the execution process of generating the risk area division result is as follows;

[0107] Substep S403 sets the intelligent warning level of geological hazards associated with the node risk level and geographic data based on the initial risk area analysis results. The process includes assigning an appropriate warning level to each risk area, such as red alert (high risk), yellow alert (medium risk) or green alert (low risk), and generating the risk area division result. The formula used is:

[0108] in, Representation Node The warning level Is a node The risk score of is a function, according to The value of is assigned a corresponding alert level.

[0109] See also Figure 6 , using the risk area division results, setting state monitoring points, through dynamic data capture and state transition analysis, assessing the probability of potential geological disasters, and conducting intelligent early warning of geological disasters. The specific steps to obtain the results of disaster probability analysis are as follows:

[0110] S501: Based on the risk area division results, the risk area is selected to set up geological status monitoring points, and the execution process of generating the status monitoring point layout by deploying seismometers and ground displacement sensors is as follows;

[0111] Substep S501 selects specific geological risk areas to set up status monitoring points based on the risk area division results. The process involves strategically deploying seismometers and ground displacement sensors to maximize monitoring efficiency and coverage. The process of selecting monitoring points is based on geological structure and historical disaster data, optimizing the spatial layout of sensors to ensure that key risk areas are effectively monitored, and generating the status monitoring point layout using the formula:

[0112] in, Indicates at location Establishing priorities for monitoring points, It's location Known risk areas The distance is the decay constant, It is a risk area The number of nodes in is the collection of all risk areas.

[0113] S502: According to the layout of the status monitoring points, dynamic data of geological changes, including the frequency of earthquake activity and soil moisture changes, are collected, and real-time analysis of the geological change data is performed to obtain the dynamic data analysis results. The execution process is as follows;

[0114] Substep S502 collects dynamic geological data such as earthquake activity frequency and soil moisture changes according to the layout of the status monitoring points. The data is transmitted through a real-time sensor network and analyzed in real time to identify potential geological activities or change trends. Data analysis includes signal processing, trend detection, and outlier identification to ensure that any key changes caused by geological activities can be responded to quickly and dynamic data analysis results can be obtained. The formula used is:

[0115] in, Indicates at time The accumulated value of geological dynamic data within It's in time Measured geological data values, is the adjustment factor.

[0116] S503: Based on the dynamic data analysis results, the probability of geological disasters at differentiated monitoring points is evaluated, geological disaster data and real-time monitoring data are integrated, and intelligent early warning of geological disasters is performed to generate the execution process of disaster occurrence probability analysis results as follows;

[0117] Based on the dynamic data analysis results, sub-step S503 integrates historical geological disaster data and current geological activity data to evaluate the probability of geological disasters at different monitoring points. The analysis process includes applying statistical models and probability theory methods, such as Bayesian networks or decision trees, to estimate and predict the disaster risks of each monitoring point and generate disaster probability analysis results. The formula used is:

[0118] in, Indicates monitoring point The probability of disaster occurrence, It is an event The historical probability of occurrence, It is an event At the monitoring point Real-time monitoring data indicators, is the weight coefficient, is the number of event types considered.

[0119] See also Figure 7 Based on the results of disaster probability analysis, the geological disaster on-site response data is integrated, and geological disaster early warning tests are conducted by simulating real-time scenarios to optimize emergency strategies and early warning guidelines. The specific steps for obtaining response strategy solutions are as follows:

[0120] S601: Using the disaster probability analysis results, integrating the response data at the geological disaster site, including the rescue team feedback and disaster impact assessment, the execution process of generating the disaster response data set is as follows;

[0121] Substep S601 uses the results of the disaster probability analysis to integrate the real-time response data of the geological disaster site, including collecting immediate feedback from the rescue team, damage assessment reports of the affected areas, and real-time monitoring data of geological activities. The integrated information can provide a more comprehensive understanding of the scope and severity of the disaster, forming a disaster response data set, using the formula:

[0122] in, represents the information completeness score of the disaster response dataset, It is The importance coefficient of the item data, is the availability of data, is the total number of data items.

[0123] S602: Using the disaster response data set, simulate the real-time scene of geological disasters of different levels, test the response time and efficiency of intelligent early warning of geological disasters, and obtain the early warning test results through cyclic testing. The execution process is as follows;

[0124] Substep S602 uses the disaster response data set to simulate real-time scenarios of geological disasters of different levels to test the response time and efficiency of the intelligent early warning system. The simulation can evaluate the performance of the early warning system in different disaster scenarios, including response speed, accuracy and resource allocation capabilities. The cyclic test helps to continuously optimize the system settings, improve the rapidity and effectiveness of disaster response, and obtain the early warning test results. The formula used is:

[0125] in, represents the average efficiency score of early warning, It is The actual response time in the test, is the expected response time, is the number of tests.

[0126] S603: Based on the warning test results, evaluate the real-time warning strategy and emergency response effect, adjust the emergency evacuation route and resource allocation plan, update the warning guide to optimize the geological disaster response efficiency, and generate the execution process of the response strategy plan as follows;

[0127] Sub-step S603 evaluates and adjusts the real-time warning strategy and emergency response effect based on the warning test results, involving real-time adjustment of emergency evacuation routes, resource allocation, and rescue operations. By analyzing the deviation between the test results and the expected goals, the warning guidelines and emergency response plans are updated to optimize the response efficiency of geological disasters and generate response strategy plans. The formula used is:

[0128] in, The total score representing the need for policy adjustment, It is The weights of the policy parameters, is the current policy parameter value, is the parameter value, is the total number of parameters.

[0129] See also Figure 8 , a geological disaster intelligent early warning system based on multi-source remote sensing data, the geological disaster intelligent early warning system based on multi-source remote sensing data is used to execute the geological disaster intelligent early warning method based on multi-source remote sensing data, the system includes:

[0130] The data processing module identifies and records the surface features, time tags and location data related to geological hazards based on multi-source remote sensing data, and performs data standardization, correlation analysis with time tags, and generates a data source feature set;

[0131] The graph model construction module constructs nodes and edges to represent the degree of association between data points and data based on the data source feature set, and generates the graph model structure by analyzing the weight of the edges;

[0132] The node analysis module uses the graph model structure to evaluate the interaction strength and influence of differentiated nodes, calibrate and classify key nodes with reference to geological activities and environmental factors, and generate a list of key nodes;

[0133] The risk warning module uses a list of key nodes to analyze potential risks, divide regions according to risk levels, monitor geological disasters in risk areas in real time, and generate risk area division results;

[0134] The early warning plan module sets up monitoring points based on the risk area division results, evaluates the probability of potential geological disasters through real-time data, and conducts intelligent early warnings, optimizes emergency strategies and early warning guidelines, and generates response strategy plans.

[0135] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent early warning method for geological disasters based on multi-source remote sensing data, characterized in that: The following steps are involved: Extract data related to geological hazards from multi-source remote sensing data, identify location information and time tags, and convert location and time information into a coordinate system to obtain a data source feature set; Based on the data source feature set, the relationship between data sources is mapped, nodes represent data points, edges represent data associations, and edge weights are analyzed to obtain a graph model structure; Applying the graph model structure, identifying the interaction intensity of geological disaster data, calculating the influence of nodes, identifying factors affecting node changes, and calibrating key nodes to obtain a key node list; By using the key node list, the nodes are divided into differentiated categories, the potential risks of geological disasters are identified and the risk areas are analyzed, early warnings are issued according to the risk levels, and the risk area division results are obtained; Using the risk area division results, set up state monitoring points, evaluate the probability of potential geological disasters through dynamic data capture and state transition analysis, conduct intelligent geological disaster warning, and obtain disaster probability analysis results; Based on the above-mentioned disaster probability analysis results, the geological disaster on-site response data is integrated, and the geological disaster early warning test is carried out by simulating real-time scenarios, and the emergency strategy and early warning guide are optimized to obtain the response strategy plan; Extract data related to geological hazards from multi-source remote sensing data, identify surface features at the locations where geological hazards occur, analyze the time attributes and time series of labeled data, and generate standardized geological hazard data; Through the standardized geological disaster data, the geographical location and timestamp of the geological disaster are extracted, the geographical location is converted into coordinates, and the timestamp is synchronized to the standard time to obtain a location time tag set; The location and time tag set is used to convert the location and time information of the geological disaster into a unified coordinate system, and a data source feature set is generated in combination with the characteristics of the geological disaster data; Mapping geological disaster data points through the data source feature set, taking each data point as a node in the graph, the node including the geographic and time information of the data point, and generating a node information set; Using the node information set, connecting related data points, constructing edges in the graph according to the fit between the data points and the distance of the geographical locations, and assigning an initialization weight based on the fit to each edge to obtain an initialized graph model; Using the initialized graph model, calculating the weights of the edges, and sorting the weights of the edges, and generating a graph model structure with reference to the impact of geological events and the degree of interaction between data points; According to the graph model structure, the interaction strength between multiple nodes is analyzed, and the influence relationship between nodes is evaluated by referring to the weight of the edges between nodes and the connection distribution degree of data points, so as to generate the node interaction degree analysis result; Based on the node interaction degree analysis results, the influence of each node is calculated, and the centrality of the node is evaluated according to the node weight and node connectivity index to obtain the node influence score; Using the node influence score, identify key factors that affect node changes, including geological activity data and environmental change records, and mark influential nodes as key nodes to generate a key node list; Based on the key node list, a cluster analysis method is used to classify the nodes according to their influence scores and geographical locations to generate node risk classification results; The node risk classification results are used to analyze the potential risks of geological disasters around the nodes of different categories, and the initialization risk area analysis results are obtained by referring to the geological disaster data and the stability of the geological structure; Based on the initialization risk area analysis results, according to the risk level and geographic data of the node, the associated geological disaster intelligent warning level is set, including red alert, yellow alert and green alert, and the risk area division result is generated; Based on the risk area division results, geological status monitoring points are set up in the selected risk areas, and the layout of the status monitoring points is generated by deploying seismometers and ground displacement sensors; According to the layout of the status monitoring points, dynamic data of geological changes, including seismic activity frequency and soil moisture changes, are collected, and real-time analysis of the geological change data is performed to obtain dynamic data analysis results; Based on the dynamic data analysis results, the probability of geological disasters at differentiated monitoring points is evaluated, geological disaster data and real-time monitoring data are integrated, intelligent early warning of geological disasters is carried out, and disaster probability analysis results are generated; Using the above disaster occurrence probability analysis results, integrating the response data at the geological disaster site, including rescue team feedback and disaster impact assessment, to generate a disaster response data set; Using the disaster response data set, simulate the real-time scenes of geological disasters of different levels, test the reaction time and efficiency of intelligent early warning of geological disasters, and obtain the early warning test results through cyclic testing; Based on the warning test results, the real-time warning strategy and emergency response effect are evaluated, the emergency evacuation routes and resource allocation plans are adjusted, the warning guidelines are updated to optimize the geological disaster response efficiency, and the response strategy plan is generated.

2. The intelligent early warning method for geological disasters based on multi-source remote sensing data according to claim 1 is characterized in that: The data source feature set includes standardized longitude and latitude coordinates and a unified time format; the graph model structure includes node characteristic analysis results, edge association strength assessment and network topology characteristics; the key node list includes risk area nodes, geological disaster occurrence nodes and monitoring data nodes; the risk area division results include the geographical boundaries and attribute characteristics of the risk area; the disaster occurrence probability analysis results include probability values, impact range and potential impact assessment; the response strategy plan includes emergency response measures, resource allocation optimization and information dissemination strategy.

3. The intelligent early warning method for geological disasters based on multi-source remote sensing data according to claim 1 is characterized in that: The formula of the cluster analysis method is as follows: in, For Node and The weighted distance between and For Node and The impact score, and For Node and The geographic location coordinates of and For Node and The geological stability and For Node and The historical frequency of disasters, , , and is the weight coefficient.

4. An intelligent early warning system for geological disasters based on multi-source remote sensing data, characterized in that: According to any one of claims 1 to 3, the method for intelligent early warning of geological disasters based on multi-source remote sensing data comprises: The data processing module identifies and records the surface features, time tags and location data related to geological hazards based on multi-source remote sensing data, and performs data standardization, correlation analysis with time tags, and generates a data source feature set; The graph model construction module constructs nodes and edges representing the degree of association between data points and data according to the data source feature set, and generates a graph model structure by analyzing the weights of the edges; The node analysis module uses the graph model structure to evaluate the interaction strength and influence of differentiated nodes, calibrates and classifies key nodes with reference to geological activities and environmental factors, and generates a key node list; The risk warning module uses the key node list to analyze potential risks, divide regions according to risk levels, monitor the geological disasters in risk areas in real time, and generate risk area division results; The early warning plan module sets up monitoring points based on the risk area division results, evaluates the probability of potential geological disasters through real-time data, and conducts intelligent early warning, optimizes emergency strategies and early warning guidelines, and generates response strategy plans.

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