Geological disaster early warning method and geological disaster early warning system

Abstract

The invention discloses a geological disaster early warning method and a geological disaster early warning system, and relates to the technical field of geological disaster early warning.The geological disaster early warning method comprises the steps that a geological disaster monitoring area is set, a data terminal and a plurality of monitoring sensors are deployed in the set geological disaster monitoring area, and meanwhile an acquisition period is set; the method comprises the following steps: periodically collecting historical geological disaster data in a geological disaster monitoring area, processing the collected historical geological disaster data in a data processing mode, and analyzing the processed historical geological disaster data in a data analysis mode after processing is completed; and meanwhile, a geological disaster simulation prediction model is constructed based on the analyzed historical geological disaster data in a data fusion and digital twinning mode, finally, geological disaster data in a geological disaster monitoring area are collected in real time and input into the geological disaster simulation prediction model, and geological disaster early warning is performed according to an output result. And the accuracy of early warning of geological disasters is improved.

Description

Technical Field

[0001] This invention relates to the field of geological disaster early warning technology, specifically to a geological disaster early warning method and a geological disaster early warning system. Background Technology

[0002] Because the distribution patterns of geological disasters in time and space are subject to both the natural environment and human activities, the evaluation and research on the susceptibility of geological disasters and the development of prediction systems are extremely important.

[0003] Existing technology, such as the invention patent application with publication number CN116246429A, discloses a geological disaster early warning system. Its method includes: a remote sensing image acquisition module, a ground elevation data acquisition module, a meteorological data acquisition module, a data processing module, a data analysis module, a monitoring module, and an early warning module. The data processing module fuses multi-temporal high-resolution remote sensing image data and ground elevation information of the target area to obtain a three-dimensional geological model and performs time-series analysis to obtain a temporal change sequence of geological conditions in the target area. The monitoring module divides the target area into key monitoring areas based on the temporal change sequence of geological conditions and conducts dynamic monitoring. The early warning module determines whether to issue a geological disaster early warning based on the dynamic monitoring results and meteorological information.

[0004] As can be seen from the above scheme, the current geological disaster early warning is based on the analysis of geological changes, which lacks a direct analysis of geological disasters, making it easy to make misjudgments and thus having certain limitations. Summary of the Invention

[0005] The purpose of this invention is to provide a geological disaster early warning method and a geological disaster early warning system, which solves the problems existing in the background art.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a geological disaster early warning method, specifically including the following steps: S1. Define the geological disaster monitoring area and deploy data terminals and multiple monitoring sensors within the defined geological disaster monitoring area; S2. After deployment, set the data acquisition cycle of the monitoring sensors and collect historical geological disaster data within the geological disaster monitoring area based on the set data acquisition cycle; The historical geological disaster data includes: image data of the monitoring area, geographical data of the monitoring area, and meteorological data of the monitoring area; S3. Process the collected historical geological disaster data using data processing methods to obtain processed historical geological disaster data, including the following steps: S31. The image data of the monitoring area in the historical geological disaster data is processed by image enhancement to obtain the enhanced image data of the monitoring area; S32. Calculate and correct the geographic data of the monitoring area based on the enhanced image data of the monitoring area to obtain the corrected geographic data of the monitoring area. S33. By supplementing the meteorological data of the monitoring area in the historical geological disaster data, the supplemented meteorological data of the monitoring area is obtained. S34. Summarize the data from steps S31, S32, and S33 to obtain the processed historical geological disaster data; S4. Analyze the processed historical geological disaster data using data analysis methods to obtain the analyzed historical geological disaster data; S5. Based on the analyzed historical geological disaster data, a geological disaster simulation and prediction model is constructed through data fusion and digital twin methods; S6. Collect geological disaster data in the geological disaster monitoring area in real time, and input the real-time collected geological disaster data into the constructed geological disaster simulation and prediction model. Based on the output results of the geological disaster simulation and prediction model, conduct geological disaster early warning.

[0007] Preferably, the step of setting up a geological disaster monitoring area and deploying data terminals and multiple monitoring sensors within the set geological disaster monitoring area includes the following steps: The center of the geological disaster monitoring area is set as the origin of the coordinate system, and a three-dimensional coordinate system is constructed based on the set origin. At the same time, a data terminal is deployed at the center of the geological disaster monitoring area. The geological hazard monitoring area is divided into smaller areas of equal size based on the constructed three-dimensional coordinate system, and monitoring sensors are deployed at the center of each smaller area.

[0008] Preferably, the process of processing the monitoring area image data in historical geological disaster data through image enhancement to obtain enhanced monitoring area image data includes the following steps: Collect the grayscale value distribution in the image data of the monitoring area and normalize the grayscale values ​​in the image data of the monitoring area; ; in, The coordinates of the pixels in the image data of the monitored area are: Gray values ​​after pixel normalization; The gray values ​​of each pixel after normalization are summarized and then the image is enhanced using gamma transformation. ; in, This represents the grayscale output value after image enhancement. , This represents the grayscale scaling factor. Indicates the gamma factor; The enhanced grayscale output values ​​of each pixel are summarized to obtain the enhanced image data of the monitoring area.

[0009] Preferably, the step of calculating and correcting the geographic data of the monitoring area based on the enhanced image data of the monitoring area to obtain the corrected geographic data of the monitoring area includes the following steps: S321. Correct the enhanced monitoring area image data to obtain the corrected monitoring area image data; Collect the camera axis distance parameters corresponding to the enhanced monitoring area image data, and correct the enhanced monitoring area image data based on the camera axis distance parameters and the enhanced monitoring area image data; The enhanced image data correction for the monitoring area is shown below: ; in, Indicates camera Wheelbase Indicates camera Wheelbase These represent the offsets between the ground plane and the camera imaging plane, respectively. This represents the camera's rotation vector. This represents the camera's translation vector. This represents the corrected image data of the monitoring area. This represents the enhanced image data of the monitoring area; S322. Calculate and correct the geographic data of the monitoring area based on the corrected image data of the monitoring area.

[0010] Preferably, the calculation and correction of the geographic data of the monitoring area based on the corrected image data of the monitoring area includes the following steps: Select any two points in the constructed three-dimensional coordinate system , The distance between the two selected points is measured to obtain the actual distance DL between the two points; Based on the selected two points, select corresponding two points in the corrected monitoring area image data. , And calculate the image distance between two points in the corrected monitoring area image data according to the distance formula; The formula for the distance between two points is as follows: ; in, Indicates the midpoint of the corrected monitoring area image data and points Image distance between; Calculate the ratio between the actual distance and the image distance; ; in, This represents the ratio between the actual distance and the image distance; The actual distance represented by each pixel in the corrected monitoring area image data is determined based on the ratio between the actual distance and the image distance. The image data of the corrected monitoring area is classified based on grayscale value matching. The geographic data of the monitoring area corresponding to each type of pixel is calculated and corrected based on the actual distance represented by each pixel, so as to determine the location and area of ​​the geographic data of the monitoring area corresponding to each type of pixel.

[0011] Preferably, the step of supplementing the meteorological data of the monitoring area in the historical geological disaster data through data completion to obtain the supplemented meteorological data of the monitoring area includes the following steps: The meteorological data of the monitoring area in the historical geological disaster data was traversed to locate the meteorological data of the monitoring area that was missing; Select m sets of meteorological data adjacent to the missing meteorological data of the monitoring area, calculate the corresponding average value, and use the calculated average value as the supplementary data to supplement the missing meteorological data of the monitoring area.

[0012] Preferably, the step of analyzing the processed historical geological disaster data through data analysis to obtain the analyzed historical geological disaster data includes the following steps: S41. Analyze the processed historical geological disaster data using fuzzy analysis and determine the historical geological disaster data grading system; The processed historical geological disaster data was compiled and submitted to experts for evaluation. Collect expert scores and calculate the subjective score for each group of processed historical geological disaster data using fuzzy analysis; The formula for calculating subjective scores is as follows: ; in, This represents the subjective score for each group of processed historical geological disaster data. This represents the score data of the z-th expert. Indicates the number of experts; Set a scoring range and divide it evenly, and determine a grading system for historical geological disaster data based on the division results; Based on the established historical geological disaster data grading system, the monitoring area geographic data standards in the historical geological disaster data of each grade are summarized and calculated. The standard for the geographical data of the monitoring area in the historical geological disaster data of each level is the average value of the geographical data of the monitoring area in the historical geological disaster data of each level. S42. Summarize historical geological disaster data of corresponding levels and determine the characteristics of geological disasters of each level through feature recognition. S43. Summarize the characteristics of geological disasters, the historical geological disaster data grading system, and the processed historical geological disaster data to obtain the analyzed historical geological disaster data.

[0013] Preferably, the process of summarizing historical geological disaster data of corresponding levels and determining the characteristics of geological disasters of each level through feature recognition includes the following steps: The monitoring area image data in the historical geological disaster data of the corresponding level is divided into k image data blocks of uniform size, and the divided image data blocks are used as the input of the convolutional neural network. The convolutional neural network includes: convolutional layers, pooling layers, and fully connected layers; Set the kernel size and stride for each convolutional layer in the convolutional neural network; Simultaneously, based on the divided image data blocks, the convolution kernel size and stride are set to move on the input image data blocks, and convolution calculation is performed during the movement to achieve feature extraction for each image data block. The formula for calculating convolution is as follows: ; in, This represents the input image data block. represents the weights of the corresponding convolution kernel, and b represents the bias value. This represents the characteristics of the output image data blocks; After feature extraction is completed, the output image data block features are expanded and combined through a fully connected layer to obtain feature data; The obtained feature data is defined as geological disaster characteristics.

[0014] Preferably, the process of constructing a geological hazard simulation and prediction model based on analyzed historical geological hazard data through data fusion and digital twin methods includes the following steps: The historical geological disaster data in the analyzed historical geological disaster data were sorted in descending order based on the collection time of each group of historical geological disaster data; The changes in the geographical data of the monitoring area in each set of historical geological disaster data were recorded using a multi-frame comparison method. Digital twin prediction weights are set based on the geographical data standards of the monitoring areas in historical geological disaster data of each level. The digital twin prediction weights are set as follows: ; in, This indicates the behavior corresponding to the changes in geographic data of the monitored area. This indicates the status of the geographic data corresponding to the current monitoring area. This represents the set of behaviors corresponding to changes in geographic data across all monitored areas. Represents the weighting function. Indicates the execution of an action. Indicates the state Execution of actions ; The geological disaster simulation and prediction model constructed based on the set digital twin prediction weights is shown below: ; in, This represents the constructed geological disaster simulation and prediction model. Indicates the predicted probability. Indicates the state.

[0015] This invention also discloses a geological disaster early warning system for implementing a geological disaster early warning method. The system includes: a data acquisition module, a data processing module, a data analysis module, a prediction model construction module, and an early warning module. The data acquisition module is used to collect geological disaster data in the geological disaster monitoring area in real time; The data processing module is used to process the real-time collected geological disaster data to obtain processed geological disaster data; The data analysis module is used to analyze the processed geological disaster data to obtain the analyzed geological disaster data; The prediction model building module is used to build a geological disaster simulation prediction model based on the analyzed geological disaster data; The early warning module is used to issue early warnings for geological disasters based on the prediction results of the geological disaster simulation prediction model.

[0016] The beneficial effects of this invention are as follows: (1) This invention sets up a geological disaster monitoring area and deploys data terminals and multiple monitoring sensors within the set geological disaster monitoring area. At the same time, it sets a collection period and periodically collects historical geological disaster data within the geological disaster monitoring area. Simultaneously, it processes the collected historical geological disaster data through data processing methods. After processing, it analyzes the processed historical geological disaster data through data analysis methods. Based on the analyzed historical geological disaster data, it constructs a geological disaster simulation prediction model through data fusion and digital twin methods. Finally, it collects geological disaster data within the geological disaster monitoring area in real time and inputs it into the geological disaster simulation prediction model. Based on the output results, it conducts geological disaster early warning, thereby improving the accuracy of geological disaster early warning.

[0017] (2) The present invention improves the reliability of geological disaster monitoring by setting the coordinate origin of the geological disaster monitoring area and constructing a three-dimensional coordinate system based on the set coordinate origin to accurately determine the location data of the geological disaster monitoring area.

[0018] (3) This invention processes the monitoring area image data in historical geological disaster data by image enhancement, and calculates and corrects the geographical data of the monitoring area by the enhanced monitoring area image data; then it completes the meteorological data of the monitoring area in historical geological disaster data by data completion, and summarizes the processed historical geological disaster data, thereby improving the efficiency of geological disaster data processing.

[0019] (4) This invention analyzes the processed historical geological disaster data through fuzzy analysis to determine the historical geological disaster data level system. At the same time, after determining the historical geological disaster data level system, it extracts the geological disaster data features under the corresponding level, thus ensuring the accuracy of geological disaster identification. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the geological disaster early warning method of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] In a specific embodiment of the present invention, Reference Figure 1 As shown, the present invention provides a geological disaster early warning method, comprising the following steps: S1. Define the geological disaster monitoring area and deploy data terminals and multiple monitoring sensors within the defined geological disaster monitoring area; S2. After deployment, set the data acquisition cycle of the monitoring sensors and collect historical geological disaster data within the geological disaster monitoring area based on the set data acquisition cycle; The historical geological disaster data includes: image data of the monitoring area, geographical data of the monitoring area, and meteorological data of the monitoring area; S3. Process the collected historical geological disaster data using data processing methods to obtain processed historical geological disaster data, including the following steps: S31. The image data of the monitoring area in the historical geological disaster data is processed by image enhancement to obtain the enhanced image data of the monitoring area; S32. Calculate and correct the geographic data of the monitoring area based on the enhanced image data of the monitoring area to obtain the corrected geographic data of the monitoring area. S33. By supplementing the meteorological data of the monitoring area in the historical geological disaster data, the supplemented meteorological data of the monitoring area is obtained. S34. Summarize the data from steps S31, S32, and S33 to obtain the processed historical geological disaster data; S4. Analyze the processed historical geological disaster data using data analysis methods to obtain the analyzed historical geological disaster data; S5. Based on the analyzed historical geological disaster data, a geological disaster simulation and prediction model is constructed through data fusion and digital twin methods; S6. Collect geological disaster data in the geological disaster monitoring area in real time, input the real-time collected geological disaster data into the constructed geological disaster simulation and prediction model, and conduct geological disaster early warning based on the output results of the geological disaster simulation and prediction model; Furthermore, referring to Figure 1 As shown, setting up a geological hazard monitoring area and deploying data terminals and multiple monitoring sensors within that area includes the following steps: The center of the geological disaster monitoring area is set as the origin of the coordinate system, and a three-dimensional coordinate system is constructed based on the set origin. At the same time, a data terminal is deployed at the center of the geological disaster monitoring area. Furthermore, based on the constructed three-dimensional coordinate system, the designated geological hazard monitoring area is divided into smaller areas of equal size, and monitoring sensors are deployed at the center of each smaller area. Furthermore, referring to Figure 1 As shown, the process of processing monitoring area image data from historical geological disaster data using image enhancement techniques to obtain enhanced monitoring area image data includes the following steps: Collect the grayscale value distribution in the image data of the monitoring area and normalize the grayscale values ​​in the image data of the monitoring area; ; in, The coordinates of the pixels in the image data of the monitored area are: Gray values ​​after pixel normalization; The gray values ​​of each pixel after normalization are summarized and then the image is enhanced using gamma transformation. ; in, This represents the grayscale output value after image enhancement. , This represents the grayscale scaling factor. Indicates the gamma factor; By summing the grayscale output values ​​of each pixel after image enhancement, the enhanced image data of the monitoring area is obtained. Furthermore, referring to Figure 1 As shown, the process of calculating and correcting the geographic data of the monitoring area based on the enhanced image data of the monitoring area to obtain the corrected geographic data of the monitoring area includes the following steps: S321. Correct the enhanced monitoring area image data to obtain the corrected monitoring area image data; Collect the camera axis distance parameters corresponding to the enhanced monitoring area image data, and correct the enhanced monitoring area image data based on the camera axis distance parameters and the enhanced monitoring area image data; The enhanced image data correction for the monitoring area is shown below: ; in, Indicates camera Wheelbase Indicates camera Wheelbase These represent the offsets between the ground plane and the camera imaging plane, respectively. This represents the camera's rotation vector. This represents the camera's translation vector. This represents the corrected image data of the monitoring area. This represents the enhanced image data of the monitoring area; S322. Calculate and correct the geographic data of the monitoring area based on the corrected image data of the monitoring area; Select any two points in the constructed three-dimensional coordinate system , The distance between the two selected points is measured to obtain the actual distance DL between the two points; Furthermore, based on the selected two points, two corresponding points are selected from the corrected monitoring area image data. , And calculate the image distance between two points in the corrected monitoring area image data according to the distance formula; The formula for the distance between two points is as follows: ; in, Indicates the midpoint of the corrected monitoring area image data and points Image distance between; Furthermore, the ratio between the actual distance and the image distance is calculated; ; in, This represents the ratio between the actual distance and the image distance; Furthermore, the actual distance represented by each pixel in the corrected monitoring area image data is determined based on the ratio between the actual distance and the image distance; Furthermore, each pixel in the corrected monitoring area image data is classified based on grayscale value matching, and the geographic data of the monitoring area corresponding to each type of pixel is calculated and corrected based on the actual distance represented by each pixel, so as to determine the location and area of ​​the geographic data of the monitoring area corresponding to each type of pixel. Furthermore, referring to Figure 1 As shown, the meteorological data of the monitoring area in the historical geological disaster data is supplemented by data completion methods. The supplemented meteorological data of the monitoring area includes the following steps: The meteorological data of the monitoring area in the historical geological disaster data was traversed to locate the meteorological data of the monitoring area that was missing; Furthermore, select m sets of meteorological data adjacent to the missing meteorological data of the monitoring area, calculate the corresponding average value, and use the calculated average value as the supplementary data to supplement the missing meteorological data of the monitoring area. Furthermore, referring to Figure 1 As shown, the processed historical geological disaster data is analyzed using data analysis methods. The analyzed historical geological disaster data includes the following steps: S41. Analyze the processed historical geological disaster data using fuzzy analysis and determine the historical geological disaster data grading system; The processed historical geological disaster data was compiled and submitted to experts for evaluation. Furthermore, expert scores were collected, and subjective scores for each group of processed historical geological disaster data were calculated using fuzzy analysis. The formula for calculating subjective scores is as follows: ; in, This represents the subjective score for each group of processed historical geological disaster data. This represents the score data of the z-th expert. Indicates the number of experts; Furthermore, a scoring range is set and evenly divided, and a historical geological disaster data grading system is determined based on the division results; Furthermore, based on the established historical geological disaster data grading system, the geographic data standards of the monitoring areas in the historical geological disaster data of each grade are summarized and calculated; The standard for the geographical data of the monitoring area in the historical geological disaster data of each level is the average value of the geographical data of the monitoring area in the historical geological disaster data of each level. S42. Summarize historical geological disaster data of corresponding levels and determine the characteristics of geological disasters of each level through feature recognition. The monitoring area image data in the historical geological disaster data of the corresponding level is divided into k image data blocks of uniform size, and the divided image data blocks are used as the input of the convolutional neural network. The convolutional neural network includes: convolutional layers, pooling layers, and fully connected layers; Furthermore, the kernel size and stride of each convolutional layer in the convolutional neural network are set; Simultaneously, based on the divided image data blocks, the convolution kernel size and stride are set to move on the input image data blocks, and convolution calculation is performed during the movement to achieve feature extraction for each image data block. The formula for calculating convolution is as follows: ; in, This represents the input image data block. represents the weights of the corresponding convolution kernel, and b represents the bias value. This represents the characteristics of the output image data blocks; Furthermore, after feature extraction is completed, the output image data block features are expanded and combined through a fully connected layer to obtain feature data; The obtained feature data is defined as geological disaster characteristics; S43. Summarize the geological hazard characteristics, historical geological hazard data grading system, and processed historical geological hazard data to obtain the analyzed historical geological hazard data; Furthermore, referring to Figure 1 As shown, the construction of a geological hazard simulation and prediction model based on analyzed historical geological hazard data through data fusion and digital twin methods includes the following steps: The historical geological disaster data in the analyzed historical geological disaster data were sorted in descending order based on the collection time of each group of historical geological disaster data; The historical geological disaster data in the analyzed historical geological disaster data were sorted in descending order based on the collection time of each group of historical geological disaster data; The changes in the geographical data of the monitoring area in each set of historical geological disaster data were recorded using a multi-frame comparison method. Digital twin prediction weights are set based on the geographical data standards of the monitoring areas in historical geological disaster data of each level. The digital twin prediction weights are set as follows: ; in, This indicates the behavior corresponding to the changes in geographic data of the monitored area. This indicates the status of the geographic data corresponding to the current monitoring area. This represents the set of behaviors corresponding to changes in geographic data across all monitored areas. Represents the weighting function. Indicates the execution of an action. Indicates the state Execution of actions ; The geological disaster simulation and prediction model constructed based on the set digital twin prediction weights is shown below: ; in, This represents the constructed geological disaster simulation and prediction model. Indicates the predicted probability. Indicates state; In one specific embodiment, the geological disaster early warning system is used to implement a geological disaster early warning method. The system includes: a data acquisition module, a data processing module, a data analysis module, a prediction model construction module, and an early warning module. The data acquisition module is used to collect geological disaster data in the geological disaster monitoring area in real time; The data processing module is used to process the real-time collected geological disaster data to obtain processed geological disaster data; The data analysis module is used to analyze the processed geological disaster data to obtain the analyzed geological disaster data; The prediction model building module is used to build a geological disaster simulation prediction model based on the analyzed geological disaster data; The early warning module is used to issue early warnings for geological disasters based on the prediction results of the geological disaster simulation prediction model.

[0024] It should be noted that The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A geological disaster early warning method, characterized by, The method comprises the following steps: S1, setting a geological disaster monitoring area, and deploying a data terminal and a plurality of monitoring sensors in the set geological disaster monitoring area; S2, after deployment, setting a data acquisition period for the monitoring sensors, and acquiring historical geological disaster data in the geological disaster monitoring area based on the set data acquisition period; The historical geological disaster data comprises monitoring area image data, monitoring area geographic data, and monitoring area meteorological data; S3, processing the acquired historical geological disaster data through a data processing method to obtain processed historical geological disaster data, comprising the following steps: S31, processing the monitoring area image data in the historical geological disaster data through an image enhancement method to obtain enhanced monitoring area image data; S32, calculating and correcting the monitoring area geographic data based on the enhanced monitoring area image data to obtain corrected monitoring area geographic data; S33, supplementing the monitoring area meteorological data in the historical geological disaster data through a data supplementing method to obtain supplemented monitoring area meteorological data; S34, summarizing the data in steps S31, S32, and S33 to obtain the processed historical geological disaster data; S4, analyzing the processed historical geological disaster data through a data analysis method to obtain analyzed historical geological disaster data; S5, constructing a geological disaster simulation prediction model through data fusion and digital twinning based on the analyzed historical geological disaster data; S6, acquiring real-time geological disaster data in the geological disaster monitoring area, inputting the acquired real-time geological disaster data into the constructed geological disaster simulation prediction model, and performing geological disaster early warning based on the output result of the geological disaster simulation prediction model. 2.The geological disaster early warning method and system according to claim 1, characterized in that, The step of setting a geological disaster monitoring area and deploying a data terminal and a plurality of monitoring sensors in the set geological disaster monitoring area comprises the following steps: Setting the center position of the geological disaster monitoring area as a coordinate origin, constructing a three-dimensional coordinate system based on the set coordinate origin, and deploying a data terminal at the center position of the geological disaster monitoring area; Based on the constructed three-dimensional coordinate system, the set geological disaster monitoring area is divided into small areas of the same size, and a monitoring sensor is deployed at the center of each divided small area. 3.The geological disaster early warning method and system according to claim 1, characterized in that, The step of processing the monitoring area image data in the historical geological disaster data through an image enhancement method to obtain enhanced monitoring area image data comprises the following steps: Acquiring the gray value distribution in the monitoring area image data, and normalizing the gray value in the monitoring area image data; ； wherein, monitoring the coordinates of the pixel points in the region image data, the coordinates being the gray value of the pixel point after the normalization processing. Summarizing the gray values of the normalized pixel points and performing image enhancement through gamma transformation; ； wherein represents the gray scale output value after image enhancement, , represents the gray scale scaling factor, represents the gamma factor; Summarizing the gray output values of the pixel points after image enhancement to obtain enhanced monitoring area image data. 4.The geological disaster early warning method and system according to claim 1, characterized in that, The step of calculating and correcting the monitoring area geographic data based on the enhanced monitoring area image data to obtain corrected monitoring area geographic data comprises the following steps: S321, correcting the enhanced monitoring area image data to obtain corrected monitoring area image data; Collect the camera axis distance parameters corresponding to the enhanced monitoring area image data, and correct the enhanced monitoring area image data based on the camera axis distance parameters and the enhanced monitoring area image data; The correction of the enhanced monitoring area image data is as follows: ； wherein, represents a camera axis, axis distance, represents a camera axis, axis distance, respectively represent a ground plane and a camera imaging plane ground offset, represents a camera rotation vector, represents a camera translation vector, represents corrected monitoring area image data, represents enhanced monitoring area image data; S322, calculate and correct the monitoring area geographic data based on the corrected monitoring area image data. 5.The geological disaster early warning method and system according to claim 4, characterized in that, The calculation and correction of the monitoring area geographic data based on the corrected monitoring area image data includes the following steps: Selecting any two points in constructing a three-dimensional coordinate system , , and measuring the distance between the two selected points to obtain the actual distance DL between the two points; selecting corresponding two points in the corrected monitoring area image data based on the selected two points , , and calculating the image distance between the two points in the corrected monitoring area image data according to the distance formula; The distance formula between two points is as follows: ； wherein, represents the image distance between the point and the point in the corrected monitoring area image data; Calculate the ratio between the actual distance and the image distance; ； wherein represents the ratio between the actual distance and the image distance; Determine the actual distance represented by each pixel point in the corrected monitoring area image data based on the ratio between the actual distance and the image distance; Classify each pixel point in the corrected monitoring area image data based on the gray value matching method, and calculate and correct the monitoring area geographic data corresponding to each type of pixel point based on the actual distance represented by each pixel point, to determine the position and area of the monitoring area geographic data corresponding to each type of pixel point. 6.The geological disaster early warning method and system according to claim 1, characterized in that, The supplementing of the monitoring area meteorological data in the historical geological disaster data through the data supplementing method to obtain the supplemented monitoring area meteorological data includes the following steps: Traverse the monitoring area meteorological data in the historical geological disaster data to locate the missing monitoring area meteorological data; Select m groups of adjacent monitoring area meteorological data of the missing monitoring area meteorological data, calculate the corresponding average value, and supplement the missing monitoring area meteorological data with the calculated average value as the supplement data. 7.The geological disaster early warning method and system according to claim 1, characterized in that, The analysis of the processed historical geological disaster data through the data analysis method to obtain the analyzed historical geological disaster data includes the following steps: S41, analyze the processed historical geological disaster data through the fuzzy analysis method, and determine the historical geological disaster data grade system; Summarize the processed historical geological disaster data, and submit the summarized historical geological disaster data to experts for scoring; Collect the expert scores and calculate the subjective score of each group of processed historical geological disaster data through the fuzzy analysis method; The subjective score calculation formula is as follows: ； wherein, denotes the subjective score of each group of processed historical geological disaster data, denotes the rating data of the zth expert, denotes the number of experts; Set the score range and divide it uniformly, and determine the historical geological disaster data grade system based on the division result; Based on the determined historical geological disaster data grade system, summarize and calculate the monitoring area geographic data standard in each grade of historical geological disaster data; The monitoring area geographic data standard in each grade of historical geological disaster data is the average value of the monitoring area geographic data in each grade of historical geological disaster data; S42, summarize the corresponding grade of historical geological disaster data, and determine the geological disaster characteristics of each grade through feature recognition method; S43, summarize the geological disaster characteristics, historical geological disaster data grade system and processed historical geological disaster data to obtain the analyzed historical geological disaster data. 8.The geological disaster early warning method and system according to claim 7, characterized in that, The summary of the corresponding grade of historical geological disaster data and the determination of the geological disaster characteristics of each grade through the feature recognition method includes the following steps: The image data of the monitoring area in the historical geological disaster data corresponding to the level is divided into k image data blocks of uniform size, and the divided image data blocks are taken as inputs of the convolutional neural network; The convolutional neural network comprises a convolutional layer, a pooling layer and a fully connected layer; The size of the convolution kernel and the convolution step of each convolutional layer in the convolutional neural network are set; Meanwhile, based on the divided image data blocks, the image data blocks are moved on the input image data blocks according to the set convolution kernel size and the set step, and convolution calculation is performed during the movement, so as to realize feature extraction of each image data block; The convolution calculation formula is as follows: ； wherein, represents an input image data block, represents a weight of a corresponding convolution kernel, and b represents a bias value, represents an output image data block feature; After feature extraction, the output image data block features are unfolded and combined through the fully connected layer to obtain feature data; The obtained feature data is set as geological disaster features. 9.The geological disaster early warning method and system according to claim 1, characterized in that, The geological disaster simulation prediction model is constructed based on the analyzed historical geological disaster data through data fusion and digital twin method, comprising the following steps: The collection time of each group of historical geological disaster data in the analyzed historical geological disaster data is arranged in descending order; Through the multi-frame comparison method, the change amount of the monitoring area geographic data in each group of historical geological disaster data is recorded; Based on the monitoring area geographic data standard in each level of historical geological disaster data, the digital twin prediction weight is set; The digital twin prediction weight is set as follows: ； wherein, represents a behavior corresponding to a change in the monitoring area geographic data, represents a state corresponding to the current monitoring area geographic data, represents a set of behaviors corresponding to changes in all monitoring area geographic data, represents a weight function, represents an executed behavior, represents that the behavior is executed at a state ;​ The geological disaster simulation prediction model is constructed based on the set digital twin prediction weight as follows: ； wherein, represents a constructed geological disaster simulation prediction model, represents a prediction probability, represents a state.

10. A system for implementing the geological disaster early warning method of claims 1-9, characterized in that, It comprises: a data acquisition module, a data processing module, a data analysis module, a prediction model construction module and an early warning module; The data acquisition module is used for real-time acquisition of geological disaster data in the geological disaster monitoring area; The data processing module is used for processing the real-time acquired geological disaster data to obtain processed geological disaster data; The data analysis module is used for analyzing the processed geological disaster data to obtain analyzed geological disaster data; The prediction model construction module is used for constructing a geological disaster simulation prediction model according to the analyzed geological disaster data; The early warning module is used for geological disaster early warning according to the prediction result of the geological disaster simulation prediction model.

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