Integrated remote sensing sensing monitoring method

Abstract

The invention discloses an integrated remote sensing sensing monitoring method, and relates to the related field of geological monitoring, and the method comprises the steps: activating an integrated remote sensing assembly, carrying out the multi-dimensional geological monitoring of a predetermined region, and obtaining the geological monitoring information; performing correlation analysis on the predetermined geological features to obtain geological feature correlation information; analyzing the geological feature association information according to a predetermined visual strategy to obtain a feature association structure chart; extracting first monitoring information; analyzing the feature association structure chart to obtain first association feature factor information corresponding to the first geological feature; performing support verification on the first monitoring information based on the first associated feature factor information to obtain a first verification result; and combining the first verification result with the geological monitoring information to generate a geological remote sensing visual map. The technical problems that in the existing tunneling process, geological monitoring is low in efficiency, and the monitoring result is not comprehensive and accurate enough are solved, and the technical effect of efficient, comprehensive and accurate geological monitoring is achieved.

Description

Technical Field

[0001] This application relates to the field of geological monitoring, and in particular, to an integrated remote sensing sensing monitoring method. Background Art

[0002] During the tunnel boring process, the complexity and uncertainty of geological conditions pose great challenges to construction. For example, a water-rich fault zone is a complex geological condition faced during tunnel boring. In such areas, faults are usually well-developed, groundwater is abundant, and the rock mechanical properties are poor. Boring a tunnel in a water-rich fault zone is difficult and has a high safety risk. To ensure the safe and efficient progress of tunnel boring, it is necessary to monitor and analyze the geological conditions of the boring area in real time. Traditional geological monitoring methods, such as surface exploration and manual sampling, are not only time-consuming and laborious but also difficult to adapt to the rapidly changing geological conditions during tunnel boring.

[0003] In the current related technologies, there are technical problems of low efficiency, incomplete and inaccurate monitoring results in the geological monitoring during tunnel boring. Summary of the Invention

[0004] This application provides an integrated remote sensing sensing monitoring method. By activating an integrated remote sensing component to perform multi-dimensional geological monitoring on a predetermined area, introducing a geological remote sensing database for correlation analysis, generating a feature correlation structure diagram, extracting and verifying the first monitoring information corresponding to the first geological feature, and combining the verification result with the geological monitoring information to generate a geological remote sensing viewable image, etc., the technical effect of efficient, comprehensive, and accurate geological monitoring is achieved.

[0005] This application provides an integrated remote sensing sensing monitoring method, including: activating an integrated remote sensing component, and performing multi-dimensional geological monitoring on a predetermined area through the integrated remote sensing component to obtain geological monitoring information; introducing a geological remote sensing database to perform correlation analysis on a predetermined geological feature to obtain geological feature correlation information; analyzing the geological feature correlation information according to a predetermined visualization strategy to obtain a feature correlation structure diagram; extracting the first monitoring information corresponding to the first geological feature in the geological monitoring information, where the first geological feature refers to any one of the predetermined geological features; analyzing the feature correlation structure diagram to obtain the first correlation feature factor information corresponding to the first geological feature; performing support verification on the first monitoring information based on the first correlation feature factor information to obtain a first verification result; and combining the first verification result with the geological monitoring information to generate a geological remote sensing viewable image of the predetermined area.

[0006] In a possible implementation, activate the integrated remote sensing component, and perform multi-dimensional geological monitoring on a predetermined area through the integrated remote sensing component to obtain geological monitoring information, and perform the following processing: Activate the integrated remote sensing component; Monitor the surface features in the predetermined geological features through the optical remote sensing device in the integrated remote sensing component to obtain surface monitoring information; Monitor the underground structure features in the predetermined geological features through the radar remote sensing device in the integrated remote sensing component to obtain underground structure monitoring information; Monitor the terrain features in the predetermined geological features through the lidar in the integrated remote sensing component to obtain terrain monitoring information; Monitor the heat source features in the predetermined geological features through the thermal infrared remote sensing device in the integrated remote sensing component to obtain heat source monitoring information; Monitor the groundwater features in the predetermined geological features through the neutron detection device in the integrated remote sensing component to obtain groundwater monitoring information; Based on the surface monitoring information, the underground structure monitoring information, the terrain monitoring information, the heat source monitoring information, and the groundwater monitoring information, form the geological monitoring information.

[0007] In a possible implementation, introduce a geological remote sensing database to perform correlation analysis on the predetermined geological features to obtain geological feature correlation information, and perform the following processing: Extract the second geological feature and the third geological feature in the predetermined geological features in sequence; Traverse the second geological feature and the third geological feature in the geological remote sensing database in sequence to obtain second monitoring information and third monitoring information respectively; Perform correlation analysis on the second monitoring information and the third monitoring information according to a predetermined correlation analysis mechanism to obtain first correlation information; Based on the first correlation information, form the geological feature correlation information.

[0008] In a possible implementation, perform correlation analysis on the second monitoring information and the third monitoring information according to a predetermined correlation analysis mechanism to obtain first correlation information, and perform the following processing: Obtain the first parameter set corresponding to the first index set in the second monitoring information; Obtain the second parameter set corresponding to the second index set in the third monitoring information; According to the predetermined correlation analysis mechanism, use the first index set as the independent variable and the second index set as the dependent variable; Perform correlation analysis of the independent variable and the dependent variable based on the first parameter set and the second parameter set to obtain the first correlation information.

[0009] In a possible implementation, the geological feature association information is analyzed according to a predetermined visual strategy to obtain a feature association structure diagram, and the following processing is performed: taking the second geological feature as the first vertex; taking the third geological feature as the second vertex; analyzing the first association information to obtain a first association degree, and matching the connection length corresponding to the first association degree to obtain an association length value; according to the predetermined visual strategy, combining the first vertex, the second vertex and the association length value to obtain the feature association structure diagram.

[0010] In a possible implementation, based on the first association feature factor information, the first monitoring information is supported and verified to obtain a first verification result, and the following processing is performed: extracting a first association feature in the first association feature factor information; matching the first association monitoring information corresponding to the first association feature in the geological monitoring information; taking the first association monitoring information as input information of the support prediction model, and predicting an output result through the support prediction model, where the output result includes a first coupling support degree; judging whether the first coupling support degree is within a predetermined coupling support degree threshold; if it is within, the verification of the first monitoring information passes, and the first verification result is generated.

[0011] In a possible implementation, the following processing is performed: if the first coupling support degree is not within the predetermined coupling support degree threshold, the verification of the first monitoring information fails, and the first verification result is generated.

[0012] In a possible implementation, the following processing is performed: the support prediction model refers to an intelligent prediction model obtained by performing supervised learning on a support degree training data set based on the neural network principle, where the support degree training data set includes association monitoring information, monitoring information and coupling support degree.

[0013] It is intended to propose an integrated remote sensing monitoring method through this application. First, the integrated remote sensing component is activated, and multi-dimensional geological monitoring is performed on a predetermined area through the integrated remote sensing component to obtain geological monitoring information. Then, a geological remote sensing database is introduced to perform association analysis on predetermined geological features to obtain geological feature association information. Next, the geological feature association information is analyzed according to a predetermined visual strategy to obtain a feature association structure diagram. Then, the first monitoring information corresponding to the first geological feature in the geological monitoring information is extracted, where the first geological feature refers to any one of the predetermined geological features. Then, the first association feature factor information corresponding to the first geological feature is analyzed from the feature association structure diagram. Furthermore, the first monitoring information is supported and verified based on the first association feature factor information to obtain a first verification result. Finally, a geological remote sensing view of the predetermined area is generated by combining the first verification result and the geological monitoring information, achieving the technical effects of efficient, comprehensive and accurate geological monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the methods according to the embodiments of this application. It should be understood that the operations in the front or below do not necessarily need to be executed precisely in sequence. On the contrary, according to the need, various steps can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0015] Figure 1 It is a schematic flowchart of an integrated remote sensing monitoring method provided by an embodiment of this application.

[0016] Figure 2 It is a schematic flowchart of the correlation analysis of predetermined geological features in an integrated remote sensing monitoring method provided by an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below.

[0018] In order to make the purpose, technical solutions and advantages of this application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limitations of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0019] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first" and "second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0020] An embodiment of the present application provides an integrated remote sensing monitoring method, as Figure 1 shown, the method includes:

[0021] Step S100, activate the integrated remote sensing component, and perform multi-dimensional geological monitoring on a predetermined area through the integrated remote sensing component to obtain geological monitoring information. Specifically, activate the integrated remote sensing component, which refers to a device or system integrating multiple remote sensing technologies and capable of capturing geological information in different dimensions simultaneously or separately, including sensors carried on satellites, airplanes or drones, etc. Start these remote sensing devices to ensure they are in working condition. Use the remote sensing component to capture the geological features of the predetermined area, including various information such as topography, landform, vegetation cover, soil moisture, groundwater level, etc., which are presented in the form of images or data. Process and organize the captured information to form geological monitoring information, providing a basis for subsequent analysis.

[0022] In a possible implementation manner, when activating the integrated remote sensing component and performing multi-dimensional geological monitoring on a predetermined area through the integrated remote sensing component to obtain geological monitoring information, step S100 further includes step S110, activate the integrated remote sensing component, and monitor the surface features in the predetermined geological features through the optical remote sensing device in the integrated remote sensing component to obtain surface monitoring information. Specifically, start and calibrate the optical remote sensing device of the integrated remote sensing component. The optical remote sensing device is a device that uses optical principles for remote detection, such as a high-resolution camera carried on a satellite or a drone. Ensure that the device is in normal working condition, and adjust parameters such as focal length and exposure to adapt to different surface features. Scan the surface of the predetermined area comprehensively according to a predetermined orbit or path. Capture the optical signals reflected by the surface, convert them into digital images or data, and perform preliminary processing, such as denoising and enhancing contrast. Based on the processed data, extract surface feature information, such as vegetation cover, soil moisture, surface temperature, etc.

[0023] Step S120, monitor the underground structure features in the predetermined geological features through the radar remote sensing device in the integrated remote sensing component to obtain underground structure monitoring information. Specifically, start and configure the radar remote sensing device. The radar remote sensing device is a device that uses radar waves for remote detection and can penetrate the surface to obtain underground structure information. Select parameters such as radar frequency and polarization mode, emit radar waves underground, and receive the reflected signals. Use radar image processing technology to extract underground structure information, such as stratigraphic distribution, faults, karst, etc. Based on the analysis results, form detailed monitoring information of the underground structure.

[0024] Step S130: Monitor the terrain features in the predetermined geological features through the lidar in the integrated remote sensing component to obtain terrain monitoring information. Specifically, start and calibrate the lidar. The lidar is a device that uses laser beams for long-distance measurement and can accurately obtain the three-dimensional coordinate information of the ground surface. Ensure the normal operation of the device, adjust the emission angle and intensity of the laser beam, scan the ground surface of the predetermined area with the laser beam, and record the emission and reflection times of the laser beam. Calculate the three-dimensional coordinates of each point on the ground surface based on the emission and reflection times of the laser beam to generate a three-dimensional terrain model. Extract terrain feature information from the three-dimensional model, such as slope, elevation, terrain undulation, etc.

[0025] Step S140: Monitor the heat source features in the predetermined geological features through the thermal infrared remote sensing device in the integrated remote sensing component to obtain heat source monitoring information. Specifically, start and calibrate the thermal infrared remote sensing device. The thermal infrared remote sensing device is a device that uses a thermal infrared sensor for long-distance detection and can capture the thermal radiation signals emitted by heat sources on the ground surface and underground. Ensure the normal operation of the device, adjust the sensitivity of the thermal infrared sensor, scan the predetermined area, and capture the thermal radiation signals emitted by heat sources on the ground surface and underground. Convert the captured thermal radiation signals into temperature information, extract the heat source features, and form detailed monitoring information of the heat source features, such as heat source location, intensity, distribution, etc.

[0026] Step S150: Monitor the groundwater features in the predetermined geological features through the neutron detection device in the integrated remote sensing component to obtain groundwater monitoring information. Specifically, start and configure the neutron detection device. The neutron detection device is a device that uses the principle of neutron scattering for groundwater detection. Select appropriate neutron source and detector parameters, emit neutrons underground, and receive the neutron signals scattered back after colliding with groundwater molecules. Use the principle of neutron scattering to calculate information such as the content, depth, and distribution of groundwater to form detailed monitoring information of groundwater, such as water content, water level, water quality, etc.

[0027] Step S160: Based on the surface monitoring information, the underground structure monitoring information, the terrain monitoring information, the heat source monitoring information, and the groundwater monitoring information, establish the geological monitoring information. Specifically, integrate the above various monitoring information to form a complete geological monitoring data set. Check and correct the integrated data to ensure the accuracy and reliability of the data. Based on the verified data, establish the geological monitoring information (the comprehensive information of geological features such as the surface, underground structure, terrain, heat source, and groundwater obtained through remote sensing monitoring means), providing a basis for subsequent analysis and visualization. This implementation method realizes the monitoring of various geological features such as the surface, underground structure, terrain, heat source, and groundwater by introducing different types of remote sensing equipment, obtains richer geological information, and provides a more comprehensive data basis for geological scientific research.

[0028] Step S200: Introduce a geological remote sensing database to conduct correlation analysis on predetermined geological features to obtain geological feature correlation information. Specifically, introduce a geological remote sensing database, that is, connect to or access a database storing geological remote sensing data, which includes historical geological data, remote sensing images, geological models, etc., to support geological monitoring and analysis work. Compare and analyze the predetermined geological features with the data in the database to find the correlation and similarity between the features in the predetermined geological features. Based on the results of the correlation analysis, extract the geological feature correlation information related to the predetermined geological features.

[0029] As Figure 2 shown, in a possible implementation method, when introducing a geological remote sensing database to conduct correlation analysis on predetermined geological features to obtain geological feature correlation information, step S200 further includes step S210: sequentially extract the second geological feature and the third geological feature in the predetermined geological features. Specifically, the predetermined geological features refer to a set of geological features that are determined in advance and need to be subjected to correlation analysis. From the predetermined list of geological features, extract two geological features in sequence as the second geological feature and the third geological feature for correlation analysis.

[0030] Step S220: Traverse the second geological feature and the third geological feature in the geological remote sensing database in sequence to obtain the second monitoring information and the third monitoring information respectively. Specifically, use database query technology to search for the monitoring data or information corresponding to the second geological feature in the geological remote sensing database to obtain the second monitoring information. Similarly, search for the monitoring data or information corresponding to the third geological feature in the geological remote sensing database to obtain the third monitoring information.

[0031] Step S230: Perform correlation analysis on the second monitoring and the third monitoring information according to a predetermined correlation analysis mechanism to obtain first correlation information. Specifically, according to a predetermined correlation analysis mechanism (a method or model determined in advance for correlation analysis, such as correlation analysis, regression analysis, causal analysis, etc.), perform correlation analysis on the second monitoring information and the third monitoring information. Analyze the correlation, causal relationship, or similarity between the two monitoring information to obtain the correlation information between them, including the similarity, correlation coefficient, causal chain, etc. of the two monitoring information.

[0032] Step S240: Construct the geological feature correlation information based on the first correlation information. Specifically, combine the obtained first correlation information with the information of the second geological feature and the third geological feature to form the geological feature correlation information. The geological feature correlation information includes the correlation relationship, correlation intensity, correlation pattern, etc. between multiple geological features. Store the geological feature correlation information in a database for subsequent analysis and visualization. This implementation method obtains rich geological monitoring data or information by introducing a geological remote sensing database, providing a basis for correlation analysis. At the same time, performing correlation analysis on the monitoring information through a predetermined correlation analysis mechanism to obtain the correlation relationship or information between geological features helps to reveal the internal connection of geological features and improves the accuracy and reliability of geological monitoring.

[0033] In a possible implementation manner, when performing correlation analysis on the second monitoring and the third monitoring information according to a predetermined correlation analysis mechanism to obtain first correlation information, step S230 further includes step S231: Obtain the first parameter set corresponding to the first index set in the second monitoring information. Specifically, identify and extract the data or information related to the first index set from the second monitoring information. The first index set is a set of indexes predefined according to geological features or monitoring objectives, used to describe or quantify certain attributes or states of the second geological feature. The first parameter set is the specific numerical values or parameters corresponding to these indexes, and these numerical values or parameters are obtained through remote sensing monitoring means and are used to quantitatively describe the state of the first index set.

[0034] Step S232: Obtain the second parameter set corresponding to the second index set in the third monitoring information. Specifically, identify and extract the data or information related to the second index set from the third monitoring information. The second index set is another set of indexes used to describe or quantify the third geological feature, and these indexes may have a certain correlation or relationship with the first index set. The second parameter set is the specific numerical values or parameters corresponding to these indexes and is used to quantitatively describe the state of the second index set.

[0035] Step S233: Take the first index set as the independent variable and the second index set as the dependent variable according to the predetermined correlation analysis mechanism. Specifically, according to the predetermined correlation analysis mechanism, determine the roles of the first index set and the second index set in the analysis. Take the index set that may affect other indexes as the independent variable (or called the explanatory variable), and take the index set that you want to understand as the dependent variable (or called the response variable). In this step, assume that the first index set may affect the second index set, so the first index set is taken as the independent variable and the second index set is taken as the dependent variable.

[0036] Step S234: Conduct the correlation analysis between the independent variable and the dependent variable based on the first parameter set and the second parameter set to obtain the first correlation information. Specifically, use statistical methods or machine learning algorithms to conduct the correlation analysis based on the first parameter set (independent variable) and the second parameter set (dependent variable), find out whether there is a certain relationship (such as linear relationship, non-linear relationship, causal relationship, etc.) between the first index set and the second index set, and quantify the strength and direction of this relationship. According to the analysis results, obtain the first correlation information, including statistical quantities such as correlation coefficient, regression coefficient or significance level, etc., which are used to describe the degree of association between the independent variable and the dependent variable. This implementation method takes the first index set as the independent variable and the second index set as the dependent variable for correlation analysis, which is based on the assumption of the potential relationship between geological features and monitoring data. This assumption helps to analyze the interaction between geological features more systematically and orderly, and find out the key influencing factors. By obtaining the first parameter set and the second parameter set for correlation analysis, the correlation information is quantified, providing a scientific basis for subsequent geological monitoring, etc.

[0037] Step S300: Analyze the geological feature correlation information according to the predetermined visualization strategy to obtain the feature correlation structure diagram. Specifically, according to the monitoring purpose and data analysis requirements, determine the visualization strategy in advance. The visualization strategy refers to the strategies and methods adopted when formulating the visualization plan, including the use of visual elements such as color, shape, size, etc. and layout design, etc. Use visualization tools or software to analyze and interpret the geological feature correlation information, and reveal its internal structure and relationship. Present the analysis results in a graphical way to form the feature correlation structure diagram, which is used to reveal the relationship between geological features.

[0038] In a possible implementation, the geological feature association information is analyzed according to a predetermined visual strategy to obtain a feature association structure diagram. Step S300 further includes step S310, where the second geological feature is taken as the first vertex, the third geological feature is taken as the second vertex, the first association information is analyzed to obtain a first association degree, and the connection length corresponding to the first association degree is matched to obtain an association length value. Specifically, the second geological feature and the third geological feature are respectively set as two vertices in the feature association structure diagram, that is, the first vertex and the second vertex. These two vertices represent two geological features that are associated and analyzed in the geological remote sensing database. The first association information obtained in step S234 is analyzed to determine the association degree between the second geological feature and the third geological feature. The association degree is a quantitative value representing the degree of correlation or mutual influence between two geological features. According to the value of the association degree, the corresponding connection length value is matched from a predetermined association length mapping table. This mapping table is preset according to statistical methods and is used to convert the association degree into the connection length in the graphical representation.

[0039] Step S320, according to the predetermined visual strategy, combining the first vertex, the second vertex and the association length value, to obtain the feature association structure diagram. Specifically, according to the predetermined visual strategy, the layout method of the feature association structure diagram is determined, including the position arrangement of the vertices, the direction of the connection lines, the overall shape of the graph, etc. On the basis of the graph layout, the first vertex and the second vertex are drawn, and according to the association length value obtained in step S310, the connection line connecting the two vertices is drawn. The length of the connection line reflects the association degree between the two geological features. Finally, the feature association structure diagram is optimized to improve its readability and aesthetics, including adjusting the positions of the vertices, the shapes of the connection lines, adding labels or annotations, etc. This implementation method, through the quantitative representation of the association degree and the association length, enables the feature association structure diagram to not only display the association relationship between geological features, but also provide information about the strength of these relationships, which helps to quickly identify key geological features and their interactions, and improves the efficiency and accuracy of geological monitoring and geological feature research.

[0040] Step S400, extract the first monitoring information corresponding to the first geological feature in the geological monitoring information, where the first geological feature refers to any one of the predetermined geological features. Specifically, based on the data analysis requirements, one of the predetermined geological features is selected as the first geological feature, and the data or image information corresponding to the first geological feature is extracted from the geological monitoring information as the first monitoring information.

[0041] Step S500: Analyze the feature association structure diagram to obtain the first associated feature factor information corresponding to the first geological feature. Specifically, analyze the feature association structure diagram to identify the associated feature factors related to the first geological feature. Based on the analysis results, extract the associated feature factor information related to the first geological feature, which may include various factors such as terrain, landform, and vegetation cover.

[0042] Step S600: Perform support verification on the first monitoring information based on the first associated feature factor information to obtain a first verification result. Specifically, by comparing and analyzing the consistency and differences between different data, use the first associated feature factor information to verify the first monitoring information to verify its accuracy and reliability. Based on the results of the verification process, obtain the first verification result for evaluating the accuracy and reliability of the first monitoring information.

[0043] In a possible implementation manner, when performing support verification on the first monitoring information based on the first associated feature factor information to obtain a first verification result, step S600 further includes step S610: Extract the first associated feature in the first associated feature factor information. Specifically, from the first associated feature factor information obtained in step S500, extract any associated feature related to the first geological feature, that is, the first associated feature. Among them, the first associated feature factor information contains multiple geological features related to the first geological feature.

[0044] Step S620: Match the first associated monitoring information corresponding to the first associated feature in the geological monitoring information. Specifically, in the obtained geological monitoring information, through analysis and comparison, locate the specific monitoring data corresponding to the first associated feature, that is, the first associated monitoring information, which provides a basis for subsequent analysis and verification.

[0045] Step S630: Use the first associated monitoring information as the input information for the support prediction model, and obtain an output result through the support prediction model. Among them, the output result includes a first coupling support degree. Specifically, the support prediction model is a mathematical model based on historical data and associated features to predict the change trend or state of the target feature, and can predict the change trend or state of the first geological feature based on the input first associated monitoring information. Input the first associated monitoring information into the support prediction model, run the support prediction model, and obtain an output result. The output result includes a first coupling support degree, indicating the degree of association between the first geological feature and the first associated feature.

[0046] Step S640: Determine whether the first coupling support degree is within a predetermined coupling support degree threshold. Specifically, the predetermined coupling support degree threshold is a quantitative criterion for evaluating whether the degree of association between the first geological feature and the first associated feature meets the requirements. Compare the first coupling support degree obtained in step S630 with the predetermined coupling support degree threshold to determine whether it meets the requirements. Step S650: If it is within the range, the verification of the first monitoring information passes, and the first verification result is generated. Specifically, if the first coupling support degree is within the range of the predetermined coupling support degree threshold, it is considered that the verification of the first monitoring information passes, that is, the first monitoring information is accurate and reliable. Based on the judgment that the verification passes, a first verification result is generated, which includes key information such as the verification pass status and the first coupling support degree. This implementation method improves the accuracy of verification by introducing the first associated feature factor information and the support prediction model and integrating multiple factors related to the first geological feature.

[0047] In a possible implementation manner, step S600 further includes step S660. If the first coupling support degree is not within the predetermined coupling support degree threshold, the verification of the first monitoring information fails, and the first verification result is generated. Specifically, in step S640, if the judgment result shows that the first coupling support degree is not within the range of the predetermined coupling support degree threshold, step S660 is executed. According to the judgment result, it is confirmed that the verification of the first monitoring information fails, that is, the association between the first monitoring information and the first associated feature is insufficient, or there are other factors causing the verification to fail. Based on the judgment that the verification fails, a corresponding first verification result is generated.

[0048] In a possible implementation, step S630 further includes step S631. The support prediction model refers to an intelligent prediction model obtained by performing supervised learning on a support degree training data set based on the principle of neural network. Among them, the support degree training data set includes association monitoring information, monitoring information, and coupling support degree. Specifically, collect and organize the support degree training data set, which includes association monitoring information, monitoring information, and coupling support degree. The association monitoring information refers to monitoring data associated with specific geological features, and these data come from a geological remote sensing database or other reliable data sources. The monitoring information refers to the data obtained during multi-dimensional geological monitoring of a predetermined area, including surface monitoring information, underground structure monitoring information, terrain monitoring information, heat source monitoring information, and groundwater monitoring information, etc. The coupling support degree is a quantitative index used to measure the degree of association or consistency between the association monitoring information and the monitoring information. Use a neural network architecture such as a multi-layer perceptron (or convolutional neural network, recurrent neural network, etc.) to divide the support degree training data set into a training set and a validation set (or test set). Use the training set data to train the neural network model, and adjust the network parameters through the backpropagation algorithm to make the prediction result of the model as close as possible to the actual coupling support degree. During the training process, use the validation set to monitor the performance of the model to prevent overfitting or underfitting. Use the validation set or test set to evaluate the performance of the model, including indicators such as accuracy, recall rate, and F1 score. Optimize the model according to the evaluation results, such as adjusting the network structure, increasing the training data, and using regularization techniques, etc. In practical applications, input the first association monitoring information as input data into the trained neural network model, and the model calculates and outputs the first coupling support degree according to the input data. This implementation method constructs a support prediction model based on the principle of neural network and uses the support degree training data set for supervised learning, making full use of the association information and features in the data, improving the accuracy of prediction, and thus improving the accuracy and reliability of geological monitoring information.

[0049] Step S700: Generate the geological remote sensing view of the predetermined area by combining the first verification result and the geological monitoring information. Specifically, integrate and summarize the first verification result, the first monitoring information, and other relevant geological monitoring information. Use visualization tools or software to present the integrated data and information in a graphical manner to form a geological remote sensing view for intuitively displaying the geological features and monitoring results of the predetermined area. Display the generated geological remote sensing view to relevant personnel or institutions to support decision-making and planning work. The embodiments of the present application adopt technical means such as performing multi-dimensional geological monitoring on a predetermined area by activating an integrated remote sensing component, introducing a geological remote sensing database for correlation analysis, generating a feature correlation structure diagram, extracting and verifying the first monitoring information corresponding to the first geological feature, and generating a geological remote sensing view by combining the verification result and the geological monitoring information, achieving the technical effect of efficient, comprehensive, and accurate geological monitoring.

[0050] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be executed in a sequence different from that in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In certain embodiments, multi-tasking and parallel processing are also possible or may be advantageous.

Claims

1. An integrated remote sensing monitoring method, characterized in that: include: activating an integrated remote sensing component, and performing multi-dimensional geological monitoring of a predetermined area through the integrated remote sensing component to obtain geological monitoring information; Introduce geological remote sensing database to conduct correlation analysis on predetermined geological features to obtain geological feature correlation information; Analyze the geological feature association information according to a predetermined visual strategy to obtain a feature association structure diagram; Extracting first monitoring information corresponding to a first geological feature in the geological monitoring information, wherein the first geological feature refers to any one of the predetermined geological features; Analyzing the feature association structure diagram to obtain first association feature factor information corresponding to the first geological feature; Performing support verification on the first monitoring information based on the first associated characteristic factor information to obtain a first verification result; The first verification result and the geological monitoring information are combined to generate a geological remote sensing visual map of the predetermined area.

2. The integrated remote sensing monitoring method according to claim 1, characterized in that: Activate the integrated remote sensing component, and use the integrated remote sensing component to perform multi-dimensional geological monitoring of the predetermined area to obtain geological monitoring information, including: Activate the integrated remote sensing component; Monitoring the surface features of the predetermined geological features by using the optical remote sensing device in the integrated remote sensing assembly to obtain surface monitoring information; Monitoring underground structure features in the predetermined geological features by using the radar remote sensing equipment in the integrated remote sensing component to obtain underground structure monitoring information; Monitoring the terrain features in the predetermined geological features by means of a laser radar in the integrated remote sensing component to obtain terrain monitoring information; Monitoring the heat source characteristics in the predetermined geological characteristics by means of the thermal infrared remote sensing device in the integrated remote sensing component to obtain heat source monitoring information; Monitoring groundwater characteristics in the predetermined geological characteristics by means of a neutron detection device in the integrated remote sensing component to obtain groundwater monitoring information; The geological monitoring information is constructed based on the surface monitoring information, the underground structure monitoring information, the terrain monitoring information, the heat source monitoring information and the groundwater monitoring information.

3. The integrated remote sensing monitoring method according to claim 1, characterized in that: The geological remote sensing database is introduced to conduct correlation analysis on the predetermined geological features to obtain the geological feature correlation information, including: Sequentially extracting a second geological feature and a third geological feature from the predetermined geological features; The second geological feature and the third geological feature are sequentially traversed in the geological remote sensing database to obtain second monitoring information and third monitoring information respectively; Performing correlation analysis on the second monitoring information and the third monitoring information according to a predetermined correlation analysis mechanism to obtain first correlation information; The geological feature associated information is established based on the first associated information.

4. The integrated remote sensing monitoring method according to claim 3, characterized in that: Performing correlation analysis on the second monitoring information and the third monitoring information according to a predetermined correlation analysis mechanism to obtain first correlation information includes: Obtaining a first parameter set corresponding to the first indicator set in the second monitoring information; Obtaining a second parameter set corresponding to the second indicator set in the third monitoring information; According to the predetermined association analysis mechanism, the first indicator set is used as an independent variable and the second indicator set is used as a dependent variable; A correlation analysis between the independent variable and the dependent variable is performed based on the first parameter set and the second parameter set to obtain the first correlation information.

5. The integrated remote sensing monitoring method according to claim 4, characterized in that: The geological feature association information is analyzed according to a predetermined visual strategy to obtain a feature association structure diagram, including: using the second geological feature as a first vertex; using the third geological feature as a second vertex; Analyze the first association information to obtain a first association degree, and match the connection length corresponding to the first association degree to obtain an association length value; According to the predetermined visual strategy, the first vertex, the second vertex and the association length value are combined to obtain the feature association structure diagram.

6. The integrated remote sensing monitoring method according to claim 1, characterized in that: The first monitoring information is supported and verified based on the first associated characteristic factor information to obtain a first verification result, including: Extracting a first correlation feature from the first correlation feature factor information; matching the first associated monitoring information corresponding to the first associated feature in the geological monitoring information; Using the first association monitoring information as input information of a support prediction model, and obtaining an output result through prediction by the support prediction model, wherein the output result includes a first coupling support degree; Determining whether the first coupling support is within a predetermined coupling support threshold; If so, the verification of the first monitoring information passes and the first verification result is generated.

7. An integrated remote sensing monitoring method according to claim 6, characterized in that: If the first coupling support is not within the predetermined coupling support threshold, the verification of the first monitoring information fails, and the first verification result is generated.

8. The integrated remote sensing monitoring method according to claim 6, characterized in that: The support prediction model refers to an intelligent prediction model obtained after supervised learning of a support training data set based on the principle of a neural network, wherein the support training data set includes associated monitoring information, monitoring information and coupled support.

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