Geological disaster monitoring method based on 5G positioning technology

Abstract

The invention relates to the technical field of geological disaster monitoring, in particular to a geological disaster monitoring method based on the 5G positioning technology, and the method comprises the steps: obtaining the geographic position information of a target region, and carrying out the displacement monitoring, underground space monitoring, underground water monitoring and meteorological monitoring of the target region based on the geographic position information, acquiring real-time displacement data, underground space detection data, underground water monitoring data and meteorological data; processing and analyzing the real-time displacement data, the underground space detection data, the underground water monitoring data and the meteorological data to obtain a geological disaster prediction result; and based on the geological disaster prediction result, giving an optimal treatment scheme and sending out early warning information. According to the invention, intelligent identification, early warning and treatment of hidden danger of geological disasters can be realized, and the safety of a monitoring area is effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster monitoring, and in particular to a geological disaster monitoring method based on 5G positioning technology. Background Art

[0002] The monitoring and early warning of geological disasters is a complex project. The core technical problem is how to achieve high-precision, real-time, and intelligent identification and early warning of collapse hazards. This involves the coordinated solution of multiple technical difficulties. First, the acquisition of geographic location information needs to overcome the limitations of insufficient accuracy of traditional positioning technology. Although 5G positioning technology has high accuracy, its stability and reliability under complex terrain conditions are still facing challenges. Secondly, when high-density displacement monitoring technology is applied over a large range and for a long time, how to ensure the continuity and consistency of monitoring data is a key issue. Furthermore, when machine learning algorithms process massive, multi-source, and heterogeneous monitoring data, how to improve the generalization and anti-interference capabilities of the model to accurately monitor geological disasters is a problem that needs to be solved urgently. In addition, the establishment of a geological disaster early warning model requires comprehensive consideration of multiple factors such as geological conditions, hydrological conditions, and human activities. How to effectively integrate these complex factors into the model to improve the accuracy and timeliness of early warning is also an important challenge. Finally, in the analysis of the formation mechanism of geological disasters and the optimization of governance plans, how to balance computational efficiency and model accuracy to achieve rapid response and scientific decision-making is another technical problem. Solving these problems requires not only breakthroughs in various technical fields, but also the cross-integration of multidisciplinary knowledge to carry out comprehensive, efficient and intelligent geological disaster monitoring and early warning. Summary of the invention

[0003] The purpose of the present invention is to provide a geological disaster monitoring method based on 5G positioning technology, which obtains the precise geographic coordinates of the target area through 5G positioning technology, adopts high-density displacement monitoring technology to monitor regional displacement data in real time, and combines underground space detection, groundwater monitoring data, and meteorological data. A geological disaster prediction model is constructed using a support vector machine to comprehensively predict the occurrence of geological disasters, and further uses an optimization algorithm to determine the best governance plan to guide targeted governance.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A geological disaster monitoring method based on 5G positioning technology, comprising:

[0006] Acquire geographic location information of a target area, and perform displacement monitoring, underground space monitoring, groundwater monitoring, and meteorological monitoring on the target area based on the geographic location information to acquire real-time displacement data, underground space detection data, groundwater monitoring data, and meteorological data;

[0007] Processing and analyzing the real-time displacement data, underground space detection data, groundwater monitoring data, and meteorological data to obtain geological disaster prediction results;

[0008] Based on the geological disaster prediction results, the best control plan is given and early warning information is issued.

[0009] Optionally, the displacement data is obtained by deploying a number of GPS locators and inclinometer sensors in the target area, the underground space detection data is obtained by deploying detection lines in the target area, using geological radar and electromagnetic wave CT imaging technology to collect electromagnetic wave response signals of underground media, and performing inversion interpretation, the groundwater monitoring data is obtained by deploying a number of water level meters and multi-parameter water quality analyzers in the target area, and the meteorological data is obtained based on the meteorological observation station to which the target area belongs.

[0010] Optionally, processing the real-time displacement data, underground space detection data, groundwater monitoring data, and meteorological data includes:

[0011] The real-time displacement data, underground space detection data, groundwater monitoring data, and meteorological data are subjected to outlier elimination, denoising, standardization, and data attribute conversion processing, and feature parameters are extracted. The feature parameters include surface deformation parameters, underground space parameters, groundwater parameters, and meteorological parameters.

[0012] Optionally, obtaining the geological disaster prediction result includes:

[0013] After the characteristic parameters are fused in time series, they are input into a pre-built geological disaster prediction model to output the geological disaster prediction results, wherein the geological disaster prediction results include geological disaster categories, geological disaster occurrence probability values, geological disaster occurrence levels, geological disaster causes and impact ranges; the geological disaster prediction model is obtained by training a support vector machine (SVM) model based on a training set, and the training set includes several types of geological disasters and corresponding characteristic parameters and causes, as well as the impact ranges corresponding to different degrees of each geological disaster.

[0014] Optionally, based on the geological disaster prediction results, providing the best governance solution includes:

[0015] Based on the predicted geological disaster categories, geological disaster occurrence probability values, geological disaster occurrence levels, geological disaster causes and impact ranges, an optimization algorithm is used to search in a preset geological disaster control solution database to determine the best control solution, wherein the geological disaster control solution database is based on geological disasters of different types and danger levels.

[0016] Optionally, an optimization algorithm is used to search in a preset geological disaster control solution database to determine the best control solution including:

[0017] Using a genetic algorithm, taking the governance effect and governance cost as optimization targets, searching for the optimal governance solution combination from the geological disaster governance solution database;

[0018] Dynamic monitoring measures are added to the optimal governance solution combination as the best governance solution, wherein the dynamic monitoring measures are real-time monitoring of displacement data, underground space detection data, groundwater monitoring data, and meteorological data during the governance process, and inputting them into the geological disaster prediction model for dynamic prediction.

[0019] Optionally, issuing the warning information includes:

[0020] Whether it is necessary to issue an early warning is determined based on the probability value of the geological disaster occurrence in the geological disaster prediction result. When the probability value of the geological disaster occurrence exceeds the preset geological disaster threshold, the geological disaster category, geological disaster probability value, geological disaster occurrence level, geological disaster cause and impact range, and the searched best governance plan are packaged as early warning information and sent to the management personnel.

[0021] Optionally, the method also includes: constructing a three-dimensional model of the target area, and visually displaying real-time monitored displacement data, underground space detection data, groundwater monitoring data, meteorological data, and corresponding geological disaster prediction results, optimal control solutions, and early warning information in the three-dimensional model.

[0022] The beneficial effects of the present invention are:

[0023] The present invention proposes a method for monitoring geological disasters based on 5G positioning technology, which uses 5G positioning technology to obtain the precise geographic coordinates of the target area, uses high-density displacement monitoring technology to monitor regional displacement data in real time, and combines underground space detection, groundwater monitoring data, and meteorological data. It uses a support vector machine to build a geological disaster prediction model to comprehensively predict the occurrence of geological disasters, and further uses an optimization algorithm to determine the best governance plan to guide targeted governance. The present invention realizes the intelligent identification, early warning, and governance of geological disaster hazards, effectively ensuring regional safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1This is a flow chart of a geological disaster monitoring method based on 5G positioning technology in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] This embodiment provides a geological disaster monitoring method based on 5G positioning technology. Figure 1 As shown, including:

[0029] Step S101, obtain the geographic location information of the target area, and perform displacement monitoring, underground space monitoring, groundwater monitoring, and meteorological monitoring on the target area based on the geographic location information to obtain real-time displacement data, underground space detection data, groundwater monitoring data, and meteorological data.

[0030] Determine the geographical location range of the target area, use 5G positioning technology to scan the target area, and obtain the geographical coordinate data within the target area; perform noise processing on the geographical coordinate data, eliminate abnormal coordinate points, and construct a geographical coordinate data set for the target area; use the geographical coordinate data set to monitor the target area, and deploy sensors to perform displacement monitoring, underground space monitoring, groundwater monitoring, and meteorological monitoring to obtain real-time displacement data, underground space detection data, groundwater monitoring data, and meteorological data. Among them, displacement data includes displacement direction, displacement rate, displacement acceleration, etc., underground space detection data includes underground cavities, pipeline spatial position and morphological information, etc., groundwater monitoring data includes groundwater level changes, water quality change dynamic information, etc., meteorological data includes temperature and humidity, wind speed, precipitation, etc.

[0031] Specifically, in this embodiment, the established geographic information database of geological disaster-prone areas is used to obtain the approximate range of the target area through spatial query, such as a certain area in a certain city, with an area of ​​about 10 square kilometers. Then, the area is scanned using 5G positioning technology to obtain high-precision geographic coordinate data, with a positioning accuracy of up to centimeters. The acquired coordinate data is subjected to noise processing, and the median filtering algorithm is used to remove abnormal points to obtain an accurate geographic coordinate data set.

[0032] In this embodiment, a number of high-precision GPS locators and inclinometer sensors are deployed in the target area. The sampling frequency is 10 Hz, the positioning accuracy is better than 1 cm, and the tilt angle measurement accuracy reaches 0.1°. The three-dimensional coordinates and tilt angle data of each monitoring point are automatically collected every hour to form a set of displacement data reflecting the surface displacement situation.

[0033] To obtain underground space detection data, geological radar, electromagnetic wave CT imaging and other technical means can be used. By laying out detection lines, collecting electromagnetic wave response signals of underground media, and performing inversion interpretation, the three-dimensional spatial position and morphological information of underground cavities, pipelines and other target bodies can be obtained. The spatial positioning accuracy can reach 1m.

[0034] At the same time, groundwater level monitoring wells and water quality monitoring points are set up in the target area. Groundwater level changes and water quality parameters are collected in real time through water level meters and multi-parameter water quality analyzers. The monitoring frequency is once per hour.

[0035] Meteorological data can be obtained according to the meteorological observation station in the target area.

[0036] Step S102: Process and analyze the real-time displacement data, underground space detection data, groundwater monitoring data, and meteorological data to obtain geological disaster prediction results.

[0037] Each monitoring point transmits data to the monitoring data center in real time through LoRa wireless communication for storage. The monitoring data center uses a distributed storage architecture and Hadoop big data processing platform to process and save the monitoring data generated every day and obtain characteristic parameters;

[0038] After the characteristic parameters are integrated in time series, they are input into the pre-built geological disaster prediction model to output the geological disaster prediction results, which include the geological disaster category, geological disaster occurrence probability value, geological disaster occurrence level, geological disaster cause and impact range.

[0039] Among them, the geological disaster prediction model is obtained by training the support vector machine SVM model based on the training set. The training set includes several types of geological disasters and their corresponding characteristic parameters and causes, as well as the impact range corresponding to the different degrees of each geological disaster.

[0040] Specifically, the processing of real-time displacement data includes: using the median filtering algorithm to process the acquired displacement data, removing outliers and high-frequency noise beyond the normal range, obtaining a smooth displacement data sequence, and extracting surface deformation parameters reflecting the displacement characteristics, such as displacement rate, displacement acceleration, and displacement accumulation.

[0041] The processing of real-time underground space detection data includes: denoising and filtering the underground space detection data, attribute conversion, etc., and extracting underground space parameters that reflect the characteristics of underground cavities, such as electromagnetic wave speed, frequency, amplitude, etc.

[0042] The processing of real-time groundwater monitoring data includes: removing outliers from groundwater monitoring data, standardizing data, and extracting groundwater parameters such as water level change rate and water quality ion concentration.

[0043] Processing of real-time meteorological data includes: removing outliers, standardizing data, and extracting meteorological parameters such as humidity, temperature, wind speed, and precipitation.

[0044] Specifically, the training process of the geological disaster prediction model in this embodiment includes:

[0045] The geological disasters in the training set and their corresponding characteristic parameters and causes, as well as the impact ranges corresponding to the different degrees of each geological disaster, are fused with the Kalman filter algorithm to eliminate data redundancy and inconsistency by establishing the state equation and observation equation and estimating the optimal value of the data using the least squares method. The principal component analysis method is used to extract features from the fused data. By calculating the eigenvalues ​​and eigenvectors, the first n principal components with a contribution rate greater than 85% are selected as key features to form an m-dimensional standardized eigenvector. The eigenvector is input into the support vector machine SVM model for training. Through 5 cross-validations, the model parameters are continuously optimized to improve the prediction accuracy, and finally a geological disaster prediction model with an accuracy rate of more than 95% is established. When the new monitoring data is input into the prediction model, the geological disaster category, geological disaster probability value, geological disaster level, geological disaster cause and impact range can be comprehensively analyzed.

[0046] Step S103: Based on the geological disaster prediction results, the best treatment plan is given and early warning information is issued;

[0047] Based on the predicted geological disaster categories, geological disaster probability values, geological disaster occurrence levels, geological disaster causes and impact ranges, an optimization algorithm is used to determine the best solution for geological disaster management, and to guide targeted management work to eliminate hidden dangers and ensure regional safety.

[0048] Whether an early warning is needed is determined based on the probability of geological disasters in the geological disaster prediction results. When the probability of geological disasters exceeds the preset geological disaster threshold, the geological disaster category, probability of geological disasters, level of geological disasters, causes and impact range of geological disasters, and the best control plan found are packaged as early warning information and sent to management personnel.

[0049] Specifically, based on the geological disaster prediction results, the predicted geological disaster location, area, depth and other parameter information is obtained, and combined with the cause and impact range of the geological disaster, the corresponding control measures are searched in the preset geological disaster control plan database, including support reinforcement, backfill compaction, drainage and other control measures.

[0050] Among them, the geological disaster control program database is constructed based on geological disasters of different types and danger levels. In the preset geological disaster control program database, the corresponding control measures are searched, including:

[0051] The genetic algorithm is used to optimize the governance effect and governance cost, and the optimal governance scheme combination is searched from the geological disaster governance scheme database to obtain a targeted geological disaster governance scheme. If the geological disaster area is large or the danger level is high, dynamic monitoring measures are added to the governance plan to obtain real-time monitoring data such as surface deformation and groundwater level in the geological disaster hazard area. According to the optimal governance plan, a detailed governance work plan is formulated, including construction technology, materials and equipment, safety measures, and schedule arrangements, to guide the orderly development of governance work. During the implementation of the governance work, the elimination of geological disaster hazards is judged through dynamic monitoring data analysis. If the monitoring data meets the preset safety threshold, it is considered that the geological disaster hazards have been eliminated. And the treated area is monitored and managed for a long time, and the regional safety status is dynamically evaluated based on the monitoring data to ensure that geological disasters will not occur and regional safety is guaranteed.

[0052] Step S104: Visually display the geological disaster prediction results, optimal management solutions, and early warning information.

[0053] A three-dimensional model of the target area is constructed, and real-time monitoring displacement data, underground space detection data, groundwater monitoring data, meteorological data, and corresponding geological disaster prediction results, optimal control solutions, and early warning information are visualized in the three-dimensional model.

[0054] Among them, the three-dimensional model is constructed through the geographic coordinate data set, which can digitally express and store the topography and geological structure information in the target area, and embed the real-time monitoring displacement data, underground space detection data, groundwater monitoring data, and meteorological data into the three-dimensional model for dynamic visualization. According to the geological disaster prediction results, the location coordinates, occurrence category, level, probability, cause and impact range of geological disasters in the target area are visualized for early warning. High-risk areas are marked with red flashing spheres, and medium and low-risk areas are marked with orange and yellow, intuitively showing the spatial distribution of geological disaster risks.

[0055] The geological disaster monitoring method based on 5G positioning technology proposed in this embodiment obtains the precise geographic coordinates of the target area through 5G positioning technology, uses high-density displacement monitoring technology to monitor regional displacement data in real time, and combines underground space detection, groundwater monitoring data, and meteorological data. The geological disaster prediction model is constructed using a support vector machine to comprehensively predict the occurrence of geological disasters, and further uses an optimization algorithm to determine the best governance plan to guide targeted governance. The present invention realizes the intelligent identification, early warning and governance of geological disaster hazards, and effectively ensures regional safety.

[0056] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A geological disaster monitoring method based on 5G positioning technology, characterized in that: include: Acquire geographic location information of a target area, and perform displacement monitoring, underground space monitoring, groundwater monitoring, and meteorological monitoring on the target area based on the geographic location information to acquire real-time displacement data, underground space detection data, groundwater monitoring data, and meteorological data; Processing and analyzing the real-time displacement data, underground space detection data, groundwater monitoring data, and meteorological data to obtain geological disaster prediction results; Based on the geological disaster prediction results, the best control plan is given and early warning information is issued.

2. The geological disaster monitoring method based on 5G positioning technology according to claim 1 is characterized in that: The displacement data is obtained by deploying a number of GPS locators and inclinometer sensors in the target area. The underground space detection data is obtained by deploying detection lines in the target area, using geological radar and electromagnetic wave CT imaging technology to collect electromagnetic wave response signals of underground media, and performing inversion interpretation. The groundwater monitoring data is obtained by deploying a number of water level meters and multi-parameter water quality analyzers in the target area. The meteorological data is obtained based on the meteorological observation station to which the target area belongs.

3. The geological disaster monitoring method based on 5G positioning technology according to claim 1 is characterized in that: Processing the real-time displacement data, underground space detection data, groundwater monitoring data, and meteorological data includes: The real-time displacement data, underground space detection data, groundwater monitoring data, and meteorological data are subjected to outlier elimination, denoising, standardization, and data attribute conversion processing, and feature parameters are extracted. The feature parameters include surface deformation parameters, underground space parameters, groundwater parameters, and meteorological parameters.

4. The geological disaster monitoring method based on 5G positioning technology according to claim 3 is characterized in that: Obtaining the geological disaster prediction results includes: After the characteristic parameters are fused in time series, they are input into a pre-built geological disaster prediction model to output the geological disaster prediction results, wherein the geological disaster prediction results include geological disaster categories, geological disaster occurrence probability values, geological disaster occurrence levels, geological disaster causes and impact ranges; the geological disaster prediction model is obtained by training a support vector machine (SVM) model based on a training set, and the training set includes several types of geological disasters and corresponding characteristic parameters and causes, as well as the impact ranges corresponding to different degrees of each geological disaster.

5. The geological disaster monitoring method based on 5G positioning technology according to claim 1 is characterized in that: Based on the geological disaster prediction results, the best governance solutions include: Based on the predicted geological disaster categories, geological disaster occurrence probability values, geological disaster occurrence levels, geological disaster causes and impact ranges, an optimization algorithm is used to search in a preset geological disaster control solution database to determine the best control solution, wherein the geological disaster control solution database is based on geological disasters of different types and danger levels.

6. The method for monitoring geological disasters based on 5G positioning technology according to claim 5, characterized in that: The optimization algorithm is used to search in the preset geological disaster control solution database to determine the best control solution, including: Using a genetic algorithm, taking the governance effect and governance cost as optimization targets, searching for the optimal governance solution combination from the geological disaster governance solution database; Dynamic monitoring measures are added to the optimal governance solution combination as the best governance solution, wherein the dynamic monitoring measures are real-time monitoring of displacement data, underground space detection data, groundwater monitoring data, and meteorological data during the governance process, and inputting them into the geological disaster prediction model for dynamic prediction.

7. The method for monitoring geological disasters based on 5G positioning technology according to claim 1, characterized in that: The issuing of the warning information includes: Whether it is necessary to issue an early warning is determined based on the probability value of the geological disaster occurrence in the geological disaster prediction result. When the probability value of the geological disaster occurrence exceeds the preset geological disaster threshold, the geological disaster category, geological disaster probability value, geological disaster occurrence level, geological disaster cause and impact range, and the searched best governance plan are packaged as early warning information and sent to the management personnel.

8. The method for monitoring geological disasters based on 5G positioning technology according to any one of claims 1 to 7, characterized in that: The method also includes: constructing a three-dimensional model of the target area, and visually displaying real-time monitored displacement data, underground space detection data, groundwater monitoring data, meteorological data, and corresponding geological disaster prediction results, optimal governance solutions, and early warning information in the three-dimensional model.

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