Intelligent monitoring and early warning method based on geological disaster cloud monitoring platform

Abstract

The invention belongs to the field of geological disaster monitoring, relates to a data analysis technology, is used for solving the problem that geological structure parameters and meteorological parameters cannot be combined for geological disaster recessive risk prediction analysis in the prior art, and particularly relates to an intelligent monitoring and early warning method based on a geological disaster cloud monitoring platform, which comprises a monitoring sub-method and a prediction sub-method. The monitoring sub-method comprises the following steps: S1, carrying out data acquisition on geological disaster parameters; s2, carrying out monitoring and early warning analysis on geological disasters; s3, carrying out alarm type analysis on geological disasters; according to the method, the concentration coefficient is generated on the basis of the distribution characteristics of the risk points, and the disaster risk type of the geological disaster monitoring area is marked through the concentration coefficient, so that the corresponding early warning signal is generated, management personnel can carry out risk management and control, and the risk processing efficiency of geological disasters is improved.

Description

Technical Field

[0001] The present invention belongs to the field of geological disaster monitoring and relates to data analysis technology, specifically an intelligent monitoring and early warning method based on a geological disaster cloud monitoring platform. Background Art

[0002] The intelligent geological disaster monitoring and early warning system uses three core modules: multi-source perception, intelligent analysis, and real-time response. It builds a disaster prevention system covering the entire process of "monitoring-early warning-disposal", significantly improving the efficiency of disaster risk identification and emergency response.

[0003] The invention patent with announcement number CN113192297B discloses a method for monitoring, predicting and warning of geological disasters based on artificial intelligence. This warning method improves the warning algorithm through warning of historical data, learning of historical data and the process of disaster occurrence and development; realizes the prediction of monitoring data; and provides warning information based on real-time data, historical data and warning information based on predicted data at the same time; however, this warning method cannot combine geological structure parameters and meteorological parameters to conduct implicit risk prediction and analysis of geological disasters, resulting in the inability of existing technologies to accurately predict geological disasters before they occur, and the actual risks of geological disasters cannot be effectively curbed.

[0004] In response to the above technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent monitoring and early warning method based on a geological disaster cloud monitoring platform, which is used to solve the problem that the existing technology cannot combine geological structure parameters and meteorological parameters to predict and analyze the hidden risks of geological disasters;

[0006] The technical problem to be solved by the present invention is: how to provide an intelligent monitoring and early warning method based on a geological disaster cloud monitoring platform that can combine geological structure parameters and meteorological parameters to predict and analyze the hidden risks of geological disasters.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An intelligent monitoring and early warning method based on a geological disaster cloud monitoring platform, including a monitoring sub-method and a prediction sub-method;

[0009] The monitoring sub-method comprises the following steps:

[0010] Step S1: Data collection of geological disaster parameters: several collection points are set up in the geological disaster monitoring area, a monitoring cycle is generated and the monitoring cycle is divided into several monitoring time periods, and a monitoring data group of the collection points is obtained at the end of the monitoring time period;

[0011] Step S2: Perform monitoring and early warning analysis on geological hazards: Generate a risk coefficient for the collection point through the collection data group of the collection point, and compare the risk coefficient with the preset risk threshold: If the risk coefficient is less than the risk threshold, the collection point is marked as a safe point; if the risk coefficient is greater than or equal to the risk threshold, the collection point is marked as a risk point; If all collection points are marked as safe points at the end of the monitoring period, execute the prediction sub-method; Otherwise, execute step S3;

[0012] Step S3: Analyze the alarm type of geological disasters: connect the risk points as the corner points of the polygon with the adjacent risk points, mark the obtained closed polygon as the risk area, mark the ratio of the area of the risk area to the area of the geological disaster monitoring area as the concentration coefficient, and mark the geological disaster risk type of the geological disaster monitoring area by the concentration coefficient.

[0013] Furthermore, in step S1, the monitoring data group includes displacement data WY, inclination data QJ and crack data LF, wherein the displacement data WY is the cumulative displacement of the acquisition point during the monitoring period, the inclination data QJ is the change in the inclination angle of the acquisition point during the monitoring period, and the crack data LF is the surface crack width of the acquisition point.

[0014] Furthermore, in step S2, the process of generating the risk coefficient of the collection point includes: forming an analysis data row vector HK from the monitoring data group of the collection point, HK = [WY, QJ, LF], generating a weight column vector LK for the monitoring data group, LK = [a1, a2, a3] T , the risk coefficient of the collection point is obtained by performing dot product calculation on the analysis data row vector HK and the weight column vector LK of all collection points.

[0015] Furthermore, in step S3, the specific process of marking the geological hazard risk type in the geological hazard monitoring area includes: comparing the concentration coefficient with the preset concentration threshold: if the concentration coefficient is less than the concentration threshold, it is determined that there is a concentrated disaster risk in the geological hazard monitoring area, a concentrated warning signal is generated, and the concentrated warning signal and the risk area are sent to the mobile phone terminal of the manager; if the concentration coefficient is greater than or equal to the concentration threshold, it is determined that there is a dispersed disaster risk in the geological hazard monitoring area, a dispersed warning signal is generated, and the dispersed warning signal and all risk points are sent to the mobile phone terminal of the manager.

[0016] Furthermore, the prediction sub-method comprises the following steps:

[0017] Step P1: Conduct multi-dimensional risk analysis on the geological disaster monitoring area: obtain the rainfall data of the collection point at the end of the monitoring period. The rainfall data is the rainfall at the location of the collection point during the monitoring period, and obtain the slope value of the location of the collection point;

[0018] Step P2: Conduct risk prediction analysis on the geological disaster monitoring area: generate a rainfall assessment set, a slope assessment set, and a risk assessment set; compare the rainfall assessment set, the slope assessment set, and the risk assessment set to obtain a triple assessment value, a rainfall assessment value, and a slope assessment value;

[0019] Step P3: Generate prediction analysis results: perform difference operation on the sum of rainfall assessment value and slope assessment value during the monitoring period and triple assessment value to obtain prediction assessment value, mark the ratio of prediction assessment value to K1 as prediction coefficient, and use the prediction coefficient to determine whether there are geological disaster risk hazards in the geological disaster monitoring area during the monitoring period.

[0020] Furthermore, in step P2, the generation process of the rainfall assessment set, the slope assessment set and the risk assessment set includes: arranging the collection points in order of rainfall data from large to small to obtain a rainfall sequence, marking the top K1 collection points in the rainfall sequence as rainfall assessment points, and all the rainfall assessment points constitute the rainfall assessment set; arranging the collection points in order of slope values from large to small to obtain a slope sequence, marking the top K1 collection points in the slope sequence as slope assessment points, and all the slope assessment points constitute the slope assessment set; arranging the collection points in order of risk coefficients from large to small to obtain a risk sequence, marking the top K1 collection points in the risk sequence as risk assessment points, and all the risk assessment points constitute the risk assessment set.

[0021] Furthermore, in step P2, the process of obtaining the triple evaluation value, rainfall evaluation value and slope evaluation value includes: marking the elements in the intersection of the rainfall evaluation set, the slope evaluation set and the risk assessment set as triple points, and marking the number of triple points as the triple evaluation value; marking the absolute value of the difference between the sequence number of the triple point in the rainfall sequence and the sequence number in the risk sequence as the rainfall coincidence value of the triple point, and summing and averaging the rainfall coincidence values of all triple points to obtain the rainfall evaluation value; marking the absolute value of the difference between the sequence number of the triple point in the slope sequence and the sequence number in the risk sequence as the slope coincidence value of the triple point, and summing and averaging the slope coincidence values of all triple points to obtain the slope evaluation value.

[0022] Furthermore, in step P3, the specific process of determining whether the geological hazard monitoring area has geological hazard risk hazards during the monitoring period includes: comparing the prediction coefficient with a preset prediction threshold: if the prediction coefficient is less than the prediction threshold, it is determined that the geological hazard monitoring area has geological hazard risk hazards during the monitoring period, generating a hazard warning signal and sending the hazard warning signal to the mobile phone terminal of the manager; if the prediction coefficient is greater than or equal to the prediction threshold, it is determined that the geological hazard monitoring area does not have geological hazard risk hazards during the monitoring period.

[0023] The present invention has the following beneficial effects:

[0024] 1. By collecting data on geological hazard parameters, we acquire monitoring data groups of all collection points in the form of point cloud data collection, and calculate the risk coefficients of the collection points through the monitoring and early warning analysis process. The risk coefficients of the collection points are used to provide feedback on the geological hazard risk level in the geological hazard monitoring area.

[0025] 2. By analyzing the alarm types of geological disasters and generating concentration coefficients based on the distribution characteristics of risk points, the disaster risk types in the geological disaster monitoring area are marked by the concentration coefficients, thereby generating corresponding early warning signals, which help managers to control risks and improve the efficiency of geological disaster risk management;

[0026] 3. Through multi-dimensional risk analysis of geological disaster monitoring areas, the geological structure parameters and meteorological parameters of the collection points are collected to provide data support for the risk prediction and analysis process. The hidden risks of geological disaster monitoring areas are evaluated by integrating multi-source data to reduce the actual probability of geological disasters.

[0027] 4. Through risk prediction analysis of geological disaster monitoring areas, prediction coefficients are generated based on the comparison results of rainfall assessment set, slope assessment set and risk assessment set. The prediction analysis results are generated through the prediction coefficients to improve the intuitiveness of the system output data. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0029] Figure 1 This is a flow chart of a method according to embodiment 1 of the present invention;

[0030] Figure 2 This is a flow chart of the method of embodiment 2 of the present invention;

[0031] Figure 3 This is a system block diagram of embodiment 3 of the present invention. DETAILED DESCRIPTION

[0032] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0033] An intelligent monitoring and early warning method based on a geological disaster cloud monitoring platform includes a monitoring sub-method and a prediction sub-method.

[0034] Example 1: Figure 1 As shown, the monitoring sub-method includes the following steps:

[0035] Step S1: Data collection of geological hazard parameters: several collection points are set in the geological hazard monitoring area, a monitoring cycle is generated and divided into several monitoring periods, and a monitoring data group of the collection point is obtained at the end of the monitoring period. The monitoring data group includes displacement data WY, inclination data QJ and crack data LF. The displacement data WY is the cumulative displacement of the collection point during the monitoring period, which is obtained by an LVDT displacement sensor. The inclination data QJ is the change in the inclination angle of the collection point during the monitoring period, which is obtained by a MEMS inclination sensor. The crack data LF is the surface crack width of the collection point, which is obtained by a laser rangefinder.

[0036] Step S2: Perform monitoring and early warning analysis on geological disasters: The monitoring data group of the collection point is used to form the analysis data row vector HK, HK = [WY, QJ, LF], and the weight column vector LK is generated for the monitoring data group, LK = [a1, a2, a3] T , perform dot product calculation on the analysis data row vector HK and the weight column vector LK of all collection points to obtain the risk coefficient of the collection point, and compare the risk coefficient with the preset risk threshold: if the risk coefficient is less than the risk threshold, the collection point is marked as a safe point; if the risk coefficient is greater than or equal to the risk threshold, the collection point is marked as a risk point; if all collection points are marked as safe points at the end of the monitoring period, the prediction sub-method is executed; otherwise, execute step S3; obtain the monitoring data group of all collection points in the form of point cloud data collection, and calculate the risk coefficient of the collection point through the monitoring and early warning analysis process, and use the risk coefficient of the collection point to provide feedback on the geological disaster risk level in the geological disaster monitoring area;

[0037] Step S3: Analyze the alarm type of geological disasters: connect the risk point as the corner point of the polygon with the adjacent risk points, mark the obtained closed polygon as the risk area, mark the ratio of the area of the risk area to the area of the geological disaster monitoring area as the concentration coefficient, and compare the concentration coefficient with the preset concentration threshold: if the concentration coefficient is less than the concentration threshold, it is determined that there is a concentrated disaster risk in the geological disaster monitoring area, generate a concentrated warning signal, and send the concentrated warning signal and the risk area to the mobile phone terminal of the manager; if the concentration coefficient is greater than or equal to the concentration threshold, it is determined that there is a dispersed disaster risk in the geological disaster monitoring area, generate a dispersed warning signal, and send the dispersed warning signal and all risk points to the mobile phone terminal of the manager; based on the distribution characteristics of the risk points, generate a concentration coefficient, mark the disaster risk type of the geological disaster monitoring area by the concentration coefficient, and thus generate a corresponding warning signal, which helps managers to carry out risk management and improve the efficiency of geological disaster risk handling.

[0038] Example 2: Figure 2 As shown, the prediction sub-method includes the following steps:

[0039] Step P1: Conduct a multi-dimensional risk analysis of the geological disaster monitoring area: obtain rainfall data at the end of the monitoring period at the collection point. The rainfall data is the rainfall amount at the location of the collection point during the monitoring period, and obtain the slope value at the location of the collection point; collect geological structure parameters and meteorological parameters at the collection point to provide data support for the risk prediction and analysis process, and comprehensively evaluate the hidden risks in the geological disaster monitoring area based on multi-source data to reduce the actual probability of geological disasters.

[0040] Step P2: Risk prediction analysis of geological disaster monitoring areas: Arrange the collection points in the order of rainfall data from large to small to obtain a rainfall sequence, mark the top K1 collection points in the rainfall sequence as rainfall assessment points, and form a rainfall assessment set from all rainfall assessment points; Arrange the collection points in the order of slope value from large to small to obtain a slope sequence, mark the top K1 collection points in the slope sequence as slope assessment points, and form a slope assessment set from all slope assessment points; Arrange the collection points in the order of risk coefficient from large to small to obtain a risk sequence, mark the top K1 collection points in the risk sequence as Risk assessment points, consisting of all risk assessment points forming a risk assessment set; marking the elements in the intersection of the rainfall assessment set, the slope assessment set and the risk assessment set as triple points, and marking the number of triple points as the triple assessment value; marking the absolute value of the difference between the sequence number of the triple point in the rainfall sequence and the sequence number in the risk sequence as the rainfall coincidence value of the triple point, and summing and averaging the rainfall coincidence values of all triple points to obtain the rainfall assessment value; marking the absolute value of the difference between the sequence number of the triple point in the slope sequence and the sequence number in the risk sequence as the slope coincidence value of the triple point, and summing and averaging the slope coincidence values of all triple points to obtain the slope assessment value;

[0041] Step P3: Generate prediction analysis results: perform difference operation on the sum of the rainfall assessment value and the slope assessment value during the monitoring period and the triple assessment value to obtain the prediction assessment value, mark the ratio of the prediction assessment value to K1 as the prediction coefficient, and compare the prediction coefficient with the preset prediction threshold: if the prediction coefficient is less than the prediction threshold, it is determined that there are geological disaster risk hazards in the geological disaster monitoring area during the monitoring period, generate a hidden danger warning signal and send the hidden danger warning signal to the mobile phone terminal of the manager; if the prediction coefficient is greater than or equal to the prediction threshold, it is determined that there are no geological disaster risk hazards in the geological disaster monitoring area during the monitoring period; generate a prediction coefficient based on the comparison results of the rainfall assessment set, the slope assessment set and the risk assessment set, and generate prediction analysis results through the prediction coefficient to improve the intuitiveness of the system output data.

[0042] Example 3: Figure 3 As shown in the figure, the intelligent monitoring and early warning system based on the geological disaster cloud monitoring platform includes a monitoring subsystem and a prediction subsystem. The monitoring subsystem includes a data acquisition module, a monitoring and early warning module, and an alarm processing module connected in sequence; the prediction subsystem includes a multi-dimensional analysis module, a prediction analysis module, and a result generation module connected in sequence.

[0043] The data acquisition module is used to collect data on geological disaster parameters to obtain monitoring data groups, and send the monitoring data groups to the monitoring and early warning module;

[0044] The monitoring and early warning module is used to monitor and analyze geological disasters;

[0045] The alarm processing module is used to analyze the alarm types of geological disasters.

[0046] The multi-dimensional analysis module is used to conduct multi-dimensional risk analysis of geological disaster monitoring areas;

[0047] The prediction and analysis module is used to conduct risk prediction and analysis in geological disaster monitoring areas;

[0048] The result generation module is used to generate predictive analysis results.

[0049] The intelligent monitoring and early warning method based on the geological disaster cloud monitoring platform sets up several collection points in the geological disaster monitoring area during operation, generates a monitoring cycle and divides the monitoring cycle into several monitoring periods, obtains the monitoring data group of the collection points at the end of the monitoring period; performs numerical calculation on the monitoring data group of the collection points to obtain the risk coefficient, and evaluates the geological disaster risk in the geological disaster monitoring area through the risk coefficient; when there is no risk, performs multi-dimensional risk analysis on the geological disaster monitoring area, generates a rainfall assessment set, a slope assessment set and a risk assessment set, compares the rainfall assessment set, the slope assessment set and the risk assessment set to obtain the prediction coefficient, and determines whether there are geological disaster risk hazards in the geological disaster monitoring area during the monitoring period through the prediction coefficient.

[0050] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

[0051] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0052] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent monitoring and early warning method based on a geological disaster cloud monitoring platform is characterized by: Includes monitoring sub-method and prediction sub-method; The monitoring sub-method comprises the following steps: Step S1: Data collection of geological disaster parameters: several collection points are set up in the geological disaster monitoring area, a monitoring cycle is generated and the monitoring cycle is divided into several monitoring time periods, and a monitoring data group of the collection points is obtained at the end of the monitoring time period; Step S2: Perform monitoring and early warning analysis on geological hazards: Generate a risk coefficient for the collection point through the collection data group of the collection point, and compare the risk coefficient with the preset risk threshold: If the risk coefficient is less than the risk threshold, the collection point is marked as a safe point; if the risk coefficient is greater than or equal to the risk threshold, the collection point is marked as a risk point; If all collection points are marked as safe points at the end of the monitoring period, execute the prediction sub-method; Otherwise, execute step S3; Step S3: Analyze the alarm type of geological disasters: connect the risk points as the corner points of the polygon with the adjacent risk points, mark the obtained closed polygon as the risk area, mark the ratio of the area of the risk area to the area of the geological disaster monitoring area as the concentration coefficient, and mark the geological disaster risk type of the geological disaster monitoring area by the concentration coefficient.

2. The intelligent monitoring and early warning method based on the geological disaster cloud monitoring platform according to claim 1 is characterized in that: In step S1, the monitoring data group includes displacement data WY, inclination data QJ and crack data LF. The displacement data WY is the cumulative displacement of the acquisition point during the monitoring period, the inclination data QJ is the change in the inclination angle of the acquisition point during the monitoring period, and the crack data LF is the surface crack width of the acquisition point.

3. The intelligent monitoring and early warning method based on the geological disaster cloud monitoring platform according to claim 1 is characterized in that: In step S2, the process of generating the risk coefficient of the collection point includes: forming an analysis data row vector HK from the monitoring data group of the collection point, HK = [WY, QJ, LF], generating a weight column vector LK for the monitoring data group, LK = [a1, a2, a3] T , the risk coefficient of the collection point is obtained by performing dot product calculation on the analysis data row vector HK and the weight column vector LK of all collection points.

4. The intelligent monitoring and early warning method based on the geological disaster cloud monitoring platform according to claim 1 is characterized in that: In step S3, the specific process of marking the geological disaster risk type in the geological disaster monitoring area includes: comparing the concentration coefficient with the preset concentration threshold: if the concentration coefficient is less than the concentration threshold, it is determined that there is a concentrated disaster risk in the geological disaster monitoring area, and a concentrated warning signal is generated and the concentrated warning signal and the risk area are sent to the mobile phone terminal of the manager; if the concentration coefficient is greater than or equal to the concentration threshold, it is determined that there is a dispersed disaster risk in the geological disaster monitoring area, and a dispersed warning signal is generated and the dispersed warning signal and all risk points are sent to the mobile phone terminal of the manager.

5. The intelligent monitoring and early warning method based on the geological disaster cloud monitoring platform according to claim 1 is characterized in that: The prediction sub-method includes the following steps: Step P1: Conduct multi-dimensional risk analysis on the geological disaster monitoring area: obtain the rainfall data of the collection point at the end of the monitoring period. The rainfall data is the rainfall at the location of the collection point during the monitoring period, and obtain the slope value of the location of the collection point; Step P2: Conduct risk prediction analysis on the geological disaster monitoring area: generate a rainfall assessment set, a slope assessment set, and a risk assessment set; compare the rainfall assessment set, the slope assessment set, and the risk assessment set to obtain a triple assessment value, a rainfall assessment value, and a slope assessment value; Step P3: Generate prediction analysis results: perform difference operation on the sum of rainfall assessment value and slope assessment value during the monitoring period and triple assessment value to obtain prediction assessment value, mark the ratio of prediction assessment value to K1 as prediction coefficient, and use the prediction coefficient to determine whether there are geological disaster risk hazards in the geological disaster monitoring area during the monitoring period.

6. The intelligent monitoring and early warning method based on the geological disaster cloud monitoring platform according to claim 1 is characterized in that: In step P2, the generation process of the rainfall assessment set, the slope assessment set and the risk assessment set includes: arranging the collection points in order of rainfall data from large to small to obtain a rainfall sequence, marking the top K1 collection points in the rainfall sequence as rainfall assessment points, and all the rainfall assessment points constitute the rainfall assessment set; arranging the collection points in order of slope values from large to small to obtain a slope sequence, marking the top K1 collection points in the slope sequence as slope assessment points, and all the slope assessment points constitute the slope assessment set; arranging the collection points in order of risk coefficients from large to small to obtain a risk sequence, marking the top K1 collection points in the risk sequence as risk assessment points, and all the risk assessment points constitute the risk assessment set.

7. The intelligent monitoring and early warning method based on the geological disaster cloud monitoring platform according to claim 1 is characterized in that: In step P2, the process of obtaining the triple evaluation value, rainfall evaluation value and slope evaluation value includes: marking the elements in the intersection of the rainfall evaluation set, the slope evaluation set and the risk assessment set as triple points, and marking the number of triple points as the triple evaluation value; marking the absolute value of the difference between the sequence number of the triple point in the rainfall sequence and the sequence number in the risk sequence as the rainfall coincidence value of the triple point, and summing and averaging the rainfall coincidence values of all triple points to obtain the rainfall evaluation value; marking the absolute value of the difference between the sequence number of the triple point in the slope sequence and the sequence number in the risk sequence as the slope coincidence value of the triple point, and summing and averaging the slope coincidence values of all triple points to obtain the slope evaluation value.

8. The intelligent monitoring and early warning method based on the geological disaster cloud monitoring platform according to claim 1 is characterized in that: In step P3, the specific process of determining whether the geological hazard monitoring area has geological hazard risk hazards during the monitoring period includes: comparing the prediction coefficient with the preset prediction threshold: if the prediction coefficient is less than the prediction threshold, it is determined that the geological hazard monitoring area has geological hazard risk hazards during the monitoring period, generating a hazard warning signal and sending the hazard warning signal to the mobile phone terminal of the manager; if the prediction coefficient is greater than or equal to the prediction threshold, it is determined that the geological hazard monitoring area does not have geological hazard risk hazards during the monitoring period.

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