A recognition and early warning method for geological disaster prevention and control

Abstract

The present invention relates to the field of geological disaster prevention and early warning technology, and in particular to a method for identifying and warning geological disaster prevention, comprising: determining the regional prevention and control type of a geological disaster monitoring area based on historical geological disaster data and corresponding geological parameter data of the geological disaster monitoring area; dividing the geological disaster monitoring area into a plurality of monitoring sub-areas based on the regional prevention and control type of the geological disaster monitoring area, and periodically collecting geological parameter data and environmental data of any monitoring sub-area; determining whether an abnormality exists in the corresponding monitoring sub-area; determining key prevention and control areas based on the geological parameter data of each monitoring sub-area, determining key disaster-causing parameters of the key prevention and control areas, and early warning indicators corresponding to the key disaster-causing parameters; determining whether an early warning is triggered based on the early warning indicators corresponding to the key disaster-causing parameters and sensor perception data, and determining an early warning method for the geological disaster monitoring area. The present invention can improve the identification accuracy of geological disaster prevention and early warning.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster prevention and early warning, and in particular to an identification and early warning method for geological disaster prevention and control. Background Art

[0002] Geological hazards refer to catastrophic events caused by natural or man-made geological processes that damage the ecological environment or alter geological structures. Common geological hazards include collapses, landslides, debris flows, ground subsidence, ground fissures, collapses, rockbursts, earthquakes, and volcanic eruptions, causing significant damage to the environment and property losses. The identification and early warning of geological hazards (such as landslides, debris flows, and collapses) are core tasks in disaster prevention and mitigation. Traditional methods of geological hazard monitoring rely primarily on manual inspections and simple instrument monitoring, which suffer from low efficiency, limited coverage, and poor real-time performance. With technological advances, geological hazard monitoring and early warning have gradually shifted from relying primarily on manual inspections without specialized knowledge to relying primarily on professional technicians and specialized monitoring equipment, and from primarily manual monitoring and data collection to automated monitoring and data collection. However, when it comes to identifying geological disasters using monitoring data, statistical models rely on historical data and single data, and cannot fully capture the multi-factor coupling mechanism of disasters. In addition, traditional threshold models or empirical formulas rely on fixed parameters (such as rainfall thresholds) and cannot adapt to the dynamic changes in geological and environmental conditions. The thresholds rely on historical statistical means and ignore extreme events or nonlinear responses, resulting in low accuracy in early warning identification and prone to false alarms and missed reports.

[0003] Chinese patent application publication number CN116564047A discloses a geological disaster monitoring and prevention system and method based on big data, including a surface displacement monitoring module, a meteorological monitoring module, a groundwater dynamic monitoring module, a parameter analysis module, an early warning module and a display terminal. The surface displacement monitoring module is used to monitor the dynamic changes of the landslide surface, the meteorological monitoring module is used to monitor meteorological changes, the groundwater dynamic monitoring module is used to monitor the dynamic changes of the groundwater level, the parameter analysis module is used to receive data and analyze whether it is different from normal conditions, the early warning module is used to receive the alarm signal of the parameter analysis module and start to alarm the display terminal, and the display terminal is used to receive the alarm signal of the early warning module and display it.

[0004] The existing technology has the following problems: warnings are issued only based on the comparison results between monitoring data and normal data ranges, the accuracy of warning identification is not high, and false alarms and missed alarms are prone to occur. Summary of the Invention

[0005] To this end, the present invention provides an identification and early warning method for geological disaster prevention and control, which is used to overcome the problems in the prior art of low accuracy of early warning identification and prone to false alarms and missed alarms.

[0006] To achieve the above objectives, the present invention provides an identification and early warning method for geological disaster prevention and control, comprising:

[0007] Step S1, determining the regional prevention and control type for the geological disaster monitoring area based on the historical geological disaster data of the geological disaster monitoring area and the corresponding geological parameter data;

[0008] Step S2: dividing the geological disaster monitoring area into a number of monitoring sub-areas based on the regional prevention and control type, and periodically collecting geological parameter data and environmental data of any monitoring sub-area;

[0009] Step S3: determining whether there is any abnormality in the corresponding monitoring sub-area based on the geological parameter data and environmental data collected during the target time period; if so, obtaining the geological parameter data and sensor perception data of each monitoring sub-area during the target time period;

[0010] Step S4, determining key prevention and control areas based on the geological parameter data of each of the monitoring sub-areas, and determining key disaster-causing parameters for the key prevention and control areas and early warning indicators corresponding to the key disaster-causing parameters;

[0011] Step S5: determining whether to trigger an early warning based on the early warning indicators corresponding to the key disaster-causing parameters and the sensor sensing data, and determining the risk level of the key prevention and control area, including low risk and high risk, when the early warning is triggered;

[0012] Step S6, determining the early warning method of the geological disaster monitoring area based on the risk level of the key prevention and control area, including:

[0013] Adjusting the collection period of the periodic collection based on the sensor perception data of the key prevention and control area;

[0014] Alternatively, an early warning prompt is generated and sent based on the area identification of the geological disaster monitoring area and the early warning indicators of the key disaster-causing parameters.

[0015] Furthermore, the step S1 includes:

[0016] Step S11, determining several key geological parameters of the geological disaster monitoring area based on the historical geological disaster data and corresponding geological parameter data;

[0017] Step S12, determining a key correlation characteristic value based on the correlation relationship of each key geological parameter data;

[0018] Step S13: determining the regional prevention and control type for the geological disaster monitoring area based on the key correlation feature value and the regional prevention and control classification model.

[0019] Furthermore, in step S2, the geological disaster monitoring area is divided into several monitoring sub-areas based on the regional prevention and control type of the geological disaster monitoring area, including:

[0020] Step S21, determining a corresponding regional prevention and control characteristic value based on the regional prevention and control type of the geological disaster monitoring area;

[0021] Step S22, determining the number of monitoring sub-areas corresponding to the geological disaster monitoring area based on the regional prevention and control characteristic value;

[0022] Step S23: dividing the geological disaster monitoring area into a plurality of monitoring sub-areas based on the number of the monitoring sub-areas.

[0023] Furthermore, in step S3, it includes:

[0024] Step S31, predicting environmental data within a future preset time period based on the environmental data collected within the target time period to obtain predicted environmental data;

[0025] Step S32, determining critical environmental data within a future preset time period based on the geological parameter data collected within the target time period;

[0026] Step S33: determining whether there is any abnormality in the corresponding monitoring sub-area based on the comparison result between the predicted environmental data and the critical environmental data.

[0027] Furthermore, in step S4, determining the key prevention and control areas includes:

[0028] Step S41, determining a standard geological parameter range of the geological disaster monitoring area based on historical geological disaster data and corresponding geological parameter data of the geological disaster monitoring area;

[0029] Step S42: determining a key prevention and control area based on the standard geological parameter range and the comparison result of the geological parameter data of each monitoring sub-area.

[0030] Furthermore, in step S42, the key prevention and control areas are determined based on the number of geological parameters in each of the monitoring sub-areas whose geological parameter data do not conform to the standard geological parameter range.

[0031] Furthermore, in the step S4, it includes:

[0032] The geological parameter data in each monitoring sub-area that does not conform to the geological parameter range of the standard geological parameter is determined as a key disaster-causing parameter, and the early warning indicator corresponding to the key disaster-causing parameter is determined based on the standard geological parameter range.

[0033] Furthermore, in the step S5, it includes:

[0034] Step S51, determining a key difference value based on the early warning indicator corresponding to the key disaster-causing parameter and the comparison result of the key disaster-causing parameter data;

[0035] Step S52, determining a perception difference value based on the sensor perception data and a preset perception standard;

[0036] Step S53: determining whether to trigger an early warning based on the key difference value and the perception difference value.

[0037] Furthermore, in step S6, determining the early warning method of the geological disaster monitoring area based on the risk level of the key prevention and control area includes:

[0038] If the risk level of the key prevention and control area is low risk, adjusting the collection period of the periodic collection based on the sensor perception data of the key prevention and control area;

[0039] If the risk level of the key prevention and control area is high risk, an early warning prompt is generated and sent based on the area identification of the geological disaster monitoring area and the early warning indicators of the key disaster-causing parameters.

[0040] Furthermore, in the step S6, it includes:

[0041] An adjustment coefficient is determined based on the key difference value and the perceived difference value, and an adjusted acquisition period is determined based on the adjustment coefficient and an acquisition period of the periodic acquisition.

[0042] Compared with the prior art, the present invention has the beneficial effect of determining the regional prevention and control type of a geological disaster monitoring area based on historical geological disaster data and corresponding geological parameter data, thereby improving the accuracy of subsequent identification and early warning, reducing data processing volume, and improving identification efficiency. By dividing a large area into multiple monitoring sub-areas based on regional prevention and control types, it can dynamically adapt to geological diversity and accurately capture local changes. By periodically collecting data from any monitoring sub-area, it can capture sudden changes in real time, ensure data continuity, and reduce data processing volume. By determining whether there are abnormal conditions in the corresponding monitoring sub-area, false alarms and missed alarms can be avoided. Only when abnormal conditions are present can relevant data from all monitoring sub-areas be obtained, which can avoid continuous transmission of large amounts of data, save computing power, and enable rapid response, improving timeliness. By identifying key prevention and control areas, it is possible to focus on core risk points. By determining key disaster-causing parameters and corresponding early warning indicators, it is possible to avoid judgment bias and avoid false alarms and missed alarms. By determining whether to trigger an early warning based on the early warning indicators of key disaster-causing parameters, it is possible to improve the accuracy of early warning identification and emergency response efficiency. By determining the risk level of key prevention and control areas, it is possible to avoid overreaction and false alarms. Determining the early warning method based on the risk level of key prevention and control areas can achieve accurate information push and improve the effectiveness and accuracy of early warnings.

[0043] Furthermore, by determining key geological parameters, the present invention can screen out geological parameters that have significantly impacted historical geological disasters in the geological disaster monitoring area, thereby reducing the amount of subsequent data processing and improving data processing efficiency. Determining key correlation characteristic values ​​based on the correlation relationships between key geological parameters can reflect the combined impact of each key geological parameter and avoid misjudgments. By determining the regional prevention and control type of the geological disaster monitoring area based on the key correlation characteristic values, the accuracy of the determination of the regional prevention and control type can be improved, further improving the accuracy of subsequent early warnings.

[0044] Furthermore, the present invention determines the regional prevention and control characteristic value based on the regional prevention and control type of the geological disaster monitoring area to determine the number of monitoring sub-areas, which can avoid resource waste or monitoring blind spots caused by uniform division and improve monitoring efficiency.

[0045] Furthermore, the present invention can predict environmental changes by predicting environmental data within a preset time period in the future, and can determine critical environmental data within a preset time period in the future based on geological parameter data collected within the target time period, thereby achieving accurate judgment. By comparing the predicted environmental data with the critical environmental data, the accuracy of identification and early warning can be improved to avoid false alarms.

[0046] Furthermore, the present invention determines the key difference value and the perception difference value, and determines whether to trigger an early warning based on the key difference value and the perception difference value, which can avoid misjudgment in a single judgment, improve the accuracy of the judgment, further improve the accuracy of the identification warning, and reduce the false alarm rate.

[0047] Furthermore, the present invention adjusts the collection cycle when the risk is low, and can timely discover potential risks and avoid missed reports by increasing the collection frequency. When the risk is high, early warning prompts are generated and sent in a timely manner, which can improve the early warning response speed and improve the accuracy of the early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of an identification and early warning method for geological disaster prevention and control according to an embodiment of the present invention;

[0049] Figure 2 Schematic diagram of the process of step S1 of the embodiment of the present invention;

[0050] Figure 3 A schematic diagram of a process for determining key prevention and control areas according to an embodiment of the present invention;

[0051] Figure 4 This is a logical decision diagram for determining whether to trigger an early warning according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0053] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0054] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0055] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0056] See also Figure 1 , which is a flow chart of an identification and early warning method for geological disaster prevention and control according to an embodiment of the present invention; an identification and early warning method for geological disaster prevention and control according to an embodiment of the present invention includes:

[0057] Step S1, determining the regional prevention and control type for the geological disaster monitoring area based on the historical geological disaster data of the geological disaster monitoring area and the corresponding geological parameter data;

[0058] During implementation, historical geological disaster data include geological disaster types (landslides, collapses, debris flows, ground fissures, ground subsidence and ground collapse, etc.), occurrence time, scale, inducing factors, environmental conditions, etc.; geological parameters include topographic parameters (slope, slope aspect, elevation, river valley landform, karst landform, etc.), rock and soil parameters (porosity, permeability coefficient, joint density, cohesion, lithology, etc.), hydrological parameters (groundwater level depth, water quality type, etc., surface water flow rate, erosion modulus, etc., annual precipitation, evaporation, etc.).

[0059] See also Figure 2 , which is a flow chart of step S1 of an embodiment of the present invention; specifically, step S1 includes:

[0060] Step S11, determining several key geological parameters of the geological disaster monitoring area based on historical geological disaster data and corresponding geological parameter data of the geological disaster monitoring area;

[0061] In a specific embodiment, a key parameter model is constructed based on the historical data of the disaster areas and the geological parameter data of the corresponding disaster areas. Those skilled in the art will appreciate that any mathematical model in the prior art that can determine the key geological parameters of the key monitoring areas, such as a logistic regression model, a neural network model, etc., falls within the scope of protection of the present invention and will not be described in detail herein. The historical disaster data and the corresponding geological parameter data of the disaster monitoring area are input into the key parameter model to obtain several key geological parameters of the disaster monitoring area output by the key parameter model.

[0062] In another specific embodiment, a correlation analysis is performed on the historical geological disaster data and the corresponding geological parameter data in the geological disaster monitoring area, for example, Pearson correlation analysis method, Spearman correlation analysis method, multiple regression analysis method, etc., and the correlation coefficient between each geological parameter and the historical geological disaster data is calculated, and the geological parameters with a high correlation with the historical geological disaster data (such as the absolute value of the correlation coefficient is greater than 0.5, or the top 3 to 5 correlation coefficients are sorted from large to small) are determined as key geological parameters.

[0063] Step S12, determining a key correlation characteristic value based on the correlation relationship of each key geological parameter data;

[0064] During implementation, correlation analysis is performed on any two key geological parameters, for example, Pearson correlation analysis, Spearman correlation analysis, covariance analysis, etc., to determine their correlation coefficients, and the key correlation characteristic value is determined based on the mean of the absolute values ​​of each correlation coefficient.

[0065] Step S13: determining the regional prevention and control type of the geological disaster monitoring area based on the key correlation feature value and the regional prevention and control classification model.

[0066] In implementation, regional prevention and control types include landslide prevention and control types, collapse prevention and control types, debris flow prevention and control types, ground fissure prevention and control types, or comprehensive prevention and control types for any of the various types of geological disasters.

[0067] It can be understood that a regional prevention and control classification model is constructed based on the regional prevention and control types of various disaster-stricken areas in historical data and the corresponding key correlation characteristic values, which is used to output the corresponding regional prevention and control type according to the determined key correlation characteristic values. The key correlation characteristic values ​​of the geological disaster monitoring area are input into the regional prevention and control classification model to obtain the regional prevention and control type of the geological disaster monitoring area output by the regional prevention and control classification model.

[0068] By determining key geological parameters, the present invention can screen out geological parameters that have significantly impacted historical geological disasters in the geological disaster monitoring area, thereby reducing the amount of subsequent data processing and improving data processing efficiency. Determining key correlation characteristic values ​​based on the correlation between key geological parameters can reflect the combined impact of each key geological parameter and avoid misjudgments. By determining the regional prevention and control type of the geological disaster monitoring area based on the key correlation characteristic values, the accuracy of the determination of the regional prevention and control type can be improved, further improving the accuracy of subsequent early warnings.

[0069] Step S2: dividing the geological disaster monitoring area into a number of monitoring sub-areas based on the regional prevention and control type, and periodically collecting geological parameter data and environmental data of any monitoring sub-area;

[0070] During implementation, the actual implementation personnel can set the collection period of periodic collection based on actual conditions. Preferably, the collection period is set to 2 minutes to 5 minutes.

[0071] It is understandable that environmental data includes rainfall, temperature, humidity, wind speed, air pressure, sunshine duration, biodiversity index, vegetation type (such as forest, grassland), etc., and there is no specific limitation on the equipment and methods for collecting geological parameter data and environmental data.

[0072] Specifically, in step S2, the geological disaster monitoring area is divided into several monitoring sub-areas based on the regional prevention and control type of the geological disaster monitoring area, including:

[0073] Step S21, determining a corresponding regional prevention and control characteristic value based on the regional prevention and control type of the geological disaster monitoring area;

[0074] During implementation, the regional prevention and control characteristic value corresponding to the geological disaster monitoring area is determined based on a preset regional prevention and control comparison table. The actual implementer can set a preset regional prevention and control comparison table based on actual conditions. For example, the regional disaster types are ranked according to the degree of damage caused by historical data (which can be assessed based on the natural and property losses caused). If the regional prevention and control type is a comprehensive prevention and control type, the corresponding degree of damage is determined based on the average degree of damage caused by the corresponding geological disaster type. The maximum value of the regional prevention and control characteristic value is determined based on the total number of regional prevention and control types. For example, if the total number is 10, the maximum value of the regional prevention and control characteristic value is 10, which corresponds to the regional prevention and control type with the greatest degree of damage.

[0075] Step S22, determining the number of monitoring sub-areas corresponding to the geological disaster monitoring area based on the regional prevention and control characteristic value;

[0076] During implementation, the regional prevention and control characteristic value is positively correlated with the degree of damage that may be caused by the regional prevention and control type corresponding to the geological disaster monitoring area. The greater the degree of damage of the corresponding regional prevention and control type, the larger the regional prevention and control characteristic value, and the regional prevention and control characteristic value is positively correlated with the number of monitoring sub-areas. The larger the regional prevention and control characteristic value, the higher the demand for monitoring, and the more refined and intensive monitoring coverage is required, and the more monitoring sub-areas corresponding to the geological disaster monitoring area are, to ensure that abnormal changes in each monitoring sub-area can be independently analyzed and responded to.

[0077] Step S23: dividing the geological disaster monitoring area into a plurality of monitoring sub-areas based on the number of the monitoring sub-areas.

[0078] During implementation, the geological disaster monitoring area is divided into grids, the geological parameters of each grid are extracted, and a clustering algorithm (for example, the K-means algorithm) is applied to cluster the grids into N monitoring sub-areas (N is the number of monitoring sub-areas). The boundaries of the sub-areas are then adjusted along geological dividing lines such as ridge lines and fault zones to ensure geological homogeneity.

[0079] The present invention determines the regional prevention and control characteristic value based on the regional prevention and control type of the geological disaster monitoring area to determine the number of monitoring sub-areas, which can avoid resource waste or monitoring blind spots caused by uniform division and improve monitoring efficiency.

[0080] Step S3: determining whether there is any abnormality in the corresponding monitoring sub-area based on the geological parameter data and environmental data collected during the target time period; if so, obtaining the geological parameter data and sensor perception data of each monitoring sub-area during the target time period;

[0081] In implementation, the sensor perception data refers to the sensor perception parameter data detected by each sensor deployed in each monitoring sub-area. The sensor perception parameters include stratum vibration, surface displacement, stratum deformation, groundwater level, soil pore water pressure, soil bearing pressure, rock strain, etc. There is no specific limitation on the type and structure of each sensor.

[0082] Specifically, step S3 includes:

[0083] Step S31, predicting environmental data within a future preset time period based on the environmental data collected within the target time period to obtain predicted environmental data;

[0084] It should be noted that those skilled in the art are aware that any prediction model in the prior art that can predict environmental data falls within the scope of protection of the present invention and will not be described in detail here.

[0085] During implementation, the actual implementation personnel can set the target time period and the preset time period based on actual conditions. Preferably, the target time period is set to a value range of 3 days to 5 days, and the preset time period is set to a value range of 1 day to 2 days.

[0086] Step S32, determining critical environmental data within a future preset time period based on the geological parameter data collected within the target time period;

[0087] In implementation, the critical environmental data is the maximum environmental data that will ensure that no geological disasters occur in the geological disaster monitoring area within a preset time period in the future based on the geological parameter data collected within the target time period. When the critical environmental data is exceeded, a geological disaster will occur.

[0088] It is understood that a critical environment prediction model can be constructed based on historical geological parameter data and historical environmental data, with the modeling objective being to predict the critical environmental thresholds of each monitoring sub-area within a preset future time period, with the output being a single-value threshold or interval threshold for the environmental parameter. Those skilled in the art will appreciate that any prior art model capable of determining critical environmental data within a preset future time period, such as a time series model or a machine learning model, falls within the scope of the present invention and will not be further described herein.

[0089] Step S33: determining whether there is any abnormality in the corresponding monitoring sub-area based on the comparison result between the predicted environmental data and the critical environmental data.

[0090] During implementation, the predicted environmental data is compared with the critical environmental data. If the predicted environmental data corresponding to any environmental parameter exceeds the critical environmental data, it is determined that there is an abnormality in the corresponding monitoring sub-area. If all the predicted environmental data do not exceed the critical environmental data, it is determined that there is no abnormality in the corresponding monitoring sub-area.

[0091] The present invention can predict environmental changes by predicting environmental data within a preset time period in the future. It can determine the critical environmental data within a preset time period in the future based on the geological parameter data collected within the target time period, and can achieve accurate judgment. By comparing the predicted environmental data with the critical environmental data, the accuracy of identification and early warning can be improved to avoid false alarms.

[0092] Step S4, determining key prevention and control areas based on the geological parameter data of each of the monitoring sub-areas, and determining key disaster-causing parameters for the key prevention and control areas and early warning indicators corresponding to the key disaster-causing parameters;

[0093] See also Figure 3 , which is a schematic diagram of a process for determining a key prevention and control area according to an embodiment of the present invention; specifically, in step S4, determining the key prevention and control area includes:

[0094] Step S41, determining a standard geological parameter range of the geological disaster monitoring area based on historical geological disaster data and corresponding geological parameter data of the geological disaster monitoring area;

[0095] In practice, the standard geological parameter range represents the geological parameter range within which no geological disasters occur in the geological disaster monitoring area. The corresponding standard range maximum or standard range minimum value can be set based on the maximum or minimum value of the geological parameters corresponding to geological disasters in history. The geological parameters corresponding to the standard geological parameter range are not greater than the standard range maximum value and not less than the standard range minimum value. For example, when a landslide occurs, the corresponding minimum groundwater level is 10m, so the maximum value of the standard range corresponding to the groundwater level is set to 10m. If the groundwater level exceeds 10m, a landslide may occur. When ground fissures occur, the corresponding maximum soil moisture content is 10%, so the maximum value of the standard range corresponding to the soil moisture content is set to 10%, and if the soil moisture content is less than 10%, ground fissures may occur.

[0096] Step S42: determining a key prevention and control area based on the standard geological parameter range and the comparison result of the geological parameter data of each monitoring sub-area.

[0097] Specifically, in step S42, the key prevention and control area is determined based on the number of geological parameters in each monitoring sub-area that do not meet the standard geological parameter range.

[0098] In implementation, if the geological parameter data in the monitoring sub-area is greater than the maximum value of the standard range or less than the minimum value of the standard range, it is determined that the corresponding geological parameter data does not meet the standard geological parameter range.

[0099] Specifically, the step S4 includes:

[0100] The geological parameter data in each monitoring sub-area that does not conform to the geological parameter range of the standard geological parameter is determined as a key disaster-causing parameter, and the early warning indicator corresponding to the key disaster-causing parameter is determined based on the standard geological parameter range.

[0101] In implementation, the early warning indicators corresponding to the key disaster-causing parameters can be determined as 1 / 5 to 1 / 6 exceeding the maximum value of the standard range or 1 / 5 to 1 / 6 below the minimum value of the standard range.

[0102] Step S5: determining whether to trigger an early warning based on the early warning indicators corresponding to the key disaster-causing parameters and the sensor sensing data, and determining the risk level of the key prevention and control area, including low risk and high risk, when the early warning is triggered;

[0103] See also Figure 4 As shown in FIG. , it is a logic determination diagram for determining whether to trigger an early warning according to an embodiment of the present invention; specifically, in the step S5, it includes:

[0104] Step S51, determining a key difference value based on the early warning indicator corresponding to the key disaster-causing parameter and the comparison result of the key disaster-causing parameter data;

[0105] In implementation, the key difference corresponding to each key disaster-causing parameter is determined based on the absolute value of the difference between each key disaster-causing parameter data and the corresponding early warning indicator, and the key difference value is determined based on the average of the ratio of the key difference of each key disaster-causing parameter to the corresponding early warning indicator.

[0106] Step S52, determining a perception difference value based on the sensor perception data and a preset perception standard;

[0107] In implementation, the perception difference corresponding to each sensor perception parameter is determined based on the absolute value of the difference between each sensor perception data and the preset perception standard of the sensor perception parameter, and the perception difference value is determined based on the average of the ratio of the perception difference of each sensor perception parameter to the corresponding perception standard.

[0108] Step S53: determining whether to trigger an early warning based on the key difference value and the perception difference value.

[0109] In implementation, if the key difference value is greater than the preset key difference threshold and the perception difference value is greater than the preset perception difference threshold, it is determined that an early warning is triggered; otherwise, it is determined that an early warning is not triggered.

[0110] It can be understood that the actual implementation personnel can set the preset key difference threshold based on the actual situation or the mean of the key difference values ​​that pass the qualification test. The actual implementation personnel can set the preset perception difference threshold based on the actual situation or the mean of the perception difference values ​​that pass the qualification test. The larger the value of the preset key difference threshold, the higher the matching requirement between the key disaster-causing parameters and the corresponding early warning indicators. The larger the value of the preset perception difference threshold, the higher the matching requirement between the sensor perception parameters and the preset perception standards. Preferably, the preset key difference threshold value range is set to 0.4~0.7, and the preset perception difference threshold value range is set to 0.5~0.8.

[0111] The present invention determines the key difference value and the perception difference value, and determines whether to trigger an early warning based on the key difference value and the perception difference value, which can avoid misjudgment of a single judgment, improve the accuracy of the judgment, further improve the accuracy of the identification warning, and reduce the false alarm rate.

[0112] Step S6, determining the early warning method of the geological disaster monitoring area based on the risk level of the key prevention and control area, including:

[0113] Adjusting the collection period of the periodic collection based on the sensor perception data of the key prevention and control area;

[0114] Alternatively, an early warning prompt is generated and sent based on the area identification of the geological disaster monitoring area and the early warning indicators of the key disaster-causing parameters.

[0115] Specifically, in step S6, determining the early warning method of the geological disaster monitoring area based on the risk level of the key prevention and control area includes:

[0116] If the risk level of the key prevention and control area is low risk, adjusting the collection period of the periodic collection based on the sensor perception data of the key prevention and control area;

[0117] If the risk level of the key prevention and control area is high risk, an early warning prompt is generated and sent based on the area identification of the geological disaster monitoring area and the early warning indicators of the key disaster-causing parameters.

[0118] The present invention adjusts the collection cycle when the risk is low, and can timely discover potential risks and avoid missed reports by increasing the collection frequency. When the risk is high, early warning prompts are generated and sent in a timely manner, which can improve the early warning response speed and improve the accuracy of early warning.

[0119] Specifically, step S6 includes:

[0120] An adjustment coefficient is determined based on the key difference value and the perceived difference value, and an adjusted acquisition period is determined based on the adjustment coefficient and an acquisition period of the periodic acquisition.

[0121] In implementation, an adjustment coefficient is determined according to the product of the key difference value and the perception difference value, and an adjusted acquisition period is determined based on the product of the adjustment coefficient and the acquisition period of the periodic acquisition.

[0122] The present invention determines the regional prevention and control type of a geological disaster monitoring area based on historical geological disaster data and corresponding geological parameter data in the geological disaster monitoring area, thereby improving the accuracy of subsequent identification and early warning, reducing the amount of data processing, and improving identification efficiency. By dividing a large area into multiple monitoring sub-areas according to the regional prevention and control type, it is possible to dynamically adapt to geological diversity and accurately capture local changes. By periodically collecting data from any monitoring sub-area, it is possible to capture sudden changes in real time, ensure data continuity, and reduce the amount of data processing. By determining whether there are abnormal conditions in the corresponding monitoring sub-area, false positives and missed positives can be avoided. Only when there are abnormal conditions, relevant data from all monitoring sub-areas is obtained, which can avoid continuous transmission of large amounts of data, save computing power, and enable rapid response and improve timeliness. By determining key prevention and control areas, core risk points can be focused on. By determining key disaster-causing parameters and corresponding early warning indicators, judgment bias can be avoided, and false positives and missed positives can be avoided. By determining whether an early warning is triggered based on the early warning indicators of key disaster-causing parameters, the accuracy of early warning identification and early warning can be improved, emergency response efficiency can be improved, and by determining the risk level of key prevention and control areas, overreaction and false positives can be avoided. Determining the early warning method based on the risk level of key prevention and control areas can achieve accurate information push and improve the effectiveness and accuracy of early warnings.

[0123] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A method for identifying and warning geological disasters, characterized in that: include: Step S1, determining the regional prevention and control type for the geological disaster monitoring area based on the historical geological disaster data of the geological disaster monitoring area and the corresponding geological parameter data; Step S2: dividing the geological disaster monitoring area into a number of monitoring sub-areas based on the regional prevention and control type, and periodically collecting geological parameter data and environmental data of any monitoring sub-area; Step S3: determining whether there is any abnormality in the corresponding monitoring sub-area based on the geological parameter data and environmental data collected during the target time period; if so, obtaining the geological parameter data and sensor perception data of each monitoring sub-area during the target time period; Step S4, determining key prevention and control areas based on the geological parameter data of each of the monitoring sub-areas, and determining key disaster-causing parameters for the key prevention and control areas and early warning indicators corresponding to the key disaster-causing parameters; Step S5: determining whether to trigger an early warning based on the early warning indicators corresponding to the key disaster-causing parameters and the sensor sensing data, and determining the risk level of the key prevention and control area, including low risk and high risk, when the early warning is triggered; Step S6, determining the early warning method of the geological disaster monitoring area based on the risk level of the key prevention and control area, including: Adjusting the collection period of the periodic collection based on the sensor perception data of the key prevention and control area; Alternatively, generating and sending an early warning prompt based on the regional identifier of the geological disaster monitoring area and the early warning indicators of the key disaster-causing parameters; Wherein, the step S1 includes: Step S11, determining several key geological parameters of the geological disaster monitoring area based on the historical geological disaster data and corresponding geological parameter data; Step S12, determining a key correlation characteristic value based on the correlation relationship of each key geological parameter data; Step S13, determining the regional prevention and control type for the geological disaster monitoring area based on the key correlation feature value and the regional prevention and control classification model; In step S2, the geological disaster monitoring area is divided into several monitoring sub-areas based on the regional prevention and control type of the geological disaster monitoring area, including: Step S21, determining a corresponding regional prevention and control characteristic value based on the regional prevention and control type of the geological disaster monitoring area; Step S22, determining the number of monitoring sub-areas corresponding to the geological disaster monitoring area based on the regional prevention and control characteristic value; Step S23: dividing the geological disaster monitoring area into a plurality of monitoring sub-areas based on the number of the monitoring sub-areas.

2. The identification and early warning method for geological disaster prevention and control according to claim 1, characterized in that: In the step S3, it includes: Step S31, predicting environmental data within a future preset time period based on the environmental data collected within the target time period to obtain predicted environmental data; Step S32, determining critical environmental data within a future preset time period based on the geological parameter data collected within the target time period; Step S33: determining whether there is any abnormality in the corresponding monitoring sub-area based on the comparison result between the predicted environmental data and the critical environmental data.

3. The identification and early warning method for geological disaster prevention and control according to claim 2, characterized in that: In step S4, determining key prevention and control areas includes: Step S41, determining a standard geological parameter range of the geological disaster monitoring area based on historical geological disaster data and corresponding geological parameter data of the geological disaster monitoring area; Step S42: determining a key prevention and control area based on the standard geological parameter range and the comparison result of the geological parameter data of each monitoring sub-area.

4. The identification and early warning method for geological disaster prevention and control according to claim 3, characterized in that: In step S42, key prevention and control areas are determined based on the number of geological parameters in each monitoring sub-area that do not meet the standard geological parameter range.

5. The identification and early warning method for geological disaster prevention and control according to claim 2 or 3, characterized in that: In the step S4, it includes: The geological parameter data in each monitoring sub-area that does not conform to the geological parameter range of the standard geological parameter is determined as a key disaster-causing parameter, and the early warning indicator corresponding to the key disaster-causing parameter is determined based on the standard geological parameter range.

6. The identification and early warning method for geological disaster prevention and control according to claim 5, characterized in that: In the step S5, it includes: Step S51, determining a key difference value based on the early warning indicator corresponding to the key disaster-causing parameter and the comparison result of the key disaster-causing parameter data; Step S52, determining a perception difference value based on the sensor perception data and a preset perception standard; Step S53: determining whether to trigger an early warning based on the key difference value and the perception difference value.

7. The identification and early warning method for geological disaster prevention and control according to claim 6, characterized in that: In step S6, determining the early warning method of the geological disaster monitoring area based on the risk level of the key prevention and control area includes: If the risk level of the key prevention and control area is low risk, adjusting the collection period of the periodic collection based on the sensor perception data of the key prevention and control area; If the risk level of the key prevention and control area is high risk, an early warning prompt is generated and sent based on the area identification of the geological disaster monitoring area and the early warning indicators of the key disaster-causing parameters.

8. The identification and early warning method for geological disaster prevention and control according to claim 7, characterized in that: In the step S6, it includes: An adjustment coefficient is determined based on the key difference value and the perceived difference value, and an adjusted acquisition period is determined based on the adjustment coefficient and an acquisition period of the periodic acquisition.

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