Geological disaster risk grading management and control method based on dual prevention mechanisms

Abstract

The invention relates to the technical field of geological disaster prevention and control, and discloses a geological disaster risk grading management and control method based on a dual prevention mechanism, and the method comprises the following specific steps: 1, constructing a geological disaster risk dynamic evaluation index system; 2, collecting and processing multi-source heterogeneous data; 3, calculating a geological disaster risk comprehensive index; 4, classifying geological disaster risk grades; 5, generating a differentiated management and control measure set matched with the risk level; and 6, dynamically updating the risk level and the management and control measures. According to the method, by constructing a dynamic evaluation index system and integrating multi-source real-time data, comprehensive and accurate quantification of geological disaster risks is achieved, the hysteresis quality of traditional static evaluation is overcome, and by directly associating the risk level with a structured and differentiated management and control measure set and establishing a dynamic update risk level based on real-time data, the risk level of the geological disaster risk can be accurately evaluated. And efficient configuration of prevention and control resources and timely adjustment of measures are ensured.

Description

Technical Field

[0001] This invention relates to the field of geological disaster prevention and control technology, specifically to a geological disaster risk classification and control method based on a dual prevention mechanism. Background Technology

[0002] With the continuous improvement of the geological disaster prevention and control system, precise management based on risk classification has become a core approach to enhance regional safety resilience and optimize disaster prevention resource allocation. Geological disaster risk classification management aims to achieve differentiated management of areas with different risk levels through a comprehensive assessment of disaster-causing factors, the vulnerability of disaster-bearing bodies, and their exposure. Especially against the backdrop of urban expansion in mountainous areas and the dense layout of major projects, the geological environment is affected by both human activities and natural disturbances, leading to increasingly complex disaster formation mechanisms. Traditional classification models based primarily on static background conditions are no longer sufficient to meet the prevention and control needs under dynamic risk evolution. There is an urgent need to construct a dual prevention mechanism that integrates pre-emptive prevention and process control to support scientific, efficient, and operable risk governance.

[0003] Among them, the geological disaster risk classification and management method based on a dual prevention mechanism focuses on organically integrating risk identification and early warning with graded response measures. By establishing a dynamic mapping relationship between risk levels and prevention and control strategies, it achieves a shift from passive response to proactive prevention and control. This method emphasizes embedding control logic in the risk assessment stage, so that the classification results not only reflect the probability of disaster occurrence and the severity of consequences, but also directly drive the deployment of differentiated and executable preventive measures, thereby improving the foresight and adaptability of the entire disaster prevention system.

[0004] However, existing technologies are mostly limited to single-dimensional risk assessment or fragmented emergency response processes, specifically: On the one hand, risk classification generally relies on static geological parameters and historical disaster data, failing to effectively couple real-time monitoring information of dynamic triggering factors such as rainfall intensity, groundwater level, and seismic activity, resulting in classification results lagging behind the actual risk status, which can easily lead to missed assessments of high-risk areas or over-defense in low-risk areas. On the other hand, even if some solutions propose a dual framework of assessment and response, there is a lack of structured rules linking risk levels and control measures. They neither set corresponding monitoring frequencies, patrol intensity, and early warning thresholds based on risk levels, nor establish a dynamic adjustment mechanism for measures, resulting in an imbalance in the allocation of prevention and control resources and low execution efficiency. Summary of the Invention

[0005] Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a geological disaster risk classification and control method based on a dual prevention mechanism, which solves the problem mentioned above that "existing technologies are mostly limited to single-dimensional risk assessment or fragmented emergency response processes".

[0006] Technical solution To achieve the above objectives, the present invention provides a method for graded management and control of geological disaster risks based on a dual prevention mechanism, comprising the following specific steps: Step 1: Construct a dynamic assessment index system for geological disaster risks. By integrating baseline geological environmental data, real-time monitoring data, and historical disaster data, establish a multi-dimensional assessment index library that includes geological structural stability, slope stability, physical and mechanical properties of soil and rock, hydrogeological conditions, intensity of human engineering activities, rainfall intensity, groundwater level dynamics, and seismic motion parameters, and determine the weight coefficients of each index. Step 2: Collect and process multi-source heterogeneous data. Utilize a sensor network deployed at geological hazard sites to automatically collect real-time monitoring multi-source heterogeneous data, including rainfall recorded by rain gauges, groundwater levels recorded by pore water pressure gauges, slope displacement recorded by inclinometers, and seismic motion parameters recorded by accelerometers. Simultaneously, extract baseline geological environmental data from the geographic information system database and obtain historical disaster data from disaster prevention and mitigation departments. Standardize, denoise, and imput missing values ​​for all data to obtain processed multi-source heterogeneous data. Step 3: Calculate the comprehensive geological disaster risk index. Based on the assessment index system constructed in Step 1 and the multi-source heterogeneous data processed in Step 2, a weighted comprehensive evaluation model is used to calculate the comprehensive geological disaster risk index for each assessment unit. The comprehensive geological disaster risk index comprehensively reflects the probability of disaster occurrence and the severity of potential consequences. Step 4: Divide the geological hazard risk level. Based on the calculated comprehensive geological hazard risk index, use the natural breakpoint method or K-means clustering algorithm to divide the assessment area into four levels: extremely high risk area, high risk area, medium risk area, and low risk area, and set a clear index threshold range for each risk level. Step 5: Generate a set of differentiated control measures adapted to the risk level. For each risk level identified in Step 4, predefine a set of control measures including monitoring frequency, patrol routes and intensity, engineering control measures, early warning thresholds, and emergency response plans. Among them, extremely high-risk areas implement 24-hour uninterrupted monitoring and daily on-site patrols; high-risk areas implement at least 2 monitoring sessions per day and at least 3 patrols per week; medium-risk areas implement at least 2 monitoring sessions per week and at least 2 patrols per month; and low-risk areas implement at least 1 monitoring session per month and quarterly patrols. Step Six: Dynamically update risk levels and control measures. Based on the continuous input of multi-source heterogeneous data from real-time monitoring data, steps three to five are periodically re-executed. When the monitored multi-source heterogeneous data indicates that the comprehensive geological disaster risk index has changed significantly and crosses the preset threshold, the risk level is automatically reclassified and the corresponding control measures are dynamically adjusted to ensure that the risk classification and control are always consistent with the current geological environment.

[0007] Preferably, the geological environment baseline data in step one includes a 1:50,000 scale regional geological map, digital elevation model, remote sensing image interpretation results, rock and soil type and distribution map, and active fault zone distribution map. The real-time monitoring data includes rainfall data sampled every minute, groundwater level data recorded every hour, and slope displacement data measured daily. The historical disaster data includes records of the location, scale, triggering factors, and losses of landslides, collapses, and debris flows that occurred in the past 50 years. The determination of the index weight coefficients adopts the analytic hierarchy process combined with the entropy weight method, wherein the weight of geological structure stability is 0.15 to 0.25, the weight of slope stability is 0.20 to 0.30, the weight of rainfall intensity is 0.10 to 0.20, and the weight of human engineering activity intensity is 0.05 to 0.15.

[0008] Preferably, the sensor network in step two includes a rain gauge, a pore water pressure gauge, an inclinometer, an accelerometer, and a global navigation satellite system receiver. The rain gauge has a measurement accuracy of ±0.1 mm, the pore water pressure gauge has a measurement range of 0 to 200 kPa and an accuracy of ±0.5% of full scale, the inclinometer has a range of ±30 degrees and a resolution of 0.001 degrees, and the data acquisition frequency is dynamically adjusted according to the risk level. The data acquisition interval in extremely high-risk areas is no more than 1 minute, and the data acquisition interval in low-risk areas is no more than 1 hour.

[0009] Preferably, the calculation formula for the weighted comprehensive evaluation model in step three is as follows: ,in, Represents a comprehensive index of geological disaster risk. Representing the The weight of each indicator, Representing the The standardized value of each indicator.

[0010] Preferably, the risk level classification in step four adopts the natural breakpoint method. The natural breakpoint method can maximize the difference between groups and minimize the difference within groups. The threshold range of the comprehensive geological disaster risk index corresponding to the extremely high risk area is greater than or equal to 0.8, the threshold range of the high risk area is 0.6 to 0.8, the threshold range of the medium risk area is 0.3 to 0.6, and the threshold range of the low risk area is less than 0.3. The spatial resolution of each assessment unit is 10 meters × 10 meters.

[0011] Preferably, the control measures defined for extremely high-risk areas in step five include: No fewer than five automatic monitoring stations shall be set up, and the monitoring data shall be transmitted to the monitoring center in real time. A warning zone with a radius of 500 meters shall be demarcated, and non-essential personnel and vehicles shall be prohibited from entering. Professional geological surveys and stability assessments shall be initiated at least once a week. Emergency supplies such as excavators and sandbags shall be prepared on site. A red warning shall be issued when the rainfall exceeds 50 mm for three consecutive hours or the slope displacement rate exceeds 5 mm per day.

[0012] Preferably, the control measures defined for high-risk areas in step five include: No fewer than three automatic monitoring stations shall be set up, and monitoring data shall be transmitted once per hour. A warning zone with a radius of 200 meters shall be delineated, and large-scale engineering activities shall be restricted. Manual inspections shall be carried out twice a month, with a focus on checking surface cracks and seepage points. An orange warning shall be issued when the rainfall exceeds 30 mm for six consecutive hours or the slope displacement rate exceeds 2 mm per day.

[0013] Preferably, the control measures defined for medium-risk areas in step five include: At least one automatic monitoring station shall be set up, and monitoring data shall be transmitted once a day. Quarterly manual inspections shall be carried out. The warning threshold shall be set so that a yellow warning is issued when the daily rainfall exceeds 100 mm or the slope displacement rate exceeds 1 mm.

[0014] Preferably, the control measures defined for low-risk areas in step five include: Macro-level monitoring is conducted using existing regional monitoring networks, without deploying dedicated monitoring equipment. Annual inspections are carried out, and a blue alert is issued when daily rainfall exceeds 150 mm.

[0015] Preferably, the dynamic updating of the risk level in step six includes: The risk level update cycle is set as follows: 1 day for extremely high-risk and high-risk areas, 7 days for medium-risk areas, and 30 days for low-risk areas. When the real-time monitored rainfall intensity data exceeds 80% of the corresponding risk level warning threshold for two consecutive update cycles, or when the slope displacement data indicates that the cumulative displacement exceeds 10 mm, the system automatically identifies the area as a key concern area and initiates a reduction in the update cycle to half of the original cycle. Adjustments to control measures are automatically sent to on-site implementation terminals within one hour of the risk level update. Beneficial effects. This invention provides a method for risk classification and management of geological disasters based on a dual prevention mechanism. It has the following beneficial effects: (1) By constructing a dynamic evaluation index system and integrating multi-source real-time data, this invention achieves comprehensive and accurate quantification of geological disaster risk, overcomes the lag of traditional static evaluation, directly links risk level with structured and differentiated control measures, and establishes a dynamic update risk level based on real-time data, ensuring efficient allocation of prevention and control resources and timely adjustment of measures, forming a closed-loop management from risk identification to control execution, improving the foresight, adaptability and operability of geological disaster risk control, and providing scientific and reliable technical support for regional disaster prevention and mitigation decision-making. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall process of the present invention; Figure 2 A schematic diagram illustrating the evaluation index system for this invention; Figure 3 This is a schematic diagram of the data acquisition and processing flow of the present invention; Figure 4 This is a schematic diagram of the risk comprehensive index calculation process of the present invention; Figure 5 This is a schematic diagram of the risk level classification process of the present invention; Figure 6 This is a schematic diagram of the process for generating the control measures set of the present invention; Figure 7 This is a schematic diagram of the process for dynamically updating risk levels according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 - Figure 7 This invention provides a geological disaster risk classification and management method based on a dual prevention mechanism, specifically including the following steps: Step 1: Construct a dynamic assessment index system for geological hazard risk. This involves integrating baseline geological environmental data, real-time monitoring data, and historical disaster data to establish a multi-dimensional assessment index library. This library includes indicators such as geological structural stability, slope stability, physical and mechanical properties of soil and rock, hydrogeological conditions, intensity of human engineering activities, rainfall intensity, groundwater level dynamics, and seismic motion parameters. The weighting coefficients for each indicator are then determined. Specifically: The geological environment baseline data includes a 1:50,000 scale regional geological map, digital elevation model, remote sensing image interpretation results, rock and soil type and distribution map, and active fault zone distribution map. The real-time monitoring data includes rainfall data sampled every minute, groundwater level data recorded every hour, and slope displacement data measured daily; the historical disaster data includes records of the location, scale, triggering factors, and losses of landslides, collapses, and debris flows that occurred in the past 50 years.

[0019] The determination of indicator weight coefficients adopts the analytic hierarchy process (AHP) combined with the entropy weight method. The weights for geological structural stability are 0.15 to 0.25, slope stability is 0.20 to 0.30, rainfall intensity is 0.10 to 0.20, and human engineering activity intensity is 0.05 to 0.15. The construction of the indicator system first involved an expert group composed of specialists from multiple fields, including geology, hydrology, remote sensing, and disaster prevention engineering. The preliminary indicator set underwent three rounds of anonymous review and revision using the Delphi method to form a consensus indicator list. Then, based on 50 years of disaster event samples from a historical disaster database, the entropy weight method was used to calculate the information entropy and objective weight of each indicator. These were then weighted and integrated with the subjective weights obtained from the AHP to finally determine the comprehensive weight coefficients of each indicator, ensuring that the indicator system is both scientifically sound and engineering-operable.

[0020] Step Two: Collect and process multi-source heterogeneous data. Utilize a sensor network deployed at geological hazard sites to automatically collect real-time monitoring data from multiple sources, including rainfall recorded by rain gauges, groundwater levels recorded by pore water pressure gauges, slope displacement recorded by inclinometers, and seismic motion parameters recorded by accelerometers. Simultaneously, extract baseline geological environmental data from a geographic information system database and obtain historical disaster data from disaster prevention and mitigation departments. All data undergoes standardization, noise reduction, and missing value imputation to obtain processed multi-source heterogeneous data. Specifically: The sensor network includes rain gauges, pore water pressure gauges, inclinometers, accelerometers, and a global navigation satellite system receiver. The rain gauges have a measurement accuracy of ±0.1 mm, the pore water pressure gauges have a measurement range of 0 to 200 kPa and an accuracy of ±0.5% of full scale, the inclinometers have a range of ±30 degrees and a resolution of 0.001 degrees, and the data acquisition frequency is dynamically adjusted according to the risk level. The data acquisition interval in extremely high-risk areas is no more than 1 minute, and the data acquisition interval in low-risk areas is no more than 1 hour.

[0021] The data processing flow is as follows: 1. Perform timestamp alignment and format standardization on the original monitoring data, and resample data with different sampling frequencies to a unified time reference using linear interpolation or spline interpolation methods; 2. A sliding window mid-range filtering algorithm was used to denoise the data from the inclinometer and pore water pressure gauge, with the window width set to 5 sampling points; 3. For missing values, if no more than 3 consecutive sampling points are missing, the weighted average of the nearest points will be used for imputation; if more than 3 sampling points are missing, the data will be marked as abnormal and a manual verification process will be triggered. 4. Convert all data into spatial vector data in a unified coordinate system, and spatially match them with the baseline data in the geographic information system to form a complete indicator dataset corresponding to each 10m × 10m evaluation unit.

[0022] Step 3: Calculate the comprehensive geological hazard risk index. Based on the assessment index system constructed in Step 1 and the multi-source heterogeneous data processed in Step 2, a weighted comprehensive evaluation model is used to calculate the comprehensive geological hazard risk index for each assessment unit. The comprehensive geological hazard risk index comprehensively reflects the probability of a disaster occurring and the severity of its potential consequences. Specifically: The calculation formula for the weighted comprehensive evaluation model is as follows:

[0023] in, Represents a comprehensive index of geological disaster risk. Representing the The weight of each indicator, Representing the The standardized value of each indicator, The standardization process uses the minimum-maximum normalization method to transform the original data into the range of 0 to 1. The formula is as follows:

[0024] in These are the original index values. and These are the minimum and maximum values ​​of the indicator in the historical dataset, respectively. For dynamic indicators such as rainfall intensity, a moving average algorithm is used to smooth short-term fluctuations. The sliding window width is 7 days, that is, the arithmetic mean of the rainfall in the past 7 days is taken as the rainfall intensity indicator value of the day, so as to eliminate the instantaneous interference of extreme rainfall on risk assessment and more accurately reflect the cumulative effect of continuous rainfall on slope stability. This calculation process is executed in parallel on the central processing server, and each assessment unit independently calculates its R value. The calculation results are stored in the spatial database in raster data format for subsequent spatial analysis and visualization.

[0025] Step 4: Classify geological hazard risk levels. Based on the calculated comprehensive geological hazard risk index, the assessment area is divided into four levels—extremely high risk, high risk, medium risk, and low risk—using either the natural discontinuity method or the K-means clustering algorithm. A specific index threshold range is set for each risk level. Specifically: The risk level classification adopts the natural breakpoint method, which can maximize the difference between groups and minimize the difference within groups. By iteratively calculating the sum of variances within groups under different grouping schemes, the boundary point that minimizes the total variance within groups is selected as the optimal breakpoint. The threshold range of the comprehensive geological disaster risk index for extremely high-risk areas is greater than or equal to 0.8, the threshold range for high-risk areas is 0.6 to 0.8, the threshold range for medium-risk areas is 0.3 to 0.6, and the threshold range for low-risk areas is less than 0.3. The spatial resolution of each assessment unit is 10 meters × 10 meters to ensure that the risk zoning has sufficient spatial accuracy to support refined management. The classification results are output in the form of vector polygon layers. Each risk zone polygon contains metadata such as its risk level attribute, area, perimeter, and the number of hidden danger points it contains, providing a spatial basis for the deployment of subsequent management and control measures.

[0026] Step 5: Generate a set of differentiated control measures adapted to the risk level. For each risk level identified in Step 4, predefine a set of control measures including monitoring frequency, patrol routes and intensity, engineering control measures, early warning thresholds, and emergency response plans. Specifically: Control measures for extremely high-risk areas include: 1. Deploy no fewer than 5 automatic monitoring stations, and transmit monitoring data to the monitoring center in real time; 2. Establish a warning zone with a radius of 500 meters, prohibiting non-essential personnel and vehicles from entering; 3. Initiate weekly professional geological surveys and stability assessments; prepare emergency supplies such as excavators and sandbags on site; 4. The warning threshold is set to issue a red warning when the rainfall exceeds 50 mm for 3 consecutive hours or the slope displacement rate exceeds 5 mm per day.

[0027] Control measures for high-risk areas include: 1. Deploy no fewer than 3 automatic monitoring stations, and transmit monitoring data once per hour; 2. Delineate a warning zone with a radius of 200 meters to restrict large-scale engineering activities; 3. Conduct manual inspections at least twice a month, focusing on checking surface cracks and seepage points; 4. The warning threshold is set at orange alert when rainfall exceeds 30 mm for 6 consecutive hours or slope displacement rate exceeds 2 mm per day.

[0028] Control measures for medium-risk areas include: 1. Deploy no less than one automatic monitoring station, and transmit monitoring data once a day; 2. Conduct quarterly manual inspections; 3. The warning threshold is set at yellow when the daily rainfall exceeds 100 mm or the slope displacement rate exceeds 1 mm per day.

[0029] Control measures for low-risk areas include: 1. Rely on the existing regional monitoring network for macro-level monitoring, without deploying dedicated monitoring equipment separately; 2. Conduct annual inspections; 3. The warning threshold is set at a blue alert when daily rainfall exceeds 150 mm. The set of control measures is stored in a structured database table format. Each record contains fields such as risk level, measure type, implementing entity, implementation frequency, technical parameters, and resource requirements to ensure the enforceability and traceability of the measures.

[0030] Step Six: Dynamically update risk levels and control measures. Based on the continuous input of real-time monitored multi-source heterogeneous data, periodically re-execute steps three through five. When the monitored multi-source heterogeneous data indicates a significant change in the comprehensive geological hazard risk index and crosses a preset threshold, automatically trigger the reclassification of risk levels and the dynamic adjustment of corresponding control measures to ensure that risk classification and control remain consistent with the current geological environment. Specifically: The dynamic update risk levels include: 1. Set the risk level update cycle: 1 day for extremely high risk areas and high risk areas, 7 days for medium risk areas, and 30 days for low risk areas; 2. When the real-time monitored rainfall intensity data exceeds 80% of the corresponding risk level warning threshold for two consecutive update cycles, or when the slope displacement data indicates that the cumulative displacement exceeds 10 mm, the system will automatically identify the area as a key concern area and start shortening the update cycle to half of the original cycle. 3. Adjustments to control measures will be automatically sent to the on-site execution terminal within 1 hour after the risk level is updated.

[0031] The update process is automatically triggered by the central dispatch module. First, the latest monitoring data and baseline data are called to recalculate the comprehensive risk index. Then, the risk level is classified. If the new level is different from the original level, the corresponding set of measures for the new level is retrieved from the control measures database. The update instruction is then pushed to the field monitoring station, the mobile terminal of the patrol personnel, and the emergency command center through the wireless communication network to achieve seamless switching of control strategies.

[0032] Furthermore, this invention also includes establishing a geological disaster risk management effectiveness evaluation module. This module quantitatively evaluates the effectiveness of management measures by comparing three indicators: the rate of change in the comprehensive geological disaster risk index before and after the implementation of management measures, the accuracy of early warnings, and the response time for emergency response. Based on the evaluation results, it optimizes the predefined set of management measures in step 5, with an optimization period of 6 months. Specifically: The formula for calculating the rate of change of the risk composite index is as follows: ,in This is a risk index prior to the implementation of the measures. This refers to the change in the index after implementation; The accuracy rate of early warning is defined as the ratio of the number of successfully warned incidents to the total number of warnings. Emergency response time refers to the time from the issuance of an early warning to the arrival of the on-site emergency response team.

[0033] The geological disaster risk management effectiveness assessment module generates an effectiveness assessment report every 6 months. If the early warning accuracy rate of a certain type of risk area is lower than 85% for two consecutive assessment cycles, or the response time exceeds the preset standard, the management measures optimization process is triggered. The expert team revises the original set of measures based on the assessment data, such as adjusting the density of monitoring stations, modifying the early warning threshold, or adding engineering management measures. The revised set of measures is updated to the database after approval, so as to realize the continuous iterative optimization of management strategies.

[0034] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A geological disaster risk classification and control method based on a dual prevention mechanism, characterized in that: The specific steps include the following: Step 1: Construct a dynamic assessment index system for geological disaster risks. By integrating baseline geological environmental data, real-time monitoring data, and historical disaster data, establish a multi-dimensional assessment index library that includes geological structural stability, slope stability, physical and mechanical properties of soil and rock, hydrogeological conditions, intensity of human engineering activities, rainfall intensity, groundwater level dynamics, and seismic motion parameters, and determine the weight coefficients of each index. Step 2: Collect and process multi-source heterogeneous data. Utilize a sensor network deployed at geological hazard sites to automatically collect real-time monitoring multi-source heterogeneous data, including rainfall recorded by rain gauges, groundwater levels recorded by pore water pressure gauges, slope displacement recorded by inclinometers, and seismic motion parameters recorded by accelerometers. Simultaneously, extract baseline geological environmental data from the geographic information system database and obtain historical disaster data from disaster prevention and mitigation departments. Standardize, denoise, and imput missing values ​​for all data to obtain processed multi-source heterogeneous data. Step 3: Calculate the comprehensive geological hazard risk index. Based on the assessment index system constructed in Step 1 and the multi-source heterogeneous data processed in Step 2, the comprehensive geological hazard risk index of each assessment unit is calculated using a weighted comprehensive evaluation model. Step 4: Divide the geological hazard risk level. Based on the calculated comprehensive geological hazard risk index, use the natural breakpoint method or K-means clustering algorithm to divide the assessment area into four levels: extremely high risk area, high risk area, medium risk area, and low risk area, and set a clear index threshold range for each risk level. Step 5: Generate a set of differentiated control measures adapted to the risk level. For each risk level identified in Step 4, predefine a set of control measures including monitoring frequency, patrol routes and intensity, engineering control measures, early warning thresholds, and emergency response plans. Step Six: Dynamically update risk levels and control measures. Based on the continuous input of real-time monitored multi-source heterogeneous data, re-execute steps three to five periodically. When the monitored multi-source heterogeneous data indicates that the comprehensive geological disaster risk index has changed significantly and crosses the preset threshold, the risk level will be automatically reclassified and the corresponding control measures will be dynamically adjusted.

2. The geological disaster risk classification and control method based on a dual prevention mechanism according to claim 1, characterized in that: The geological environment baseline data includes a 1:50,000 scale regional geological map, digital elevation model, remote sensing image interpretation results, rock and soil type and distribution map, and active fault zone distribution map; the real-time monitoring data includes rainfall data sampled every minute, groundwater level data recorded every hour, and slope displacement data measured daily. The historical disaster data includes records of the location, scale, triggering factors, and losses of landslides, collapses, and debris flows that occurred in the past 50 years. The weight coefficients of the indicators are determined using the analytic hierarchy process combined with the entropy weight method, where the weight for geological structural stability is 0.15 to 0.25, the weight for slope stability is 0.20 to 0.30, the weight for rainfall intensity is 0.10 to 0.20, and the weight for human engineering activity intensity is 0.05 to 0.

15.

3. The geological disaster risk classification and control method based on a dual prevention mechanism according to claim 1, characterized in that: The sensor network includes rain gauges, pore water pressure gauges, inclinometers, accelerometers, and global navigation satellite system receivers; the data acquisition frequency is dynamically adjusted according to the risk level, with a data acquisition interval of no more than 1 minute in extremely high-risk areas and no more than 1 hour in low-risk areas.

4. The geological disaster risk classification and control method based on a dual prevention mechanism according to claim 1, characterized in that: The calculation formula for the weighted comprehensive evaluation model is as follows: ,in, Represents a comprehensive index of geological disaster risk. Representing the The weight of each indicator, Representing the The standardized value of each indicator, .

5. The geological disaster risk classification and control method based on a dual prevention mechanism according to claim 1, characterized in that: The threshold range of the comprehensive geological disaster risk index for extremely high-risk areas is greater than or equal to 0.8, the threshold range for high-risk areas is 0.6 to 0.8, the threshold range for medium-risk areas is 0.3 to 0.6, and the threshold range for low-risk areas is less than 0.3; the spatial resolution of each assessment unit is 10 meters × 10 meters.

6. The geological disaster risk classification and control method based on a dual prevention mechanism according to claim 1, characterized in that: The control measures corresponding to the extremely high-risk areas include: No fewer than 5 automatic monitoring stations shall be set up, and the monitoring data shall be transmitted to the monitoring center in real time. A warning zone with a radius of 500 meters shall be delineated, and professional geological surveys and stability assessments shall be initiated at least once a week. Excavators and sandbags shall be prepared as emergency supplies on site. A red warning shall be issued when the rainfall exceeds 50 mm for 3 consecutive hours or the slope displacement rate exceeds 5 mm per day.

7. The geological disaster risk classification and control method based on a dual prevention mechanism according to claim 1, characterized in that: The control measures corresponding to the high-risk areas include: Deploy no fewer than three automatic monitoring stations, transmitting monitoring data once per hour; delineate a warning zone with a radius of at least 200 meters; conduct manual inspections at least twice a month; and set the warning threshold to issue an orange alert when rainfall exceeds 30 mm for at least six consecutive hours or the slope displacement rate exceeds 2 mm per day. The control measures corresponding to the medium-risk areas include: At least one automatic monitoring station shall be set up, and monitoring data shall be transmitted at least once a day. Quarterly manual inspections shall be carried out. The warning threshold shall be set so that a yellow warning is issued when the daily rainfall exceeds 100 mm or the slope displacement rate exceeds 1 mm.

8. The geological disaster risk classification and control method based on a dual prevention mechanism according to claim 1, characterized in that: The control measures corresponding to the low-risk areas include: Macro-level monitoring is carried out by relying on the existing regional monitoring network, without deploying dedicated monitoring equipment separately; Annual inspections will be conducted; a blue alert will be issued when the daily rainfall exceeds 150 mm.

9. The geological disaster risk classification and control method based on a dual prevention mechanism according to claim 1, characterized in that: The dynamic update risk levels include: The risk level update cycle is set as follows: 1 day for extremely high-risk and high-risk areas, 7 days for medium-risk areas, and 30 days for low-risk areas. When the real-time monitored rainfall intensity data exceeds 80% of the corresponding risk level warning threshold for two consecutive update cycles, or when the slope displacement data indicates that the cumulative displacement exceeds 10 mm, the system automatically identifies the area as a key concern area and initiates a reduction in the update cycle to half of the original cycle. Adjustments to control measures are automatically sent to the on-site execution terminal within one hour of the risk level update.

10. A geological disaster risk classification and control method based on a dual prevention mechanism according to claim 1, characterized in that: It also includes the establishment of a geological disaster risk management effectiveness evaluation module. This module quantitatively evaluates the effectiveness of management measures by comparing three indicators: the rate of change of the comprehensive geological disaster risk index before and after the implementation of management measures, the accuracy of early warning, and the response time for handling emergencies. Based on the evaluation results, the predefined set of management measures is optimized every 6 months.

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