Regional geological disaster risk intelligent assessment method and system

Abstract

The invention discloses an intelligent assessment method and system for regional geological disaster risk, and relates to the technical field of intelligent assessment. During operation of the system, various real-time data related to debris flow are acquired by integrating real-time sensors and satellite remote sensing data, original data are preprocessed and stored in a data storage system; based on the collected data, a physical model and a machine learning model are constructed, a debris flow disaster risk index MRI, a rainfall intensity coefficient RIC, a geological vulnerability coefficient GVC and a hydrodynamic pressure coefficient HPC are obtained after calculation, the probability of debris flow occurrence is predicted, and a result is output according to the model. The debris flow disaster risk index MRI is compared with a preset first safety threshold value M and a preset second safety threshold value N, the risk level of debris flow occurrence is evaluated, different areas are managed in a classified mode, debris flow early warning is given out according to real-time monitoring data and risk evaluation results, and automatic emergency response measures are supported.

Description

Technical Field

[0001] The present invention relates to the field of intelligent assessment technology, and in particular to a method and system for intelligent assessment of regional geological disaster risk. Background Art

[0002] Regional geological disasters Debris flow is one of the common serious natural disasters in mountainous and hilly areas. It is usually caused by external factors such as heavy rainfall, snowmelt, earthquakes, etc. It is characterized by strong suddenness, great destructive power, and wide impact range. The occurrence of debris flow often causes serious consequences such as traffic interruption, house damage, casualties, etc., posing a huge threat to the local social economy and ecological environment. Traditional debris flow monitoring and early warning methods mostly rely on a single data source or empirical judgment, and cannot achieve accurate prediction and rapid response to the occurrence of debris flow.

[0003] However, the existing debris flow disaster monitoring methods have many shortcomings when dealing with complex environments and changing meteorological conditions. Traditional disaster monitoring systems often lack real-time integration of multi-source data and cannot effectively capture the key causes of debris flow, such as sudden heavy rainfall or drastic changes in geological conditions. In addition, the warning mechanism of the existing system mostly relies on historical experience or human judgment, lacks in-depth analysis and modeling of complex data, resulting in low warning accuracy, false alarms and missed reports. The above-mentioned defects are mainly due to the lack of analysis of debris flow inducing factors by existing monitoring technologies, as well as the limited ability of data processing and model building. When heavy rainfall occurs or geological conditions change in the region, the traditional system often cannot issue warnings in time due to its slow data integration speed and lack of real-time analysis capabilities, resulting in delayed emergency response after the disaster occurs. This lag not only delays the rescue time, but may also aggravate the degree of damage caused by the disaster, resulting in serious economic losses and casualties. In addition, the existing system cannot adaptively adjust the model parameters, making it difficult to effectively respond to dynamic changes in the environment, thereby increasing the error range of the prediction. The fundamental reason for the existing defects lies in the insufficient real-time monitoring and modeling capabilities of complex geological and meteorological conditions. Therefore, the intelligent assessment system for debris flow disasters needs to be further optimized to cope with rapidly changing environments and sudden disasters and improve the accuracy and timeliness of early warnings. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and system for intelligently assessing the risk of regional geological disasters, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a regional geological disaster risk intelligent assessment system, including a data acquisition module, a data preprocessing module, a debris flow model construction and analysis module, a risk assessment module, an early warning emergency response module, and a model optimization module; The data acquisition module is used to collect a variety of real-time data related to debris flow, including meteorological data, geological data and hydrological data, by integrating real-time sensors and satellite remote sensing data; The data preprocessing module is used to clean, format and reduce noise of the original data, complete missing data, remove abnormal data, and store it in the data storage system; The debris flow model construction and analysis module is used to build a physical model and a machine learning model based on the collected data, and obtain after calculation: debris flow disaster risk index MRI, rainfall intensity coefficient RIC, geological vulnerability coefficient GVC and hydrodynamic pressure coefficient HPC, and predict the probability of debris flow. The model will combine the key parameters of terrain, rainfall and soil moisture content for analysis; The risk assessment module is used to evaluate the risk level of debris flow occurrence and classify and manage different areas by comparing the debris flow disaster risk index MRI with the preset first safety threshold M and second safety threshold N according to the model output results; The early warning emergency response module is used to issue debris flow warnings based on real-time monitoring data and risk assessment results. Warning information is transmitted to relevant departments and the public through text messages, alarms and application notifications, supporting automated emergency response measures, including closing roads and evacuating people; The model optimization module is used to self-adjust and optimize the debris flow prediction model using historical data and real-time feedback.

[0006] Preferably, the data acquisition module includes a meteorological data acquisition unit, a geological data acquisition unit and a hydrological data acquisition unit; The meteorological data collection unit is used to collect meteorological data related to debris flow in real time, including rainfall, rainfall intensity and rainfall duration, by integrating automatic rain gauge, weather station and radar rainfall monitoring system, to obtain: total rainfall P t , Rainfall intensity I r and the cumulative rainfall threshold P th ; The geological data acquisition unit is used to collect data related to geological fragility in real time, including terrain slope, soil moisture content and rock fragmentation, by integrating GPS equipment, laser rangefinder and soil moisture sensor, to obtain: slope S, soil moisture content W s , rock crushing degree R f and vegetation coverage V c ; The hydrological data acquisition unit is used to collect hydrological data related to hydrodynamic pressure in real time through a current meter, an ultrasonic water level meter and a pressure sensor, including surface water flow, water depth and river runoff information, and obtain: surface runoff flow Q, water depth D, slope effect S e、River Basin Area A f and the basin area normalization factor A max .

[0007] Preferably, the data preprocessing module includes a data preprocessing unit; The data preprocessing unit is used to clean the collected raw data, remove noise and invalid data, convert the cleaned data into a unified format, ensure that data from different sources can be compatible in subsequent analysis, complete missing data, process missing key data points through interpolation or machine learning algorithms, and store them in a data storage system.

[0008] Preferably, the debris flow model construction and analysis module includes a physical model construction unit, a machine learning model construction unit and a risk coefficient calculation unit; The physical model building unit is used to build a physical model for debris flow analysis based on topography, rainfall and soil moisture parameters, including a soil erosion model and a slope stability model; The machine learning model building unit is used to build a machine learning model for debris flow probability prediction based on historical data and real-time collected data, including a neural network and a random forest model; The risk coefficient calculation unit is used to calculate the debris flow disaster risk index MRI and its three key coefficients: rainfall intensity coefficient RIC, geological vulnerability coefficient GVC and hydrodynamic pressure coefficient HPC through a model, so as to predict the probability of debris flow occurrence; The debris flow disaster risk index MRI is calculated by the following formula: ; In the formula, RIC represents rainfall intensity coefficient, GVC represents geological vulnerability coefficient, and HPC represents hydrodynamic pressure coefficient. They represent the weight coefficients of rainfall intensity coefficient RIC, geological vulnerability coefficient GVC and hydrodynamic pressure coefficient HPC respectively; The rainfall intensity coefficient RIC is calculated by the following formula: ; Where P t represents the total rainfall, I r represents rainfall intensity, P th represents the cumulative rainfall threshold, T r Indicates the duration of rainfall, w t represents the time weight factor, T max Indicates the upper limit of reference rainfall time.

[0009] Preferably, the geological vulnerability coefficient GVC is calculated by the following formula: ; In the formula, S represents the slope, W s represents soil moisture content, R f Indicates the rock fragmentation, V c represents the vegetation coverage rate, Bg represents the probability of debris flow affected by bedrock fragility, Respectively represent the slope S, soil moisture W s and vegetation coverage V c The weight value of .

[0010] Preferably, the hydrodynamic pressure coefficient HPC is calculated by the following formula: ; In the formula, Q represents the surface runoff flow, D represents the water depth, S e represents the slope effect, A f represents the river basin area, A max Represents the watershed area normalization factor.

[0011] Preferably, the risk assessment module includes a threshold comparison unit and a regional risk classification unit; The threshold comparison unit is used to compare the debris flow disaster risk index MRI with the preset first safety threshold M and second safety threshold N to evaluate the risk level of debris flow: When the debris flow disaster risk index MRI is less than or equal to the first safety threshold M, it is assessed as the first risk level. Within this range, the probability of debris flow is less than 30%, and it does not pose a threat to the personnel, infrastructure and ecological environment in the area. Under this condition, the system maintains routine monitoring and no emergency response measures are required; When the debris flow disaster risk index MRI is greater than the first safety threshold M and less than or equal to the second safety threshold N, it is assessed as the second risk level. At this time, the probability of debris flow is 40%-50%. It is recommended to strengthen monitoring and remind relevant departments to take preventive measures, including raising the warning level, initiating local emergency preparations, and paying close attention to weather and geological changes in high-risk areas; When the debris flow disaster risk index MRI is greater than the second safety threshold N, it is assessed as the third risk level. At this time, the risk of debris flow is higher than 50%. The emergency response mechanism needs to be activated immediately, the relevant departments need to be notified, and the public needs to be warned. Road closures, personnel evacuations, and emergency deployment of rescue resources are required to ensure personal safety and minimize property losses. The regional risk classification unit is used to classify and manage the monitoring areas based on different risk levels, and determine the priority of each area to facilitate the planning and execution of subsequent emergency response.

[0012] Preferably, the early warning emergency response module includes an early warning generation unit and an emergency measure execution unit; The warning generation unit is used to automatically generate debris flow warning information based on real-time monitoring data and risk assessment results, including detailed content of potential risks and the scope of the affected area, and transmit the debris flow warning information to relevant government departments and the public through SMS, alarm bells and application notification channels; The emergency measure execution unit is used to automatically trigger preset emergency response measures according to the early warning information, including closing roads in the affected area, evacuating personnel and initiating the deployment of rescue resources.

[0013] Preferably, the model optimization module includes a historical data feedback unit and a model adjustment unit; The historical data feedback unit is used to collect historical data and feedback information after the debris flow disaster occurs, and optimize and adaptively adjust the parameters of the existing physical model and machine learning model according to the feedback data; The model adjustment unit is used to continuously update and iterate the model based on new data during system operation to ensure the adaptability and robustness of the debris flow risk prediction model under different environments and conditions.

[0014] A regional geological disaster risk intelligent assessment method comprises the following steps: Step 1: Collect a variety of real-time data related to debris flow, including meteorological data, geological data and hydrological data, by integrating real-time sensors and satellite remote sensing data; Step 2: Clean, format and reduce noise of the original data, complete missing data, remove abnormal data, and store it in the data storage system; Step 3: Based on the collected data, physical models and machine learning models are constructed to obtain the following after calculation: debris flow risk index MRI, rainfall intensity coefficient RIC, geological vulnerability coefficient GVC and hydrodynamic pressure coefficient HPC, and predict the probability of debris flow. The model will combine key parameters such as topography, rainfall and soil moisture for analysis; Step 4: According to the model output results, the debris flow disaster risk index MRI is compared with the preset first safety threshold M and second safety threshold N to evaluate the risk level of debris flow and classify and manage different areas; Step 5: Based on real-time monitoring data and risk assessment results, a debris flow warning is issued. The warning information is delivered to relevant departments and the public through SMS, alarm bells and application notifications, supporting automated emergency response measures, including road closures and evacuations; Step 6: Use historical data and real-time feedback to self-adjust and optimize the debris flow prediction model.

[0015] The present invention provides a method and system for intelligently assessing the risk of regional geological disasters, which has the following beneficial effects: (1) When the system is running, it integrates real-time sensors and satellite remote sensing data to collect a variety of real-time data related to debris flows, preprocesses the raw data, and stores it in the data storage system. Based on the collected data, it builds a physical model and a machine learning model to obtain the following after calculation: debris flow disaster risk index MRI, rainfall intensity coefficient RIC, geological vulnerability coefficient GVC and hydrodynamic pressure coefficient HPC. It predicts the probability of debris flow occurrence. According to the model output results, the debris flow disaster risk index MRI is compared with the preset first safety threshold M and second safety threshold N to evaluate the risk level of debris flow occurrence, and classify and manage different areas. According to the real-time monitoring data and risk assessment results, it issues debris flow warnings and supports automated emergency response measures, including road closures and evacuation of personnel. It uses historical data and real-time feedback to self-adjust and optimize the debris flow prediction model.

[0016] (2) This regional geological disaster risk intelligent assessment system realizes real-time monitoring and accurate assessment of debris flow risks through the collaborative work of six modules. The data acquisition module uses sensors, satellite remote sensing and other technologies to obtain important data such as meteorology, geology, and hydrology from multiple dimensions; the data preprocessing module effectively cleans and completes the original data to ensure high-quality data input; the debris flow model construction and analysis module combines physical models with machine learning algorithms to accurately calculate the debris flow disaster risk index and predict the probability of potential disasters. Through the collaborative work of these modules, the system can comprehensively and real-time assess the risk of debris flow, providing a solid data foundation for subsequent early warning and response.

[0017] (3) Compared with traditional debris flow monitoring methods, this intelligent assessment system has achieved significant improvements in risk assessment and early warning response. First, the risk assessment module can automatically classify and manage areas of different risk levels by comparing the debris flow disaster risk index with the preset safety threshold, and put forward targeted early warning and emergency response measures. Secondly, the early warning emergency response module relies on real-time data and model output to quickly generate early warning information, and transmit it to relevant departments and the public through multiple channels in real time, greatly improving the timeliness and accuracy of the early warning. Compared with traditional systems, it reduces human judgment and experience dependence, and significantly reduces the risk of false alarms and missed alarms.

[0018] (4) Through the model optimization module, the system also has self-learning and self-adaptation capabilities, and can automatically optimize the prediction model based on historical disaster data and real-time feedback, continuously improving the prediction accuracy and system adaptability. This optimization mechanism enables the system to effectively respond to dynamic changes in environmental and geological conditions and maintain the long-term effectiveness and reliability of predictions. Overall, compared with existing technical means, this system not only significantly improves the accuracy in data integration, model construction and risk assessment, but also brings a qualitative leap in the speed and reliability of early warning and emergency response, providing strong technical support for reducing the losses caused by debris flow disasters and improving the efficiency of emergency rescue. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a block diagram of a regional geological disaster risk intelligent assessment system of the present invention; Figure 2 The present invention is a schematic diagram of the steps of a method for intelligently assessing the risk of regional geological disasters. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Example 1 The present invention provides a regional geological disaster risk intelligent assessment system, please refer to Figure 1 , including data acquisition module, data preprocessing module, debris flow model construction and analysis module, risk assessment module, early warning emergency response module, and model optimization module; The data acquisition module is used to collect a variety of real-time data related to debris flow, including meteorological data, geological data and hydrological data, by integrating real-time sensors and satellite remote sensing data; The data preprocessing module is used to clean, format and reduce noise of the original data, complete missing data, remove abnormal data, and store it in the data storage system; The debris flow model construction and analysis module is used to build a physical model and a machine learning model based on the collected data, and obtain after calculation: debris flow disaster risk index MRI, rainfall intensity coefficient RIC, geological vulnerability coefficient GVC and hydrodynamic pressure coefficient HPC, and predict the probability of debris flow. The model will combine the key parameters of terrain, rainfall and soil moisture content for analysis; The risk assessment module is used to evaluate the risk level of debris flow occurrence and classify and manage different areas by comparing the debris flow disaster risk index MRI with the preset first safety threshold M and second safety threshold N according to the model output results; The early warning emergency response module is used to issue debris flow warnings based on real-time monitoring data and risk assessment results. Warning information is transmitted to relevant departments and the public through text messages, alarms and application notifications, supporting automated emergency response measures, including closing roads and evacuating people; The model optimization module is used to self-adjust and optimize the debris flow prediction model using historical data and real-time feedback.

[0022] In this embodiment, by integrating real-time sensors and satellite remote sensing data, various real-time data related to debris flows are collected, the original data is preprocessed and stored in a data storage system, and a physical model and a machine learning model are constructed based on the collected data. After calculation, the following are obtained: a debris flow disaster risk index MRI, a rainfall intensity coefficient RIC, a geological vulnerability coefficient GVC and a hydrodynamic pressure coefficient HPC, and the probability of debris flow occurrence is predicted. According to the model output results, the debris flow disaster risk index MRI is compared with the preset first safety threshold M and the second safety threshold N to evaluate the risk level of debris flow occurrence, and different areas are classified and managed. According to the real-time monitoring data and risk assessment results, a debris flow warning is issued to support automated emergency response measures, including closing roads and evacuating personnel, and the debris flow prediction model is self-adjusted and optimized using historical data and real-time feedback.

[0023] Example 2 This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the data acquisition module includes a meteorological data acquisition unit, a geological data acquisition unit and a hydrological data acquisition unit; The meteorological data collection unit is used to collect meteorological data related to debris flow in real time, including rainfall, rainfall intensity and rainfall duration, by integrating automatic rain gauge, weather station and radar rainfall monitoring system, to obtain: total rainfall P t , Rainfall intensity I r and the cumulative rainfall threshold P th ; The geological data acquisition unit is used to collect data related to geological fragility in real time, including terrain slope, soil moisture content and rock fragmentation, by integrating GPS equipment, laser rangefinder and soil moisture sensor, to obtain: slope S, soil moisture content W s , rock crushing degree R f and vegetation coverage V c ; The hydrological data acquisition unit is used to collect hydrological data related to hydrodynamic pressure in real time through a current meter, an ultrasonic water level meter and a pressure sensor, including surface water flow, water depth and river runoff information, and obtain: surface runoff flow Q, water depth D, slope effect S e 、River Basin Area A f and the basin area normalization factor A max .

[0024] The data preprocessing module includes a data preprocessing unit; The data preprocessing unit is used to clean the collected raw data, remove noise and invalid data, convert the cleaned data into a unified format, ensure that data from different sources can be compatible in subsequent analysis, complete missing data, process missing key data points through interpolation or machine learning algorithms, and store them in a data storage system.

[0025] In this embodiment, by integrating automated meteorological, geological and hydrological data acquisition units, the system can acquire and process multi-dimensional data related to debris flows in real time, including key factors such as rainfall, topography, soil, and hydrology. The data preprocessing module further ensures high-quality data input, and ensures that data from different sources can be processed compatibly by removing noise, formatting, and completing data. The overall system can improve the accuracy and efficiency of data processing, provide a reliable foundation for subsequent debris flow risk assessment and prediction, and thus improve the accuracy and response speed of disaster warnings, and reduce the false alarm and missed alarm rates in risk management.

[0026] Example 3 This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the debris flow model construction and analysis module includes a physical model construction unit, a machine learning model construction unit and a risk coefficient calculation unit; The physical model building unit is used to build a physical model for debris flow analysis based on topography, rainfall and soil moisture parameters, including a soil erosion model and a slope stability model; The machine learning model building unit is used to build a machine learning model for debris flow probability prediction based on historical data and real-time collected data, including a neural network and a random forest model; The risk coefficient calculation unit is used to calculate the debris flow disaster risk index MRI and its three key coefficients: rainfall intensity coefficient RIC, geological vulnerability coefficient GVC and hydrodynamic pressure coefficient HPC through a model, so as to predict the probability of debris flow occurrence; The debris flow disaster risk index MRI is calculated by the following formula: ; In the formula, RIC represents rainfall intensity coefficient, GVC represents geological vulnerability coefficient, and HPC represents hydrodynamic pressure coefficient. They represent the weight coefficients of rainfall intensity coefficient RIC, geological vulnerability coefficient GVC and hydrodynamic pressure coefficient HPC respectively; The rainfall intensity coefficient RIC is calculated by the following formula: ; Where P t represents the total rainfall, I r represents rainfall intensity, P th represents the cumulative rainfall threshold, T r Indicates the duration of rainfall, w t represents the time weight factor, T max Indicates the upper limit of reference rainfall time.

[0027] The geological vulnerability coefficient GVC is calculated by the following formula: ; In the formula, S represents the slope, W s represents soil moisture content, R f Indicates the rock fragmentation, V c represents the vegetation coverage rate, Bg represents the probability of debris flow affected by bedrock fragility, Respectively represent the slope S, soil moisture W s and vegetation coverage V c The weight value of .

[0028] The hydrodynamic pressure coefficient HPC is calculated by the following formula: ; In the formula, Q represents the surface runoff flow, D represents the water depth, S e represents the slope effect, A f represents the river basin area, A max Represents the watershed area normalization factor.

[0029] In this embodiment, by combining the construction of physical models with machine learning models, the system can comprehensively consider various factors that cause debris flows, including topography, rainfall, soil moisture content, etc., and construct soil erosion models and slope stability models to improve the accuracy of the analysis. At the same time, the machine learning model continuously optimizes the probability prediction of debris flow based on historical data and real-time data, thereby improving the accuracy and flexibility of the prediction. By calculating the rainfall intensity coefficient, geological vulnerability coefficient, and hydrodynamic pressure coefficient, the system can quantify the debris flow disaster risk index and achieve an accurate assessment of the probability of debris flow occurrence. The overall effect significantly improves the scientificity and accuracy of debris flow risk prediction, and provides a reliable basis for relevant disaster prevention and mitigation decisions.

[0030] Example 4 This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the risk assessment module includes a threshold comparison unit and a regional risk classification unit; The threshold comparison unit is used to compare the debris flow disaster risk index MRI with the preset first safety threshold M and second safety threshold N to evaluate the risk level of debris flow: When the debris flow disaster risk index MRI is less than or equal to the first safety threshold M, it is assessed as the first risk level. Within this range, the probability of debris flow is less than 30%, and it does not pose a threat to the personnel, infrastructure and ecological environment in the area. Under this condition, the system maintains routine monitoring and no emergency response measures are required; When the debris flow disaster risk index MRI is greater than the first safety threshold M and less than or equal to the second safety threshold N, it is assessed as the second risk level. At this time, the probability of debris flow is 40%-50%. It is recommended to strengthen monitoring and remind relevant departments to take preventive measures, including raising the warning level, initiating local emergency preparations, and paying close attention to weather and geological changes in high-risk areas; When the debris flow disaster risk index MRI is greater than the second safety threshold N, it is assessed as the third risk level. At this time, the risk of debris flow is higher than 50%. The emergency response mechanism needs to be activated immediately, the relevant departments need to be notified, and the public needs to be warned. Road closures, personnel evacuations, and emergency deployment of rescue resources are required to ensure personal safety and minimize property losses. The regional risk classification unit is used to classify and manage the monitoring areas based on different risk levels, and determine the priority of each area to facilitate the planning and execution of subsequent emergency response.

[0031] The early warning emergency response module includes an early warning generation unit and an emergency measure execution unit; The warning generation unit is used to automatically generate debris flow warning information based on real-time monitoring data and risk assessment results, including detailed content of potential risks and the scope of the affected area, and transmit the debris flow warning information to relevant government departments and the public through SMS, alarm bells and application notification channels; The emergency measure execution unit is used to automatically trigger preset emergency response measures according to the early warning information, including closing roads in the affected area, evacuating personnel and initiating the deployment of rescue resources.

[0032] The model optimization module includes a historical data feedback unit and a model adjustment unit; The historical data feedback unit is used to collect historical data and feedback information after the debris flow disaster occurs, and optimize and adaptively adjust the parameters of the existing physical model and machine learning model according to the feedback data; The model adjustment unit is used to continuously update and iterate the model based on new data during system operation to ensure the adaptability and robustness of the debris flow risk prediction model under different environments and conditions.

[0033] In this embodiment, the beneficial effect of the present invention is that, through the risk assessment mechanism of threshold comparison and regional risk classification, the system can accurately classify based on different levels of debris flow disaster risk index MRI, ensuring that areas with different risk levels receive corresponding management and monitoring. The early warning emergency response module can achieve timely notification to relevant departments and the public by automatically generating and publishing debris flow early warning information, and trigger automated emergency measures, including road closures and personnel evacuation, greatly improving the speed and efficiency of emergency response. The model optimization module enables the system to adapt to the changing environment through real-time data feedback and continuous model adjustment, ensuring long-term prediction accuracy and robustness, thereby further improving the prevention and control capabilities of debris flow disasters and the efficiency of post-disaster recovery.

[0034] Example 5 A regional geological disaster risk intelligent assessment method, please refer to Figure 2 , specifically: including the following steps: Step 1: Collect a variety of real-time data related to debris flow, including meteorological data, geological data and hydrological data, by integrating real-time sensors and satellite remote sensing data; Step 2: Clean, format and reduce noise of the original data, complete missing data, remove abnormal data, and store it in the data storage system; Step 3: Based on the collected data, physical models and machine learning models are constructed to obtain the following after calculation: debris flow risk index MRI, rainfall intensity coefficient RIC, geological vulnerability coefficient GVC and hydrodynamic pressure coefficient HPC, and predict the probability of debris flow. The model will combine key parameters such as topography, rainfall and soil moisture for analysis; Step 4: According to the model output results, the debris flow disaster risk index MRI is compared with the preset first safety threshold M and second safety threshold N to evaluate the risk level of debris flow and classify and manage different areas; Step 5: Based on real-time monitoring data and risk assessment results, a debris flow warning is issued. The warning information is delivered to relevant departments and the public through SMS, alarm bells and application notifications, supporting automated emergency response measures, including road closures and evacuations; Step 6: Use historical data and real-time feedback to self-adjust and optimize the debris flow prediction model.

[0035] In this embodiment, efficient monitoring, assessment and early warning of debris flow risks are achieved through a systematic process. By integrating real-time sensors and satellite remote sensing data, the system can comprehensively and accurately collect multi-dimensional data; the data preprocessing step ensures the accuracy and completeness of the data, thereby improving the reliability of model analysis. Based on physical models and machine learning models, the system can accurately calculate the debris flow disaster risk index MRI and classify and manage the risk levels of different regions. The real-time early warning mechanism notifies relevant departments and the public through multiple channels, quickly responds to potential disasters, and greatly improves the timeliness and accuracy of the early warning. The model self-optimization function ensures that the system maintains efficient prediction capabilities in the face of a changing environment, thereby effectively reducing the risks and losses caused by debris flow disasters.

[0036] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A regional geological disaster risk intelligent assessment system, characterized by: It includes data acquisition module, data preprocessing module, debris flow model construction and analysis module, risk assessment module, early warning emergency response module, and model optimization module; The data acquisition module is used to collect a variety of real-time data related to debris flow, including meteorological data, geological data and hydrological data, by integrating real-time sensors and satellite remote sensing data; The data preprocessing module is used to clean, format and reduce noise of the original data, complete missing data, remove abnormal data, and store it in the data storage system; The debris flow model construction and analysis module is used to build a physical model and a machine learning model based on the collected data, and obtain after calculation: debris flow disaster risk index MRI, rainfall intensity coefficient RIC, geological vulnerability coefficient GVC and hydrodynamic pressure coefficient HPC, and predict the probability of debris flow. The model will combine the key parameters of terrain, rainfall and soil moisture content for analysis; The risk assessment module is used to evaluate the risk level of debris flow occurrence and classify and manage different areas by comparing the debris flow disaster risk index MRI with the preset first safety threshold M and second safety threshold N according to the model output results; The early warning emergency response module is used to issue debris flow warnings based on real-time monitoring data and risk assessment results. Warning information is transmitted to relevant departments and the public through text messages, alarms and application notifications, supporting automated emergency response measures, including closing roads and evacuating people; The model optimization module is used to self-adjust and optimize the debris flow prediction model using historical data and real-time feedback.

2. The regional geological disaster risk intelligent assessment system according to claim 1, characterized in that: The data acquisition module includes a meteorological data acquisition unit, a geological data acquisition unit and a hydrological data acquisition unit; The meteorological data collection unit is used to collect meteorological data related to debris flow in real time, including rainfall, rainfall intensity and rainfall duration, by integrating automatic rain gauge, weather station and radar rainfall monitoring system, to obtain: total rainfall P t , Rainfall intensity I r and the cumulative rainfall threshold P th ; The geological data acquisition unit is used to collect data related to geological fragility in real time, including terrain slope, soil moisture content and rock fragmentation, by integrating GPS equipment, laser rangefinder and soil moisture sensor, to obtain: slope S, soil moisture content W s 、Rock crushing degree R f and vegetation coverage V c ; The hydrological data acquisition unit is used to collect hydrological data related to hydrodynamic pressure in real time through a current meter, an ultrasonic water level meter and a pressure sensor, including surface water flow, water depth and river runoff information, and obtain: surface runoff flow Q, water depth D, slope effect S e 、River Basin Area A f and the basin area normalization factor A max .

3. A regional geological disaster risk intelligent assessment system according to claim 2, characterized in that: The data preprocessing module includes a data preprocessing unit; The data preprocessing unit is used to clean the collected raw data, remove noise and invalid data, convert the cleaned data into a unified format, ensure that data from different sources can be compatible in subsequent analysis, complete missing data, process missing key data points through interpolation or machine learning algorithms, and store them in a data storage system.

4. The regional geological disaster risk intelligent assessment system according to claim 1, characterized in that: The debris flow model construction and analysis module includes a physical model construction unit, a machine learning model construction unit and a risk coefficient calculation unit; The physical model building unit is used to build a physical model for debris flow analysis based on topography, rainfall and soil moisture parameters, including a soil erosion model and a slope stability model; The machine learning model building unit is used to build a machine learning model for debris flow probability prediction based on historical data and real-time collected data, including a neural network and a random forest model; The risk coefficient calculation unit is used to calculate the debris flow disaster risk index MRI and its three key coefficients: rainfall intensity coefficient RIC, geological vulnerability coefficient GVC and hydrodynamic pressure coefficient HPC through a model, so as to predict the probability of debris flow occurrence; The debris flow disaster risk index MRI is calculated by the following formula: ； In the formula, RIC represents rainfall intensity coefficient, GVC represents geological vulnerability coefficient, and HPC represents hydrodynamic pressure coefficient. They represent the weight coefficients of rainfall intensity coefficient RIC, geological vulnerability coefficient GVC and hydrodynamic pressure coefficient HPC respectively; The rainfall intensity coefficient RIC is calculated by the following formula: ； Where P t represents the total rainfall, I r represents rainfall intensity, P th represents the cumulative rainfall threshold, T r Indicates the duration of rainfall, w t represents the time weight factor, T max Indicates the upper limit of reference rainfall time.

5. The regional geological disaster risk intelligent assessment system according to claim 3 is characterized by: The geological vulnerability coefficient GVC is calculated by the following formula: ； In the formula, S represents the slope, W s represents soil moisture content, R f Indicates the rock fragmentation, V c represents the vegetation coverage rate, Bg represents the probability of debris flow affected by bedrock fragility, Respectively represent the slope S, soil moisture W s and vegetation coverage V c The weight value of .

6. The regional geological disaster risk intelligent assessment system according to claim 3 is characterized by: The hydrodynamic pressure coefficient HPC is calculated by the following formula: ； In the formula, Q represents the surface runoff flow, D represents the water depth, S e represents the slope effect, A f A is the river basin area. max Represents the watershed area normalization factor.

7. The regional geological disaster risk intelligent assessment system according to claim 1 is characterized by: The risk assessment module includes a threshold comparison unit and a regional risk classification unit; The threshold comparison unit is used to compare the debris flow disaster risk index MRI with the preset first safety threshold M and second safety threshold N to evaluate the risk level of debris flow: When the debris flow disaster risk index MRI is less than or equal to the first safety threshold M, it is assessed as the first risk level. Within this range, the probability of debris flow is less than 30%, and it does not pose a threat to the personnel, infrastructure and ecological environment in the area. Under this condition, the system maintains routine monitoring and no emergency response measures are required; When the debris flow disaster risk index MRI is greater than the first safety threshold M and less than or equal to the second safety threshold N, it is assessed as the second risk level. At this time, the probability of debris flow is 40%-50%. It is recommended to strengthen monitoring and remind relevant departments to take preventive measures, including raising the warning level, initiating local emergency preparations, and paying close attention to weather and geological changes in high-risk areas; When the debris flow disaster risk index MRI is greater than the second safety threshold N, it is assessed as the third risk level. At this time, the risk of debris flow is higher than 50%. The emergency response mechanism needs to be activated immediately, the relevant departments need to be notified, and the public needs to be warned. Road closures, personnel evacuations, and emergency deployment of rescue resources are required to ensure personal safety and minimize property losses. The regional risk classification unit is used to classify and manage the monitoring areas based on different risk levels, and determine the priority of each area to facilitate the planning and execution of subsequent emergency response.

8. The regional geological disaster risk intelligent assessment system according to claim 1 is characterized by: The early warning emergency response module includes an early warning generation unit and an emergency measures execution unit; The warning generation unit is used to automatically generate debris flow warning information based on real-time monitoring data and risk assessment results, including detailed content of potential risks and the scope of the affected area, and transmit the debris flow warning information to relevant government departments and the public through SMS, alarm bells and application notification channels; The emergency measure execution unit is used to automatically trigger preset emergency response measures according to the early warning information, including closing roads in the affected area, evacuating personnel and initiating the deployment of rescue resources.

9. The regional geological disaster risk intelligent assessment system according to claim 1, characterized in that: The model optimization module includes a historical data feedback unit and a model adjustment unit; The historical data feedback unit is used to collect historical data and feedback information after the debris flow disaster occurs, and optimize and adaptively adjust the parameters of the existing physical model and machine learning model according to the feedback data; The model adjustment unit is used to continuously update and iterate the model based on new data during system operation to ensure the adaptability and robustness of the debris flow risk prediction model under different environments and conditions.

10. A method for intelligently assessing the risk of regional geological disasters, applied to a system for intelligently assessing the risk of regional geological disasters as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Collect a variety of real-time data related to debris flow, including meteorological data, geological data and hydrological data, by integrating real-time sensors and satellite remote sensing data; Step 2: Clean, format and reduce noise of the original data, complete missing data, remove abnormal data, and store it in the data storage system; Step 3: Based on the collected data, physical models and machine learning models are constructed to obtain the following after calculation: debris flow risk index MRI, rainfall intensity coefficient RIC, geological vulnerability coefficient GVC and hydrodynamic pressure coefficient HPC, and predict the probability of debris flow. The model will combine key parameters such as topography, rainfall and soil moisture for analysis; Step 4: According to the model output results, the debris flow disaster risk index MRI is compared with the preset first safety threshold M and second safety threshold N to evaluate the risk level of debris flow and classify and manage different areas; Step 5: Based on real-time monitoring data and risk assessment results, a debris flow warning is issued. The warning information is delivered to relevant departments and the public through SMS, alarm bells and application notifications, supporting automated emergency response measures, including road closures and evacuations; Step 6: Use historical data and real-time feedback to self-adjust and optimize the debris flow prediction model.

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