A vibration monitoring and early warning system for high slope landslides in the lower reservoir

Abstract

A vibration monitoring and early warning system for high slope landslides in the lower reservoir belongs to the technical field of vibration monitoring. To solve the problem that the existing monitoring and early warning system has poor comprehensiveness and low accuracy in risk prediction; the present invention includes a monitoring unit, a comprehensive coefficient calculation unit and a risk level judgment unit. Among them, the comprehensive coefficient calculation unit includes an index quantification module, a matrix construction and weight allocation module and a comprehensive coefficient calculation module. It comprehensively considers multiple factors such as formation characteristics, slope geological conditions, rock mass characteristics, rock mass structure, slope height and slope gradient, constructs a dynamic matrix and uses a scientific weight allocation method to calculate the comprehensive coefficient, automatically adjusts the risk level division standard according to the change trend of the fused risk indicators, adapts to the dynamic changes of slope stability, issues accurate early warnings in a timely manner, overcomes the problem that the existing monitoring and early warning system only focuses on a single indicator or a few indicators, and improves the comprehensiveness of the evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of vibration monitoring, and in particular to a vibration monitoring and early warning system for landslides on high slopes of lower reservoirs. Background Art

[0002] With the continued expansion of water conservancy project construction, slope blasting excavation has become widely used. While slope blasting excavation can efficiently excavate large amounts of earth and rock, the vibrations generated during blasting can adversely affect the surrounding environment and structures, such as destabilizing the surrounding geological structure and even damaging nearby buildings.

[0003] Currently commonly used monitoring and early warning systems often only focus on a single indicator or a few indicators, lack a systematic and comprehensive evaluation system, and find it difficult to fully consider the combined impact of multiple factors. The vibration problem caused by blasting and excavation of the slopes of water conservancy projects is a complex dynamic process. Traditional evaluation methods find it difficult to quantitatively describe the vibration characteristics and impacts, which greatly limits the accuracy and reliability of risk assessment. Summary of the Invention

[0004] The purpose of the present invention is to provide a vibration monitoring and early warning system for landslides on high slopes of lower reservoirs, which solves the problem of poor comprehensiveness and low accuracy of risk estimation of existing monitoring and early warning systems in the background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a vibration monitoring and early warning system for landslides on high slopes of lower reservoirs, comprising:

[0006] A monitoring unit, which is used to obtain relevant data of the slope in a comprehensive and real-time manner, and includes a monitoring point layout module, a sensor configuration module, and a data acquisition and transmission module;

[0007] A comprehensive coefficient calculation unit is used to comprehensively evaluate the stability of the slope, including:

[0008] Index quantification module: used to quantify stratum characteristics, slope geological conditions, rock mass characteristics, rock mass structure, slope height and slope gradient;

[0009] Matrix construction and weight assignment module: used to construct a dynamic matrix to reflect the values of monitoring points under different indicators, and consider the mutual influence between different monitoring points. The spatial correlation matrix is introduced. The spatial correlation matrix should accurately reflect the spatial relationship and interaction between monitoring points. The weight of each indicator is determined by combining principal component analysis and genetic algorithm, and the importance of each indicator to slope stability is comprehensively considered.

[0010] Comprehensive coefficient calculation module: using formula Calculate the comprehensive coefficient for each monitoring point, where represents the comprehensive coefficient of the i-th monitoring point, Part mainly reflects the traditional comprehensive calculation based on index weights and the values of monitoring points under each index, Part introduces spatial correlation, considering the differences between different monitoring points and the influence of their relationships on the comprehensive coefficient of the current monitoring point;

[0011] Risk level judgment unit: used to judge the risk level of the slope. The risk level judgment unit includes a multi-modal data fusion module, a dynamic risk level division module, and a warning decision-making module.

[0012] Furthermore, the monitoring point layout module layouts multiple monitoring points according to the slope topography, geological structure, and the area likely to be affected by blasting, focusing on covering potential landslide areas, weak rock mass structures, and key parts at the top and bottom of the slope, and adjusts the monitoring point density according to the slope risk level.

[0013] Furthermore, the sensor configuration module selects high-precision vibration sensors, displacement sensors, and stress sensors to monitor the vibration, displacement, and stress changes of the slope in real time.

[0014] Furthermore, the data acquisition and transmission module uses a data collector to collect sensor data in real time. The data collector configures corresponding gateways and terminal nodes through the LoRa wireless communication technology to transmit the collected data to the monitoring center.

[0015] Furthermore, the formation characteristics include lithology, hardness, permeability, and formation age. The slope geological conditions add the quantification of groundwater activities, including the influence of groundwater level, water flow velocity, and water quality on slope stability. The rock mass characteristics include strength, elastic modulus, and anisotropy degree, which are comprehensively determined through laboratory tests, on-site monitoring, and numerical simulation methods. The rock mass structure includes the density, aperture, connectivity, direction, and distribution law of joints and fractures. The slope height is quantified in intervals, and the risk growth rate is different in different intervals. The slope gradient considers the inclination angle and the slope change rate.

[0016] Furthermore, in the comprehensive coefficient calculation module, the formula where is the weight optimized by the genetic algorithm, is the value of the i-th monitoring point under the j-th index. In the comprehensive coefficient calculation module, in the formula k is the spatial correlation coefficient, used to adjust the influence degree of spatial correlation in the comprehensive coefficient calculation, represents the summation operation for all other monitoring points except the i-th monitoring point, It represents the summation of six indicators, namely formation characteristics, slope geological conditions, rock mass characteristics, rock mass structure, slope height, and slope gradient. is the spatial correlation coefficient between the m-th and the i-th monitoring points, reflecting the similarity degree of each indicator between different monitoring points. represents the difference between the i-th monitoring point and other monitoring points in the n-th indicator.

[0017] Furthermore, the multi-modal data fusion module introduces the surface temperature change of the slope, acoustic emission signal monitoring data, vibration monitoring quantity, and comprehensive coefficient for multi-modal data fusion. The calculation formula of the multi-modal fusion module is ; where R is the risk index, used to quantitatively evaluate the risk degree of slope landslide; n represents the number of monitoring points; represents the cumulative operation on n monitoring points. i takes values from 1 to n in sequence, covering all monitoring points; represents the weight coefficient of the i-th monitoring point, which is determined by the importance and location factors of the monitoring point; V i represents the vibration monitoring quantity of the i-th monitoring point, C i represents the comprehensive coefficient of the i-th monitoring point, T i represents the temperature change of the i-th monitoring point, S i represents the acoustic emission intensity of the i-th monitoring point. is the weight coefficient of the corresponding data, and the weight coefficient is determined by the analytic hierarchy process according to historical data and expert experience.

[0018] Furthermore, the dynamic risk level division module uses a fuzzy logic system to automatically adjust the division standard of the risk level according to the change trend of the fused risk index. The risk levels are set as four levels: low risk, medium risk, high risk, and extremely high risk, and are divided by setting threshold intervals. Calculate the mean value and the standard deviation ;

[0019] When it is low risk;

[0020] When it is medium risk; [[ID=4l]]

[0021] When it is high risk;

[0022] When it is extremely high risk.

[0023] Furthermore, the early warning decision-making module adopts a reinforcement learning algorithm to optimize the early warning decision according to the actual effect after early warning. When the risk level increases, the system automatically analyzes the reasons and provides corresponding recommended measures.

[0024] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0025] A vibration monitoring and early warning system for high slope landslides of the lower reservoir provided by the present invention comprehensively considers multiple factors such as formation characteristics, slope geological conditions, rock mass characteristics, rock mass structure, slope height, and slope gradient, constructs a dynamic matrix and uses a scientific weight distribution method to calculate the comprehensive coefficient, automatically adjusts the risk level division standard according to the change trend of the fused risk indicators, adapts to the dynamic changes of slope stability, issues accurate early warnings in a timely manner, overcomes the problem that the existing monitoring and early warning systems only focus on a single indicator or a few indicators, and improves the comprehensiveness of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a module schematic diagram of the present invention;

[0027] Figure 2 It is a safety risk factor structure diagram of the index quantification module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] In order to solve the technical problem of poor comprehensiveness of the existing monitoring and early warning systems and low accuracy of risk prediction, as Figure 1 - Figure 2 shown, the following preferred technical solutions are provided:

[0030] A vibration monitoring and early warning system for high slope landslides of the lower reservoir includes a monitoring unit, a comprehensive coefficient calculation unit, and a risk level judgment unit.

[0031] The monitoring unit is used to comprehensively and real-time obtain relevant data of the slope;

[0032] The monitoring unit includes:

[0033] A monitoring point layout module: The monitoring point layout module reasonably arranges multiple monitoring points according to the slope topography, geological structure, and areas likely to be affected by blasting, focuses on covering potential landslide areas, weak rock mass structures, and key parts at the top and bottom of the slope, and adjusts the density of monitoring points according to the slope risk level to ensure the comprehensiveness and representativeness of the monitoring data. At the same time, considering the geological differences and blasting impact degrees in different regions, the monitoring points are finely arranged in layers and regions to improve the accuracy and effectiveness of monitoring;

[0034] Sensor Configuration Module: The sensor configuration module selects high-precision vibration sensors, displacement sensors, and stress sensors to monitor the vibration, displacement, and stress changes of the slope in real time;

[0035] Data Acquisition and Transmission Module: The data acquisition and transmission module uses a data collector to collect sensor data in real time. The data collector configures corresponding gateways and terminal nodes through LoRa wireless communication technology and transmits the collected data to the monitoring center.

[0036] Comprehensive Coefficient Calculation Unit: The comprehensive coefficient calculation unit is used to comprehensively evaluate the stability of the slope; the comprehensive coefficient calculation unit includes:

[0037] Index Quantification Module: It is used to quantify the formation characteristics, slope geological conditions, rock mass characteristics, rock mass structure, slope height, and slope gradient. Among them, the formation characteristics include lithology, hardness, permeability, and formation age. The quantification of slope geological conditions adds the quantification of groundwater activities, including the influence of groundwater level, water flow velocity, and water quality on slope stability. The rock mass characteristics include strength, elastic modulus, and anisotropy degree, which are comprehensively determined through laboratory tests, on-site monitoring, and numerical simulation methods. The rock mass structure includes the density, aperture, connectivity, direction, and distribution law of joints and fractures. The slope height is quantified in intervals, and the risk growth rate is different in different intervals. The slope gradient considers the inclination angle and the slope change rate;

[0038] Matrix Construction and Weight Allocation Module: It is used to construct a dynamic matrix to reflect the values of monitoring points under different indicators, and consider the mutual influence between different monitoring points. A spatial correlation matrix is introduced, and the spatial correlation matrix should accurately reflect the spatial relationship and interaction between monitoring points. The weights of each indicator are determined by combining the principal component analysis method and the genetic algorithm, comprehensively considering the importance of each indicator to slope stability;

[0039] [[ID=1�]]Comprehensive Coefficient Calculation Module: Use the formula to calculate the comprehensive coefficient of each monitoring point, where represents the comprehensive coefficient of the i-th monitoring point, The part mainly reflects the comprehensive calculation based on the traditional index weights and the values of monitoring points under each index, where is the weight optimized by the genetic algorithm, is the value of the i-th monitoring point under the j-th index, The part introduces spatial correlation and considers the differences between different monitoring points and the influence of their relationships on the comprehensive coefficient of the current monitoring point. Among them, k is the spatial correlation coefficient, which is used to adjust the influence degree of spatial correlation in the comprehensive coefficient calculation, represents the summation operation for all other monitoring points except the i-th monitoring point, It represents the summation of six indicators, namely formation characteristics, slope geological conditions, rock mass characteristics, rock mass structure, slope height, and slope gradient. is the spatial correlation coefficient between the m-th and the i-th monitoring points, which reflects the similarity degree among different monitoring points in each indicator. If the numerical change trends of two monitoring points are similar in a certain indicator, then the spatial correlation coefficient between them will be relatively large. represents the difference between the i-th monitoring point and other monitoring points in the n-th indicator. By considering this difference and combining it with the spatial correlation coefficient, the relative relationship of the current monitoring point with other monitoring points in space can be reflected, thereby further affecting the calculation of the comprehensive coefficient.

[0040] Risk level judgment unit: used to judge the risk level of the slope;

[0041] The risk level judgment unit includes:

[0042] Multimodal data fusion module: The multimodal data fusion module introduces the surface temperature change of the slope, acoustic emission signal monitoring data, vibration monitoring quantity, and comprehensive coefficient for multimodal data fusion. The calculation formula of the multimodal fusion module is ; where R is the risk index, used to quantitatively evaluate the risk degree of slope landslide; n represents the number of monitoring points; represents the cumulative operation for n monitoring points. i takes values from 1 to n in sequence, covering all monitoring points; represents the weight coefficient of the i-th monitoring point, which is determined by the importance and location factors of the monitoring point; V i represents the vibration monitoring quantity of the i-th monitoring point, C i represents the comprehensive coefficient of the i-th monitoring point, T i represents the temperature change of the i-th monitoring point, S i represents the acoustic emission intensity of the i-th monitoring point, is the weight coefficient of the corresponding data, and the weight coefficient is determined by the analytic hierarchy process based on historical data and expert experience.

[0043] Dynamic risk level division module: The dynamic risk level division module uses a fuzzy logic system to automatically adjust the risk level division criteria according to the change trend of the fused risk index. For example, when the monitoring data shows that the slope stability is gradually decreasing, tighten the risk level division criteria; when the slope stability improves, appropriately relax the criteria. The dynamic adjustment process should be timely and accurate, and be able to adapt to the dynamic changes of the slope state. The risk levels are set as four levels: low risk, medium risk, high risk, and extremely high risk, and threshold intervals are set for division. Calculate the mean value and the standard deviation ;

[0044] When it is a low risk;

[0045] When it is a medium risk;

[0046] When it is a high risk;

[0047] When it is an extremely high risk;

[0048] Early warning decision-making module: The early warning decision-making module adopts a reinforcement learning algorithm to optimize the early warning decision according to the actual effect after early warning. When the risk level rises, the system automatically analyzes the reasons and provides corresponding recommended measures.

[0049] Specifically, first, according to the slope topography, geological structure, and the areas likely to be affected by blasting, a number of monitoring points are reasonably arranged, focusing on covering potential landslide areas, weak rock mass structures, and key parts at the top and bottom of the slope. The density of monitoring points is adjusted according to the slope risk level. At the same time, considering the geological differences and blasting influence degrees in different regions, a refined layout is carried out in a hierarchical and regional manner. High-precision vibration sensors, displacement sensors, and stress sensors are selected to monitor the vibration, displacement, and stress changes of the slope in real time. The data collector is used to collect the sensor data in real time. By adopting LoRa wireless communication technology, configuring the corresponding gateway and terminal nodes, the collected data is transmitted to the monitoring center;

[0050] Subsequently, the formation characteristics, slope geological conditions, rock mass characteristics, rock mass structure, slope height, and slope gradient are quantified to construct a dynamic matrix to reflect the values of monitoring points under different indexes. Considering the mutual influence between different monitoring points, a spatial correlation matrix is introduced. The principal component analysis method and the genetic algorithm are combined to determine the weights of each index. Considering the importance of each index to the slope stability, the comprehensive coefficient of each monitoring point is calculated using the formula;

[0051] Finally, the surface temperature change of the slope, the monitoring data of acoustic emission signals, the vibration monitoring quantity, and the comprehensive coefficient are introduced for multi-modal data fusion. The convolutional neural network in deep learning is used for data fusion to automatically extract features and integrate the relationships between different types of data. Using the fuzzy logic system, according to the change trend of the fused risk index, the division standard of the risk level is automatically adjusted. The risk level is set to four levels: low risk, medium risk, high risk, and extremely high risk. It is divided by calculating the mean and standard deviation of the fused risk index and setting the threshold interval. The reinforcement learning algorithm is used to optimize the early warning decision according to the actual effect after early warning. When the risk level rises, the system automatically analyzes the reasons and provides corresponding recommended measures.

[0052] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0053] As mentioned above, the above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes, should be covered within the protection scope of the present invention.

Claims

1. A vibration monitoring and early warning system for high slope landslides in the lower reservoir, characterized in that, Including: A monitoring unit, which is used to comprehensively and real-time obtain relevant data of the slope. The monitoring unit includes a monitoring point layout module, a sensor configuration module, and a data acquisition and transmission module; A comprehensive coefficient calculation unit, which is used to comprehensively evaluate the stability of the slope, including: An index quantification module: used to quantify the formation characteristics, slope geological conditions, rock mass characteristics, rock mass structure, slope height, and slope gradient; A matrix construction and weight assignment module: used to construct a dynamic matrix to reflect the values of monitoring points under different indicators, consider the mutual influence between different monitoring points, introduce a spatial correlation matrix, which should accurately reflect the spatial relationship and interaction between monitoring points, and use a combination of principal component analysis and genetic algorithm to determine the weights of each indicator, comprehensively considering the importance of each indicator to the slope stability; Comprehensive coefficient calculation module: Using the formula to calculate the comprehensive coefficient of each monitoring point, where represents the comprehensive coefficient of the i-th monitoring point, The part mainly reflects the comprehensive calculation based on the traditional index weights and the values of the monitoring points under each index, The part introduces spatial correlation and considers the differences between different monitoring points and the impact of their relationships on the comprehensive coefficient of the current monitoring point; A risk level judgment unit: used to judge the risk level of the slope. The risk level judgment unit includes a multi-modal data fusion module, a dynamic risk level division module, and a warning decision-making module; In the comprehensive coefficient calculation module, the formula where is the weight optimized by the genetic algorithm, is the value of the i-th monitoring point under the j-th index; In the comprehensive coefficient calculation module, the formula where k is the spatial correlation coefficient, which is used to adjust the influence degree of spatial correlation in the comprehensive coefficient calculation, represents the summation operation for all other monitoring points except the i-th monitoring point, represents the summation of six indicators including formation characteristics, slope geological conditions, rock mass characteristics, rock mass structure, slope height, and slope gradient, is the spatial correlation coefficient between the m-th and the i-th monitoring points, which reflects the similarity degree of different monitoring points in each indicator, represents the difference between the i-th monitoring point and other monitoring points in the n-th indicator.

2. The vibration monitoring and early warning system for high slope landslide of the lower reservoir according to claim 1, characterized in that: The monitoring point layout module layouts multiple monitoring points according to the slope topography, geological structure, and areas likely to be affected by blasting, focusing on covering potential landslide areas, weak rock mass structures, key parts of the slope top and bottom, and adjusts the monitoring point density according to the slope risk level.

3. The vibration monitoring and early warning system for high slope landslide of the lower reservoir according to claim 1, characterized in that: The sensor configuration module selects high-precision vibration sensors, displacement sensors, and stress sensors to monitor the vibration, displacement, and stress changes of the slope in real time.

4. The vibration monitoring and early warning system for the landslide of the high slope of the lower reservoir according to claim 1, characterized in that: The data acquisition and transmission module uses a data collector to collect sensor data in real time. The data collector configures corresponding gateways and terminal nodes through LoRa wireless communication technology and transmits the collected data to the monitoring center.

5. The vibration monitoring and early warning system for high slope landslide of the lower reservoir according to claim 1, wherein: The formation characteristics include lithology, hardness, permeability, and formation age. The slope geological conditions add the quantification of groundwater activities, including the influence of groundwater level, water flow velocity, and water quality on the slope stability. The rock mass characteristics include strength, elastic modulus, and anisotropy degree, which are comprehensively determined by laboratory tests, on-site monitoring, and numerical simulation methods. The rock mass structure includes the density, aperture, connectivity, direction, and distribution law of joints and fractures. The slope height is quantified in intervals, and the risk growth rate is different in different intervals. The slope gradient considers the inclination angle and slope change rate.

6. The vibration monitoring and early warning system for the high slope landslide of the lower reservoir according to claim 1, characterized in that: The multi-modal data fusion module introduces the surface temperature change of the slope, the monitoring data of acoustic emission signals, the vibration monitoring quantity and the comprehensive coefficient for multi-modal data fusion. The calculation formula of the multi-modal fusion module is ; where R is the risk index, used to quantitatively evaluate the risk degree of slope landslide; n represents the number of monitoring points; represents the cumulative operation on n monitoring points, where i takes values from 1 to n in sequence, covering all monitoring points; represents the weight coefficient of the i-th monitoring point, which is determined by the importance and location factors of the monitoring point; V i represents the vibration monitoring quantity of the i-th monitoring point, C i represents the comprehensive coefficient of the i-th monitoring point, T i represents the temperature change of the i-th monitoring point, S i represents the acoustic emission intensity of the i-th monitoring point, is the weight coefficient of the corresponding data, and the weight coefficient is determined by the analytic hierarchy process according to historical data and expert experience.

7. The vibration monitoring and early warning system for the high slope landslide of the lower reservoir according to claim 1, characterized in that: The dynamic risk level classification module uses a fuzzy logic system to automatically adjust the classification criteria of risk levels according to the changing trend of the fused risk indicators. The risk levels are set to four levels: low risk, medium risk, high risk, and extremely high risk, and threshold intervals are set for classification, and the mean value of the fused risk indicators is calculated and standard deviation ; When it is a low risk; When it is a medium risk; When it is a high risk; When it is an extremely high risk.

8. The vibration monitoring and early warning system for the high slope landslide of the lower reservoir according to claim 1, characterized in that: The warning decision-making module uses a reinforcement learning algorithm to optimize the warning decision according to the actual effect after warning. When the risk level rises, the system automatically analyzes the reasons and provides corresponding recommended measures.

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