Reservoir landslide evolution state sensing method fusing distributed inclinometry and GNSS (Global Navigation Satellite System)

Abstract

The invention discloses a reservoir landslide evolution state sensing method fusing distributed inclinometry and GNSS, and relates to the technical field of landslide monitoring, comprising the following steps: arranging a distributed inclinometer and installing a global navigation satellite system; the method comprises the following steps: acquiring monitoring data through an inclinometer and a global navigation satellite system, and processing noise; data fusion is carried out, and landslide dynamic states are output in real time. According to the method, organic combination of macroscopic deformation information and local deformation data is realized, the overall motion trend and local deformation characteristics of the landslide can be more accurately captured, the timeliness and practicability of data acquisition are improved, noise and abnormal values in the data are effectively removed, the signal-to-noise ratio of the data is improved, and the data acquisition efficiency is improved. The method provides a more accurate basis for subsequent data analysis, improves the precision and reliability of landslide dynamic estimation, enables the monitoring result to be more accurate, provides timely information support for landslide early warning and emergency response, and ensures the continuity and stability of a monitoring system.

Description

Technical Field

[0001] The present invention relates to the technical field of landslide monitoring, and particularly to a method for sensing the evolution state of a reservoir landslide by integrating distributed inclinometry and GNSS. Background Art

[0002] Landslides are the main types of geological disasters threatening the safety of reservoir areas. The identification of their evolution state is directly related to the stability evaluation and potential risk assessment of landslides. Currently, the identification of the evolution state mostly relies on surface monitoring means, such as remote sensing technology and the Global Navigation Satellite System (GNSS), to capture the overall deformation trend of landslides. However, the monitoring information of surface deformation is often insufficient to comprehensively reveal the complex mechanical behaviors inside the landslide body, especially the activities and stress distributions of deep sliding surfaces.

[0003] In this context, the research on deep deformation monitoring of reservoir landslides has emerged as an important means to understand the internal mechanical behaviors of landslides and predict their evolution. Deep monitoring technologies, such as deep inclinometry, acoustic emission, etc., can not only capture deep deformation information that cannot be detected on the surface, but also provide key data on the morphology, displacement, slip rate, etc. of the sliding surface inside the landslide body. The combination of these deep data and surface monitoring data can form a more complete landslide evolution model, which helps to reveal the triggering mechanism, instability process and development trend of landslides, and ultimately provides a more reliable scientific basis for the formulation of landslide prevention and control strategies.

[0004] The deep deformation monitoring methods for reservoir landslides include a variety of technical means; among them, distributed inclinometry, as an important deep displacement monitoring method, has unique advantages in the deep deformation monitoring of reservoir landslides; by deploying inclinometers inside the landslide body, the deep deformation of the landslide body can be monitored in real time, providing continuous monitoring of the deformation of the landslide body; compared with other monitoring methods, distributed inclinometers have the advantages of convenient installation, stable data, and rapid response, and are especially suitable for deep deformation monitoring; its real-time performance and reliability make it an indispensable important technical means in the deep deformation monitoring of reservoir landslides; of course, distributed inclinometers also have their disadvantages. The main components of current distributed inclinometers are inertial measurement units (IMUs) made of microelectro-mechanical systems (MEMS). IMUs have significant advantages in short-term tracking; due to its high-frequency data acquisition ability, IMUs can accurately capture the changes in displacement, velocity, and acceleration within a short period of time, providing high-precision real-time measurement data, which makes IMUs perform particularly well in application scenarios that require rapid response and high time resolution; but specifically for the deep deformation monitoring of reservoir landslides, it is difficult to perceive the relatively slow slope deformation under the heading accuracy that can be measured in the static working mode of the sensor, thus affecting the authenticity of the data; and if only GNSS monitoring is used, since the time from the slope accelerating deformation to slope failure is often very short, and the accuracy of real-time GNSS solution is relatively low, it is difficult to monitor the displacement changes in the landslide accelerating deformation stage; therefore, when applying IMUs to the monitoring of low-speed slope deformation, the GNSS measurement data should be reasonably combined according to the monitoring needs to obtain results that can reflect the true slope deformation situation.

[0005] In terms of the layout method of distributed inclinometers, the traditional layout method usually adopts equidistant layout. The disadvantages of this method are mainly reflected in the waste of monitoring points and the redundancy of monitoring equipment; since the equidistant layout does not consider the actual complexity of the terrain and environment, it often leads to the setting of too many monitoring points in areas with relatively small changes or areas that do not require intensive monitoring, which not only causes waste of monitoring resources but also increases the costs of equipment maintenance and management; in addition, this evenly distributed method cannot conduct key monitoring on key areas such as the slip zone, reducing the efficiency and response ability of the overall monitoring system.

[0006] In addition, the collected data is affected by factors such as the inherent noise of the sensor itself, temperature changes, electromagnetic interference, and mechanical vibration; these noises will affect the measurement accuracy of the IMU, resulting in errors and instabilities in the data, thus affecting the accurate tracking of displacement, velocity, and acceleration; in high-precision applications such as landslide monitoring, these noises may mask the true deformation signal and reduce the reliability and accuracy of the monitoring system; therefore, corresponding technical means are needed to reduce the influence of noise and improve the accuracy and stability of the measurement results. Summary of the Invention

[0007] In view of the above problems, the present invention is proposed.

[0008] Therefore, the technical problems solved by the present invention are: the existing landslide monitoring technologies have problems such as low data accuracy, difficulty in reflecting the landslide evolution state in real time, inability to achieve the fusion of distributed inclinometer and GNSS data, and how to monitor the landslide dynamics in real time and accurately.

[0009] To solve the above technical problems, the present invention provides the following technical solution: a method for perceiving the evolution state of reservoir landslides by fusing distributed inclinometer and GNSS, including arranging distributed inclinometers and installing a global navigation satellite system; collecting monitoring data through the inclinometer and the global navigation satellite system and performing noise processing; performing data fusion and outputting the landslide dynamics in real time.

[0010] As a preferred solution of the method for perceiving the evolution state of reservoir landslides by fusing distributed inclinometer and GNSS according to the present invention, wherein: the arranging of the distributed inclinometers and the installation of the global navigation satellite system include collecting the landslide topography, geomorphology, and the length of the landslide body and sliding zone through on-site investigation and drilling, and organizing and analyzing the data. Through comprehensive analysis of the data, the most suitable points are selected for the arrangement of the inclinometer drilling and the installation of the global navigation satellite system.

[0011] As a preferred solution of the method for perceiving the evolution state of reservoir landslides by fusing distributed inclinometer and GNSS according to the present invention, wherein: the arranging of the distributed inclinometers and the installation of the global navigation satellite system also include determining the specific length of the distributed inclinometers through systematic collection of landslide information and selection of drilling points, and adopting different arrangement schemes for the landslide body and sliding zone parts, including sparse arrangement in the landslide body area to cover the monitored landslide body, provide macroscopic deformation information of the whole landslide, and capture the overall movement trend of the landslide. In the sliding zone area, an encryption arrangement strategy is adopted to increase the density of the measurement and control units and obtain local deformation data.

[0012] As a preferred solution of the reservoir landslide evolution state perception method integrating distributed inclinometry and GNSS according to the present invention, wherein: the acquisition of monitoring data includes setting the sampling interval and measurement period of the inclinometer according to the characteristics of landslide activities and monitoring requirements. When the landslide activities are relatively frequent or the landslide activities are located in important monitoring areas, the sampling interval is shortened to capture deformation changes. After the setting is completed, the inclinometer and GNSS equipment are started for automatic recording, and the surface and deep deformation data are automatically collected within the set period.

[0013] As a preferred solution of the reservoir landslide evolution state perception method integrating distributed inclinometry and GNSS according to the present invention, wherein: the noise processing includes noise processing based on 2LE-CEEMDAN, including data preprocessing, initial decomposition, double-layer entropy ratio calculation, adaptive noise injection, signal decomposition and reconstruction.

[0014] Data preprocessing includes data acquisition and cleaning, and removing noise and outliers in the acceleration and angular velocity data obtained by the IMU sensor.

[0015] Initial decomposition includes using the EMD method to preliminarily decompose the preprocessed IMU signal to obtain the intrinsic mode functions.

[0016] The double-layer entropy ratio calculation includes calculating the first-layer entropy ratio and the second-layer entropy ratio.

[0017] When calculating the first-layer entropy ratio, calculate the local entropy value of each IMF to measure the useful information and noise components contained. The samples of the IMF are expressed as:

[0018] imf i =[imf i,1 ,imf i,2 ,…,imf i,n

[0019] wherein, imf i is the i-th intrinsic mode function, and n is the number of intrinsic mode functions, indicating the total number of all IMFs extracted during the landslide monitoring process.

[0020] Calculate the sample entropy value of the IMF, expressed as:

[0021]

[0022] wherein, H i is the i-th entropy value, indicating the uncertainty or chaos degree of information under a specific state, p k is the probability of the k-th histogram interval, and N is the total number of histogram intervals.

[0023] ​When calculating the entropy ratio of the second layer, calculate the entropy value of the overall signal, determine the overall noise level and decomposition accuracy, and calculate the signal sample, expressed as:

[0024] X = [x1, x2, …, x n

[0025] where X is the overall data vector, and x n is the nth observation value, representing the state result finally collected during monitoring. n is the total number of observation values, which is used to define the scale of the data set.

[0026] Calculate the entropy value of the signal sample, expressed as:

[0027]

[0028] where H total represents the overall entropy value; q k is the probability of the kth histogram, and N is the total number of histogram intervals.

[0029] Adaptive noise injection includes initial noise injection, noise adjustment strategy, and iterative decomposition.

[0030] During initial noise injection, inject noise into the signal according to the initial noise level, and perform the first round of CEEMDAN decomposition. The signal after injecting noise is expressed as:

[0031] x noisy = x + σn

[0032] where x noisy represents the noisy observation value, the noise level is σ, and the noise signal is n.

[0033] Execute the noise adjustment strategy. According to the calculation result of the double-layer entropy ratio, adaptively adjust the intensity of noise injection. The current noise level is σ. When the overall entropy value is ΔH fotal < tol, then reduce the noise level, σ = σ × 0.9.

[0034] When the overall entropy value is ΔH fotal ≥ tol, then increase the noise level, σ = σ × 1.1.

[0035] Perform iterative decomposition, repeat noise injection and CEEMDAN decomposition until the noise level and the signal decomposition result reach stability.

[0036] Perform signal decomposition and reconstruction. Select some IMFs through the intrinsic mode function for signal reconstruction, remove noise and retain useful information.

[0037] ​As a preferred solution of the reservoir landslide evolution state perception method integrating distributed inclinometry and GNSS according to the present invention, wherein: the data fusion includes data fusion based on UKF, including a prediction step and an update step.

[0038] The prediction step includes generating Sigma points, starting from the current state vector x k and covariance matrix P k Generate a set of sigma points, calculate the mean and covariance of the state distribution, expressed as:

[0039]

[0040] wherein, X k is the generated sigma point matrix, L is the dimension of the state vector, λ is the scaling parameter, λ = α 2 (L + k) - L.

[0041] Propagate the generated sigma points to the next moment through the state transition function f(·):

[0042] X k+1|k = f(X k , u k )

[0043] wherein, X k+1|k represents the state estimate at time step k, speculates the system state at time k + 1, given the information at the current time k, and ux represents the control input IMU data, including acceleration and angular velocity.

[0044] Calculate the predicted state mean and covariance P k+1|k , expressed as:

[0045]

[0046] wherein, and are weights, and Q k is the process noise covariance matrix.

[0047] The update step includes mapping the predicted sigma points to the observation space through the observation function h(·), expressed as:

[0048] z k+1|k = h(X k+1|k )

[0049] Calculate the predicted observation mean and observation covariance P zz , expressed as:

[0050]

[0051] Among them, R k is the observation noise covariance matrix.

[0052] Calculate the covariance P of the state and the observation xz , which is expressed as:

[0053]

[0054] Calculate the Kalman gain K k+1 , which is expressed as:

[0055]

[0056] Update the state and covariance according to the observation data, which is expressed as:

[0057]

[0058] Among them, is the transpose of the Kalman gain.

[0059] As a preferred solution of the reservoir landslide evolution state perception method that fuses distributed inclinometry and GNSS according to the present invention, wherein: the real-time output of landslide dynamic estimation includes, after the fusion of IMU and GNSS data, using the unscented Kalman filter to output the accurate position information, attitude, and speed estimation of the observation point, organically fusing the two through the unscented Kalman filter. When the GNSS signal is unstable or fails, short-term precise positioning is maintained through the IMU, and the GNSS corrects the cumulative error of the IMU to output continuous state estimation.

[0060] Another object of the present invention is to provide a reservoir landslide evolution state perception system that fuses distributed inclinometry and GNSS, which can collect monitoring data through an inclinometer and a global navigation satellite system and perform noise processing, solving the problem of data noise interference in the current landslide monitoring technology.

[0061] As a preferred solution of the reservoir landslide evolution state perception system that fuses distributed inclinometry and GNSS according to the present invention, wherein: it includes an equipment installation module, a noise processing module, and a data fusion module.

[0062] The equipment installation module is used to deploy distributed inclinometers and install a global navigation satellite system; the noise processing module is used to collect monitoring data through an inclinometer and a global navigation satellite system and perform noise processing; the data fusion module is used to perform data fusion and output landslide dynamics in real time.

[0063] A computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of a method for perceiving the evolution state of a reservoir landslide by integrating distributed inclinometry and GNSS.

[0064] A computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, it implements the steps of a method for perceiving the evolution state of a reservoir landslide by integrating distributed inclinometry and GNSS.

[0065] Advantages of the present invention: The method for perceiving the evolution state of a reservoir landslide by integrating distributed inclinometry and GNSS provided by the present invention arranges distributed inclinometers and installs a global navigation satellite system, realizing the organic combination of macroscopic deformation information and local deformation data, which helps to more accurately capture the overall movement trend and local deformation characteristics of the landslide. By collecting monitoring data through inclinometers and the global navigation satellite system and performing noise processing, the timeliness and practicality of data collection are improved, effectively removing noise and outliers in the data, enhancing the signal-to-noise ratio of the data, providing a more accurate basis for subsequent data analysis, performing data fusion, and real-time output of landslide dynamics, improving the accuracy and reliability of landslide dynamics estimation, making the monitoring results more accurate, providing timely information support for landslide early warning and emergency response, and ensuring the continuity and stability of the monitoring system. The present invention achieves better results in terms of monitoring accuracy, data collection efficiency, and system robustness. Description of the Drawings

[0066] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0067] Figure 1 It is the overall flowchart of a method for perceiving the evolution state of a reservoir landslide by integrating distributed inclinometry and GNSS provided by the first embodiment of the present invention.

[0068] Figure 2 It is the single-section tilt model diagram of the distributed inclinometer sub-array of a method for perceiving the evolution state of a reservoir landslide by integrating distributed inclinometry and GNSS provided by the second embodiment of the present invention.

[0069] Figure 3 It is the schematic diagram of the non-uniform layout of the distributed inclinometers of a method for perceiving the evolution state of a reservoir landslide by integrating distributed inclinometry and GNSS provided by the second embodiment of the present invention.

[0070] Figure 4Schematic diagram of the installation cross-section of the inclinometer for a method of sensing the evolution state of reservoir landslides by integrating distributed inclinometry and GNSS provided in the second embodiment of the present invention.

[0071] Figure 5 Schematic diagram of the layout of distributed inclinometry and GNSS for a method of sensing the evolution state of reservoir landslides by integrating distributed inclinometry and GNSS provided in the second embodiment of the present invention.

[0072] Figure 6 Flowchart of data fusion of distributed inclinometry and GNSS based on unscented Kalman filter for a method of sensing the evolution state of reservoir landslides by integrating distributed inclinometry and GNSS provided in the second embodiment of the present invention.

[0073] Figure 7 Overall flowchart of a system for sensing the evolution state of reservoir landslides by integrating distributed inclinometry and GNSS provided in the third embodiment of the present invention. Specific implementation manners

[0074] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific implementation manners of the present invention will be given in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all 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 scope of protection of the present invention.

[0075] Embodiment 1, referring to Figure 1 This is an embodiment of the present invention, which provides a method for sensing the evolution state of reservoir landslides by integrating distributed inclinometry and GNSS, including:

[0076] S1: Deploy distributed inclinometers and install a global navigation satellite system.

[0077] Furthermore, deploying distributed inclinometers and installing a global navigation satellite system includes collecting landslide topography, geomorphology, and the length of the sliding mass and sliding zone through on-site exploration and drilling, and organizing and analyzing the data. Through comprehensive analysis of the data, the most suitable points are selected for the deployment of inclinometer drilling and the installation of the global navigation satellite system.

[0078] It should be noted that deploying distributed inclinometers and installing a global navigation satellite system also includes determining the specific length of the distributed inclinometers through systematic collection of landslide information and selection of drilling points, and adopting different deployment schemes for the sliding mass and sliding zone parts. For example, sparse deployment is adopted in the sliding mass area to cover the monitored landslide body, provide macroscopic deformation information of the whole landslide, and capture the overall movement trend of the landslide. In the sliding zone area, a dense deployment strategy is adopted to increase the density of measurement and control units and obtain local deformation data.

[0079] It should also be noted that GNSS (Global Navigation Satellite System) is a system that provides positioning, navigation, and timing services through a satellite network. In landslide monitoring, GNSS is widely used for real-time monitoring and data recording of ground displacement.

[0080] By deploying distributed inclinometers and installing the Global Navigation Satellite System, comprehensive coverage and precise positioning of the landslide monitoring area are achieved. Through comprehensive data analysis of the landslide topography, geomorphology, and the length of the sliding mass and sliding zone, the rationality and scientificity of the installation positions of the inclinometers and GNSS systems are ensured, thereby improving the representativeness and accuracy of the monitoring data. According to the landslide information collection results, the specific lengths and deployment schemes of the distributed inclinometers are optimized, enabling the monitoring system to be adaptively deployed according to the characteristics of different regions. Sparse deployment is adopted in the sliding mass area to effectively capture the overall movement trend of the landslide, while dense deployment is adopted in the key sliding zone area to improve the ability to collect local deformation data. This not only provides macroscopic deformation information of the overall landslide but also enables the acquisition of fine deformation data of key areas through dense deployment, providing a solid foundation for subsequent data analysis and landslide early warning. This deployment method also improves the robustness of the monitoring system, enabling it to operate stably under different environments and conditions and ensuring the reliability of long-term monitoring.

[0081] S2: Collect monitoring data through inclinometers and the Global Navigation Satellite System, and perform noise processing.

[0082] Furthermore, collecting monitoring data includes setting the sampling interval and measurement period of the inclinometer according to the characteristics of landslide activities and monitoring requirements. When landslide activities are relatively frequent or the landslide is located in an important monitoring area, the sampling interval is shortened to capture deformation changes. After setting, start the inclinometer and GNSS devices for automatic recording, and automatically collect surface and deep deformation data within the set period.

[0083] It should be noted that performing noise processing includes noise processing based on 2LE-CEEMDAN, including data preprocessing, initial decomposition, double-layer entropy ratio calculation, adaptive noise injection, signal decomposition, and reconstruction.

[0084] Data preprocessing includes data acquisition and cleaning, removing noise and outliers from the acceleration and angular velocity data obtained by the IMU sensor.

[0085] Initial decomposition includes using the EMD method to perform preliminary decomposition on the preprocessed IMU signal to obtain intrinsic mode functions.

[0086] Double-layer entropy ratio calculation includes calculating the first-layer entropy ratio and the second-layer entropy ratio.

[0087] When calculating the first-layer entropy ratio, calculate the local entropy value of each IMF to measure the useful information and noise components contained. The samples of the IMF are expressed as:

[0088] imf i =[imf i,1 , imf i,2 , …, imf i,n

[0089] where imf i is the i-th intrinsic mode function, and n is the number of intrinsic mode functions, indicating the total number of all IMFs extracted during landslide monitoring.

[0090] Calculate the sample entropy value of the IMF, which is expressed as:

[0091]

[0092] where H i is the i-th entropy value, representing the uncertainty or degree of chaos of information in a specific state, p k is the probability of the k-th histogram interval, and N is the total number of histogram intervals.

[0093] When calculating the second-layer entropy ratio, calculate the entropy value of the overall signal to determine the overall noise level and decomposition accuracy. The signal samples are expressed as:

[0094] X = [x1, x2, …, x n

[0095] where X is the overall data vector, and x n is the n-th observed value, representing the state result finally collected during monitoring. n is the total number of observed values, used to define the scale of the data set.

[0096] Calculate the sample entropy value of the signal, which is expressed as:

[0097]

[0098] where H total represents the overall entropy value; q k is the probability of the k-th histogram, and N is the total number of histogram intervals.

[0099] Adaptive noise injection includes initial noise injection, noise adjustment strategy, and iterative decomposition.

[0100] During initial noise injection, inject noise into the signal according to the initial noise level and perform the first round of CEEMDAN decomposition. The signal after injecting noise is expressed as:

[0101] x​​noisy = x + σn

[0102] Wherein, x noisy represents the noisy observation value, the noise level is σ, and the noise signal is n.

[0103] Execute the noise adjustment strategy. According to the calculation result of the double-layer entropy ratio, adaptively adjust the intensity of noise injection. The current noise level is σ. When the overall entropy value is ΔH fotal < tol, then reduce the noise level, σ = σ × 0.9.

[0104] When the overall entropy value is ΔH fotal ≥ tol, then increase the noise level, σ = σ × 1.1.

[0105] Perform iterative decomposition, repeat noise injection and CEEMDAN decomposition until the noise level and the signal decomposition result 20 reach stability.

[0106] Perform signal decomposition and reconstruction. Select some IMFs through the intrinsic mode function for signal reconstruction, remove noise and retain useful information.

[0107] It should also be noted that the monitoring data is collected through an inclinometer and a global navigation satellite system, and the steps of noise processing are carried out. Through the 2LE-CEEMDAN double-layer entropy ratio complete ensemble empirical mode decomposition algorithm, it combines the double-layer entropy ratio mechanism and the EMD empirical mode decomposition algorithm, and is used to process non-stationary signals. The core idea is to measure the complexity and uncertainty by calculating the entropy value of the signal, so as to adaptively adjust the injection of noise, optimize the signal decomposition effect, reduce the noise interference in the measurement data, and improve the accuracy of signal processing; The 2LE-CEEMDAN method is used to process the collected data for noise, including data preprocessing, initial decomposition, double-layer entropy ratio calculation, adaptive noise injection, signal decomposition and reconstruction. It effectively removes the noise and outliers in the data. The data preprocessing link ensures the accuracy of subsequent analysis by removing obvious noise and outliers. The double-layer entropy ratio calculation and adaptive noise injection strategy improve the accuracy of signal decomposition by precisely controlling the noise level, making the reconstructed signal closer to the real landslide deformation situation. It not only improves the signal-to-noise ratio of the data, reduces the risk of false alarms and missed alarms, but also improves the speed and efficiency of data processing, providing reliable data support for real-time monitoring and early warning.

[0108] S3: Perform data fusion and output the landslide dynamics in real time.

[0109] Furthermore, performing data fusion includes performing data fusion based on UKF, including a prediction step and an update step.

[0110] The prediction step includes generating Sigma points, from the current state vector xk and covariance matrix P k Generate a set of sigma points, and calculate the mean and covariance of the state distribution, expressed as:

[0111]

[0112] where, X k is the generated sigma point matrix, L is the dimension of the state vector, λ is the scaling parameter, λ = α 2 (L + κ) - L.

[0113] Propagate the generated sigma points to the next moment through the state transition function f(·):

[0114] X k+1|k = f(X k , u k )

[0115] where, X k+1|k represents the state estimate at time step k, predicts the system state at time k+1, given the information at the current time k, u k represents the control input IMU data, including acceleration and angular velocity.

[0116] Calculate the predicted state mean and covariance P k+1|k , expressed as:

[0117]

[0118] where, and are weights, Q k is the process noise covariance matrix.

[0119] The update step includes mapping the predicted sigma points to the observation space through the observation function h(·), expressed as:

[0120] z k+1|k = h(X k+1|k )

[0121] Calculate the predicted observation mean and observation covariance P zz , expressed as:

[0122]

[0123] where, R k is the observation noise covariance matrix.

[0124] Calculate the covariance P of the state and observation xz , expressed as:

[0125]

[0126] Calculate the Kalman gain K k+1 , which is expressed as:

[0127]

[0128] Update the state and covariance according to the observation data, which is expressed as:

[0129]

[0130] where is the transpose of the Kalman gain.

[0131] It should be noted that the real-time output of landslide dynamic estimation includes, after the fusion of IMU and GNSS data, using the unscented Kalman filter to output the accurate position information, attitude, and velocity estimation of the observation points, and organically fusing the two through the unscented Kalman filter. When the GNSS signal is unstable or fails, precise positioning is maintained for a short time through the IMU, and the GNSS corrects the cumulative error of the IMU to output continuous state estimation.

[0132] It should also be noted that the UKF (Unscented Kalman Filter) is an advanced filtering algorithm for nonlinear system state estimation. By generating a set of sample points (sigma points), it approximately models the nonlinear relationship, thereby improving the accuracy of state estimation. The UKF is used in landslide monitoring to fuse GNSS and IMU data to further improve the estimation accuracy of position and attitude. By effectively processing the characteristics of different sensor data, the UKF can maintain a high estimation accuracy in the case of signal occlusion or instability, ensuring the continuity and reliability of landslide dynamic monitoring.

[0133] The IMU (Inertial Measurement Unit) is a combination of sensors used to measure the acceleration and angular velocity of an object, usually including an accelerometer and a gyroscope. The IMU can provide instant data on the motion state of the object.

[0134] The steps of data fusion and real-time output of landslide dynamics achieve precise perception and real-time tracking of the landslide evolution state. The IMU and GNSS data are fused based on the UKF algorithm, including a prediction step and an update step. In the prediction step, the Sigma points are generated and the mean and covariance of the state distribution are calculated, providing a basis for state transition. In the update step, the Kalman gain is calculated and the state and covariance are updated using the observation data, enabling the system to maintain short-term precise positioning using the IMU when the GNSS signal is unstable or fails. At the same time, the GNSS data corrects the cumulative error of the IMU, thereby outputting a continuous and more accurate state estimate. Through advanced data fusion technology, not only the environmental adaptability and data continuity of the monitoring system are improved, but also accurate and reliable landslide dynamic information can be obtained in a complex and changeable environment. The real-time output of landslide dynamic estimation provides timely and effective decision-making support for landslide warning and emergency response, enhancing the practicality and emergency handling ability of landslide monitoring.

[0135] Example 2, referring to Figures 2 - 6 , which is an embodiment of the present invention, provides a method for perceiving the evolution state of reservoir landslides by integrating distributed inclinometry and GNSS. In order to verify the beneficial effects of the present invention, scientific demonstrations are carried out through economic benefit calculations and simulation experiments.

[0136] First, obtain landslide data. Based on a detailed engineering geological survey, record the location, scope, crack distribution of the landslide body, and the contact surface between the landslide body and the sliding bed. Collect soil and rock samples and conduct a hydrographic survey to understand the groundwater flow situation. On the basis of on-site reconnaissance, use technical means such as borehole sampling and ground-penetrating radar to obtain internal data of the landslide body, and determine detailed information such as the thickness of the landslide, the depth of the slip surface, and the main sliding direction. Draw a landslide profile according to these data to show the internal structure of the landslide body and the location of the slip surface, providing an intuitive basis for site selection. According to the results of on-site reconnaissance and profile analysis, then select appropriate monitoring points to arrange GNSS receivers and inclinometers. GNSS receivers are usually installed at the front edge, middle, and rear edge of the landslide body to monitor the overall displacement, while inclinometers are arranged along the depth of the landslide body, especially near the slip surface, to ensure effective monitoring of the internal deformation of the landslide body. Install a reliable power supply system and data transmission device to ensure the long-term stable operation of the equipment. During the installation process, equipment debugging and calibration are also required to ensure the accuracy of data collection, and preliminary data collection tests are carried out to verify the normal working state of the equipment. By monitoring the change of the accelerometer in the gravity field, the rotation angles of a single section on the X, Y, and Z axes are calculated. Using the rotation angle and the known section length L, the deformations ΔX, ΔY, and ΔZ of each section of the array displacement meter can be accurately calculated, that is As Figure 2 shown; The specific layout pattern of the distributed inclinometer is asFigure 3 As shown, different from the equidistant layout of conventional inclinometers, the spacing between the measurement and control units in the sliding mass is larger than that in the sliding zone, and the layout is denser at the sliding zone, so that more sensor data at the sliding zone can be obtained to achieve the purpose of centralized monitoring of the sliding zone; the specific layout cross-sectional view is as Figure 4 shown. After drilling is completed, first fix the top rigid casing preliminarily, then put the pre-prepared PVC protection pipe into the monitoring hole. After the PVC pipe is installed, backfill fine sand between the PVC pipe and the hole wall. The displacement meter is placed in the PVC pipe, and dry fine sand is selected to backfill between the displacement meter and the PVC pipe; specifically, on the entire landslide, the schematic diagram of the distributed inclinometer is as Figure 5 shown; the data acquisition of the inclinometer is completed by the acquisition system. The data acquisition of the inclinometer is completed by a dedicated digital acquisition device. After setting the sampling interval, the monitoring data is transmitted to the monitoring center through the wireless 20 transmission module and is processed in real time using the remote operation system; 2LE-CEEMDAN noise processing is specifically applied to the IMU data detected by the inclinometer and is mainly denoised according to the following steps: data acquisition: obtain the IMU acceleration and angular velocity data from the landslide inclinometer to form the original signal; noise introduction: add adaptive noise to each original signal to generate a set of noisy signals; IMF extraction: perform CEEMDAN on each noisy signal, extract the IMFs, and average them to obtain the final IMFs; entropy ratio calculation: calculate the double-layer entropy ratio of each IMF to evaluate its effectiveness; IMF selection: adaptively select effective IMFs according to the value of the double-layer entropy ratio; signal reconstruction: add the selected IMFs to obtain the denoised signal; UKF data fusion: first define the state, calculate the state vector, expressed as:

[0137]

[0138] where, (x, y, z) represents the position, (v x , v y , v z ) is the velocity, (φ, ψ) is the attitude angle, which are the roll angle, pitch angle and yaw angle respectively.

[0139] The IMU data is used for state prediction. The state transition function f(·) describes the state transition of the system from time k to k + 1. The IMU provides the acceleration (a x , a y , a z ) and angular velocity (w x , w v , w z ). The transfer function is expressed as:

[0140] x k+1 = f(x k , u k) + w k

[0141] The state transition equation, expressed as:

[0142]

[0143] where Δt is the time step, and w k is the process noise, following a zero - mean Gaussian distribution.

[0144] Define the observation model, including calculating the observation function h(x) to describe the relationship between the observed value and the state, expressed as:

[0145] z k = h(x k ) + v k

[0146] Calculate the observation equation, expressed as:

[0147]

[0148] where z k is the observation vector (the position provided by GNSS), and V k is the observation noise, following a zero - mean Gaussian distribution.

[0149] The steps of the UKF algorithm include generating sigma points, generating sigma points from the state vector x k and the covariance matrix P k ; propagating the sigma points through the state transition function f(·) to obtain the predicted sigma points X k+1|k ; calculating the predicted state mean and covariance P k+1|k using the predicted sigma points; the update steps include propagating the predicted sigma points through the observation function h(·) to obtain the predicted observation sigma points Z k+1|k ; calculating the predicted observation mean and the observation covariance P z using the predicted observation sigma points; calculating the covariance of the state and the observation P xz using the predicted sigma points and the predicted observation sigma points; calculating the Kalman gain K k+1 using the covariance of the state and the observation and the observation covariance; updating the state vector and the covariance matrix P k+1; Output position estimation, velocity estimation, and attitude estimation. Through the UKF, IMU and GNSS data can be effectively fused. IMU data provides high-frequency motion information, while GNSS data provides an accurate position reference. The UKF uses the unscented transform to handle non-linear relationships and maintains the accuracy of the state and covariance, making the fused state estimation more robust. Moreover, the position information, attitude, and velocity estimation of the output observation points are more accurate. The specific process of fusing IMU and GNSS data is as follows Figure 6 shown. First, input data, mainly including IMU measurements and GNSS data. IMU measurements are mainly used for state prediction in the subsequent Kalman filter fusion process, and the sigma points are propagated through the state transition function. GNSS data is mainly used for state update. The predicted points are mapped to the observation space through the observation function, and the state vector and covariance matrix are continuously updated by calculating the mean, covariance, and Kalman gain of the observation state. Finally, repeat the above prediction and update steps, continuously perform time update according to IMU data, and perform measurement update according to GNSS data to achieve the fusion of the two.

[0150] Example 3, referring to Figure 7 , which is an embodiment of the present invention, provides a reservoir landslide evolution state perception system that fuses distributed inclinometry and GNSS, including an equipment installation module, a noise processing module, and a data fusion module.

[0151] Among them, the equipment installation module is used to deploy distributed inclinometers and install the Global Navigation Satellite System; the noise processing module is used to collect monitoring data through the inclinometers and the Global Navigation Satellite System and perform noise processing; the data fusion module is used to perform data fusion and output the landslide dynamics in real time.

[0152] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, etc., which can store program codes.

[0153] The logic and / or steps represented in the flowchart or otherwise described herein can, for example, be considered as a definable sequence of executable instructions for implementing logical functions, which can be embodied specifically in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with the instruction execution system, apparatus, or device.

[0154] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection portion (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.

[0155] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

[0156] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for perceiving the evolution state of reservoir landslides by integrating distributed inclinometry and GNSS, characterized in that, include: Deployment of distributed inclinometers and installation of a global navigation satellite system; Monitoring data is collected through inclinometers and global navigation satellite systems, and noise processing is performed; Perform data fusion and output landslide dynamics in real time.

2. The method for perceiving the evolution state of reservoir landslide by integrating distributed inclinometer and GNSS according to claim 1, characterized in that: The deployment of distributed inclinometers and installation of a global navigation satellite system include collecting data on landslide topography, landforms, and sliding body and belt length through field surveys and drilling, and organizing and analyzing the data. Through comprehensive analysis of the data, the most suitable points are selected for the deployment of inclinometer drilling holes and the installation of a global navigation satellite system.

3. The method for perceiving the evolution state of reservoir landslide by integrating distributed inclinometer and GNSS according to claim 2, characterized in that: The deployment of distributed inclinometers and installation of a global navigation satellite system also includes determining the specific length of the distributed inclinometers through systematic collection of landslide information and selection of drilling points, and adopting different deployment plans for the sliding body and sliding belt areas, including sparse deployment in the sliding body area to cover and monitor the landslide body, provide overall macroscopic deformation information of the landslide, and capture the overall movement trend of the landslide; in the sliding belt area, adopt a dense deployment strategy to increase the density of measurement and control units and obtain local deformation data.

4. The method for perceiving the evolution state of reservoir landslide by integrating distributed inclinometer and GNSS according to claim 3, wherein: The monitoring data collection includes setting the sampling interval and measurement period of the inclinometer according to the characteristics of the landslide activity and the monitoring needs. When the landslide activity is more frequent or the landslide activity is located in an important monitoring area, the sampling interval is shortened to capture deformation changes. After the setting is completed, the inclinometer and GNSS equipment are started to automatically record and automatically collect surface and deep deformation data within the set period.

5. The method for perceiving the evolution state of reservoir landslide by integrating distributed inclinometer and GNSS according to claim 4, characterized in that: The noise processing includes performing noise processing based on 2LE-CEEMDAN, including data preprocessing, initial decomposition, double-layer entropy ratio calculation, adaptive noise injection, signal decomposition and reconstruction; Data preprocessing includes data acquisition and cleaning, removing noise and outliers from the acceleration and angular velocity data obtained by the IMU sensor; The initial decomposition includes using the EMD method to perform preliminary decomposition of the preprocessed IMU signal to obtain the intrinsic mode function; The calculation of the double-layer entropy ratio includes calculating the first-layer entropy ratio and the second-layer entropy ratio; When calculating the first-layer entropy ratio, the local entropy value of each IMF is calculated to measure the useful information and noise components contained. The sample of IMF is expressed as: imf i = [imf i,1 , imf i,2 , …, imf i,n ​ where, imf i is the i-th intrinsic mode function, and n is the number of intrinsic mode functions, indicating the total number of all IMFs extracted during landslide monitoring; Calculate the sample entropy of IMF, expressed as: Among them, H i is the i-th entropy value, representing the uncertainty or degree of chaos of information in a specific state, and p k is the probability of the k-th histogram interval, and N is the total number of histogram intervals; When calculating the second-layer entropy ratio, the entropy value of the overall signal is calculated, the overall noise level and decomposition accuracy are determined, and the signal samples are calculated, which is expressed as: X = [x1, x2, …, x n ​ where X is the overall data vector, and x n is the nth observation value, representing the state result finally collected during the monitoring. n is the total number of observation values, which is used to define the scale of the data set; Calculate the entropy value of the signal sample, expressed as: Among them, H total represents the overall entropy value; q k is the probability of the k-th histogram, and N is the total number of histogram intervals; Adaptive noise injection includes initial noise injection, noise adjustment strategy, and iterative decomposition; During the initial noise injection, noise is injected into the signal according to the initial noise level, and the first round of CEEMDAN decomposition is performed. The signal after noise injection is expressed as: x noisy = x + σn where x noisy represents the noisy observation with noise level σ and noise signal n; Execute the noise adjustment strategy. According to the calculation result of the double-layer entropy ratio, adaptively adjust the intensity of noise injection. The current noise level is σ. When the overall entropy value is ΔH fotal <When it is less than the tolerance tol, reduce the noise level, σ = σ × 0.9; When the overall entropy value is ΔH fotal ≥tol, increase the noise level, σ = σ × 1.1; Perform iterative decomposition, repeating noise injection and CEEMDAN decomposition until the noise level and signal decomposition results 20 reach stability; Perform signal decomposition and reconstruction, select part of the IMF through the intrinsic mode function to reconstruct the signal, remove noise and retain useful information.

6. The method for perceiving the evolution state of reservoir landslide by integrating distributed inclinometer and GNSS according to claim 5, wherein: The data fusion includes data fusion based on UKF, including a prediction step and an update step; The prediction step includes generating sigma points from the current state vector x k and covariance matrix P k Generate a set of sigma points and calculate the mean and covariance of the state distribution, expressed as: where X k is the generated sigma point matrix, L is the dimension of the state vector, λ is the scaling parameter, and λ = α 2 (L + κ) - L; The generated sigma point is propagated to the next moment through the state transfer function f(·): X k+1|k = f(X k , u k ) where, X k+1|k represents the state estimate at time step k, predicting the system state at time k+1, given the information at the current time k, u k represents the control input IMU data, including acceleration and angular velocity; Calculate the predicted state mean and covariance P k+1|k , denoted as: Among them, and are weights, and Q k is the process noise covariance matrix; The update step includes mapping the predicted sigma points to the observation space through the observation function h(·), expressed as: z k+1|k = h(X k+1|k ) Calculate the predicted observation mean and the observation covariance P zz , expressed as: wherein, R k is the observation noise covariance matrix; Calculate the covariance P of the state and the observation xz , expressed as: Calculate the Kalman gain K k+1 , which is expressed as: Updating the state and covariance according to the observation data, expressed as: Among them, is the transpose of the Kalman gain.

7. The method for perceiving the evolution state of reservoir landslide by integrating distributed inclinometer and GNSS according to claim 6, characterized in that: The real-time output of landslide dynamic estimation includes, after the fusion of IMU and GNSS data, using the unscented Kalman filter to output the accurate position information, attitude, and velocity estimation of the observation points, organically fusing the two through the unscented Kalman filter. When the GNSS signal is unstable or fails, short-term precise positioning is maintained through the IMU, and the GNSS corrects the cumulative error of the IMU to output continuous state estimation.

8. A system adopting the reservoir landslide evolution state perception method integrating distributed inclinometer and GNSS as described in any one of claims 1 to 7, characterized in that: Including a device installation module, a noise processing module, and a data fusion module; The device installation module is used to deploy distributed inclinometers and install a global navigation satellite system; The noise processing module is used to collect monitoring data through the inclinometer and the global navigation satellite system and perform noise processing; The data fusion module is used to perform data fusion and output the landslide dynamics in real time.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for perceiving the evolution state of reservoir landslides by fusing distributed inclinometry and GNSS according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for perceiving the evolution state of reservoir landslides by fusing distributed inclinometry and GNSS according to any one of claims 1 to 7.

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