Landslide real-time monitoring method and system based on multi-sensor fusion and intelligent regulation and control

Abstract

The invention relates to the technical field of landslide treatment, and discloses a landslide real-time monitoring method and system based on multi-sensor fusion and intelligent regulation, and the method comprises the steps: placing at least one sensor in a metal reinforcement device and at a key position of a landslide body, and carrying out the real-time monitoring of landslide body data; water component imaging data of the slope body are obtained in real time under the rainfall condition; the rainfall is obtained according to landslide mass water component imaging data; valve opening degrees of the drainage channel and the diversion channel are automatically adjusted based on the rainwater amount; calculating the rainwater amount to obtain stability parameter distribution; obtaining safety coefficients according to the stability parameter distribution, and obtaining the instability probability of the slope body through the safety coefficients of the multiple grid units; the structural design of the metal reinforcing pile and the reinforcing frame is optimized through finite element analysis, and adjustment is conducted. According to the invention, through real-time monitoring, data analysis and automatic control of multi-source data, the detection precision and early warning capability of the landslide mass are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of landslide control, and in particular to a real-time landslide monitoring method and system based on multi-sensor fusion and intelligent regulation. Background Art

[0002] Landslide refers to a natural phenomenon in which soil or rock on a slope slides down the slope as a whole or in a dispersed manner along a certain weak surface or weak zone under the action of gravity, due to factors such as river scouring, groundwater activity, rainwater soaking, earthquakes and artificial slope cutting. The moving rock (soil) is called a displaced body or sliding body, and the underlying rock (soil) that has not moved is called a sliding bed. At present, in emergency prevention and control of landslides, multiple piles are usually arranged on the landslide surface to stabilize the landslide, but the multiple piles are usually independent, and there is a lack of measures to connect and reinforce the multiple piles, which affects the effect of landslide prevention and control, making the effect of landslide prevention and control need to be improved.

[0003] Prior art 1, Chinese patent, patent number: 202411585519.0, involves the fields of geological engineering and civil engineering, and in particular, involves a test method for the influence of loess water content changes on slope stability. The content includes: collecting and preparing loess samples, measuring the physical parameters of loess samples, and constructing a multidimensional physical property matrix of loess samples; real-time monitoring of pore water pressure, and calculating the shear stress of loess samples based on the dynamic change data of pore water pressure; based on the shear stress of loess samples and the multidimensional physical property matrix of loess samples, dynamically updating the dynamic stability parameters of the slope, and comprehensively evaluating the instability risk of the slope. Although it solves the problem that traditional slope monitoring technology lacks systematic research on the mechanical properties of loess, resulting in poor accuracy and reliability of prediction results; lacks real-time update of slope stability, resulting in the inability to form an accurate dynamic monitoring and recursive prediction mechanism; cannot issue early warning signals in time, resulting in insufficient emergency response to landslides; however, it does not consider the impact on landslide prevention and control, resulting in the failure of metal reinforcement equipment.

[0004] Prior art 2, Chinese patent, patent number: 202411108782.0 discloses an interface-type landslide highway emergency maintenance and treatment structure and method, the method includes the following steps: Step 1: Calculate the shear reinforcement of the pile foundation according to the thrust; Step 2: Close the highway traffic at the landslide section, mill and excavate the road surface layer and base layer of the highway at the landslide section, and locate the positions of the consolidated pile foundation and the non-consolidated pile foundation; Step 3: Drill holes and pile foundation construction, and tie beam construction between non-consolidated pile foundations; Step 4: Tie the steel skeleton of the raft slab and pour the raft slab concrete to realize the consolidation of the consolidated pile foundation and the raft slab, and the non-consolidated pile foundation and tie beam are supported on the bottom of the raft slab to complete the construction of the first section of the treatment unit; Repeat the above steps to carry out the construction of other treatment units; Step 5: Connect the highway in the non-landslide section with the raft slab at the end of the landslide section through a slab, and carry out landslide control in the landslide section of the slope. Although the highway at the landslide section could be separated from the slope, allowing the operating highway to resume traffic as soon as possible and greatly shortening the construction period, drainage channels were not considered, resulting in unreasonable rainwater drainage management.

[0005] Prior art three, Chinese patent, patent number: 202410304622.7 relates to the field of landslide control technology, and in particular to a landslide control slope drainage and reinforcement system. It includes a slope body, on which an inner slope drainage structure and an outer slope drainage structure are arranged, the inner drainage structure includes a seamless steel pipe and a permeable foam concrete column, the exposed end of the inner drainage structure is connected to the slope intercepting ditch, the surface of the seamless steel pipe is provided with permeable holes and a filter net, the slope intercepting ditch is connected to the slope drainage ditch, and the slope drainage ditch is connected to the slope foot drainage ditch. Although it has the following effects: the setting of the inner drainage structure allows the water inside the slope to enter the permeable foam concrete through the permeable holes, and the water is automatically drained by using the internal and external pressure difference at both ends of the pipe body, the exposed end is connected to the slope surface intercepting ditch, and finally the internal water is discharged from the slope through the external drainage structure, and at the same time the inner drainage structure has a reinforcement effect on the soft soil layer, effectively reducing the probability of landslides in rainy seasons; but detection and early warning are not considered, and timely treatment cannot be carried out when a landslide occurs.

[0006] At present, the existing technologies 1, 2 and 3 lack consideration of the impact on landslide prevention, drainage channels, detection and early warning, resulting in the problem that the landslide prevention effect needs to be further improved. In order to solve the above problems, the present invention provides a landslide emergency prevention technology. Summary of the invention

[0007] The main purpose of the present invention is to provide a landslide emergency prevention technology to solve the problem that the prior art lacks consideration of the impact on landslide prevention, drainage channels, detection and early warning, resulting in the need to further improve the landslide prevention effect.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A landslide emergency prevention and control technology specifically includes the following steps:

[0010] At least one sensor is placed inside the metal reinforcement and at key locations of the landslide body to monitor the landslide body data in real time, and the monitored landslide body data is divided into multiple grid units; and water component imaging data of the slope body is obtained in real time under rainfall conditions;

[0011] Among them, the water component data is the moisture content of each grid unit; the sensors include displacement sensors, stress sensors, humidity sensors, and rain monitoring sensors, etc.;

[0012] Compare the water composition imaging data of the landslide body with the preset water composition threshold to obtain the rainfall amount; automatically adjust the valve opening of the drainage channel and the diversion channel according to the rainfall amount; calculate according to the rainfall amount to obtain the stability parameter distribution;

[0013] The safety factor is obtained according to the distribution of stability parameters, and the instability probability of the slope is obtained through the safety factors of multiple grid units; if the instability probability reaches the starting threshold, the grouting equipment is automatically controlled to start; when the grouting reaches the grouting threshold, the grouting equipment is turned off; the structural design of the metal reinforcement piles and reinforcement frames is optimized and adjusted through finite element analysis.

[0014] As a further improvement of the present invention, the process of obtaining the water component imaging data of the slope body in real time under rainfall conditions specifically includes the following steps:

[0015] At least one sensor is placed inside the metal reinforcement and at key positions of the landslide body to monitor the landslide body data in real time, and the landslide body data is divided to obtain a plurality of grid units; a plurality of electrodes are arranged according to the plurality of grid units;

[0016] Measure the resistivity of multiple grids; obtain the resistivity of any position of the landslide in real time; perform suction balance on the resistivity to obtain the corresponding resistance value, and obtain the corresponding moisture content based on the resistance value;

[0017] The corresponding resistivity is obtained according to the resistance value, and the moisture content is fitted with the resistivity to obtain the corresponding relationship between the resistivity and the moisture content; the resistivity at different positions is discretized in the corresponding grid units to obtain the resistivity corresponding to each grid unit; the resistivity of each grid unit is input into the corresponding relationship between the resistivity and the moisture content to obtain the moisture content of each grid unit.

[0018] As a further improvement of the present invention, the process of obtaining the stability parameter distribution specifically includes the following steps

[0019] The water composition imaging data of the landslide body is compared with the preset water composition threshold, and the features of the landslide body data are extracted in combination with the evolution law of the landslide body itself and the landslide deformation mechanism to obtain the rainfall landslide influence feature set;

[0020] Among them, the rainfall landslide impact feature set includes historical displacement data features, rainfall factor features, stability coefficient features, rainfall infiltration depth features, and time factor features;

[0021] Generate a predicted displacement sequence based on the historical displacement data characteristics and the rainfall landslide impact feature set, and use the predicted displacement sequence to determine the predicted landslide time; issue an early warning based on the predicted landslide time and upload it to the central control system;

[0022] The central control system analyzes the landslide time and automatically adjusts the valve opening of the drainage channel and diversion channel according to the analysis results; and analyzes the rainfall volume of the predicted results to obtain the stability parameter distribution.

[0023] As a further improvement of the present invention, the process of obtaining the rainfall landslide impact feature set specifically includes the following steps:

[0024] Collect the characteristics of historical displacement data; combine the evolution law of the landslide body itself and the mechanism of rainfall on landslide deformation, and analyze the rainfall factor based on the historical average rainfall, previous effective rainfall and number of rainy days;

[0025] The importance of rainfall factors is determined to determine the characteristics of rainfall factors; the stability coefficient and rainfall penetration depth characteristics are determined based on the permeability coefficient, shear strength and landslide slope; based on the rainfall in different months, characteristic analysis is performed to determine the characteristics of time factors;

[0026] According to the similarity between the time factor characteristics, the time characteristics are classified; according to the historical displacement data characteristics, rainfall factor characteristics, stability coefficient characteristics, rainfall infiltration depth characteristics and time factor characteristics, the rainfall landslide impact feature set is determined.

[0027] As a further improvement of the present invention, the process of predicting the landslide time and providing early warning specifically comprises the following steps:

[0028] The deformation acceleration point is calculated based on the curve between the inverse of the landslide velocity and time; based on the deformation acceleration point, the predicted landslide time is determined using the predicted displacement sequence; the difference between the predicted landslide time and the current time is used as an early warning indicator;

[0029] Construct a landslide monitoring and early warning mechanism based on early warning indicators and landslide migration rate; detect and warn landslides according to the landslide monitoring and early warning mechanism; and upload the real-time monitored landslide early warning data to the central control system;

[0030] The central control system analyzes the real-time monitored landslide warning data in combination with the rainfall-landslide impact feature set to predict the landslide phenomenon that will occur at the corresponding time.

[0031] As a further improvement of the present invention, the process of obtaining the stability parameter distribution specifically includes the following steps:

[0032] The landslide phenomenon occurring at the corresponding time of the landslide is analyzed and calculated in combination with the historical landslide phenomenon to obtain the valve switch value; the valve opening of the drainage channel and the diversion channel is automatically adjusted according to the valve switch value;

[0033] Based on the rainfall landslide influence feature set, the soil-water characteristic relationship is obtained; based on the relationship between soil resistivity and volume water, the relationship between matrix suction and soil resistance is obtained; by introducing suction stress, the relationship between suction stress and water content is obtained;

[0034] The stress at any position of the landslide body is obtained, and the suction stress is used to replace the pore pressure to obtain the relationship between the suction stress and the effective stress; according to the relationship between the suction stress and the effective stress, combined with the rainfall, the stability parameter distribution is obtained.

[0035] As a further improvement of the present invention, the process of optimizing the structural design of metal reinforcement piles and reinforcement frames by finite element analysis specifically includes the following steps:

[0036] Obtain the safety factor of each grid cell according to the stability parameter distribution; obtain the total number of grid cells and count the number of grid cells with a local safety factor less than 1; obtain the percentage of the number of grid cells with a local safety factor less than 1 in the total number of grid cells and analyze its distribution to obtain the instability probability of the slope;

[0037] The instability probability is compared with the preset instability probability threshold. When the instability probability reaches the start threshold, the grouting equipment is automatically controlled to start. When the grouting reaches the grouting threshold, the grouting equipment is turned off and the instability probability is recalculated. If it is greater than the start threshold, the grouting equipment is automatically controlled to start. Iterate in sequence until it is lower than the start threshold.

[0038] Features are extracted from the stability parameter distribution, and the rainfall-landslide impact feature data set is adjusted according to the extracted features for data fusion. A dynamic map is established in a high-dimensional data space for the adjusted rainfall-landslide impact feature data set at different time points, and the quantitative results of the distribution of influencing factors at each position of the metal reinforcement piles and reinforcement frames are obtained.

[0039] As a further improvement of the present invention, the process of obtaining the quantitative results of the distribution of the influencing factors at each position of the metal reinforcement pile and the reinforcement frame specifically includes the following steps:

[0040] The adjusted rainfall impact characteristic data set at each time point is combined into a high-dimensional vector, and the preprocessed and normalized standardized data is obtained through data preprocessing and normalization; the covariance matrix is ​​calculated based on the standardized data, and the covariance matrix is ​​eigen-decomposed to obtain the eigenvalues ​​and corresponding eigenvectors;

[0041] Based on the size of the eigenvalue, the largest eigenvalue and the corresponding eigenvector are selected to form the principal component. Based on the principal component, the standardized data is projected to map the high-dimensional data to the low-dimensional space, and the data points at each time point are plotted in the position space to construct a dynamic spectrum.

[0042] According to the distribution and change trend of the data points in the dynamic spectrum, quantitative analysis is performed to obtain the statistical summary of the impact at each time point or each area; the quantitative distribution results of the influencing factors of the metal reinforcement piles and reinforcement frames are recorded; the structure of the metal reinforcement piles and reinforcement frames is adjusted according to the quantitative results;

[0043] Among them, quantitative analysis includes examining the distribution of data points in low-dimensional space, changes in time series, and identifying existing anomalies.

[0044] As a further improvement of the present invention, the process of adjusting the metal reinforcement pile and the reinforcement frame structure according to the quantification result specifically includes the following steps:

[0045] Based on the dynamic spectrogram, analyze the distribution of data points at each time point or each area; identify the change pattern and potential abnormal points of the data in the time series; quantify the data distribution in the dynamic spectrogram;

[0046] Calculate the cumulative contribution points of the eigenvalues ​​and eigenvectors at each time point or each region, evaluate the explanatory power of different principal components on data changes based on the cumulative contribution points, and the variance contribution value of each time point or each region, and determine the contribution of the principal components to the main change direction of the data;

[0047] According to the identification of key data and abnormal points in the dynamic spectrum, the structural design of metal reinforcement piles and reinforcement frames is adjusted according to the contribution of the principal component to the main change direction of the data; the adjusted data is recalculated, and the calculation result is compared with the preset structural threshold. If it is greater, it is readjusted; it is iterated in sequence until the calculation result is less than the preset structural threshold.

[0048] To achieve the above object, the present invention also provides the following technical solutions:

[0049] A real-time landslide monitoring system based on multi-sensor fusion and intelligent control, including:

[0050] The bottom ends of multiple metal reinforcement piles are driven into the slope surface of the landslide body in a matrix manner. The interior of the metal reinforcement piles is a hollow structure. The outer wall of the metal reinforcement piles is provided with multiple grouting ports, which are evenly distributed on the outer wall of the metal reinforcement piles. Concrete slurry is then injected into the multiple metal reinforcement piles. The concrete slurry penetrates into the landslide body through the grouting ports. After the concrete slurry solidifies, the metal reinforcement piles are fixed inside the slope of the landslide body, so that the landslide body is grout-reinforced.

[0051] The plurality of metal reinforcement piles are divided into four vertical rows, and reinforcement frames are provided on the tops of the four rows of metal reinforcement piles, and the bottoms of the four reinforcement frames are fixedly installed on the tops of the four vertical rows of metal reinforcement piles by bolts;

[0052] A drainage channel is dug on one side of the landslide surface of the landslide body, and multiple diversion channels are dug on the landslide surface of the landslide body. The multiple diversion channels are in an inclined state, and the downward inclined end of the drainage channel is communicated with the interior of the drainage channel.

[0053] The present invention drives a plurality of metal reinforcement piles into the slope surface of the landslide body in a matrix manner, and then injects concrete slurry into the plurality of metal reinforcement piles. The concrete slurry penetrates into the landslide body through the grouting port, and the metal reinforcement piles can be fixed inside the slope of the landslide body, so that the landslide body is reinforced to support the sliding force of the sliding body and stabilize the landslide. Then, through the cooperation of a plurality of reinforcement frames and connecting rods, the plurality of metal reinforcement piles can be connected together to increase the stability between the plurality of metal reinforcement piles, so that the plurality of metal reinforcement piles form a whole, and the effect of landslide prevention and control is increased. The metal reinforcement piles and the anchor rod are connected by the cooperation of the connecting lock ring, the reinforcement seat and the anchor rod. The reinforcement frame is further tightened to prevent the metal reinforcement piles from sliding down, thereby further improving the effect of landslide prevention and control; by adopting the coordination of anchor rods, drainage channels and filter screens, rainwater on the landslide surface is intercepted into multiple diversion channels on rainy days, and discharged into the drainage channels through the diversion channels, and then discharged through the drainage channels, so that the landslide surface of the landslide body can be drained and drained, and then the rainwater on the landslide surface of the landslide body can be drained and managed. At the same time, through the setting of the filter screen, leaves, dead branches or other garbage in the water can be filtered to prevent garbage from entering the drainage channels and diversion channels, causing blockages in the drainage channels and diversion channels. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of the steps of an embodiment of a method for real-time monitoring of landslides based on multi-sensor fusion and intelligent regulation of the present invention;

[0055] Figure 2 It is a schematic flow chart of the steps of obtaining the water component imaging data of the slope body in real time under rainfall conditions in one embodiment of the real-time landslide monitoring method based on multi-sensor fusion and intelligent control of the present invention;

[0056] Figure 3 A schematic flow chart of steps for obtaining stability parameters according to an embodiment of a landslide real-time monitoring method based on multi-sensor fusion and intelligent regulation of the present invention;

[0057] Figure 4 A schematic flow chart of the steps of obtaining a rainfall landslide influence feature set according to an embodiment of a landslide real-time monitoring method based on multi-sensor fusion and intelligent regulation of the present invention;

[0058] Figure 5 A schematic flow chart of the steps of predicting the time of landslide and providing early warning according to an embodiment of the real-time landslide monitoring method based on multi-sensor fusion and intelligent regulation of the present invention;

[0059] Figure 6 A schematic flow chart of steps for obtaining stability parameter distribution in an embodiment of a landslide real-time monitoring method based on multi-sensor fusion and intelligent regulation of the present invention;

[0060] Figure 7 This is a schematic flow chart of the steps of optimizing the structural design of metal reinforcement piles and reinforcement frames through finite element analysis in one embodiment of the landslide real-time monitoring method based on multi-sensor fusion and intelligent regulation of the present invention;

[0061] Figure 8 A schematic diagram of the steps of an embodiment of the present invention for obtaining the quantitative results of the distribution of the influencing factors at each position of the metal reinforcement pile and the reinforcement frame;

[0062] Fig. 9 This is a schematic flow chart of the steps of adjusting the metal reinforcement piles and reinforcement frame structures according to the quantification results in one embodiment of the landslide real-time monitoring method based on multi-sensor fusion and intelligent regulation of the present invention;

[0063] Fig.10 It is a functional module diagram of an embodiment of a landslide real-time monitoring system based on multi-sensor fusion and intelligent regulation of the present invention;

[0064] Fig.11 A schematic diagram of functional modules of an embodiment of a pile connection reinforcement system of the present invention;

[0065] Fig.12 A schematic diagram of functional modules of an embodiment of a drainage protection system of the present invention;

[0066] Fig.13 It is a structural schematic diagram of an embodiment of an electronic device of the present invention;

[0067] Fig.14 It is a schematic structural diagram of an embodiment of the storage medium of the present invention. DETAILED DESCRIPTION

[0068] 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.

[0069] The terms "first", "second" and "third" in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0070] Reference to an "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0071] like Figure 1 As shown, this embodiment provides an embodiment of a landslide real-time monitoring method based on multi-sensor fusion and intelligent regulation. In this embodiment, the landslide real-time monitoring method based on multi-sensor fusion and intelligent regulation specifically includes the following steps:

[0072] Step S1: placing at least one sensor inside the metal reinforcement and at key positions of the landslide body to monitor the landslide body data in real time, dividing the monitored landslide body data into multiple grid units; and obtaining the water component imaging data of the slope body in real time under rainfall conditions;

[0073] Among them, the water component data is the moisture content of each grid unit; the sensors include displacement sensors, stress sensors, humidity sensors, and rain monitoring sensors, etc.;

[0074] Step S2: Compare the water component imaging data of the landslide body with the preset water component threshold to obtain the amount of rainwater; automatically adjust the valve opening of the drainage channel and the diversion channel according to the amount of rainwater; calculate according to the amount of rainwater to obtain the stability parameter distribution;

[0075] Step S3: Obtain the safety factor according to the stability parameter distribution, and obtain the instability probability of the slope through the safety factors of multiple grid units; if the instability probability reaches the start threshold, the grouting equipment is automatically controlled to start; when the grouting reaches the grouting threshold, the grouting equipment is turned off; the structural design of the metal reinforcement piles and reinforcement frames is optimized through finite element analysis and adjusted.

[0076] Preferably, in step S1 of the present embodiment, a variety of sensors are placed inside the metal reinforcement and at key positions of the landslide body, including displacement sensors, stress sensors, humidity sensors, and rain monitoring sensors, etc., for real-time monitoring of the landslide body data; the monitored landslide body data is divided into multiple grid units for subsequent analysis and processing; under rainfall conditions, water component imaging data of the slope body is obtained in real time, including the moisture content of each grid unit; through the deployment of multiple sensors, the present embodiment can obtain multi-dimensional data of the landslide body in real time, including displacement, stress, humidity, and rainfall, etc., to provide basic data for subsequent analysis; dividing the landslide body into multiple grid units is helpful to more finely analyze the deformation characteristics and water component distribution of the landslide body, and improve the accuracy and reliability of monitoring; by obtaining water component imaging data, the moisture content distribution inside the landslide body can be intuitively understood, providing an important basis for evaluating the stability of the landslide. In step S2, the water component imaging data of the landslide body is compared with the preset water component threshold to determine the rainfall; the valve opening of the drainage system and the diversion channel is automatically adjusted according to the rainfall to control the moisture content of the landslide body; the stability parameter distribution is calculated according to the rainfall to evaluate the overall stability of the landslide body. This embodiment automatically adjusts the drainage system to timely remove excess water in the landslide body to prevent the risk of landslide caused by water accumulation; the stability parameter distribution is calculated using rainfall and water component data to dynamically evaluate the stability of the landslide body and provide a scientific basis for early warning and prevention; combined with real-time monitoring data and preset thresholds, the system can quickly respond to rainfall events, adjust drainage measures in time, and reduce the possibility of landslides. In step S3, the safety factor is calculated according to the stability parameter distribution to evaluate the instability probability of the landslide body; the instability probability of the slope body is comprehensively evaluated through the safety factors of multiple grid units; when the instability probability reaches the threshold of the start interval, the grouting equipment is automatically started; otherwise, the equipment is shut down; this embodiment optimizes the structural design of metal reinforcement piles and reinforcement frames through finite element analysis to improve overall stability. This embodiment can provide early warning of the possibility of landslides by calculating the safety factor and the probability of instability, thereby providing a time window for emergency response; when the probability of instability reaches a threshold, the system automatically starts the grouting equipment for reinforcement, thereby reducing manual intervention and improving the response speed; this embodiment optimizes the design of the reinforcement structure through finite element analysis, thereby improving the overall stability and durability of the landslide body and reducing the risk of future landslides.

[0077] Furthermore, if Figure 2 As shown, the process of obtaining the water component imaging data of the slope body in real time under rainfall conditions in step S1 specifically includes the following steps:

[0078] Step S11: placing at least one sensor inside the metal reinforcement and at key positions of the landslide body to monitor the landslide body data in real time, dividing the landslide body data into a plurality of grid units; and arranging a plurality of electrodes according to the plurality of grid units;

[0079] Step S12: measuring the resistivity of multiple grids; obtaining the resistivity of any position of the landslide body in real time; performing suction balance on the resistivity to obtain the corresponding resistance value, and obtaining the corresponding water content according to the resistance value;

[0080] Step S13: Obtain the corresponding resistivity according to the resistance value, fit the water content and the resistivity to obtain the corresponding relationship between the resistivity and the water content; discretize the resistivity at different positions in the corresponding grid units to obtain the resistivity corresponding to each grid unit; input the resistivity of each grid unit into the corresponding relationship between the resistivity and the water content to obtain the water content of each grid unit.

[0081] Preferably, in step S11 of this embodiment, at least one sensor is placed inside the metal reinforcement and at key positions of the landslide body to monitor the landslide body data in real time. These sensors may include inclination sensors, displacement sensors, etc., which can monitor parameters such as deformation, displacement and vibration of the landslide body; and through the arrangement of sensor groups, monitoring points are formed, and data such as vibration frequency, inclination offset increment, inclination offset direction and displacement increment of the landslide body are collected in real time; in step S12, the landslide body data is divided to obtain multiple grid units, and each grid unit is used as a basic unit for resistivity measurement; multiple electrodes are arranged according to the grid units, and the resistivity distribution inside the landslide body is measured using a high-density resistivity method; the resistivity of multiple grids is measured to obtain resistivity data at any position of the landslide body in real time; the resistivity is subjected to suction balance processing to obtain the corresponding resistance value, and the water content is calculated based on the resistance value; in step S13, the water content and the resistivity are fitted to obtain the corresponding relationship between the resistivity and the water content; the resistivity at different positions is discretized in the corresponding grid units to obtain the resistivity corresponding to each grid unit; the resistivity of each grid unit is input into the corresponding relationship between the resistivity and the water content to obtain the water content of each grid unit. In this embodiment, the deformation and moisture changes of the landslide body can be monitored in real time and accurately by using a sensor group and a high-density resistivity method, thereby improving the accuracy and real-time performance of landslide early warning. Real-time monitoring of the resistivity changes of the landslide body can quickly respond to changes in the internal structure of the landslide body, thereby providing reliable data support for landslide early warning. Resistivity imaging technology and multivariate data fusion methods can be used to achieve multidimensional imaging and analysis of the internal structure of the landslide body, and accurately determine the location of the potential slip surface and the stability of the landslide body. Combined with data such as soil moisture content and rainfall, a multivariate data fusion model is constructed to improve the accuracy and real-time performance of landslide prediction. The high-density resistivity method can measure the resistivity distribution of the object being measured without disturbing it, thereby improving the spatial resolution of the monitoring data. The design and optimization of the electrode network can meet the needs of large-area risk point inspection and variable-density and variable-range monitoring of key areas.

[0082] Furthermore, if Figure 3 As shown, the process of obtaining the stability parameter distribution in step S2 specifically includes the following steps:

[0083] Step S21: Compare the water component imaging data of the landslide body with the preset water component threshold, and extract features from the landslide body data in combination with the evolution law of the landslide body itself and the landslide deformation mechanism to obtain a rainfall landslide influence feature set;

[0084] Among them, the rainfall landslide impact feature set includes historical displacement data features, rainfall factor features, stability coefficient features, rainfall infiltration depth features, and time factor features;

[0085] Step S22: Generate a predicted displacement sequence for the rainfall landslide impact feature set based on the historical displacement data characteristics, and use the predicted displacement sequence to determine the predicted landslide time; issue an early warning based on the predicted landslide time and upload it to the central control system;

[0086] Step S23: The central control system analyzes the landslide time and automatically adjusts the valve opening of the drainage channel and the diversion channel according to the analysis results; and analyzes the rainfall volume of the predicted results to obtain the stability parameter distribution.

[0087] Preferably, in step S21 of this embodiment, by comparing the water component imaging data of the landslide body with the preset water component threshold, combined with the evolution law of the landslide body itself and the landslide deformation mechanism, the characteristics of the landslide body data are extracted; the rainfall landslide impact feature set is extracted from the landslide body data, including historical displacement data features, rainfall factor features, stability coefficient features, rainfall infiltration depth features and time factor features; step S21 can more accurately identify the potential risk of landslides by combining the evolution law and deformation mechanism of the landslide body itself, and provide a scientific basis for subsequent warnings; and by extracting multiple features, the stability of the landslide can be comprehensively evaluated, thereby improving the comprehensive performance of the early warning system. In step S22, based on the historical displacement data features, a predicted displacement sequence is generated for the rainfall landslide impact feature set, the predicted displacement sequence is used to determine the time when the landslide may occur, and an early warning is issued according to the predicted time; the early warning information is uploaded to the central control system for further processing and response; step S22 can dynamically predict the time when the landslide occurs by predicting the displacement sequence, thereby achieving early warning and reducing disaster losses; uploading the early warning information to the central control system can achieve multi-departmental coordinated response and improve emergency handling efficiency. In step S23, the central control system analyzes the landslide time and automatically adjusts the valve opening of the drainage channel and the diversion channel according to the analysis results; the predicted rainfall is analyzed to obtain the stability parameter distribution of the landslide body, and the drainage and diversion measures can be adjusted in time through the automatic adjustment function of the central control system to reduce the landslide risk; and through the analysis of rainfall and stability parameters, the landslide body can be managed in a refined manner to improve the governance effect.

[0088] Furthermore, if Figure 4 As shown, the process of obtaining the rainfall landslide impact feature set in step S21 specifically includes the following steps:

[0089] Step S211: Collect historical displacement data features; combine the evolution law of the landslide body itself and the mechanism of rainfall on landslide deformation, and analyze the rainfall factor based on the historical average rainfall, previous effective rainfall and rainfall days;

[0090] Step S212: Determine the characteristics of rainfall factors by calculating the importance of rainfall factors; determine the stability coefficient and rainfall penetration depth characteristics according to the permeability coefficient, shear strength and landslide slope; perform characteristic analysis based on the rainfall in different months to determine the characteristics of time factors;

[0091] Step S213: classify the time features according to the similarity between the time factor features; determine the rainfall landslide impact feature set according to the historical displacement data features, rainfall factor features, stability coefficient features, rainfall infiltration depth features and time factor features.

[0092] Preferably, in step S211, the historical displacement data of the landslide body is monitored to extract its evolution law; combined with the evolution law of the landslide body itself, the mechanism of rainfall on landslide deformation is analyzed, including the historical average rainfall, the previous effective rainfall and the number of rainy days; through the combined analysis of historical displacement data and rainfall factors, the landslide deformation trend can be predicted more accurately, and the medium- and long-term prediction error can be reduced; the impact of rainfall on landslide deformation is considered to make the model more adaptable and practical. Step S212 analyzes the importance of rainfall factors and extracts key features; determines the stability coefficient and rainfall penetration depth characteristics based on the permeability coefficient, shear strength and landslide slope; performs feature analysis based on the rainfall in different months to determine the time factor characteristics; in step S212, the importance of rainfall factors is analyzed to optimize model parameters, improve the model's explanatory power and prediction accuracy; and through the stability coefficient and penetration depth characteristics, the stability of the landslide is better evaluated to provide a scientific basis for prevention and control measures. Step S213 classifies the time features according to the similarity between the time factor features; determines the rainfall landslide impact feature set by combining the historical displacement data features, rainfall factor features, stability coefficient features, rainfall infiltration depth features and time factor features; through the determination of the comprehensive feature set, it is possible to comprehensively evaluate the risk level of landslide under different conditions and provide more comprehensive data support for landslide prevention and control; combines the feature analysis of multiple factors to improve the accuracy and timeliness of landslide warning.

[0093] Furthermore, if Figure 5 As shown, the process of predicting the landslide time and providing early warning in step S22 specifically includes the following steps:

[0094] Step S221: Calculate the deformation acceleration point based on the curve between the inverse of the landslide velocity and time; determine the predicted landslide time based on the deformation acceleration point using the predicted displacement sequence; and use the difference between the predicted landslide time and the current time as an early warning indicator;

[0095] Step S222: construct a landslide monitoring and early warning mechanism based on early warning indicators and landslide migration rate; detect and warn the landslide according to the landslide monitoring and early warning mechanism; and upload the real-time monitored landslide early warning data to the central control system;

[0096] Step S223: The central control system analyzes the real-time monitored landslide warning data in combination with the rainfall landslide impact feature set to predict the landslide phenomenon that will occur at the corresponding time.

[0097] Preferably, in step S221 of this embodiment, the relationship between the inverse of the landslide velocity and time is analyzed to identify the deformation acceleration point, and the landslide time is predicted based on this point; the displacement data after the deformation acceleration point is used in combination with the landslide creep theory to establish a landslide time prediction model to improve the prediction accuracy; the difference between the predicted landslide time and the current time is used as an early warning indicator to evaluate the urgency of the landslide; in step S221, the time of landslide occurrence can be more accurately predicted by the inverse velocity method and the identification of the deformation acceleration point, reducing the possibility of false alarms and missed alarms; the calculation of the early warning indicator provides a scientific basis for real-time monitoring and early warning, can timely discover landslide risks, and provide a time window for emergency response. In step S222, a comprehensive early warning system is constructed to dynamically monitor and warn the landslide by combining the early warning index and the landslide migration rate; the real-time monitored landslide early warning data is uploaded to the central control system to ensure the timeliness and effectiveness of the information; in step S222, by combining the early warning index and the landslide migration rate, the early warning system can more comprehensively assess the landslide risk and reduce the false alarm or omission caused by a single indicator; the uploading of real-time data enables the central control system to obtain the latest landslide information in a timely manner, providing reliable data support for emergency response. In step S223, the central control system analyzes the real-time monitoring data in combination with the rainfall landslide impact feature set to predict the specific time of the landslide; and by analyzing the impact of rainfall and other environmental factors on the landslide, predict the specific phenomena when the landslide occurs, such as crack expansion, displacement acceleration, etc.; combined with the impact characteristics of environmental factors such as rainfall, the time and specific phenomena of the landslide can be more accurately predicted, providing a scientific basis for disaster prevention and mitigation; by predicting the specific phenomena of the landslide in this embodiment, the relevant departments can take targeted emergency measures in advance, such as personnel evacuation, reinforcement of facilities, etc., to minimize the losses caused by the disaster.

[0098] Furthermore, if Figure 6 As shown, the process of obtaining the stability parameter distribution in step S23 specifically includes the following steps:

[0099] Step S231: Analyze and calculate the landslide phenomenon occurring at the corresponding time of the landslide in combination with the historical landslide phenomenon to obtain the valve switch value; automatically adjust the valve opening of the drainage channel and the diversion channel according to the valve switch value;

[0100] Step S232: based on the rainfall landslide influence feature set, the soil-water characteristic relationship is obtained; based on the relationship between soil resistivity and volume water, the relationship between matrix suction and soil resistance is obtained; suction stress is introduced to obtain the relationship between suction stress and water content;

[0101] Step S233: Obtain the stress at any position of the landslide body, use suction stress to replace pore pressure, and obtain the relationship between suction stress and effective stress; analyze the relationship between suction stress and effective stress in combination with rainfall to obtain stability parameter distribution.

[0102] Among them, in step S231, the valve switch value is calculated and the valve opening is adjusted. The historical data is usually presented in the form of time series, such as annual rainfall or monthly displacement. In order to eliminate the dimension difference, the historical data is normalized:

[0103]

[0104] Where, T ref is the reference time, used to convert historical data into dimensionless values; T norm,i represents the normalized value of the i-th historical data; T history,i Represents the i-th historical data (such as rainfall, displacement, etc.).

[0105] Combine the normalized historical data with the current feature parameters and assign different weight coefficients k1 and k2:

[0106]

[0107] In the formula, k1 and k2 are determined through experiments or experience, reflecting the influence of historical data and current data on the valve switching value; B switch Indicates the valve switch value, which is the weighted result of comprehensive historical data and current characteristic parameters; P current Indicates the characteristic parameters of the current landslide phenomenon (such as current rainfall, current displacement, etc.); n indicates the number of historical data;

[0108] The adjustment of the valve opening is based on an exponential function, ensuring a smooth transition of the opening as the switching value changes:

[0109]

[0110] When V switch When V is close to 0, the valve opening θ is also close to 0; switch Much larger than V ref When the valve opening θ approaches the maximum opening θ max ;

[0111] Step S232 soil-water characteristic relationship and suction stress calculation. The soil-water characteristic relationship describes the relationship between soil moisture content and matrix suction:

[0112]

[0113] In the formula, the moisture content of the soil is w It has a nonlinear relationship with the matrix suction ψ; the parameters α, n and m are determined by fitting the experimental data. The parameters reflect the pore distribution and water holding capacity of the soil; the residual water content θ r and saturated moisture content θ s It is the basic physical property of soil;

[0114] The relationship between matrix suction and soil resistance. Matrix suction ψ is calculated through soil resistivity ρ e Indirect measurement:

[0115] ψ=a·ln(ρ e )+b

[0116] In the formula, soil resistivity ρ e and moisture contentθ w Closely related; by fitting the experimental data, the parameters a and b are determined, and the logarithmic relationship between matrix suction and resistivity is established;

[0117] The relationship between suction stress and water content, suction stress σ suction is a function of soil moisture content:

[0118]

[0119] The suction stress decreases with the increase of water content. By fitting the experimental data, the parameters c, d and e are determined, and the power function relationship between the suction stress and water content is established.

[0120] Step S233: Relationship between suction stress and effective stress and distribution of stability parameters. In unsaturated soil, suction stress σ suction Replaces the role of pore water pressure:

[0121] σ′=σ-u a +χ·σ suction

[0122] Where, the total stress σ is determined by the soil deadweight and external load; the pore air pressure u a Usually close to atmospheric pressure; the weight coefficient χ reflects the contribution of suction stress to effective stress, and its value is related to the saturation of the soil; σ′ represents effective stress, which represents the actual stress between particles in the soil;

[0123] Stability parameters (safety factor F s ) is calculated based on the Mohr-Coulomb criterion:

[0124]

[0125] In the formula, the shear strength τ f Determined by the effective cohesion c′ and the effective internal friction angle φ′:

[0126] τ f =c′+σ′·tan(φ′)

[0127] The shear stress τ is determined by the soil weight and the slope inclination:

[0128] τ=γ·H·sin(α)

[0129] Safety factor F s It is the ratio of shear strength to shear stress, which reflects the stability of the landslide; it quantitatively analyzes the distribution of stability parameters of the landslide and provides a scientific basis for landslide prevention and control; γ represents the gravity of the soil, which represents the weight of the soil per unit volume; H represents the height of the landslide body; α represents the inclination angle of the landslide surface.

[0130] Preferably, in step S231 of this embodiment, the valve switch value is calculated by analyzing the historical landslide phenomenon at the corresponding time of the landslide and combining with the real-time monitoring data; according to the valve switch value, the valve opening of the drainage channel and the diversion channel is automatically adjusted to realize the dynamic management of the drainage in the landslide area; step S231 can more effectively remove the water accumulation in the landslide area and reduce the possibility of landslide by adjusting the valve opening in real time; combined with historical data and real-time monitoring, it can more accurately predict the landslide risk, take measures in advance, and reduce the impact of landslide on the environment and infrastructure. In step S232, based on the influence feature set of rainfall landslide, the soil-water characteristic relationship is established; and through the relationship between soil resistivity and volume water, the suction stress is introduced to obtain the relationship between the suction stress and the water content; the suction stress is used to replace the pore pressure to obtain the relationship between the suction stress and the effective stress; step S232 can more accurately evaluate the possibility of landslide and its stability parameter distribution by establishing the soil-water characteristic relationship and the suction stress model; combined with parameters such as rainfall and soil suction, the landslide risk can be identified earlier, providing a scientific basis for disaster prevention and mitigation. Step S233 obtains the stress at any position of the landslide body, uses suction stress to replace pore pressure, and obtains the relationship between suction stress and effective stress; analyzes in combination with the rainfall amount to obtain the stability parameter distribution; Step S233 can dynamically evaluate the stability of the landslide by real-time monitoring of the stress changes of the landslide body and combining with the rainfall amount, and timely adjust the drainage and support measures; According to the stability parameter distribution, a landslide control plan can be formulated more scientifically to improve the control effect.

[0131] Furthermore, if Figure 7 As shown, the process of optimizing the structural design of the metal reinforcement piles and the reinforcement frame by finite element analysis in step S3 specifically includes the following steps:

[0132] Step S31: Obtain the safety factor of each grid cell according to the stability parameter distribution; obtain the total number of grid cells, and count the number of grid cells with a local safety factor less than 1; obtain the percentage of the number of grid cells with a local safety factor less than 1 in the total number of grid cells and analyze its distribution to obtain the instability probability of the slope body;

[0133] Step S32: Compare the instability probability with the preset instability probability threshold. When the instability probability reaches the start threshold, the grouting equipment is automatically controlled to start. When the grouting reaches the grouting threshold, the grouting equipment is turned off, and the instability probability is recalculated. If it is greater than the start threshold, the grouting equipment is automatically controlled to start. Iterate in sequence until it is lower than the start threshold.

[0134] Step S33: extract features from the stability parameter distribution, adjust the rainfall-landslide impact feature data set according to the extracted features, and perform data fusion, so that the adjusted rainfall-landslide impact feature data set at different time points is in a high-dimensional data space, and a dynamic map is established to obtain the quantitative results of the distribution of influencing factors at each position of the metal reinforcement pile and the reinforcement frame.

[0135] Preferably, in step S31 of this embodiment, the slope is divided into multiple grid units, and the safety factor of each grid unit is calculated by the distribution of stability parameters; the slope is divided into multiple grid units, and the safety factor of each grid unit is calculated by the distribution of stability parameters; step S31 can more accurately evaluate the instability probability of the slope through grid analysis, providing a basis for subsequent early warning and control; real-time acquisition of the distribution of the instability probability is helpful to timely discover potential landslide risks and improve the accuracy of early warning. In step S32, the instability probability is compared with the preset threshold, and when the instability probability reaches the start threshold, the grouting equipment is automatically started; when the grouting reaches the grouting threshold, the grouting equipment is turned off; the instability probability is recalculated, and if it is still greater than the start threshold, the grouting equipment is continued to be started until the instability probability is lower than the threshold; this embodiment reduces manual intervention and improves response speed and efficiency by automatically controlling the grouting equipment; the grouting amount is dynamically adjusted according to the real-time instability probability to ensure the stability of the slope while avoiding waste of resources. In step S33, features are extracted from the stability parameter distribution, and the rainfall landslide impact feature data set is adjusted according to these features; a dynamic map is established in the high-dimensional data space to quantify the distribution of influencing factors at each position of the metal reinforcement piles and reinforcement frames; through the high-dimensional dynamic map, the distribution of influencing factors at different positions can be more intuitively understood, providing a scientific basis for engineering design and construction; reinforcement measures are dynamically adjusted according to real-time data to improve the accuracy and effectiveness of the reinforcement effect.

[0136] Furthermore, if Figure 8 As shown, the process of obtaining the quantitative results of the distribution of the influencing factors of each position of the metal reinforcement pile and the reinforcement frame in step S33 specifically includes the following steps:

[0137] Step S331: combining the adjusted rainfall impact feature data set at each time point into a high-dimensional vector, and obtaining the preprocessed and normalized standardized data through data preprocessing and normalization; calculating the covariance matrix according to the standardized data, and performing eigendecomposition on the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors;

[0138] Assuming there are m′ time points and each time point has n′ features (such as rainfall, temperature, humidity, etc.), the data set can be represented as a matrix X with a dimension of m′×n′. In order to eliminate the dimensional differences between the features, the data needs to be standardized. The standardized formula is:

[0139]

[0140] In the formula, μ is the mean vector of each feature; σ″ is the standard deviation vector of each feature; the standardized data X 标准化 The mean is 0 and the variance is 1, which is convenient for calculating the covariance matrix;

[0141] Calculation of the covariance matrix, standardized data X 标准化 It is directly used to calculate the covariance matrix C. The formula of the covariance matrix is:

[0142]

[0143] Where: X 标准化 is the standardized data matrix; X 标准化 T Yes X 标准化 The transposed matrix of is (n′×m′); m′-1 is the number of samples minus one, which is used to unbiasedly estimate the covariance matrix; the covariance matrix (\mathbf{C}) describes the linear correlation between each feature;

[0144] Characteristic decomposition, perform characteristic decomposition on the covariance matrix C, the formula is:

[0145] Cv l =λ l v l

[0146] Where C is the covariance matrix with dimension (n′×m′); l is the lth eigenvalue, which indicates the variance of the data in the direction of the corresponding eigenvector; v L is the lth eigenvector, which represents the change pattern of the data in the corresponding direction;

[0147] The goal of eigendecomposition is to find the eigenvalues ​​and eigenvectors of the covariance matrix C such that:

[0148] C=VΛV T

[0149] Where V is the eigenvector matrix, and each column is an eigenvector v i , the dimension is (n′×n′); Λ is a diagonal matrix, and the elements on the diagonal are the eigenvalues ​​λ i, the dimension is (n′×n′).

[0150] Step S332: Based on the size of the eigenvalue, select the largest eigenvalue and the corresponding eigenvector to form the principal component; project the standardized data based on the principal component, map the high-dimensional data to the low-dimensional space, and draw the data points at each time point in the position space to construct a dynamic spectrum;

[0151] Step S333: Perform quantitative analysis based on the distribution and change trend of the data points in the dynamic spectrum to obtain a statistical summary of the impact at each time point or each area; record the quantitative results of the distribution of the influencing factors of the metal reinforcement piles and reinforcement frames; and adjust the structure of the metal reinforcement piles and reinforcement frames according to the quantitative results;

[0152] Among them, quantitative analysis includes examining the distribution of data points in low-dimensional space, changes in time series, and identifying existing anomalies.

[0153] Preferably, this embodiment combines the adjusted rainfall impact feature data set at each time point into a high-dimensional vector, and obtains the preprocessed and normalized standardized data through data preprocessing and normalization. This step ensures that all features are on the same scale, which helps to improve the accuracy of subsequent analysis; the standardization method usually includes subtracting the mean and dividing by the standard deviation, so that the mean of the data is 0 and the variance is 1; the covariance matrix is ​​calculated based on the standardized data, and the covariance matrix describes the linear relationship between the data and is a symmetric matrix; the covariance matrix is ​​subjected to eigenvalue decomposition to obtain the eigenvalue and the corresponding eigenvector. The eigenvalue represents the variance of the data in the feature space, and the eigenvector represents the direction of the data in the feature space; based on the size of the eigenvalue, the largest eigenvalue and the corresponding eigenvector are selected to form the principal component. These principal components are used to project the high-dimensional data into a low-dimensional space, thereby achieving dimensionality reduction; the standardized data is projected into a new space composed of the selected principal components to obtain the reduced-dimensional data; the data points at each time point are plotted in the low-dimensional space to construct a dynamic spectrogram. The dynamic spectrum can intuitively display the distribution of data points in the time series; perform quantitative analysis, check the distribution of data points in low-dimensional space and their changing trends, and identify abnormal points. Through principal component analysis, high-dimensional data is projected into low-dimensional space while retaining the main information of the original data as much as possible. This helps to reduce computational complexity and improve the efficiency of data analysis; by selecting the direction of the maximum variance as the principal component, noise and redundant information can be effectively removed, thereby improving the performance and accuracy of the model; the dynamic spectrum can intuitively display the distribution of data points in the time series, helping to identify abnormal points or trend changes; it is of great significance for monitoring and adjusting the structure of metal reinforcement piles and reinforcement frames; quantitative analysis can provide a statistical summary of the impact of each time point or region, providing a basis for subsequent structural adjustments.

[0154] Furthermore, if Fig. 9 As shown, the process of adjusting the metal reinforcement pile and the reinforcement frame structure according to the quantification result in step S333 specifically includes the following steps:

[0155] Step S3331: Based on the dynamic spectrogram, analyze the distribution of data points at each time point or each area; identify the change pattern and potential abnormal points of the data in the time series; quantify the distribution of data in the dynamic spectrogram;

[0156] Step S3332: Calculate the cumulative contribution points of the eigenvalues ​​and eigenvectors at each time point or each region, evaluate the explanatory power of different principal components on data changes based on the cumulative contribution points, and the variance contribution value of each time point or each region, and determine the contribution of the principal component to the main change direction of the data;

[0157] Step S3333: Identify key data and abnormal points based on the dynamic spectrum, and adjust the structural design of metal reinforcement piles and reinforcement frames based on the contribution of the principal component to the main change direction of the data; recalculate the adjusted data, compare the calculation result with the preset structural threshold, and readjust if it is greater than the preset structural threshold; iterate in sequence until the calculation result is less than the preset structural threshold.

[0158] Among them, step S3331 represents dynamic spectrum analysis and data quantification, and the dynamic spectrum data distribution quantification formula is:

[0159]

[0160] In the formula, Q dyn (t,r) represents the dynamic spectrum quantization value at time point t and region r; S ij (t,r) represents the data point in the i-th row and j-th column at time point t and region r; μ ij (t,r) represents the data mean at time point t and region r; σ ij (t,r) represents the standard deviation of the data at time point t and region r; t0 represents the reference time point; τ represents the time attenuation coefficient, which controls the time weight; n″,m″ represents the number of rows and columns of the dynamic spectrum;

[0161] Outlier identification formula:

[0162]

[0163] Where A(t,r) represents the quantified value of the outlier at time point t and region r; S i (t,r) represents the i-th data point at time t and region r; μ i (t,r) indicates the data mean at time point t and region r; σ i(t,r) represents the standard deviation of the data at time point t and region r; r0 represents the reference region; ρ represents the spatial attenuation coefficient, which controls the weight of the region; k represents the total number of data points;

[0164] Step S3332 represents principal component analysis and contribution value calculation, and the characteristic value cumulative contribution point formula is:

[0165]

[0166] In the formula, C cum (t,r) represents the cumulative contribution point of the eigenvalues ​​at time point t and region r; λ p (t,r) represents the pth eigenvalue at time point t and region r; α′ represents the attenuation coefficient, which controls the eigenvalue weight;

[0167] Variance contribution formula:

[0168]

[0169] Where V cont (t,r) represents the variance contribution value of time point t and region r; λ p (t,r) represents the pth eigenvalue at time point t and region r; φ p (t,r) represents the pth eigenvector of time point t and region r; P represents the total number of principal components;

[0170] Step S3333 represents structural design and iterative adjustment, and the structural adjustment formula is:

[0171]

[0172] Where D adj (t,r) represents the structural adjustment value at time point t and region r; λ p (t,r) represents the pth eigenvalue at time point t and region r; φ p (t,r) represents the pth eigenvector of time point t and region r;

[0173] Iterative adjustment formula:

[0174]

[0175] In the formula, I iter (t,r) represents the iterative adjustment value of time point t and region r; D adj (t,r) represents the structural adjustment value; the above formula realizes the quantitative optimization of the metal reinforcement pile and reinforcement frame structure through dynamic spectrum analysis, principal component analysis and iterative adjustment; each formula contains mathematical operations and weight control to ensure the accuracy and reliability of the calculation results.

[0176] Preferably, in step S3331 of this embodiment, the distribution of data points at each time point or region is analyzed through a dynamic spectrum to identify the change pattern and potential anomalies of the data in the time series; the data distribution in the dynamic spectrum is quantified to more accurately describe the distribution characteristics of the data; through dynamic spectrum analysis, this embodiment can timely discover the change pattern and potential anomalies in the data, providing a basis for subsequent data processing; quantifying the data distribution helps to more accurately describe the characteristics of the data, thereby improving the accuracy of the overall data analysis; in step S3332, the eigenvalues ​​and eigenvectors of each time point or region are calculated, and their cumulative contribution points are evaluated; based on the cumulative contribution points, the explanatory power of different principal components on data changes and the variance contribution value of each time point or region are evaluated to determine the contribution of the principal component to the main change direction of the data; in this embodiment, through principal component analysis, high-dimensional data is projected into a low-dimensional space, while retaining the main information, reducing noise, and improving data processing efficiency; the explanatory power of the principal component is evaluated to help understand the main change direction of the data, thereby providing a scientific basis for subsequent adjustments. In step S3333, the structural design of the metal reinforcement piles and reinforcement frames is adjusted according to the key data and abnormal points identified in the dynamic spectrum and the results of the principal component analysis; the adjusted data is recalculated and compared with the preset structural threshold. If it is greater than the threshold, it is readjusted, and it is iterated in sequence until the calculated result is less than the threshold; this embodiment optimizes the structural design of the metal reinforcement piles and reinforcement frames through the results of the dynamic spectrum and the principal component analysis, and improves the safety and stability of the structure; and through iterative optimization, it is ensured that the final design results meet the preset safety standards and reduce potential risks.

[0177] like Figure 10-12As shown, the present embodiment also provides an embodiment of a real-time monitoring system for landslides based on multi-sensor fusion and intelligent regulation. In the present embodiment, the real-time monitoring system for landslides based on multi-sensor fusion and intelligent regulation is applied to the real-time monitoring method for landslides based on multi-sensor fusion and intelligent regulation in the above-mentioned embodiment. The real-time monitoring system for landslides based on multi-sensor fusion and intelligent regulation includes driving the bottom ends of a plurality of metal reinforcement piles 2 into the slope surface of the landslide body 1 in a matrix manner. The interior of the metal reinforcement piles 2 is a hollow structure. The outer wall of the metal reinforcement piles 2 is provided with a plurality of grouting ports 21. The plurality of grouting ports 21 are evenly distributed on the outer wall of the metal reinforcement piles 2. Then, concrete slurry is injected into the plurality of metal reinforcement piles 2. The concrete slurry penetrates into the landslide body 1 through the grouting ports 21, and waits for the concrete slurry to penetrate into the landslide body 1. After the slurry solidifies, the metal reinforcement piles 2 can be fixed inside the slope of the landslide body 1, so that the landslide body 1 can be grout-reinforced; the multiple metal reinforcement piles 2 are divided into four vertical rows, and the tops of the four rows of metal reinforcement piles 2 are each provided with a reinforcement frame 3, and the number of the reinforcement frames 3 is four. The bottoms of the four reinforcement frames 3 can be fixedly installed on the tops of the four vertical rows of metal reinforcement piles 2 by bolts; a drainage channel 8 is dug on one side of the landslide surface of the landslide body 1, and multiple diversion channels 9 are dug on the landslide surface of the landslide body 1. The multiple diversion channels 9 are in an inclined state, and the downward inclined ends of the drainage channels 8 are connected to the inside of the drainage channels 8; four reinforcement seats 6 are evenly arranged on the top surface of the landslide body 1, and the tops of the four reinforcement frames 3 are fixedly installed with connecting lock rings 5, and the other ends of the connecting lock rings 5 ​​are installed on one side of the reinforcement seats 6.

[0178] The bottom of the four reinforcement seats 6 are provided with anchor rods 7, and the reinforcement seats 6 can be fixed to the top surface of the landslide body 1 by driving the anchor rods 7 into the interior of the top surface of the landslide body 1; multiple connecting rods 4 are provided between the four reinforcement frames 3, and the two ends of the connecting rods 4 are fixedly connected between the two reinforcement frames 3 by bolts. The four reinforcement frames 3 are connected together by setting multiple reinforcement frames 3 to increase the stability between the four reinforcement frames 3; multiple diversion channels 9 are used to intercept and divert rainwater on the landslide surface of the landslide body 1. When it rains, the rainwater falls on the landslide surface of the landslide body 1 The rainwater flows into multiple diversion channels 9, and is discharged into the drainage channel 8 through the downward inclined end of the diversion channel 9, and then discharged through the drainage channel 8, so that the landslide surface of the landslide body 1 can be drained and drained, and then the rainwater on the landslide surface of the landslide body 1 can be drained and managed; the tops of the drainage channels 8 and the diversion channels 9 are fixedly installed with filter screens, and leaves, dead branches or other garbage in the water are filtered through the filter screen to prevent garbage from entering the drainage channels 8 and the diversion channels 9, causing blockage in the drainage channels 8 and the diversion channels 9.

[0179] Both sides of the top of the multiple metal reinforcement piles 2 are fixedly connected with connecting plates 22, and a threaded opening 23 is opened on the connecting plate 22. The bottom of the reinforcement frame 3 is opened with a threaded installation opening corresponding to the threaded opening 23. When installing the metal reinforcement piles 2 and the reinforcement frame 3, bolts can be used to fix the connecting plates 22 and the top of the metal reinforcement piles 2 to the bottom of the reinforcement frame 3 through the threaded opening 23; the depth of the bottom ends of the multiple metal reinforcement piles 2 is 1.3 to 1.5 times the thickness of the landslide body 1.

[0180] Preferably, when in use, the bottom ends of the plurality of metal reinforcement piles 2 are driven into the slope surface of the landslide body 1 in a matrix manner, so that the depth of the bottom ends of the plurality of metal reinforcement piles 2 is 1.3 to 1.5 times the thickness of the landslide body 1, and then the plurality of metal reinforcement piles 2 are injected with concrete slurry, and the concrete slurry penetrates into the landslide body 1 through the grouting port 21, and the concrete slurry is waited to solidify, and the metal reinforcement piles 2 can be fixed inside the slope of the landslide body 1, so that the landslide body 1 is grout-reinforced, and then the bottom part of the plurality of reinforcement frames 3 is The top of the plurality of vertical metal reinforcement piles 2 is separately placed, and the connection plate 22 and the top of the metal reinforcement pile 2 are fixedly installed on the bottom of the reinforcement frame 3 by bolts through the threaded opening 23, and then the connection rods 4 are installed between the plurality of reinforcement frames 3 by bolts, so that the plurality of reinforcement frames 3 are connected together by the plurality of connection rods 4, and the stability between the plurality of reinforcement frames 3 is increased, and then the anchor rod 7 at the bottom of the reinforcement seat 6 is driven into the top surface of the landslide body 1, and the reinforcement seat 6 is fixed, and the reinforcement seat 6 is connected to the metal reinforcement pile 2 and the locking ring 5. The reinforcement frame 3 is further tightened to prevent the metal reinforcement pile 2 from sliding down, further improving the effect of landslide prevention and control. When it rains, the rainwater on the landslide surface is intercepted into multiple diversion channels 9, and discharged into the drainage channel 8 through the diversion channel 9, and then discharged through the drainage channel 8, so that the landslide surface of the landslide body 1 can be drained and drained, and then the rainwater on the landslide surface of the landslide body 1 can be drained and managed; by driving multiple metal reinforcement piles into the slope surface of the landslide body in a matrix manner, and then Concrete slurry is injected and penetrates into the landslide body through the grouting port, so that the landslide body is reinforced to support the sliding force of the sliding body and stabilize the landslide. Then, through the cooperation of multiple reinforcement frames and connecting rods, multiple metal reinforcement piles are connected together to increase the stability between the multiple metal reinforcement piles. Through the cooperation of connecting locking rings, reinforcement seats and anchor rods, the metal reinforcement piles and reinforcement frames are further tightened to prevent the metal reinforcement piles from sliding down, thereby further improving the effect of landslide prevention and control.

[0181] In this embodiment, when it rains, rainwater on the landslide surface is intercepted into multiple diversion channels 9, and discharged into the drainage channel 8 through one end of the diversion channel 9, and then discharged through the drainage channel 8, so that the landslide surface of the landslide body 1 can be drained and drained, and then the rainwater on the landslide surface of the landslide body 1 can be drained and managed. At the same time, through the setting of the filter screen, leaves, dead branches or other garbage in the water can be filtered to prevent garbage from entering the drainage channel 8 and the diversion channel 9, causing blockage in the drainage channel 8 and the diversion channel 9. By driving the anchor rod 7 at the bottom of the reinforcement seat 6 into the top surface of the landslide body 1, the reinforcement seat 6 is fixedly installed, and the reinforcement seat 6 is further fastened to the reinforcement frame 3 and the metal reinforcement pile 2 through the connecting lock ring 5 to prevent the metal reinforcement pile 2 from sliding down, further improving the effect of landslide prevention and control. A drainage channel 8 is dug on one side of the landslide surface of the landslide body 1, and multiple diversion channels 9 are dug on the landslide surface of the landslide body 1. The multiple diversion channels 9 are in an inclined state, and the downward inclined end of the drainage channel 8 is communicated with the inside of the drainage channel 8. The multiple diversion channels 9 are used to intercept and divert rainwater on the landslide surface of the landslide body 1. When it rains, rainwater falling on the landslide surface of the landslide body 1 flows into the multiple diversion channels 9, and is discharged into the drainage channel 8 through the downward inclined end of the diversion channel 9, and then discharged through the drainage channel 8, so that the landslide surface of the landslide body 1 can be drained and drained, and then the rainwater on the landslide surface of the landslide body 1 can be drained and managed. Filters are fixedly installed on the tops of the drainage channel 8 and the diversion channel 9, and leaves, dead branches or other garbage in the water are filtered through the filter to prevent garbage from entering the drainage channel 8 and the diversion channel 9, causing blockage in the drainage channel 8 and the diversion channel 9.

[0182] In this embodiment, the bottom ends of multiple metal reinforcement piles 2 are driven into the slope surface of the landslide body 1 in a matrix manner, so that the depth of the bottom ends of the multiple metal reinforcement piles 2 is 1.3 to 1.5 times the thickness of the landslide body 1, thereby ensuring the stability of the metal reinforcement piles 2. Then, concrete slurry is injected into the multiple metal reinforcement piles 2, and through the setting of the grouting port 21, part of the concrete slurry inside the metal reinforcement piles 2 can penetrate into the landslide body 1. After the concrete slurry solidifies, the metal reinforcement piles 2 can be fixed inside the slope of the landslide body 1, so that the landslide body 1 is grouting reinforced. Then, the bottoms of the multiple reinforcement frames 3 are respectively placed on the tops of the multiple vertical metal reinforcement piles 2, and the metal reinforcement piles 2 are fixedly connected to the reinforcement frames 3 by bolts, so as to increase the stability between the vertical metal reinforcement piles 2. Then, connecting rods 4 are installed between the multiple reinforcement frames 3 by bolts, so that the multiple reinforcement frames 3 are connected together by the multiple connecting rods 4, and the stability between the multiple metal reinforcement piles 2 is further increased.

[0183] like Fig.13As shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device 10 includes a processor 81 and a memory 82 coupled to the processor 81.

[0184] The memory 82 stores program instructions for implementing the real-time landslide monitoring method based on multi-sensor fusion and intelligent regulation according to any of the above embodiments.

[0185] The processor 81 is used to execute program instructions stored in the memory 82 to perform real-time landslide monitoring based on multi-sensor fusion and intelligent regulation.

[0186] The processor 81 may also be referred to as a CPU (Central Processing Unit). The processor 81 may be an integrated circuit chip having signal processing capabilities. The processor 81 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0187] Further, Fig.14 This is a schematic diagram of the structure of a storage medium of an embodiment of the present application. The storage medium 9 of the embodiment of the present application stores program instructions 91 that can implement all the above methods, wherein the program instructions 91 can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0188] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0189] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units. The above is only an implementation mode of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present invention.

[0190] The specific implementation methods of the invention are described in detail above, but they are only examples, and the invention is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the invention, therefore, the equalization, modification, improvement, etc. made without departing from the spirit and principle of the invention should be included in the scope of the invention.

Claims

1. A real-time landslide monitoring method based on multi-sensor fusion and intelligent control, characterized in that: The real-time landslide monitoring method based on multi-sensor fusion and intelligent control specifically includes the following steps: At least one sensor is placed inside the metal reinforcement and at key locations of the landslide body to monitor the landslide body data in real time, and the monitored landslide body data is divided into multiple grid units; and water component imaging data of the slope body is obtained in real time under rainfall conditions; The water component data is the water content of each grid unit; the sensors include displacement sensors, stress sensors, humidity sensors and rain monitoring sensors; Compare the water composition imaging data of the landslide body with the preset water composition threshold to obtain the rainfall amount; automatically adjust the valve opening of the drainage channel and the diversion channel according to the rainfall amount; calculate according to the rainfall amount to obtain the stability parameter distribution; The safety factor is obtained according to the distribution of stability parameters, and the instability probability of the slope is obtained through the safety factors of multiple grid units; if the instability probability reaches the starting threshold, the grouting equipment is automatically controlled to start; when the grouting reaches the grouting threshold, the grouting equipment is turned off; the structural design of the metal reinforcement piles and reinforcement frames is optimized and adjusted through finite element analysis.

2. The real-time landslide monitoring method based on multi-sensor fusion and intelligent control according to claim 1 is characterized in that: The process of obtaining the water component imaging data of the slope in real time under rainfall conditions specifically includes the following steps: At least one sensor is placed inside the metal reinforcement and at key positions of the landslide body to monitor the landslide body data in real time, and the landslide body data is divided to obtain a plurality of grid units; a plurality of electrodes are arranged according to the plurality of grid units; Measure the resistivity of multiple grids; obtain the resistivity of any position of the landslide in real time; perform suction balance on the resistivity to obtain the corresponding resistance value, and obtain the corresponding moisture content based on the resistance value; The corresponding resistivity is obtained according to the resistance value, and the moisture content is fitted with the resistivity to obtain the corresponding relationship between the resistivity and the moisture content; the resistivity at different positions is discretized in the corresponding grid units to obtain the resistivity corresponding to each grid unit; the resistivity of each grid unit is input into the corresponding relationship between the resistivity and the moisture content to obtain the moisture content of each grid unit.

3. The real-time landslide monitoring method based on multi-sensor fusion and intelligent control according to claim 2 is characterized in that: The process of obtaining the stability parameter distribution includes the following steps: The water composition imaging data of the landslide body is compared with the preset water composition threshold, and the features of the landslide body data are extracted in combination with the evolution law of the landslide body itself and the landslide deformation mechanism to obtain the rainfall landslide influence feature set; Among them, the rainfall landslide impact feature set includes historical displacement data features, rainfall factor features, stability coefficient features, rainfall infiltration depth features, and time factor features; Generate a predicted displacement sequence based on the historical displacement data characteristics and the rainfall landslide impact feature set, and use the predicted displacement sequence to determine the predicted landslide time; issue an early warning based on the predicted landslide time and upload it to the central control system; The central control system analyzes the landslide time and automatically adjusts the valve opening of the drainage channel and diversion channel according to the analysis results; and analyzes the rainfall volume of the predicted results to obtain the stability parameter distribution.

4. The real-time landslide monitoring method based on multi-sensor fusion and intelligent control according to claim 3 is characterized in that: The process of obtaining the rainfall-landslide impact feature set specifically includes the following steps: Collect the characteristics of historical displacement data; combine the evolution law of the landslide body itself and the mechanism of rainfall on landslide deformation, and analyze the rainfall factor based on the historical average rainfall, previous effective rainfall and number of rainy days; The importance of rainfall factors is determined to determine the characteristics of rainfall factors; the stability coefficient and rainfall penetration depth characteristics are determined based on the permeability coefficient, shear strength and landslide slope; based on the rainfall in different months, characteristic analysis is performed to determine the characteristics of time factors; According to the similarity between the time factor characteristics, the time characteristics are classified; according to the historical displacement data characteristics, rainfall factor characteristics, stability coefficient characteristics, rainfall infiltration depth characteristics and time factor characteristics, the rainfall landslide impact feature set is determined.

5. The real-time landslide monitoring method based on multi-sensor fusion and intelligent regulation according to claim 3 is characterized in that: The process of predicting landslide time and issuing early warning includes the following steps: The deformation acceleration point is calculated based on the curve between the inverse of the landslide velocity and time; based on the deformation acceleration point, the predicted landslide time is determined using the predicted displacement sequence; the difference between the predicted landslide time and the current time is used as an early warning indicator; Construct a landslide monitoring and early warning mechanism based on early warning indicators and landslide migration rate; detect and warn landslides according to the landslide monitoring and early warning mechanism; and upload the real-time monitored landslide early warning data to the central control system; The central control system analyzes the real-time monitored landslide warning data in combination with the rainfall-landslide impact feature set to predict the landslide phenomenon that will occur at the corresponding time.

6. The real-time landslide monitoring method based on multi-sensor fusion and intelligent regulation according to claim 3 is characterized in that: The process of obtaining the stability parameter distribution specifically includes the following steps: The landslide phenomenon occurring at the corresponding time of the landslide is analyzed and calculated in combination with the historical landslide phenomenon to obtain the valve switch value; the valve opening of the drainage channel and the diversion channel is automatically adjusted according to the valve switch value; Based on the rainfall landslide influence feature set, the soil-water characteristic relationship is obtained; based on the relationship between soil resistivity and volume water, the relationship between matrix suction and soil resistance is obtained; by introducing suction stress, the relationship between suction stress and water content is obtained; The stress at any position of the landslide body is obtained, and the suction stress is used to replace the pore pressure to obtain the relationship between the suction stress and the effective stress; according to the relationship between the suction stress and the effective stress, combined with the rainfall, the stability parameter distribution is obtained.

7. The real-time landslide monitoring method based on multi-sensor fusion and intelligent regulation according to claim 1 is characterized in that: The process of optimizing the structural design of metal reinforcement piles and reinforcement frames through finite element analysis includes the following steps: Obtain the safety factor of each grid cell according to the stability parameter distribution; obtain the total number of grid cells and count the number of grid cells with a local safety factor less than 1; obtain the percentage of the number of grid cells with a local safety factor less than 1 in the total number of grid cells and analyze its distribution to obtain the instability probability of the slope; The instability probability is compared with the preset instability probability threshold. When the instability probability reaches the start threshold, the grouting equipment is automatically controlled to start. When the grouting reaches the grouting threshold, the grouting equipment is turned off and the instability probability is recalculated. If it is greater than the start threshold, the grouting equipment is automatically controlled to start. Iterate in sequence until it is lower than the start threshold. Features are extracted from the stability parameter distribution, and the rainfall-landslide impact feature data set is adjusted according to the extracted features for data fusion. A dynamic map is established in a high-dimensional data space for the adjusted rainfall-landslide impact feature data set at different time points, and the quantitative results of the distribution of influencing factors at each position of the metal reinforcement piles and reinforcement frames are obtained.

8. The real-time landslide monitoring method based on multi-sensor fusion and intelligent regulation according to claim 7 is characterized in that: The process of obtaining the quantitative results of the distribution of the influencing factors at each position of the metal reinforcement pile and the reinforcement frame specifically includes the following steps: The adjusted rainfall impact characteristic data set at each time point is combined into a high-dimensional vector, and the preprocessed and normalized standardized data is obtained through data preprocessing and normalization; the covariance matrix is ​​calculated based on the standardized data, and the covariance matrix is ​​eigen-decomposed to obtain the eigenvalues ​​and corresponding eigenvectors; Based on the size of the eigenvalue, the largest eigenvalue and the corresponding eigenvector are selected to form the principal component; based on the principal component, the standardized data is projected to map the high-dimensional data to the low-dimensional space, and the data points at each time point are plotted in the position space to construct a dynamic spectrum; According to the distribution and change trend of the data points in the dynamic spectrum, quantitative analysis is performed to obtain the statistical summary of the impact at each time point or each area; the quantitative distribution results of the influencing factors of the metal reinforcement piles and reinforcement frames are recorded; the structure of the metal reinforcement piles and reinforcement frames is adjusted according to the quantitative results; Among them, quantitative analysis includes examining the distribution of data points in low-dimensional space, changes in time series, and identifying existing anomalies.

9. The method for real-time monitoring of landslides based on multi-sensor fusion and intelligent regulation according to claim 8 is characterized in that: The process of adjusting the metal reinforcement piles and reinforcement frame structure according to the quantitative results specifically includes the following steps: Based on the dynamic spectrogram, analyze the distribution of data points at each time point or each area; identify the change pattern and potential abnormal points of the data in the time series; quantify the data distribution in the dynamic spectrogram; Calculate the cumulative contribution points of the eigenvalues ​​and eigenvectors at each time point or each region, evaluate the explanatory power of different principal components on data changes based on the cumulative contribution points, and the variance contribution value of each time point or each region, and determine the contribution of the principal components to the main change direction of the data; According to the identification of key data and abnormal points in the dynamic spectrum, the structural design of metal reinforcement piles and reinforcement frames is adjusted according to the contribution of the principal component to the main change direction of the data; the adjusted data is recalculated, and the calculation result is compared with the preset structural threshold. If it is greater, it is readjusted; it is iterated in sequence until the calculation result is less than the preset structural threshold.

10. A real-time landslide monitoring system based on multi-sensor fusion and intelligent regulation, which is applied to the real-time landslide monitoring method based on multi-sensor fusion and intelligent regulation as claimed in any one of claims 1 to 9, characterized in that: The landslide real-time monitoring system based on multi-sensor fusion and intelligent control includes: The bottom ends of multiple metal reinforcement piles are driven into the slope surface of the landslide body in a matrix manner. The interior of the metal reinforcement piles is a hollow structure. The outer wall of the metal reinforcement piles is provided with multiple grouting ports, which are evenly distributed on the outer wall of the metal reinforcement piles. Concrete slurry is then injected into the multiple metal reinforcement piles. The concrete slurry penetrates into the landslide body through the grouting ports. After the concrete slurry solidifies, the metal reinforcement piles are fixed inside the slope of the landslide body, so that the landslide body is grout-reinforced. The plurality of metal reinforcement piles are divided into four vertical rows, and reinforcement frames are provided on the tops of the four rows of metal reinforcement piles, and the bottoms of the four reinforcement frames are fixedly installed on the tops of the four vertical rows of metal reinforcement piles by bolts; A drainage channel is dug on one side of the landslide surface of the landslide body, and multiple diversion channels are dug on the landslide surface of the landslide body. The multiple diversion channels are in an inclined state, and the downward inclined end of the drainage channel is communicated with the interior of the drainage channel.

Images