A landslide monitoring and early warning method based on multi-sensor intelligent detector

Abstract

The present invention discloses a landslide monitoring and early warning method based on a multi-sensor intelligent detector, comprising the following steps: according to the on-site conditions of the hidden danger point, in the landslide area to be monitored, multiple monitoring points are set at intervals along the horizontal direction and the vertical direction, and a multi-sensor intelligent monitoring instrument is deployed at each monitoring point; and multiple multi-sensor intelligent monitoring instruments are electrically connected to a main control server. The present invention deploys multiple multi-sensor intelligent monitoring instruments on the displacement surfaces of landslides, collapses and other geological disasters to form a matrix-type surface monitoring of disaster bodies such as landslides and collapses, so that any slip or change in any direction under the matrix-type monitoring can be sensed and monitored, and the spatial three-dimensional dynamic presentation of the matrix monitoring surface is realized through software, and when the monitoring data reaches a certain level, a warning alarm can be issued at the same time.

Description

Technical Field

[0001] The invention relates to the technical field of geological disaster prevention and control engineering, and in particular to a landslide monitoring and early warning method based on a multi-sensor intelligent detector. Background Art

[0002] Landslides are the most common geological disasters. Once they occur, they will bring unpredictable dangers to local residents and cause significant losses to the country's economic construction. By monitoring landslides, giving landslide warnings in real time and predicting their deformation trends, residents can be temporarily evacuated from the landslide site during a period of high risk to ensure their lives.

[0003] At present, in terms of landslide early warning, the Chinese patent application with patent number ZL97199034.4 discloses an earthquake detection system technology using wireless telemetry technology, which is only used for landslide detection, but cannot detect and warn mountain landslides, and the system is complex and costly; the Chinese patent application with announcement number CN106327803 discloses a weighted Markov landslide early warning method based on mean-standard deviation, which uses the mean-standard deviation method to determine the state of each displacement velocity in the displacement velocity set, and uses a weighted Markov chain to predict the displacement velocity prediction state the next day. When the displacement velocity state the day before is abnormal, the displacement velocity state on the day is abnormal, and the displacement velocity prediction state the next day is abnormal, a landslide early warning signal is issued. However, the disadvantage of the above method is that it is necessary to collect at least two days of displacement velocity states for comparison before it can be displayed whether the next day's velocity is abnormal, so it cannot give a very accurate landslide early warning. Summary of the invention

[0004] The purpose of the present invention is to provide a landslide monitoring and early warning method based on a multi-sensor intelligent detector to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a landslide monitoring and early warning method based on a multi-sensor intelligent detector, comprising the following steps:

[0006] According to the on-site conditions of the potential hazard points, multiple monitoring points are set up in the horizontal and vertical directions in the landslide area that needs to be monitored, and a multi-sensor intelligent monitoring instrument is deployed at each monitoring point;

[0007] Electrically connecting a plurality of multi-sensor intelligent monitoring instruments to a main control server respectively;

[0008] Multiple multi-sensor intelligent monitoring instruments obtain the acceleration in the X-axis direction, the Y-axis direction and the Z-axis direction of each monitoring point in real time, and send them to the main control server. The main control server records the acceleration in the X-axis direction, the Y-axis direction and the Z-axis direction of each monitoring point received in correspondence with the GPS coordinates of each monitoring point;

[0009] The main control server processes the received acceleration in the X-axis direction, the Y-axis direction and the Z-axis direction of each monitoring point respectively, and converts them into displacement in the X-axis direction, the Y-axis direction and the Z-axis direction respectively, and records the displacement in the X-axis direction, the Y-axis direction and the Z-axis direction of each monitoring point in correspondence with the GPS coordinates of each monitoring point;

[0010] The real-time monitoring surface is constructed using the displacement of each monitoring point in the X-axis direction, the displacement of the Y-axis direction, and the displacement of the Z-axis direction and the GPS coordinates of each monitoring point.

[0011] The main control server compares the displacement of each monitoring point on the real-time monitoring surface in the X-axis direction, the Y-axis direction and the Z-axis direction one by one, selects the monitoring point with the largest displacement in a certain direction, and compares the maximum displacement of the monitoring point with the threshold. When the maximum displacement exceeds the threshold, the main control server sends an alarm signal.

[0012] Preferably, the main control server converts the received acceleration in the X-axis direction, the acceleration in the Y-axis direction and the acceleration in the Z-axis direction into displacement in the X-axis direction, the displacement in the Y-axis direction and the displacement in the Z-axis direction respectively in the same manner.

[0013] Preferably, the main control server converts the received acceleration in the X-axis direction into the displacement in the X-axis direction including the following steps:

[0014] According to the acceleration definition formula, we have

[0015]

[0016] Among them, u(t) is the displacement corresponding to time t, a(t) is the theoretical value of acceleration corresponding to time t, is the actual value of acceleration measured at time t;

[0017] Assume that equation (1) is a Π function of displacement u(t),

[0018]

[0019] Among them, u(t) is the displacement corresponding to time t, a(t) is the theoretical value of acceleration corresponding to time t, is the actual value of acceleration measured at time t, T1 and T2 are two time points;

[0020] Discretize equation (1) using the trapezoidal rule, and we have

[0021]

[0022] in, is a (2k+1)-order diagonal weighted matrix, except that the first and last elements are equal to All diagonal elements are 1, a k is the theoretical value of acceleration of the kth interpolation node, is the actual value of acceleration measured at time t, and Δt is a fixed time period;

[0023] The acceleration a obtained at a certain point p p (u), discretized by the central finite difference method, we can get the formula:

[0024]

[0025] For all time steps, rewrite (3) into matrix form:

[0026]

[0027] in,

[0028] a is the acceleration component of the corresponding point, Δt is a fixed time period;

[0029] Substituting formula (4) into formula (2), it can be simplified to

[0030]

[0031] Among them, L a is a (2k+1)-order diagonal weighted matrix, except that the first and last elements are equal to All diagonal elements are 1, is the acceleration measurement value, Δt is a fixed time period;

[0032] Using regularization techniques and adding additional conditions, we have

[0033]

[0034] Among them, E (u) represents a function of displacement u, where u is the calculated displacement and u st is the static displacement, r is an arbitrary minimum value, ||uu st||2 is the square norm of the difference function, which is used to characterize the two functions u and u st The degree of approximation;

[0035] Rewrite equation (6) as

[0036]

[0037] Among them, E (u) represents a function about the displacement u, where u is the calculated displacement and r is an arbitrarily determined minimum value;

[0038] Applying the regularization condition equation as a penalty function to the original minimization problem (5) yields (8):

[0039]

[0040] Among them, L a is a (2k+1)-order diagonal weighted matrix, except that the first and last elements are equal to All diagonal elements are 1,

[0041] u is the calculated displacement, λ is the optimal regularization factor;

[0042] Solving equation (8) yields the analytical solution for the displacement:

[0043]

[0044] Where L = L a ·L c , L a is a (2k+1)-order diagonal weighted matrix, except that the first and last elements are equal to All diagonal elements are 1, u is the calculated displacement, λ is the optimal regularization factor,

[0045] Let the first three terms be C, and simplify equation (9) to

[0046]

[0047] Where C = (L T L+λ 2 I) -1 L T L a , is the acceleration measurement value, Δt is a fixed time period;

[0048] Substituting the acceleration measurement value of any point in a fixed time period into equation (10) can obtain the displacement of the point.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. The present invention deploys multiple multi-sensor intelligent monitoring instruments on the displacement surfaces of landslides, collapses and other geological disasters to form a matrix-type surface monitoring of landslides, collapses and other disaster bodies, so that any slip or change in any direction under the matrix-type monitoring can be sensed and monitored, and the spatial three-dimensional dynamic presentation of the matrix monitoring surface can be realized through software, and when the monitoring data reaches a certain level, early warning alarms can be issued at the same time;

[0051] 2. The present invention uses the characteristics of thermal imaging temperature measurement to collect matrix-type surface monitoring data with a short data time span for detection and early warning, with high accuracy and high practicality, and is worthy of promotion;

[0052] 3. The method of deriving displacement from acceleration adopted by the present invention is more convenient and concise than the traditional method of obtaining displacement by quadratic integration of acceleration. Although the multi-sensor intelligent detector adopted by the present invention can only measure acceleration, it is dozens of times cheaper than the detector that can measure displacement, and is more economically practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of the method of the present invention.

[0054] Figure 2 It is a deployment diagram of the multi-sensor intelligent monitoring instrument of the present invention. DETAILED DESCRIPTION

[0055] 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.

[0056] In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "front end", "rear end", "two ends", "one end", "the other end" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0057] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "provided with", "connected", etc. should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0058] See also Figure 1 , Figure 2 The present invention provides an embodiment: a landslide monitoring and early warning method based on a multi-sensor intelligent detector, comprising the following steps:

[0059] S1. According to the on-site conditions of the potential hazard points, in the landslide area that needs to be monitored, such as Figure 2 As shown, multiple monitoring points are set at intervals along the horizontal direction and the vertical direction, and a multi-sensor intelligent monitoring instrument is deployed at each monitoring point to obtain the acceleration in the X-axis direction, the Y-axis direction and the Z-axis direction of each monitoring point;

[0060] S2, electrically connecting a plurality of multi-sensor intelligent monitoring instruments to a main control server respectively;

[0061] S3, multiple multi-sensor intelligent monitoring instruments obtain the acceleration in the X-axis direction, the Y-axis direction and the Z-axis direction of each monitoring point in real time, and send them to the main control server, and the main control server records the received acceleration in the X-axis direction, the Y-axis direction and the Z-axis direction of each monitoring point in correspondence with the GPS coordinates of each monitoring point;

[0062] S4. The main control server processes the received acceleration in the X-axis direction, the acceleration in the Y-axis direction, and the acceleration in the Z-axis direction of each monitoring point, and converts them into displacement in the X-axis direction, the displacement in the Y-axis direction, and the displacement in the Z-axis direction of each monitoring point, and records the displacement in the X-axis direction, the displacement in the Y-axis direction, and the displacement in the Z-axis direction of each monitoring point in correspondence with the GPS coordinates of each monitoring point;

[0063] S5, constructing a real-time monitoring surface using the displacement of each monitoring point in the X-axis direction, the displacement of each monitoring point in the Y-axis direction, and the displacement of each monitoring point in the Z-axis direction and the GPS coordinates of each monitoring point;

[0064] S6. The master control server compares the displacement in the X-axis direction, the displacement in the Y-axis direction, and the displacement in the Z-axis direction of each monitoring point on the surface under real-time monitoring one by one, selects the monitoring point with the largest displacement in a certain direction, and compares the maximum displacement of this monitoring point with a threshold. When the maximum displacement exceeds the threshold, the master control server issues an alarm signal.

[0065] Specifically, when the master control server compares the displacement in the X-axis direction, the displacement in the Y-axis direction, and the displacement in the Z-axis direction of each monitoring point on the surface under real-time monitoring one by one, it selects the monitoring point with the largest displacement in a certain direction, and compares the displacement of this monitoring point with a threshold. The threshold can be set in segments. Taking 30mm and 100mm as examples, when the maximum displacement exceeds 30mm and is less than 100mm, the master control server marks this monitoring point and multiple adjacent monitoring points as a key monitoring area, prompts the on-site personnel to conduct key inspections in this area, and continuously counts the displacement in the X-axis direction, the displacement in the Y-axis direction, and the displacement in the Z-axis direction of this key monitoring area within a monitoring cycle; when the maximum displacement exceeds 100mm, the master control server marks this monitoring point and multiple adjacent monitoring points as a landslide warning area and gives a warning signal. At the same time, it compiles all the data collected from this landslide warning area and adjacent landslide monitoring areas and sends them to the expert analysis system for comprehensive analysis. The expert checks the relevant data information on-site or through the network for analysis, discussion, and formulation of solutions.

[0066] Preferably, within the time interval T1 < t < T2, the master control server processes the acceleration in the X-axis direction, the acceleration in the Y-axis direction, and the acceleration in the Z-axis direction of each received monitoring point respectively, and converts them into the displacement in the X-axis direction, the displacement in the Y-axis direction, and the displacement in the Z-axis direction respectively. Since the processing methods in the three directions are the same, the processing method in one of the directions is listed for description. Specifically, it includes the following steps:

[0067] According to the acceleration definition formula,

[0068]

[0069] where u(t) is the displacement corresponding to the moment t, and a(t) is the theoretical value of the acceleration corresponding to the moment t. is the actual measured value of the acceleration at the moment t.

[0070] Therefore, a Π function of the displacement u(t) as shown in formula (1) can be assumed. When this function takes the minimum value, the actual measured value of the acceleration is infinitely close to the theoretical value of the acceleration.

[0071]

[0072] Among them, u(t) is the displacement corresponding to time t, a(t) is the theoretical value of acceleration corresponding to time t, is the actual value of acceleration measured at time t, and T1 and T2 are two time points.

[0073] Preferably, equation (1) is discretized by the trapezoidal rule,

[0074]

[0075] in, is a (2k+1)-order diagonal weighted matrix, except that the first and last elements are equal to All diagonal elements are 1, a k is the theoretical value of acceleration of the kth interpolation node, is the actual value of acceleration measured at time t, and Δt is a fixed time period.

[0076] Preferably, the acceleration a obtained at a certain point p is p (u), discretized by the central finite difference method, we can get the formula:

[0077]

[0078] Preferably, for all time steps, (3) is rewritten in matrix form:

[0079]

[0080] in,

[0081] a is the acceleration component of the corresponding point, and Δt is a fixed time period.

[0082] Substituting formula (4) into formula (2), we can obtain:

[0083]

[0084] Among them, L a is a (2k+1)-order diagonal weighted matrix, except that the first and last elements are equal to All diagonal elements are 1, is the acceleration measurement value, and Δt is a fixed time period.

[0085] Preferably, since the linear algebra operator L = L a ·L cThe rank deficiency makes it impossible to produce a unique displacement solution for the minimization problem of the equation under a given measured acceleration, and the equation does not specify appropriate boundary conditions, making the minimization problem of the equation ill-posed. In order to solve the ill-posed problem here, it is necessary to use regularization technology and add additional conditions to solve it. According to the requirements, the calculated displacement needs to be kept near the static displacement of the given system, so the following formula can be obtained:

[0086]

[0087] Among them, E (u) represents a function of displacement u, where u is the calculated displacement and u st is the static displacement, r is an arbitrary minimum value, ||uu st ||2 is the square norm of the difference function, which is used to characterize the two functions u and u st degree of approximation.

[0088] Since the static displacement should be equal to zero, the above equation (6) can be rewritten as

[0089]

[0090] Among them, E (u) represents a function about the displacement u, where u is the calculated displacement and r is an arbitrarily determined minimum value.

[0091] Preferably, the regularization condition equation is applied as a penalty function to the original minimization problem equation (5), and the equation (8) shown below is obtained:

[0092]

[0093] Among them, L a is a (2k+1)-order diagonal weighted matrix, except that the first and last elements are equal to All diagonal elements are 1,

[0094] u is the calculated displacement, and λ is the optimal regularization factor.

[0095] The minimization problem in the above formula is a quadratic problem about the unknown displacement vector. Therefore, the analytical solution of the displacement can be obtained from formula (8):

[0096]

[0097] Where L = L a ·L c , L a is a (2k+1)-order diagonal weighted matrix, except that the first and last elements are equal to All diagonal elements are 1, u is the calculated displacement, λ is the optimal regularization factor,

[0098] Let the first three terms be C, and equation (9) can be simplified to the following:

[0099]

[0100] Where C = (L T L+λ 2 I) -1 L T L a , is the acceleration measurement value, and Δt is a fixed time period.

[0101] At this point, we get the acceleration measurement value Formula (10) for the displacement u is obtained by substituting the acceleration measurement value of any point in a fixed time period into the formula (10).

[0102] The above method is a process of obtaining the displacement in one of the accelerations in the X-axis direction, the Y-axis direction and the Z-axis direction of a monitoring point within a fixed time period measured by a multi-sensor intelligent detector. Similarly, the displacements in the other two directions can be obtained, and the displacements in the X-axis direction, the Y-axis direction and the Z-axis direction u (u x, u y, u z ).

[0103] At this time, the displacement u(u x, u y, u z ) After that, the displacement distribution on the entire real-time monitoring surface can be obtained by interpolation.

[0104] Specifically, you can use Matlab to implement surface interpolation. The following is a sample code for using the griddata function to perform surface interpolation, generating a spatial surface based on discrete point coordinate interpolation.

[0105] clear;clc;

[0106] [X,Y,Z] = peaks(30);

[0107] position=[13,26;20,24;16,27;16,21;16,24;9,19;9,15;7,17;

[0108] 12,17;9,17;14,16;14,11;11,12;16,12;14,13;17,11;16,5;13,8;20,8;17,8;22,19;22,12;20,16;25,16;22,16];

[0109] p1 = position(:,1);

[0110] p2=position(:,2);

[0111] x = zeros(1,length(p1));

[0112] y=zeros(1,length(p1));

[0113] z = zeros(1,length(p1));

[0114] for i=1:length(p1)

[0115] x(i)=X(p1(i),p2(i));

[0116] y(i)=Y(p1(i),p2(i));

[0117] z(i)=Z(p1(i),p2(i));

[0118] end

[0119] subplot(1,2,1)

[0120] surf(X,Y,Z)

[0121] hold on

[0122] plot3(x,y,z,'or','MarkerFaceColor','r');

[0123] hold off

[0124] ylim([-2,2]);

[0125] [Xi,Yi,Zi]=griddata(x,y,z,linspace(min(x),max(x),30)',linspace(min(y),max(y),30),'v4');

[0126] % 'linear': triangle-based linear interpolation (default algorithm);

[0127] % 'cubic': triangle-based cubic interpolation;

[0128] % 'nearest': nearest neighbor interpolation method;

[0129] % 'v4': griddata algorithm in MATLAB 4.

[0130] subplot(1,2,2)

[0131] surf(Xi,Yi,Zi)

[0132] hold on

[0133] plot3(x,y,z,'or','MarkerFaceColor','r');

[0134] hold off

[0135] Further, the code to obtain the displacement vector diagram is as follows:

[0136] clear;clc;

[0137] [X,Y,Z] = peaks(30);

[0138] [U,V,W] = surfnorm(X,Y,Z);

[0139] quiver3(X,Y,Z,U,V,W,0.5,'r'); % Arrow length, color

[0140] hold on

[0141] surf(X,Y,Z,'LineStyle','none'); % grid lines are not displayed

[0142] It is also possible to group multiple monitoring points into one area, set up a data acquisition terminal in the area, collect data information measured by each monitoring point in the area in real time through the data acquisition terminal, and transmit the data information to the main control server through wireless communication methods such as 2G, 3G or satellite communication.

[0143] Here, considering that some areas have a long-term downward trend or lateral shift trend, the displacement allowed to occur needs to be adjusted accordingly. Therefore, the specific situation and threshold of the early warning are determined by the actual situation. Several multi-sensor intelligent detectors can be deployed first, and the displacement of multiple points in the X-axis direction, Y-axis direction, and Z-axis direction obtained without landslides are taken as the maximum value. This is repeated several times, and the average value of the maximum displacement each time is taken as the threshold. Once the threshold is exceeded, the server of the main control center will issue an alarm prompt and send an alarm signal to warn of the landslide.

[0144] The present invention provides a landslide monitoring and early warning method based on a multi-sensor intelligent detector. By deploying multiple multi-sensor intelligent monitoring instruments on the displacement surface of landslides, collapses and other geological disasters, a matrix-type surface monitoring of disaster bodies such as landslides and collapses is formed, and slippage or changes in any direction under matrix-type monitoring can be sensed and monitored. The spatial three-dimensional dynamic presentation of the matrix monitoring surface is realized through software, and when the monitoring data reaches a certain level, a warning alarm can be issued at the same time. The present invention collects matrix-type surface monitoring data with a small data time span for detection and early warning, has high accuracy and high practicality, and is worthy of promotion.

[0145] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A landslide monitoring and early warning method based on a multi-sensor intelligent detector, characterized in that: The following steps are involved: According to the on-site conditions of the potential hazard points, multiple monitoring points are set up in the horizontal and vertical directions in the landslide area that needs to be monitored, and a multi-sensor intelligent monitoring instrument is deployed at each monitoring point; Electrically connecting a plurality of multi-sensor intelligent monitoring instruments to a main control server respectively; Multiple multi-sensor intelligent monitoring instruments obtain the acceleration in the X-axis direction, the Y-axis direction and the Z-axis direction of each monitoring point in real time, and send them to the main control server. The main control server records the acceleration in the X-axis direction, the Y-axis direction and the Z-axis direction of each monitoring point received in correspondence with the GPS coordinates of each monitoring point; The main control server processes the received acceleration in the X-axis direction, the Y-axis direction and the Z-axis direction of each monitoring point respectively, and converts them into displacement in the X-axis direction, the Y-axis direction and the Z-axis direction respectively, and records the displacement in the X-axis direction, the Y-axis direction and the Z-axis direction of each monitoring point in correspondence with the GPS coordinates of each monitoring point; The real-time monitoring surface is constructed using the displacement of each monitoring point in the X-axis direction, the displacement of the Y-axis direction, and the displacement of the Z-axis direction and the GPS coordinates of each monitoring point. The main control server compares the displacement of each monitoring point on the real-time monitoring surface in the X-axis direction, the Y-axis direction and the Z-axis direction one by one, selects the monitoring point with the largest displacement in a certain direction, and compares the maximum displacement of the monitoring point with the threshold value. When the maximum displacement exceeds the threshold value, the main control server sends an alarm signal; The main control server converts the received acceleration in the X-axis direction into the displacement in the X-axis direction, including the following steps: According to the acceleration definition formula, we have Among them, u(t) is the displacement corresponding to time t, a(t) is the theoretical value of acceleration corresponding to time t, is the actual value of acceleration measured at time t; Assume that equation (1) is a Π function of displacement u(t), Among them, u(t) is the displacement corresponding to time t, a(t) is the theoretical value of acceleration corresponding to time t, is the actual value of acceleration measured at time t, T1 and T2 are two time points; Discretize equation (1) using the trapezoidal rule, and we have in, is a (2k+1)-order diagonal weighted matrix, except that the first and last elements are equal to All diagonal elements are 1, a k is the theoretical value of acceleration of the kth interpolation node, is the actual value of acceleration measured at time t, and Δt is a fixed time period; The acceleration a obtained at a certain point p p (u), discretized by the central finite difference method, we can get the formula: For all time steps, rewrite (3) into matrix form: in, a is the acceleration component of the corresponding point, Δt is a fixed time period; Substituting formula (4) into formula (2), it can be simplified to Among them, L a is a (2k+1)-order diagonal weighted matrix, except that the first and last elements are equal to All diagonal elements are 1, is the acceleration measurement value, Δt is a fixed time period; Using regularization techniques and adding additional conditions, we have Among them, E (u) represents a function of displacement u, where u is the calculated displacement and u st is the static displacement, r is an arbitrary minimum value, ||uu st ||2 is the square norm of the difference function, which is used to characterize the two functions u and u st The degree of approximation; Rewrite equation (6) as Among them, E (u) represents a function about the displacement u, where u is the calculated displacement and r is an arbitrarily determined minimum value; Applying the regularization condition equation as a penalty function to the original minimization problem (5) yields (8): Among them, L a is a (2k+1)-order diagonal weighted matrix, except that the first and last elements are equal to All diagonal elements are 1, u is the calculated displacement, λ is the optimal regularization factor; solving equation (8) yields the analytical solution of the displacement: Where L = L a ·L c , L a is a (2k+1)-order diagonal weighted matrix, except that the first and last elements are equal to All diagonal elements are 1, u is the calculated displacement, λ is the optimal regularization factor, Let the first three terms be C, and simplify equation (9) to Where C = (L T L+λ 2 I) -1 L T L a , is the acceleration measurement value, Δt is a fixed time period; Substituting the acceleration measurement value of any point in a fixed time period into equation (10) can obtain the displacement of the point.

2. The landslide monitoring and early warning method based on a multi-sensor intelligent detector according to claim 1 is characterized by: The main control server converts the received acceleration in the X-axis direction, the acceleration in the Y-axis direction and the acceleration in the Z-axis direction into displacement in the X-axis direction, the displacement in the Y-axis direction and the displacement in the Z-axis direction respectively in the same manner.

3. The landslide monitoring and early warning method based on a multi-sensor intelligent detector according to claim 1 is characterized in that: The multiple monitoring points are grouped into one area, a data acquisition terminal is set up in the area, the data information measured by each monitoring point in the area is collected in real time by the data acquisition terminal, and the data information is transmitted to the main control server through 2G, 3G or satellite wireless communication.

4. The landslide monitoring and early warning method based on a multi-sensor intelligent detector according to claim 1 is characterized in that: The method for determining the threshold value comprises the following steps: The displacements in the X-axis direction, Y-axis direction and Z-axis direction of multiple points obtained without landslide are collected and the maximum value is taken. This is repeated several times and the average value of the maximum displacement values ​​is taken as the threshold.

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