Landslide displacement monitoring method, system and device, and storage medium

Abstract

Disclosed in embodiments of the present application are a landslide displacement monitoring method, system and device, and a storage medium. By taking the minimum total mean square error as an objective, the optimal weighting factor corresponding to each piece of landslide displacement data can be adaptively searched, so that a fused data value is optimal. The optimization method can fully consider differences in errors among the landslide displacement data, thereby obtaining a more accurate and reliable data fusion result. Thus, weighted fusion is performed on all the landslide displacement data on the basis of a first weighting factor of each piece of landslide displacement data, to obtain landslide displacement deformation data at a landslide displacement monitoring point, so that the landslide displacement deformation data enables comprehensive and effective determination of landslide deformation stages, and more closely reflects actual landslide deformation.

Description

A method, system, device, and storage medium for monitoring landslide displacement. [Technical Field]

[0001] This invention relates to the field of landslide prediction technology, and in particular to a method, system, device and storage medium for monitoring landslide displacement. [Background Technology]

[0002] In geological hazard monitoring and analysis, the most important aspect is the discriminant analysis of the deformation stages of landslides. However, in practical applications, simply comparing and analyzing the data collected at each monitoring point is insufficient to fully utilize the information in this data, thus failing to achieve accurate monitoring of the true situation of landslides. [Summary of the Invention]

[0003] In view of this, the present invention provides a method, system, device and storage medium for monitoring landslide displacement.

[0004] The specific technical solution of the first embodiment of the present invention is as follows: a method for monitoring landslide displacement, the method comprising: acquiring landslide monitoring data from landslide displacement monitoring points; the landslide monitoring data including landslide displacement data obtained by different displacement sensors monitoring the landslide displacement monitoring points; acquiring the total mean square error of all landslide displacement data; acquiring a first weighting factor for each landslide displacement data with the goal of minimizing the total mean square error; using the first weighting factor of each landslide displacement data to perform weighted fusion of all landslide displacement data to obtain landslide displacement deformation data of the landslide displacement monitoring points; the landslide displacement deformation data including displacement data after fusion of landslide displacement data from different time periods; acquiring landslide displacement deformation data from different time periods; and monitoring landslide displacement at the landslide displacement monitoring points based on the landslide displacement deformation data from different time periods.

[0005] Preferably, obtaining the total mean square error of all landslide displacement data includes: obtaining the variance of each landslide displacement data and a second weighting factor for each landslide displacement data; the second weighting factor is a preset weighting factor; and obtaining the total mean square error based on the variance of each landslide displacement data and the second weighting factor.

[0006] Preferably, the total mean square error is obtained using the following formula:

[0007] Where, σ 2 Let W be the total mean square error, n be the number of landslide displacement data, and W be the total mean square error. p The second weighting factor for the p-th landslide displacement data is... Let be the variance of the p-th landslide displacement data.

[0008] Preferably, obtaining the variance of each landslide monitoring data includes: obtaining the variance of the first landslide displacement data based on the cross-correlation coefficient between the first landslide displacement data and the second landslide displacement data, and the autocorrelation coefficient of the first landslide displacement data; the first landslide displacement data and the second landslide displacement data are any one of the landslide displacement data in the landslide monitoring data.

[0009] Preferably, the difference between the cross-correlation coefficient and the autocorrelation coefficient is the variance of the first landslide displacement data.

[0010] Preferably, the first weighting factor is obtained using the following formula:

[0011] in, The first weighting factor for the p-th landslide displacement data is... Let p be the variance of the p-th landslide displacement data. Let be the variance of the i-th landslide displacement data.

[0012] Preferably, after obtaining the landslide monitoring data of the landslide displacement monitoring points, the method further includes: performing data missing interpolation and data smoothing and denoising processing on the landslide monitoring data to obtain optimized landslide monitoring data; and using the optimized landslide monitoring data as the landslide monitoring data to return the total mean square error of obtaining all landslide displacement data.

[0013] The specific technical solution of the second embodiment of the present invention is as follows: a landslide displacement monitoring system, the system comprising: a first data acquisition module, a total mean square error acquisition module, a target optimization module, a data fusion module, a second data acquisition module, and a monitoring module; the first data acquisition module is used to acquire landslide monitoring data of landslide displacement monitoring points; the landslide monitoring data includes landslide displacement data obtained by different displacement sensors monitoring the landslide displacement monitoring points; the total mean square error acquisition module is used to acquire the total mean square error of all landslide displacement data; the target optimization module is used to acquire a first weighting factor for each landslide displacement data with the goal of minimizing the total mean square error; the data fusion module is used to use the first weighting factor of each landslide displacement data to perform weighted fusion of all landslide displacement data to obtain landslide displacement deformation data of the landslide displacement monitoring points; the landslide displacement deformation data includes displacement data after fusion of landslide displacement data from different time periods; the second data acquisition module is used to acquire landslide displacement deformation data from different time periods; the monitoring module is used to monitor the landslide displacement of the landslide displacement monitoring points based on the landslide displacement deformation data from different time periods.

[0014] The specific technical solution of the third embodiment of the present invention is as follows: a landslide displacement monitoring device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method as described in any one of the first embodiments of this application.

[0015] The specific technical solution of the fourth embodiment of the present invention is as follows: a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor performs the steps of the method as described in any one of the first embodiments of this application.

[0016] Implementing the embodiments of the present invention will have the following beneficial effects:

[0017] This invention acquires landslide monitoring data from landslide displacement monitoring points; obtains the total mean square error of all landslide displacement data; obtains a first weighting factor for each landslide displacement data with the goal of minimizing the total mean square error; uses the first weighting factor of each landslide displacement data to perform weighted fusion of all landslide displacement data to obtain landslide displacement deformation data of the landslide displacement monitoring points; acquires landslide displacement deformation data for different time periods; and performs landslide displacement monitoring at the landslide displacement monitoring points based on the landslide displacement deformation data for different time periods.

[0018] By minimizing the total mean square error, the optimal weighting factor corresponding to each landslide displacement data point can be adaptively found, resulting in optimal fused data values. This optimization method fully considers the error differences between individual landslide displacement data points, thus obtaining more accurate and reliable data fusion results. Therefore, by using the first weighting factor of each landslide displacement data point to weight and fuse all landslide displacement data, landslide displacement deformation data at monitoring points can be obtained. This allows the landslide displacement deformation data to comprehensively and effectively identify landslide deformation stages and more closely reflect the actual landslide deformation situation. [Attached Image Description]

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 is a flowchart of the steps for monitoring landslide displacement;

[0021] Figure 2 is a schematic diagram of landslide displacement data at different time periods;

[0022] Figure 3 is a schematic diagram of the merged landslide displacement data at different time periods;

[0023] Figure 4 is a schematic diagram of the landslide displacement monitoring system;

[0024] Figure 5 is a diagram of the internal structure of a computer device;

[0025] Among them, 201 is the first data acquisition module; 202 is the total mean square error acquisition module; 203 is the target optimization module; 204 is the data fusion module; 205 is the second data acquisition module; and 206 is the monitoring module.

Detailed Implementation Methods

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0027] The terms "first," "second," etc., used in the specification, claims, and drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to such processes, methods, products, or apparatus.

[0028] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0029] To more effectively utilize deformation monitoring data from various types and locations, these data can be fused. Currently, commonly used landslide monitoring data fusion algorithms include Kalman filtering, wavelet transform, and weighted fusion. This application is based on GNSS monitoring technology and landslide displacement and deformation data collected by displacement gauges, and employs a weighted fusion method to perform data-level fusion processing on the displacement changes obtained from landslide body monitoring.

[0030] (1) Multiple landslide monitoring displacement sensors are installed in areas where landslides and other geological disasters are prone to occur in power transmission line projects to monitor the displacement changes of landslide bodies that may landslide.

[0031] (2) Monitoring data preprocessing:

[0032] When monitoring landslides, both the monitoring instruments themselves and the external environment can affect the monitoring data to varying degrees, reducing the quality of the data. By performing data preprocessing such as removing irrelevant information (gross errors), data interpolation and completion, and data smoothing and denoising, higher-quality monitoring sequences can be obtained, further improving the accuracy and reliability of data-level fusion results and applications.

[0033] Please refer to Figure 1, which is a flowchart of a landslide displacement monitoring method according to the first embodiment of this application. This method enables landslide displacement deformation data to comprehensively and effectively identify the landslide deformation stage and more closely reflect the actual landslide deformation situation. The method includes:

[0034] Step 101: Obtain landslide monitoring data from landslide displacement monitoring points; the landslide monitoring data includes landslide displacement data obtained by different displacement sensors monitoring the landslide displacement monitoring points;

[0035] Step 102: Obtain the total mean square error of all landslide displacement data;

[0036] Step 103: Obtain the first weighting factor for each landslide displacement data with the goal of minimizing the total mean square error;

[0037] Step 104: Use the first weighting factor of each landslide displacement data to perform weighted fusion of all landslide displacement data to obtain the landslide displacement deformation data of the landslide displacement monitoring point; the landslide displacement deformation data includes the displacement data after fusion of landslide displacement data from different time periods;

[0038] Step 105: Obtain landslide displacement and deformation data for different time periods;

[0039] Step 106: Monitor the landslide displacement at the monitoring points based on the landslide displacement deformation data from different time periods.

[0040] Specifically, multiple displacement sensors are used to monitor landslide displacement monitoring points and obtain landslide displacement data from these monitoring points. The total mean square error (TMS) reflects the difference between the landslide displacement data and the actual displacement data. The first weighting factor for each landslide displacement data is obtained with the goal of minimizing the TMS. The landslide displacement data is then weighted and fused using the first weighting factor of each landslide displacement data, which minimizes the difference between the fused landslide displacement deformation data and the actual displacement data.

[0041] The method in this embodiment aims to minimize the total mean square error by adaptively finding the optimal weighting factor for each landslide displacement data point, thus achieving the optimal fused data value. This optimization method fully considers the error differences between various landslide displacement data points, resulting in more accurate and reliable data fusion results. Therefore, by using the first weighting factor of each landslide displacement data point to perform weighted fusion on all landslide displacement data, the landslide displacement deformation data of the monitoring points is obtained. This allows the landslide displacement deformation data to comprehensively and effectively identify the landslide deformation stage and more closely reflect the actual landslide deformation situation.

[0042] In a specific embodiment, obtaining the total mean square error of all landslide displacement data includes: obtaining the variance of each landslide displacement data and a second weighting factor for each landslide displacement data; the second weighting factor is a preset weighting factor; and obtaining the total mean square error based on the variance of each landslide displacement data and the second weighting factor.

[0043] Specifically, let the variances of the n displacement monitoring sensors be respectively The true value to be estimated is X. Each sensor's data corresponds to one true value, and the number of measurements is the same. The measurements of each displacement sensor are X1, X2, ..., X... n The preset first weighting factors for each group of measurements are W1, W2, ..., W... n The preliminary true value X obtained by fusing multiple measurements is then... * The value satisfies the following expression:

[0044] In a specific embodiment, the total mean square error is obtained using the following formula:

[0045] Where, σ 2 Let W be the total mean square error, n be the number of landslide displacement data, and W be the total mean square error. p The second weighting factor for the p-th landslide displacement data is... Let be the variance of the p-th landslide displacement data.

[0046] In a specific embodiment, the first weighting factor is obtained using the following formula:

[0047] in, The first weighting factor for the p-th landslide displacement data is... Let p be the variance of the p-th landslide displacement data. Let be the variance of the i-th landslide displacement data. Specifically, the number of second weighting factors is the same as the number of measurements, meaning each measurement has a corresponding weighting factor.

[0048] In a specific embodiment, obtaining the variance of each landslide monitoring data includes: obtaining the variance of the first landslide displacement data based on the cross-correlation coefficient between the first landslide displacement data and the second landslide displacement data, and the autocorrelation coefficient of the first landslide displacement data; the first landslide displacement data and the second landslide displacement data are any one of the landslide displacement data in the landslide monitoring data.

[0049] Specifically, the displacement values ​​collected by the two independent displacement monitoring sensors are Xp and Xq, respectively, and the corresponding measurement errors are Vp and Vq, respectively, that is: X p =X+V p X q =X+V q

[0050] The variance of the displacement monitoring sensor p is:

[0051] The cross-correlation coefficient R between Xp and Xq pq For: R pq =E[X p X q ] = E[X 2

[0052] The autocorrelation coefficient R of Xp pp for:

[0053] In a specific embodiment, the difference between the cross-correlation coefficient and the autocorrelation coefficient is the variance of the first landslide displacement data. Specifically, the variance of the landslide displacement data is obtained using the following formula:

[0054] In a specific embodiment, after obtaining the landslide monitoring data of the landslide displacement monitoring points, the method further includes: performing data missing interpolation and data smoothing and denoising processing on the landslide monitoring data to obtain optimized landslide monitoring data; using the optimized landslide monitoring data as the landslide monitoring data, and returning the step of obtaining the total mean square error of all landslide displacement data.

[0055] Specifically, during landslide monitoring, both the monitoring instruments themselves and the external environment can influence the monitoring data to varying degrees, reducing its quality. Preprocessing the monitoring data, including removing irrelevant information (gross errors), data interpolation for completion, and data smoothing and denoising, aims to obtain higher-quality monitoring sequences and further improve the accuracy and reliability of data fusion results and applications. In practical applications, surface displacement monitoring data commonly suffers from missing data, primarily during the uniform deformation and accelerated deformation stages. Lagrange interpolation is used to fill in the missing data in landslide monitoring data, providing a high-quality dataset for multi-source landslide data fusion. Figure 2 shows the three-dimensional displacement changes during the uniform deformation stage of the landslide monitoring data. The "peaks and spikes" in the monitoring sequence are smoothed, preserving effective displacement characteristics while making the displacement trend clearer. Least squares and weighted moving average methods are selected for data smoothing and denoising based on the characteristics of the landslide deformation stages. Even with data noise, the least squares method can still provide reasonable fitting results. It has a smaller impact on outliers and can handle them better, making it more reliable when dealing with noisy or outlier data. The weighted moving average method, by assigning different weights to observations at different times, can better handle trend changes in data, thus providing more accurate predictions.

[0056] In specific embodiments, geological disasters such as landslides often exhibit suddenness due to geological environment. As can be seen from the cumulative displacement deformation, the accelerated deformation stage of loess landslides is very short-lived. Therefore, to analyze the accelerated deformation stage more accurately and reliably, the interval in the equal-interval processing should be reduced, and the sample size of the accelerated deformation stage observation sequence should be increased. In this application, the accelerated deformation stage is spaced at two-hour intervals, and the other two stages are spaced at days. The average value of the data is used to obtain the equally spaced observation sequence. Therefore, the monitoring sequence is divided into two stages: Stage I (initial deformation stage + uniform deformation stage) and Stage II (accelerated deformation stage). The weighted fusion data from Stage I and Stage II are fused, and the fused data is shown in Figure 3.

[0057] The comparative analysis in Figure 3 shows that the fused data analysis results are more stable than the actual monitoring data analysis results, and the deformation curves are smoother and more complete, which is beneficial for more accurate information capture when making stage judgments on landslides. To further analyze and evaluate the adaptive weighted fusion results, the results are compared with the data before fusion. The fused data and monitoring data are processed separately, and the improved tangent angle is compared and analyzed as an example. Here, the displacement data collected by each sensor and the fused data are used to calculate the improved tangent angle before and after reaching 80°, and the statistics are shown in Table 1 below.

[0058] Table 1 Improved Tangent Crossover Analysis

[0059] The relevant technology utilizes the mathematical method of improved tangent angle to further divide the accelerated stage of landslide deformation into three sub-stages: when the improved tangent angle is >45°, the landslide is in the initial accelerated deformation stage; when the improved tangent angle is >80°, the landslide is in the intermediate accelerated deformation stage; and when the improved tangent angle is >85°, the landslide is in the pre-slide stage. According to the table above, monitoring point DCF11 indicates that the landslide entered the intermediate acceleration phase on September 20, 2019, and the accelerated phase on September 27, 2019; monitoring point HF07 indicates that the landslide entered the intermediate acceleration phase on October 4, 2019; monitoring points DCF14 and HF06 indicate that the landslide entered the intermediate acceleration phase on September 30, 2019, and the accelerated phase on October 1, 2019; and monitoring point DCF15 and the adaptive weighted fusion results indicate that the landslide entered the intermediate acceleration phase on October 1, 2019, and the accelerated phase on October 2, 2019.

[0060] In summary, the results show that adaptive weighting can effectively fuse the five sets of deformation data. The fused data can comprehensively and effectively identify the landslide deformation stages and more closely reflect the actual landslide deformation situation. The fused data analysis curve is smoother and more complete, and can better reflect and capture the overall deformation characteristics of the landslide. The fused analysis results are superior to the results of data analysis from single landslide deformation monitoring points.

[0061] In a specific embodiment, please refer to Figure 4, which is a structural schematic diagram of a landslide displacement monitoring system according to a second embodiment of this application. The system includes: a first data acquisition module 201, a total mean square error acquisition module 202, a target optimization module 203, a data fusion module 204, a second data acquisition module 205, and a monitoring module 206. The first data acquisition module 201 is used to acquire landslide monitoring data from landslide displacement monitoring points. The landslide monitoring data includes landslide displacement data obtained by different displacement sensors monitoring the landslide displacement monitoring points. The total mean square error acquisition module 202 is used to acquire the total mean square error of all landslide displacement data. The optimization module 203 is used to obtain a first weighting factor for each landslide displacement data with the goal of minimizing the total mean square error; the data fusion module 204 is used to use the first weighting factor of each landslide displacement data to perform weighted fusion of all landslide displacement data to obtain landslide displacement deformation data of the landslide displacement monitoring point; the landslide displacement deformation data includes displacement data after fusion of landslide displacement data from different time periods; the second data acquisition module 205 is used to acquire landslide displacement deformation data from different time periods; the monitoring module 206 is used to monitor the landslide displacement at the landslide displacement monitoring point based on the landslide displacement deformation data from different time periods.

[0062] Specifically, the system in this embodiment uses multiple displacement sensors to monitor landslide displacement monitoring points and obtain landslide displacement data from these monitoring points. The total mean square error (TMS) reflects the difference between the landslide displacement data and the actual displacement data. The first weighting factor for each landslide displacement data is obtained with the goal of minimizing the TMS. The landslide displacement data is then weighted and fused using the first weighting factor of each landslide displacement data, which minimizes the difference between the fused landslide displacement deformation data and the actual displacement data.

[0063] The system in this embodiment adaptively finds the optimal weighting factor corresponding to each landslide displacement data point by minimizing the total mean square error, thus achieving the optimal fused data value. This optimization method fully considers the error differences between individual landslide displacement data points, resulting in more accurate and reliable data fusion results. Therefore, by using the first weighting factor of each landslide displacement data point to perform weighted fusion on all landslide displacement data, the system obtains landslide displacement deformation data from the monitoring points. This allows the landslide displacement deformation data to comprehensively and effectively identify the landslide deformation stage and more closely reflect the actual landslide deformation situation.

[0064] In a specific embodiment, the third embodiment of this application provides a landslide displacement monitoring device, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the method described in any one of the first embodiments of this application. The monitoring device in this embodiment, by aiming to minimize the total mean square error, can adaptively find the optimal weighting factor corresponding to each landslide displacement data point, so that the fused data value reaches its optimal state. This optimization method can fully consider the error differences between various landslide displacement data points, thereby obtaining more accurate and reliable data fusion results. Therefore, by using the first weighting factor of each landslide displacement data point to perform weighted fusion of all landslide displacement data to obtain landslide displacement deformation data of the monitoring points, the landslide displacement deformation data can comprehensively and effectively determine the landslide deformation stage and more closely reflect the actual landslide deformation situation.

[0065] In a specific embodiment, the fourth embodiment of this application provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program causes the processor to perform the steps of the method described in any one of the first embodiments of this application. The storage medium in this embodiment, by aiming to minimize the total mean square error, can adaptively find the optimal weighting factor corresponding to each landslide displacement data point, so that the fused data value reaches its optimal state. This optimization method can fully consider the error differences between various landslide displacement data points, thereby obtaining a more accurate and reliable data fusion result. Therefore, by using the first weighting factor of each landslide displacement data point to perform weighted fusion of all landslide displacement data to obtain landslide displacement deformation data of landslide displacement monitoring points, the landslide displacement deformation data can comprehensively and effectively determine the landslide deformation stage and more closely reflect the actual landslide deformation situation.

[0066] Figure 5 illustrates the internal structure of a computer device in one embodiment. This computer device can be a terminal or a server. Referring to Figure 5, the computer device includes a processor, memory, etc., connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program enables the processor to implement the methods in this embodiment. The internal memory may also store a computer program, which, when executed by the processor, enables the processor to perform the methods in this embodiment. Those skilled in the art will understand that the structure shown in Figure 5 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0067] The above embodiments merely illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

[0068] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments for application in other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method of monitoring landslide displacement, characterized by, The method includes: Acquire landslide monitoring data from landslide displacement monitoring points; the landslide monitoring data includes landslide displacement data obtained by different displacement sensors monitoring the landslide displacement monitoring points; Obtain the total mean square error of all landslide displacement data; The first weighting factor for each landslide displacement data is obtained with the goal of minimizing the total mean square error; The landslide displacement deformation data of the landslide displacement monitoring point is obtained by weighting and fusing all the landslide displacement data using the first weighting factor of each landslide displacement data; the landslide displacement deformation data includes the displacement data after fusing landslide displacement data from different time periods; Obtain landslide displacement and deformation data at different time periods; Landslide displacement monitoring is performed at the landslide displacement monitoring points based on landslide displacement and deformation data from different time periods.

2. The method of monitoring landslide displacement according to claim 1, wherein, The total mean square error of obtaining all landslide displacement data includes: Obtain the variance of each landslide displacement data point, and the second weighting factor for each landslide displacement data point; the second weighting factor is a preset weighting factor. The total mean square error is obtained based on the variance of each landslide displacement data and the second weighting factor.

3. The method of monitoring landslide displacement according to claim 2, wherein, The total mean square error is obtained using the following equation: where σ 2 is the total mean square error, n is the number of landslide displacement data, W p is the second weighting factor of the pth landslide displacement data, Let be the variance of the p-th landslide displacement data.

4. The method of claim 2, wherein The variance of each landslide monitoring data point is obtained, including: The variance of the first landslide displacement data is obtained based on the cross-correlation coefficient between the first landslide displacement data and the second landslide displacement data, as well as the autocorrelation coefficient of the first landslide displacement data; the first landslide displacement data and the second landslide displacement data are any one of the landslide displacement data in the landslide monitoring data.

5. The method of monitoring landslide displacement as claimed in claim 4, wherein, The difference between the cross-correlation coefficient and the autocorrelation coefficient is the variance of the first landslide displacement data.

6. The method of claim 1, wherein, The first weighting factor is obtained using the following equation: wherein a first weighting factor for the pth landslide displacement data, variance of the pth landslide displacement data, Let be the variance of the i-th landslide displacement data.

7. The method of claim 1, wherein After acquiring the landslide monitoring data from the landslide displacement monitoring points, the process also includes: The landslide monitoring data is subjected to data missing interpolation and data smoothing and noise reduction to obtain optimized landslide monitoring data; The optimized landslide monitoring data is used as the landslide monitoring data, and the step of obtaining the total mean square error of all landslide displacement data is returned.

8. A landslide displacement monitoring system, characterized by, The system includes: a first data acquisition module, a total mean square error acquisition module, a target optimization module, a data fusion module, a second data acquisition module, and a monitoring module; The first data acquisition module is used to acquire landslide monitoring data from landslide displacement monitoring points; the landslide monitoring data includes landslide displacement data obtained by different displacement sensors monitoring the landslide displacement monitoring points; The total mean square error acquisition module is used to acquire the total mean square error of all landslide displacement data; The target optimization module is used to obtain the first weighting factor for each landslide displacement data with the goal of minimizing the total mean square error. The data fusion module is used to perform weighted fusion of all landslide displacement data using the first weighting factor of each landslide displacement data to obtain landslide displacement deformation data of the landslide displacement monitoring point; the landslide displacement deformation data includes displacement data after fusion of landslide displacement data from different time periods; The second data acquisition module is used to acquire landslide displacement and deformation data for different time periods; The monitoring module is configured to monitor landslide displacement of the landslide displacement monitoring point based on landslide displacement deformation data of the different time periods.

9. A landslide displacement monitoring device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the computer program, when executed by the processor, causes the processor to perform the steps of the method according to any one of claims 1 to 7.

10. A computer readable storage medium storing a computer program, characterized in that, The computer program, when executed by the processor, causes the processor to perform the steps of the method according to any one of claims 1 to 7.

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