A method for monitoring millimeter-level displacement changes in landslide settlement

Abstract

This application relates to the field of settlement displacement monitoring technology, specifically to a method for monitoring millimeter-level displacement changes in landslide settlement. The method includes: acquiring three-dimensional coordinate data of each monitoring point in the landslide body; calculating the characteristic values ​​of the directional temporal changes in settlement displacement at each monitoring point at each acquisition time to obtain the correlation difference coefficient between different monitoring points; obtaining the adjusted similarity distance between different monitoring points to extract the nearest neighbor monitoring points for each monitoring point; obtaining the confidence weight of each monitoring point based on the average distribution level of the correlation difference coefficient between each monitoring point and its nearest neighbor monitoring points; and combining the landslide settlement displacement change data of each monitoring point to obtain a comprehensive evaluation result of the landslide settlement displacement change data, thereby assessing and monitoring the landslide settlement displacement change state. This application can improve the monitoring accuracy of landslide settlement.

Description

Technical Field

[0001] This application relates to the field of settlement displacement monitoring technology, specifically to a method for monitoring millimeter-level displacement changes in landslide settlement. Background Technology

[0002] Although Global Navigation Satellite System (GNSS) technology is widely used in landslide monitoring, its monitoring results are susceptible to interference from various factors when millimeter-level accuracy is required, leading to significant displacement monitoring errors. Specifically, ionospheric delay and tropospheric refraction dynamically change over time and space, interfering with the propagation path of satellite signals; dense vegetation or tall buildings often present in landslide areas can easily trigger multipath effects, where satellite signals are superimposed on direct signals after being reflected from the ground, affecting observation accuracy; in addition, the complex geological environment of landslide areas may cause surface vibrations (such as vehicle traffic, construction activities, etc.), causing slight displacement of the receiver and creating transient interference. These environmental interference factors can cause deviations in actual location monitoring.

[0003] Traditional millimeter-level monitoring methods fail to fully consider the directional consistency of local displacement changes in landslide displacement monitoring, resulting in significant errors in comprehensive monitoring and analysis due to environmental interference, which in turn reduces the reliability of landslide settlement and displacement monitoring. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a method for monitoring millimeter-level displacement changes in landslide settlement, thereby resolving existing issues.

[0005] The millimeter-level displacement change monitoring method for landslide settlement proposed in this application adopts the following technical solution:

[0006] One embodiment of this application provides a method for monitoring millimeter-level displacement changes in landslide settlement, including the following steps:

[0007] Obtain the three-dimensional coordinate data of each monitoring point in the landslide body;

[0008] By assessing the similarity between the changes in the three-dimensional coordinates of each monitoring point at each acquisition time and subsequent acquisition times, the characteristic value of the directional temporal change of the settlement displacement at each acquisition time is obtained. Based on the degree of difference between different monitoring points regarding the characteristic value and the degree of similarity between different monitoring points regarding the changes in the three-dimensional coordinates at adjacent acquisition times, the correlation difference coefficient between different monitoring points is obtained.

[0009] Based on the distance relationship between different monitoring points and the correlation difference coefficient, the adjusted similarity distance between different monitoring points is obtained in order to extract the nearest monitoring points of each monitoring point;

[0010] Based on the average distribution level of the correlation difference coefficient between each monitoring point and its neighboring monitoring points, the confidence weight of each monitoring point is obtained. Combined with the landslide settlement and displacement change data of each monitoring point, a comprehensive evaluation result of the landslide settlement and displacement change data is obtained to evaluate and monitor the landslide settlement and displacement change status.

[0011] Preferably, for each monitoring point, all three-dimensional coordinate data collected up to the current moment are sorted in chronological order, and the displacement vector of the change in three-dimensional coordinate data between adjacent moments is calculated, wherein the direction of the displacement vector is from the three-dimensional coordinate data corresponding to the earlier collection moment to the three-dimensional coordinate data corresponding to the later collection moment.

[0012] Preferably, the process for obtaining the characteristic values ​​of the directional temporal change of the settlement displacement at each monitoring point at each acquisition time is as follows:

[0013] For each monitoring point's three-dimensional coordinate data at each acquisition time, the cosine similarity of the displacement vector between each acquisition time and the three-dimensional coordinate data of each subsequent acquisition time is obtained, and the mean of all cosine similarities is used as the characteristic value of the directional temporal change of settlement displacement at each monitoring point at each acquisition time.

[0014] Preferably, the method for obtaining the correlation difference coefficient between the different monitoring points is as follows:

[0015] Among them, H x,y d represents the correlation coefficient between the x-th and y-th monitoring points; x,y DTW distance of the feature sequences between the x-th and y-th monitoring points; μ x and μ y Let t represent the sum of the Euclidean distances between the three-dimensional coordinate data of all adjacent acquisition times corresponding to the x-th and y-th monitoring points, respectively. x,y The characteristic coefficient representing the change in displacement direction between the x-th and y-th monitoring points is exp(), which represents an exponential function with the natural constant as the base.

[0016] Preferably, all the feature values ​​corresponding to each monitoring point are arranged according to the acquisition time to form a feature sequence for each monitoring point, and the average value of the cosine similarity of the displacement vectors between different monitoring points at all the same adjacent acquisition times is calculated as the feature coefficient of the displacement direction change between different monitoring points.

[0017] Preferably, the method for obtaining the adjusted similarity distance between the different monitoring points is as follows:

[0018] In the formula, s x,y p represents the adjusted similarity distance between the x-th and y-th monitoring points; x,yh represents the Euclidean distance between the x-th and y-th monitoring points; x,y The normalized result of the correlation difference coefficient between the x-th and y-th monitoring points.

[0019] Preferably, the step of extracting the nearest neighbor monitoring points of each monitoring point further includes: taking the three-dimensional coordinate data of all monitoring points as input, and using the K-nearest neighbor algorithm to obtain the nearest neighbor monitoring points of each monitoring point, wherein the adjusted similarity distance between different monitoring points is used as the similarity distance between different monitoring points in the K-nearest neighbor algorithm.

[0020] Preferably, the confidence weight of each monitoring point is obtained by the following method: Where σ x w represents the confidence weight of the x-th monitoring point. x and w i represents the landslide settlement displacement monitoring deviation values ​​at the x-th and i-th monitoring points, respectively; n represents the number of monitoring points.

[0021] Preferably, the mean of the normalized correlation difference coefficients between each monitoring point and all its neighboring monitoring points is used as the landslide settlement displacement monitoring deviation value for each monitoring point.

[0022] Preferably, the method for obtaining the comprehensive evaluation results of the landslide settlement and displacement change data is as follows: Where θc represents the comprehensive evaluation result of the landslide settlement and displacement change data c, σ k b represents the confidence weight of the k-th monitoring point. k,c The value of c represents the landslide settlement and displacement change data at the k-th monitoring point, and n represents the number of monitoring points.

[0023] This application has at least the following beneficial effects:

[0024] This application takes into account that in the monitoring of millimeter-level displacement of landslide settlement, the error is relatively large due to the interference of environmental factors such as ionospheric delay, tropospheric refraction, multipath effect and surface vibration. Therefore, this application adopts a monitoring mode that combines GNSS reference station and monitoring point to collect and analyze data in the monitoring area.

[0025] This application combines the local directional consistency characteristics of landslide settlement and displacement changes, and compares and analyzes the differences in directional temporal consistency and displacement directional similarity characteristics of different monitoring points. Compared with traditional data association analysis methods, the method of this application can accurately capture the directional characteristics between monitoring points during landslide settlement, highlight the dynamic correlation characteristics of different monitoring points in the direction of displacement change, accurately screen the nearest monitoring points in the judgment of local directional change consistency of each monitoring point, and analyze the influence characteristics of environmental interference on different monitoring points in the actual monitoring process based on the screening results, thereby determining the confidence weight of each monitoring point for settlement and displacement change monitoring and analysis. Its beneficial effect is that by fully combining the local directional consistency characteristics of landslide settlement and displacement monitoring, it accurately analyzes the interference characteristics of different monitoring points affected by the environment, realizes accurate monitoring and analysis of landslide settlement and displacement, and improves the accuracy of monitoring landslide settlement and displacement changes at the millimeter level. Attached Figure Description

[0026] To more clearly illustrate the technical solutions and advantages 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.

[0027] Figure 1 A flowchart illustrating the steps of a method for monitoring millimeter-level displacement changes in landslide settlement provided in this application. Detailed Implementation

[0028] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method for monitoring millimeter-level displacement changes in landslide settlement according to this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0029] Unless otherwise defined, terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes said element. Furthermore, the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items. All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0030] The following, in conjunction with the accompanying drawings, details the specific scheme of a method for monitoring millimeter-level displacement changes in landslide settlement provided in this application.

[0031] This application provides an embodiment of a method for monitoring millimeter-level displacement changes in landslide settlement. For details, please refer to [link to specific documentation]. Figure 1 This includes the following steps:

[0032] Step 1: Obtain the three-dimensional coordinate data of each monitoring point in the landslide.

[0033] In this embodiment, a "GNSS base station + monitoring point" networking mode is adopted: 12 base stations are deployed in the stable area outside the landslide influence range. In this embodiment, choke coil antennas are used to minimize multipath effects. 60 monitoring points are deployed at key parts of the landslide body, such as the top, middle, and bottom of the slope. All equipment uses millimeter-precision dual-frequency GNSS receivers with a sampling frequency of 1Hz to ensure that a set of three-dimensional coordinate data (X, Y, Z) is acquired every second. The collected data is wirelessly transmitted to the monitoring center and stored in RINEX format to ensure the integrity of the original observation values.

[0034] Step 2: By measuring the similarity between the three-dimensional coordinate changes of each monitoring point at each acquisition time and subsequent acquisition times, the characteristic value of the directional temporal change of the settlement displacement at each acquisition time is obtained. Based on the degree of difference of the characteristic values ​​of different monitoring points and the degree of similarity of the three-dimensional coordinate changes of different monitoring points with respect to adjacent acquisition times, the correlation difference coefficient between different monitoring points is obtained.

[0035] In actual monitoring, the monitoring center needs to comprehensively analyze the received monitoring data. Considering that multipath effects and environmental interference factors may cause deviations in displacement monitoring at different monitoring points, resulting in local displacement anomalies, the accuracy of displacement change analysis at different monitoring points is affected, increasing the error of the final landslide settlement displacement monitoring results and reducing the accuracy of monitoring millimeter-level displacement changes in landslide settlement. Therefore, in view of the above situation, combined with the local directional consistency characteristics of landslide settlement displacement, the settlement displacement change characteristics of different monitoring points are analyzed, so as to accurately monitor and analyze the millimeter-level displacement changes of landslide settlement.

[0036] In this embodiment, for each monitoring point, all three-dimensional coordinate data collected up to the current moment are sorted in chronological order of collection time. Then, the displacement vector of the three-dimensional coordinate data changes between adjacent moments is calculated. The direction of the displacement vector points from the three-dimensional coordinate data corresponding to the earlier collection moment to the three-dimensional coordinate data corresponding to the later collection moment. Typically, if landslide settlement does not reach a high-risk warning level, the displacement changes at different monitoring points will exhibit a certain degree of consistency, showing significant local directional consistency. Therefore, to accurately analyze the local monitoring anomalies caused by interference in actual monitoring, a feature value of the directional temporal change of settlement displacement is constructed based on the difference between the displacement change characteristics collected at each monitoring point and the subsequent overall change characteristics. The larger the feature value, the higher the directional consistency of displacement changes during the monitoring process.

[0037] Specifically, the process for obtaining the characteristic values ​​of the directional temporal changes in settlement displacement at each acquisition time is as follows:

[0038] For the three-dimensional coordinate data of each monitoring point at each acquisition time, the cosine similarity of the displacement vector between the three-dimensional coordinate data of that acquisition time and the three-dimensional coordinate data of each subsequent acquisition time is obtained, and the mean of all cosine similarities is used as the characteristic value of the directional temporal change of the settlement displacement at that acquisition time.

[0039] Through the above calculations, the characteristic values ​​of the directional temporal variation of settlement displacement at each acquisition time prior to the current time can be obtained.

[0040] Furthermore, the feature values ​​corresponding to each monitoring point at all acquisition times are arranged into a sequence according to the acquisition time, which is used as the feature sequence of each monitoring point; the DTW distance between the feature sequences of each monitoring point and each other monitoring point is calculated. The larger the DTW distance, the more significant the difference in the directional consistency of displacement change characteristics between different monitoring points.

[0041] Furthermore, in this embodiment, the cosine similarity of the displacement vectors at each identical adjacent acquisition time between different monitoring points is calculated. The mean of all cosine similarities is used as the characteristic coefficient of the displacement direction change between different monitoring points. The larger the characteristic coefficient, the closer the directionality of the settlement displacement between different monitoring points. Taking into account the differences in the temporal consistency of the direction between different monitoring points and the similarity characteristics of the displacement changes, a correlation difference coefficient of the settlement displacement monitoring changes between each monitoring point and every other monitoring point is constructed. The larger the correlation difference coefficient, the more significant the nearest neighbor change characteristics of the settlement displacement changes between monitoring points are, based on the temporal consistency characteristics of the displacement change direction of each monitoring point and the similarity characteristics of the displacement direction between different monitoring points. In this embodiment, the specific calculation formula for the correlation difference coefficient is as follows:

[0042] Among them, H x,y d represents the correlation coefficient between the x-th and y-th monitoring points; x,y DTW distance of the feature sequences between the x-th and y-th monitoring points; μ x and μ y Let |μ| represent the sum of the Euclidean distances between the three-dimensional coordinate data of all adjacent acquisition times corresponding to the x-th and y-th monitoring points, respectively. x -μ y The larger the difference in |, the greater the difference in the corresponding displacement change; t x,y The characteristic coefficient representing the change in displacement direction between the x-th and y-th monitoring points is exp(), which represents an exponential function with the natural constant as the base. x,y The smaller the calculation result of -1), the greater the difference in displacement direction between monitoring points.

[0043] It should be noted that, compared with traditional methods for analyzing data changes between monitoring points, the above analysis and calculation combines landslide settlement change characteristics. By integrating the differences in directional temporal consistency and the similarity of spatial displacement direction to calculate the correlation difference coefficient, this embodiment can more comprehensively capture the correlation characteristics between different monitoring points during landslide settlement, highlight the dynamic correlation characteristics of different monitoring points in the direction of spatial displacement change, and improve the accuracy and reliability of the analysis of correlation changes between monitoring points.

[0044] Step 3: Based on the distance relationship between different monitoring points and the correlation difference coefficient, obtain the adjusted similarity distance between different monitoring points to extract the nearest monitoring points of each monitoring point.

[0045] Based on the above analysis, and considering that landslides with high risk of settlement typically exhibit regional directional disorder, while those with medium to low risk and changing settlement displacement trends typically show consistent displacement changes in local areas, the analysis of correlation characteristics between different monitoring points is used to screen neighboring monitoring points with similar monitoring characteristics during the actual monitoring process. Specifically, the three-dimensional coordinate data of all monitoring points are used as input, and the K-nearest neighbor algorithm is used to obtain the nearest monitoring points for each monitoring point, where the number of nearest monitoring points K is 10.

[0046] Furthermore, considering that determining the similarity distance between different monitoring points significantly impacts the accuracy of extracting nearest neighbor monitoring points using the K-nearest neighbor algorithm, this embodiment adjusts the calculation based on the displacement direction variation characteristics of landslide settlement to accurately obtain nearest neighbor monitoring points with similar settlement monitoring characteristics to each monitoring point. Specifically, during landslide settlement displacement monitoring, to accurately analyze the impact of monitoring errors caused by interference at each monitoring point on the overall settlement displacement prediction and evaluation, the calculation of the similarity distance between different monitoring points is adjusted based on the directional characteristics of landslide settlement. The adjustment formula is as follows:

[0047] Where s x,y p represents the adjusted similarity distance between the x-th and y-th monitoring points; x,y h represents the Euclidean distance between the x-th and y-th monitoring points; x,y The normalized result of the correlation difference coefficient between the x-th and y-th monitoring points; based on the similarity distance determined above, the K nearest neighbor monitoring points of each monitoring point are obtained through the K nearest neighbor algorithm. The specific implementation process of the K nearest neighbor algorithm is well known to those skilled in the art and will not be described in detail here.

[0048] Step 4: Based on the average distribution level of the correlation difference coefficient between each monitoring point and its neighboring monitoring points, obtain the confidence weight of each monitoring point. Combine the displacement change data of each monitoring point to obtain the comprehensive evaluation result of the landslide settlement displacement change data, so as to evaluate and monitor the landslide settlement displacement change state.

[0049] Furthermore, based on the neighboring monitoring points of each monitoring point identified above, a comprehensive analysis of the confidence weight for the settlement displacement change prediction analysis of each monitoring point is performed. Specifically, the mean of the normalized correlation difference coefficients between each monitoring point and all its neighboring monitoring points is calculated and used as the landslide settlement displacement monitoring deviation value w for each monitoring point. The larger the characteristic value, the greater the possibility of settlement displacement monitoring error due to interference during the actual monitoring process. Based on the accurate analysis of settlement displacement monitoring deviation, the confidence weight for the trend change analysis of each monitoring point is calculated, and the specific calculation formula is as follows: Where σ x w represents the confidence weight of the x-th monitoring point. x and w i represents the landslide settlement displacement monitoring deviation values ​​at the x-th and i-th monitoring points, respectively; n represents the number of monitoring points.

[0050] Among them, the larger the deviation value of landslide settlement displacement monitoring, the greater the error in the displacement change of the current monitoring point due to environmental interference, and the smaller the confidence weight of the corresponding settlement displacement analysis comprehensive evaluation.

[0051] Based on the above analysis of the spatial orientation correlation and neighboring characteristic differences in the landslide settlement and displacement monitoring process, a multi-index evaluation system including displacement rate, displacement acceleration, and spatial consistency is constructed to output the overall state and risk level of landslide settlement.

[0052] Specifically, the landslide settlement displacement change data of each monitoring point is calculated. In this embodiment, the displacement change data includes displacement rate, displacement acceleration and displacement gradient. It should be noted that in actual application scenarios, implementers can select displacement change data at their own discretion. This embodiment does not impose any special restrictions on this.

[0053] For each type of displacement change data, based on the confidence weight of the comprehensive assessment of settlement displacement analysis at different monitoring points, the comprehensive assessment result for each type of landslide settlement displacement change data is calculated. Specifically, for each type of displacement change data, the calculation formula for its corresponding comprehensive assessment result is as follows: Where θc represents the comprehensive evaluation result of the landslide settlement and displacement change data c, σ k b represents the confidence weight of the k-th monitoring point. k,c This represents the value of c, which represents the landslide settlement and displacement change data at the k-th monitoring point.

[0054] Furthermore, preferably, in this embodiment, based on the above calculation results, the comprehensive evaluation results of displacement rate, displacement acceleration, and displacement gradient during the landslide displacement monitoring process are obtained respectively. Specifically, the calculation process of displacement rate, displacement acceleration, and spatial displacement gradient (displacement difference per unit distance) based on the obtained displacement data is a technique known to those skilled in the art and will not be elaborated further. This allows for accurate judgment and evaluation of the landslide settlement and displacement state. Specifically, in this embodiment, when the comprehensive evaluation result of the displacement rate at the monitoring point is <0.5 mm / day, and the comprehensive evaluation result of the displacement acceleration is <0.1 mm / day... 2 When the comprehensive evaluation result of spatial displacement gradient is <0.2mm / m, it is judged as a "steady state"; when the comprehensive evaluation result of displacement rate increases to 1-3mm / day, and the comprehensive evaluation result of acceleration is >0.1mm / day... 2 When the displacement direction of more than 80% of the monitoring points is consistent, it is judged as "potential sliding risk"; when the comprehensive evaluation result of the displacement rate is >5mm / day, the acceleration increases sharply, and the comprehensive evaluation result of the displacement gradient is >1mm / m, it is judged as "high risk".

[0055] Based on the above comprehensive analysis, the impact of millimeter-level displacement monitoring errors caused by multipath effects and environmental anomalies on the actual landslide monitoring can be avoided. The accurate displacement changes of landslide settlement can be captured as a whole, and timely early warnings can be issued.

[0056] It is understood that references to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include the specific features, structures, or characteristics described in connection with that embodiment. Therefore, the appearance of phrases such as "in one embodiment," "in some embodiments," "in other embodiments," or "in still other embodiments" in different parts of this specification does not necessarily refer to the same embodiment, but rather means "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0057] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous. Moreover, the sequence numbers of the steps in the embodiments do not imply a specific order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments in this specification.

[0058] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A landslide subsidence millimeter level displacement change monitoring method, characterized in that, The method comprises the following steps: obtaining three-dimensional coordinate data of each monitoring point of the landslide body; obtaining eigenvalues of directionality time sequence changes of settlement displacement changes of each monitoring point at each collection time through similarity between three-dimensional coordinate changes of each monitoring point at each collection time and at subsequent collection times, and obtaining a correlation difference coefficient between different monitoring points according to a difference degree of the eigenvalues of different monitoring points and similarity between three-dimensional coordinate changes of adjacent collection times between different monitoring points; obtaining an adjusted similar distance between different monitoring points according to a distance relationship between different monitoring points and the correlation difference coefficient, to extract a near neighbor monitoring point of each monitoring point; obtaining a confidence weight of each monitoring point according to an average distribution level of the correlation difference coefficient of each monitoring point and its near neighbor monitoring point, obtaining a comprehensive evaluation result of each landslide settlement displacement change data, to evaluate and monitor a landslide settlement displacement change state, in combination with the landslide settlement displacement change data of each monitoring point.

2. The method of claim 1, wherein, For each monitoring point, all three-dimensional coordinate data collected up to the current time are sorted in time sequence, and a displacement vector of three-dimensional coordinate data changes between adjacent time is calculated, wherein a direction of the displacement vector is from three-dimensional coordinate data corresponding to a preceding collection time to three-dimensional coordinate data corresponding to a subsequent collection time.

3. The method of claim 2, wherein the landslide settlement is monitored in millimeter level displacement changes. The eigenvalues of directionality time sequence changes of settlement displacement changes of each monitoring point at each collection time are obtained by: for three-dimensional coordinate data of each monitoring point at each collection time, obtaining a cosine similarity of displacement vectors between three-dimensional coordinate data of each collection time and subsequent collection times, and taking an average of all cosine similarities as the eigenvalues of directionality time sequence changes of settlement displacement changes of each monitoring point at each collection time.

4. The method of claim 2, wherein the landslide settlement is monitored in millimeter level displacement changes. The correlation difference coefficient between different monitoring points is obtained by: where H x,y denotes the correlation difference coefficient between the xth and yth monitoring points; d x,y denotes the DTW distance of the feature sequence between the xth and yth monitoring points; μ x and μ y denote the sum of the Euclidean distances between the three-dimensional coordinate data of all adjacent acquisition time points corresponding to the xth and yth monitoring points, respectively; t x,y denotes the feature coefficient of the displacement direction change between the xth and yth monitoring points, and exp() denotes the exponential function with the natural constant as the base.

5. The method of claim 4, wherein the landslide settlement is monitored in millimeter level displacement changes. arranging all the eigenvalues corresponding to each monitoring point according to collection time to form a feature sequence of each monitoring point, and taking an average of cosine similarities of displacement vectors between different monitoring points at all same adjacent collection times as a feature coefficient of displacement direction changes between different monitoring points.

6. The method of claim 1, wherein, The adjusted similar distance between different monitoring points is obtained by: where s x,y represents the adjusted similarity distance between the xth and yth monitoring points; p x,y represents the Euclidean distance between the xth and yth monitoring points; h x,y represents the normalized result of the correlation difference coefficient between the xth and yth monitoring points.

7. The method of claim 1, wherein, The near neighbor monitoring point of each monitoring point is further obtained by taking three-dimensional coordinate data of all monitoring points as input, and using a K-nearest neighbor algorithm to obtain the near neighbor monitoring point of each monitoring point, wherein the adjusted similar distance between different monitoring points is taken as a similar distance between different monitoring points in the K-nearest neighbor algorithm.

8. The method of claim 1, wherein, The confidence weight of each monitoring point is obtained by: wherein σ x represents the confidence weight of the xth monitoring point, w x and w i respectively represent the landslide subsidence displacement monitoring deviation values of the xth and ith monitoring points; and n represents the number of monitoring points.

9. The method of claim 8, wherein the landslide settlement is monitored in millimeter level displacement changes. An average of normalized processing results of the correlation difference coefficient of each monitoring point and all its near neighbor monitoring points is taken as a landslide settlement displacement monitoring deviation value of each monitoring point.

10. The method of claim 1, wherein, The method for obtaining the comprehensive evaluation result of the landslide subsidence displacement change data is: Wherein θc represents the comprehensive evaluation result of the landslide subsidence displacement change data c, σ k represents the confidence weight of the kth monitoring point, b k,c represents the value of the landslide subsidence displacement change data c of the kth monitoring point, and n represents the number of monitoring points.

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