A method and device for monitoring settlement displacement of deep foundation pit

Abstract

The application relates to the technical field of settlement displacement auxiliary monitoring, in particular to a deep foundation pit settlement displacement auxiliary monitoring method and device, which comprises the following steps: collecting each kind of monitoring data of each monitoring point in a deep foundation pit, obtaining the feature value difference between different monitoring points under the same kind of monitoring data, obtaining the metric distance between different monitoring points under the same kind of monitoring data, clustering and dividing all the monitoring points under the same kind of monitoring data, and carrying out modal decomposition to obtain the feature coefficient of each modal component of the monitoring data of each monitoring point in each cluster under the same kind of monitoring data, then obtaining the interference adjustment value of the monitoring data of all the monitoring points in each cluster under the same kind of monitoring data in each modal component, and carrying out filtering processing, reconstructing and restoring all the modal components, and predicting each kind of monitoring data, and then assisting in monitoring the settlement displacement of the deep foundation pit. The application can improve the precision of the deep foundation pit settlement displacement auxiliary monitoring.

Description

Technical Field

[0001] This application relates to the field of settlement displacement auxiliary monitoring technology, specifically to a method and device for settlement displacement auxiliary monitoring of deep foundation pits. Background Technology

[0002] Deep foundation pit engineering is widely used in projects such as subways, underground shopping malls, and basements of high-rise buildings. Due to the large excavation depth, complex geological conditions, and sensitive surrounding environment of deep foundation pits, problems such as soil deformation and displacement of support structures can lead to serious consequences such as ground settlement, building tilting, and even collapse. Therefore, real-time and accurate auxiliary monitoring can promptly grasp the deformation trends of the foundation pit and surrounding soil, providing data support for optimizing engineering design and construction plans, effectively preventing safety accidents, ensuring construction safety and the stability of the surrounding environment, and also helping to improve the overall quality and management level of deep foundation pit projects.

[0003] However, current auxiliary monitoring of settlement displacement in deep foundation pits still faces many challenges in the process from data acquisition to result prediction. In actual monitoring, the complex monitoring environment leads to significant differences in interference in different areas. For example, changes in water content and temperature cause interference at different frequencies in the actual monitoring results. Traditional filtering methods, when monitoring multiple points for settlement displacement in deep foundation pits, do not fully consider the differences in the impact of different interference frequencies under the complex environment of the actual monitoring process. This results in poor data processing quality, affecting the accuracy of subsequent settlement displacement prediction and analysis, and making it difficult to meet the needs of deep foundation pit engineering for accurate monitoring and early warning of settlement displacement. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide an auxiliary monitoring method and device for settlement displacement of deep foundation pits. The specific technical solution adopted is as follows:

[0005] This application provides an auxiliary monitoring method for settlement displacement of deep foundation pits, including the following steps:

[0006] Collect each type of monitoring data at each monitoring point in the deep foundation pit;

[0007] Based on the fluctuation characteristics of each type of monitoring data at different monitoring points, a sample set of each type of monitoring data is obtained. By the difference in feature values ​​between different monitoring points in each type of monitoring data sample set, and combined with the difference in monitoring data between different monitoring points under the same type of monitoring data, the metric distance between different monitoring points under the same type of monitoring data is obtained, and then all monitoring points under the same type of monitoring data are clustered.

[0008] By analyzing the differences between modal components in the same frequency band after modal decomposition of monitoring data at different monitoring points in each cluster under the same monitoring data, the characteristic coefficients of monitoring data at each monitoring point in each cluster for each modal component are obtained. Combined with the discrete range of the corresponding modal component data, the interference adjustment value of monitoring data at all monitoring points in each cluster for each modal component is obtained. Then, filtering processing is performed on each modal component of monitoring data at each monitoring point in each cluster under each type of monitoring data.

[0009] The modal components of the filtered monitoring data at each monitoring point are reconstructed and restored to predict each type of monitoring data, thereby assisting in the monitoring of settlement displacement of deep foundation pits.

[0010] Preferably, each monitoring data sample set further includes:

[0011] The three feature values ​​of the monitoring time series of each type of monitoring data at each monitoring point are used as the X-axis, Y-axis and Z-axis coordinate values ​​to establish a three-dimensional rectangular coordinate system. Each three-dimensional rectangular coordinate system corresponds to one type of monitoring data. The mapping result of each three-dimensional rectangular coordinate system is used as the monitoring sample set of each type of monitoring data, that is, the sample set of each type of monitoring data.

[0012] Preferably, the three characteristic values ​​of the monitoring time series of each type of monitoring data at each monitoring point are:

[0013] The monitoring data for each type of data at each monitoring point are arranged according to the collection time sequence to obtain the monitoring time sequence sequence of each type of monitoring data at each monitoring point;

[0014] The three characteristic values ​​are, in order: the variance, range, and trend statistics of the monitoring time series of each type of monitoring data at each monitoring point.

[0015] Preferably, the method for calculating the metric distance between different monitoring points under the same monitoring data is as follows:

[0016] ;

[0017] In the formula, For the same type of monitoring data, the first The monitoring point and the first The metric distance between monitoring points; For each monitoring data sample set, the first The monitoring point and the first Euclidean distance of eigenvalues ​​between monitoring points; Indicates the first under the same monitoring data The monitoring point and the first The DTW distance between monitoring points in the time series.

[0018] Preferably, the clustering of all monitoring points under the same monitoring data further includes:

[0019] For each monitoring point, the metric distance between different monitoring points under the same monitoring data is used as the cluster distance for clustering.

[0020] Preferably, obtaining the characteristic coefficients of each modal component of the monitoring data at each monitoring point in each cluster includes:

[0021] The DTW distances of similar frequency band modal components between monitoring data at different monitoring points in each cluster under the same monitoring data are statistically analyzed, and the mean of all DTW distances is used as the characteristic coefficient of each modal component of the monitoring data at each monitoring point in each cluster under the same monitoring data.

[0022] Preferably, the method for obtaining the similar frequency band modal components is as follows:

[0023] The modal components of the same frequency band in the monitoring data at different monitoring points under the same monitoring data are regarded as the modal components of similar frequency bands.

[0024] Preferably, the method for calculating the interference modulation value of each modal component for the monitoring data at all monitoring points in each cluster is as follows:

[0025] ;

[0026] In the formula, For the same type of monitoring data, the monitoring data at all monitoring points in each cluster at the first... Interference adjustment values ​​for each modal component; For each cluster under the same monitoring data, the first... The monitoring data at the monitoring point is the first The standard deviation of the data corresponding to each modal component; For each cluster under the same monitoring data, the first... The monitoring data at the monitoring point is the first Characteristic coefficients of each modal component; For the monitoring data at all monitoring points in each cluster under the same monitoring data, the first... The sum of the characteristic coefficients of each modal component; n is the total number of monitoring points in each cluster.

[0027] Preferably, the step of filtering each modal component of the monitoring data at each monitoring point in each cluster under each type of monitoring data further includes:

[0028] The standard deviation of the data corresponding to each modal component of the monitoring data at each monitoring point in each cluster under each type of monitoring data, and the mean of the interference modulation value of the monitoring data at each modal component of all monitoring points in each cluster under the same type of monitoring data, are used as the standard deviation parameter of the filter, and then the filtering process is performed on each modal component of the monitoring data at each monitoring point in each cluster under each type of monitoring data.

[0029] This application embodiment also provides a settlement displacement auxiliary monitoring device for deep foundation pits. The device stores a computer program, and when the computer program is executed by a processor, it implements the steps of any of the above-described settlement displacement auxiliary monitoring methods for deep foundation pits.

[0030] As can be seen from the above, the settlement displacement auxiliary monitoring method and device for deep foundation pits provided in this application have at least the following beneficial effects:

[0031] This application fully considers the impact characteristics of regional interference in different frequency bands under a multi-monitoring mode, and accurately preprocesses settlement monitoring data to improve the accuracy of deep foundation pit settlement displacement prediction and analysis, thereby enhancing the precision of settlement displacement monitoring and early warning in deep foundation pit engineering. During deep foundation pit settlement displacement monitoring, the complex environment of the deep foundation pit leads to significant differences in the interference effects experienced by different monitoring points, resulting in the monitoring data of each monitoring point being affected by interference factors within different frequency ranges. Combining a multi-monitoring strategy, the application divides all monitoring points under the same monitoring data by using the characteristic values ​​between different monitoring points. It then analyzes the data deviation caused by interference at different monitoring points in similar frequency bands, and optimizes the processing of each type of collected monitoring data by analyzing the interference characteristics of different monitoring points in different frequency bands under the same monitoring data. Attached Figure Description

[0032] 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.

[0033] Figure 1 A flowchart illustrating the steps of an auxiliary monitoring method for settlement and displacement of deep foundation pits provided in this application;

[0034] Figure 2 A flowchart illustrating the steps of the distance measurement method provided in this application. Detailed Implementation

[0035] 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 and apparatus for auxiliary monitoring of settlement displacement in deep foundation pits proposed in 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.

[0036] Unless otherwise specified and limited, 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.

[0037] The following description, in conjunction with the accompanying drawings, details the specific scheme of the auxiliary monitoring method and device for settlement displacement of deep foundation pits provided in this application.

[0038] Please see Figure 1 The document illustrates a flowchart of a method for auxiliary monitoring of settlement displacement in a deep foundation pit according to an embodiment of this application, including the following steps:

[0039] Step 1: Collect each type of monitoring data at each monitoring point in the deep foundation pit.

[0040] In the auxiliary monitoring of settlement and displacement in deep foundation pits, the reasonable layout of monitoring points is the foundation for obtaining effective data. Specifically, based on the design drawings of the foundation pit, the geological survey report, and the surrounding environmental conditions, monitoring points are first laid out at certain intervals at key parts of the foundation pit's support structure and along the edge of the foundation pit in the surrounding soil. For the foundation pit support structure, a monitoring point is laid out at the top of each support pile and at every first preset distance on the capping beam; and monitoring points are laid out at every second preset distance along the edge of the foundation pit in the surrounding soil. The key parts include, but are not limited to, the pile tops, capping beams, and support nodes. The first preset distance ranges from 10 to 15 meters, and the second preset distance ranges from 5 to 10 meters. Preferably, in this embodiment, the first preset distance is set to 5 meters, and the second preset distance is set to 10 meters.

[0041] Monitoring equipment will be deployed at each monitoring point. For settlement monitoring, a high-precision level or hydrostatic leveling system will be used; for tilt and displacement monitoring, a total station and displacement sensors will be used. A sampling frequency will be set on the monitoring equipment: data will be collected hourly during the active construction period and a preset number of times daily during the stable construction period, with the monitoring time and equipment number parameters recorded. The collected monitoring data includes settlement, horizontal displacement, and tilt. After collection, the data will be transmitted in real-time to the data processing center via 4G or 5G wireless networks. The preset number of times ranges from 2 to 4 times; in this embodiment, it is set to 3 times.

[0042] Step 2: Based on the fluctuation characteristics of each type of monitoring data at different monitoring points, obtain a sample set for each type of monitoring data. By combining the differences in feature values ​​between different monitoring points in each type of monitoring data sample set with the differences in monitoring data between different monitoring points under the same type of monitoring data, obtain the metric distance between different monitoring points under the same type of monitoring data, and then cluster all monitoring points under the same type of monitoring data.

[0043] Due to the complex construction environment of deep foundation pits and their susceptibility to geological changes caused by the environment, complex interference factors exist during the actual data collection process. This leads to significant differences in the interference effects at different monitoring points, resulting in noticeable variations in the preprocessing of each type of monitoring data collected from each point. Based on the above analysis, a comprehensive comparison of each type of monitoring data is conducted under a monitoring strategy involving multiple monitoring points. Data with similar changing characteristics during the monitoring process are categorized, and the interference characteristics of each monitoring point at different frequency ranges are determined. This allows for the optimization and adjustment of the preprocessing effect of each type of monitoring data during the monitoring process, achieving accurate auxiliary monitoring of settlement and displacement in deep foundation pits.

[0044] Specifically, in the process of auxiliary monitoring of settlement and displacement in deep foundation pits, factors such as rainfall, changes in water content, and surrounding construction may cause interference within the monitoring area at different frequency ranges, leading to significant errors in each type of monitoring data. This deviation in actual monitoring data caused by environmental interference affects the accuracy of auxiliary monitoring of settlement and displacement in deep foundation pits. Therefore, considering the monitoring strategy characteristics of multiple monitoring points, for each deployed monitoring point, each type of monitoring data is arranged according to the acquisition time sequence to obtain a monitoring time sequence sequence for each type of monitoring data at each monitoring point. The variance, range, and trend statistics of the monitoring time sequence sequence for each type of monitoring data are then calculated to form the characteristic values ​​of the monitoring time sequence sequence for each type of monitoring data, reflecting the degree of fluctuation, range of change, and trend characteristics of different monitoring data at each monitoring point. Preferably, in this embodiment, the trend statistics are obtained through the MK trend verification algorithm; the specific process will not be elaborated further.

[0045] Furthermore, considering that changes in environmental factors may also cause interference characteristics and abnormal changes in different frequency ranges for each monitoring data point, for example, vibrations caused by surrounding building construction or heavy vehicle traffic may cause high-frequency, small-amplitude fluctuations in the data, a comprehensive comparison of the degree, range, and trend of fluctuations in different monitoring data points at each monitoring point is conducted to identify differences between different monitoring points.

[0046] Therefore, the variance, range, and trend statistics of the characteristic values ​​of the monitoring time series of each type of monitoring data at each monitoring point are calculated and used as the X-axis, Y-axis, and Z-axis coordinate values, respectively, to establish a three-dimensional rectangular coordinate system. Then, all monitoring points are mapped to the three-dimensional rectangular coordinate system according to the characteristic values ​​of the monitoring time series of each type of monitoring data. One three-dimensional rectangular coordinate system corresponds to one type of monitoring data, and the coordinates in the three-dimensional rectangular coordinate system are the characteristic values ​​of each type of monitoring data at different monitoring points. The mapping result is used as the monitoring sample set of each type of monitoring data, that is, the sample set of each type of monitoring data, to accurately divide monitoring points with similar monitoring data, and then accurately compare the interference impact characteristics in different frequency ranges.

[0047] Based on the above analysis, and considering the differences in characteristic values ​​of different monitoring points within each monitoring data sample set, and combined with the differences in monitoring time series between different monitoring points under the same monitoring data, the metric distance between different monitoring points under the same monitoring data is calculated. In this embodiment, the specific calculation formula is as follows:

[0048] ;

[0049] In the formula, For the same type of monitoring data, the first The monitoring point and the first The metric distance between monitoring points; For each monitoring data sample set, the first The monitoring point and the first Euclidean distance of eigenvalues ​​between monitoring points; Indicates the first under the same monitoring data The monitoring point and the first The DTW distance between monitoring points in the time series.

[0050] in, The larger the value, the greater the difference in the variation characteristics of each monitoring data due to interference during the actual monitoring process; The larger the value, the greater the difference between different monitoring points under the same monitoring data. The flowchart of the distance measurement method provided in this embodiment is as follows: Figure 2 As shown.

[0051] To highlight the data deviation characteristics caused by interference factors within different frequency ranges during the monitoring process, for all monitoring points under the same monitoring data, the DBSCAN algorithm is used to obtain the clustering results of all monitoring points under the same monitoring data, combining the metric distance between different monitoring points under the same monitoring data. Preferably, in this embodiment, the DBSCAN algorithm is a well-known technology, and the specific process will not be described in detail.

[0052] Step 3: By analyzing the differences between modal components in the same frequency band after modal decomposition of monitoring data at different monitoring points in each cluster under the same monitoring data, the characteristic coefficients of monitoring data at each monitoring point in each cluster for each modal component are obtained. Combined with the discrete range of the corresponding modal component data, the interference adjustment value of monitoring data at all monitoring points in each cluster for each modal component is obtained. Then, filtering is performed on each modal component of monitoring data at each monitoring point in each cluster under each type of monitoring data.

[0053] Furthermore, for each cluster after clustering, the monitoring time-series sequence of monitoring data at all monitoring points within each cluster under the same monitoring data is used as input. A variational mode decomposition algorithm is employed to obtain the modal components of the monitoring time-series sequence of monitoring data at each monitoring point within each cluster under the same monitoring data. The number of modal components is a preset number; in this embodiment, the preset number is set to 5, corresponding to 5 different frequency bands. These are then numbered sequentially as 1, 2, 3, 4, and 5 according to the decomposition order, corresponding to the monitoring data of the monitoring data at each monitoring point under each monitoring data in different frequency band modal components. Preferably, in this embodiment, the variational mode decomposition algorithm is a known technique, and the specific process will not be elaborated further.

[0054] Based on each monitoring point under each type of monitoring data with similar data fluctuations, and combined with the differences in data changes caused by interference in different frequency bands during actual monitoring, the data characteristics of the monitoring data at each monitoring point after clustering under the same type of monitoring data are compared in different frequency band modal components. The interference adjustment value of the monitoring data at all monitoring points in each cluster under the same type of monitoring data is calculated for each modal component.

[0055] Therefore, the modal components of the same frequency band at different monitoring points under the same monitoring data are taken as similar frequency band modal components. The DTW distance between similar frequency band modal components of monitoring data at different monitoring points in each cluster under the same monitoring data is calculated. The larger the DTW distance, the greater the difference in similar frequency band modal components between monitoring data at different monitoring points under the same monitoring data. The mean of all DTW distances is taken as the characteristic coefficient of each modal component of monitoring data at each monitoring point in each cluster under the same monitoring data.

[0056] Based on the above analysis, according to the characteristic coefficients of each modal component of the monitoring data at each monitoring point in each cluster under the same monitoring data, and combined with the discrete range of the corresponding data for each modal component of the monitoring data at each monitoring point in each cluster under the same monitoring data, the interference modulation value of the monitoring data at all monitoring points in each cluster under the same monitoring data for each modal component is calculated. In this embodiment, the specific calculation formula is as follows:

[0057] ;

[0058] In the formula, For the same type of monitoring data, the monitoring data at all monitoring points in each cluster at the first... Interference adjustment values ​​for each modal component; For each cluster under the same monitoring data, the first... The monitoring data at the monitoring point is the first The standard deviation of the data corresponding to each modal component; For each cluster under the same monitoring data, the first... The monitoring data at the monitoring point is the first Characteristic coefficients of each modal component; For the monitoring data at all monitoring points in each cluster under the same monitoring data, the first... The sum of the characteristic coefficients of each modal component; n is the total number of monitoring points in each cluster.

[0059] in, This indicates the comparison result of the difference in modal component data in similar frequency bands at different monitoring points in each cluster under the same monitoring data. The larger the value, the more significant the difference in modal components in similar frequency bands caused by interference. This represents the comparison of similar frequency band modal components of the monitoring data at all monitoring points in each cluster under the same monitoring data. The larger the value, the more significant the interference effect of the monitoring data at each monitoring point in each cluster on the corresponding frequency range.

[0060] Furthermore, a Gaussian filter is used to filter each modal component of the monitoring data at each monitoring point in each cluster under each type of monitoring data. Specifically, for each modal component of the monitoring data at each monitoring point in each cluster under each type of monitoring data, the standard deviation of the data corresponding to each modal component, and the mean of the interference modulation value of the monitoring data at all monitoring points in each cluster under the same type of monitoring data, are used as the standard deviation parameter of the Gaussian filter, and then Gaussian filtering is performed on each modal component data. Preferably, in this embodiment, the Gaussian filter is a known technology, and the specific process will not be described in detail.

[0061] Therefore, in cases where data deviations occur at different monitoring points under the same monitoring data due to interference in actual monitoring processes, the characteristics of each monitoring point under the same monitoring data are analyzed and clustered. This allows for accurate analysis of the impact of interference factors in different frequency ranges during actual monitoring, leading to interference characteristics at different monitoring points. Furthermore, the parameters in the filtering process of each modal component of the monitoring data at each monitoring point in each cluster under the same monitoring data are adjusted. The more significant the interference, the larger the filtering parameters of the corresponding frequency band modal components, thus reducing the impact of interference factors on deep foundation settlement displacement monitoring.

[0062] Step 4: Reconstruct and restore all modal components of the filtered monitoring data at each monitoring point to predict each type of monitoring data, thereby assisting in the monitoring of settlement displacement of deep foundation pits.

[0063] After the above processing, all modal components after modal decomposition of the filtered monitoring data at each monitoring point in each cluster under the same monitoring data are reconstructed and restored. The reconstructed data is each type of monitoring data after preprocessing the settlement displacement of each monitoring point. Preferably, in this embodiment, the reconstruction and restoration process is a well-known technique, and the specific process will not be described in detail.

[0064] Based on the preprocessed settlement displacement data of each monitoring point, the ARIMAX prediction algorithm is used to predict the settlement displacement of the deep foundation pit. The prediction results are compared with pre-set warning thresholds. If the predicted settlement displacement data of each monitoring point exceeds the warning threshold, the system immediately issues a warning signal and notifies relevant personnel via SMS, email, etc. The specific warning thresholds can be set according to the actual construction requirements for deep foundation pits. Simultaneously, the monitoring data and prediction results are displayed in the form of time-settlement curves and displacement distribution maps based on monitoring point locations, intuitively presenting the changing trend of deep foundation pit settlement displacement. This provides accurate data for engineering managers to formulate reasonable construction plans and safety protection measures, achieving effective auxiliary monitoring and risk control of deep foundation pit settlement displacement. Preferably, in this embodiment, the ARIMAX prediction algorithm is a known technology, and the specific process will not be described in detail.

[0065] Based on the same inventive concept as the above method, this application embodiment also provides a settlement displacement auxiliary monitoring device for deep foundation pits. The device stores a computer program, and when the computer program is executed by a processor, it implements the steps of any of the above-described settlement displacement auxiliary monitoring methods for deep foundation pits.

[0066] It is understood 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.

[0067] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0068] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the protection scope of this application.

Claims

1. A method for auxiliary monitoring of settlement and displacement in deep foundation pits, characterized in that, Includes the following steps: Collect each type of monitoring data at each monitoring point in the deep foundation pit; Based on the fluctuation characteristics of each type of monitoring data at different monitoring points, a sample set of each type of monitoring data is obtained. By the difference in feature values ​​between different monitoring points in each type of monitoring data sample set, and combined with the difference in monitoring data between different monitoring points under the same type of monitoring data, the metric distance between different monitoring points under the same type of monitoring data is obtained, and then all monitoring points under the same type of monitoring data are clustered. By analyzing the differences between modal components in the same frequency band after modal decomposition of monitoring data at different monitoring points in each cluster under the same monitoring data, the characteristic coefficients of monitoring data at each monitoring point in each cluster for each modal component are obtained. Combined with the discrete range of the corresponding modal component data, the interference adjustment value of monitoring data at all monitoring points in each cluster for each modal component is obtained. Then, filtering processing is performed on each modal component of monitoring data at each monitoring point in each cluster under each type of monitoring data. All modal components of the filtered monitoring data at each monitoring point are reconstructed and restored to predict each type of monitoring data, thereby assisting in the monitoring of settlement displacement of deep foundation pits. The acquisition of the characteristic coefficients of each modal component of the monitoring data at each monitoring point in each cluster includes: The DTW distance between similar frequency band modal components of monitoring data at different monitoring points in each cluster under the same monitoring data is statistically analyzed, and the mean of all DTW distances is used as the characteristic coefficient of each modal component of the monitoring data at each monitoring point in each cluster under the same monitoring data. The method for calculating the interference modulation value of each modal component for the monitoring data at all monitoring points in each cluster is as follows: ； In the formula, For the same type of monitoring data, the monitoring data at all monitoring points in each cluster at the first... Interference adjustment value for each modal component; For each cluster under the same monitoring data, the first... The monitoring data at the monitoring point is the first The standard deviation of the data corresponding to each modal component; For each cluster under the same monitoring data, the first... The monitoring data at the monitoring point is the first Characteristic coefficients of each modal component; For the monitoring data at all monitoring points in each cluster under the same monitoring data, the first... The sum of the characteristic coefficients of each modal component; n is the total number of monitoring points in each cluster.

2. The method for auxiliary monitoring of settlement displacement in deep foundation pits as described in claim 1, characterized in that, Each monitoring data sample set further includes: The three feature values ​​of the monitoring time series of each type of monitoring data at each monitoring point are used as the X-axis, Y-axis and Z-axis coordinate values ​​to establish a three-dimensional rectangular coordinate system. Each three-dimensional rectangular coordinate system corresponds to one type of monitoring data. The mapping result of each three-dimensional rectangular coordinate system is used as the monitoring sample set of each type of monitoring data, that is, the sample set of each type of monitoring data.

3. The method for auxiliary monitoring of settlement displacement in deep foundation pits as described in claim 2, characterized in that, The three characteristic values ​​of the monitoring time series of each type of monitoring data at each monitoring point are: The monitoring data for each type of data at each monitoring point are arranged according to the collection time sequence to obtain the monitoring time sequence sequence of each type of monitoring data at each monitoring point; The three characteristic values ​​are, in order: the variance, range, and trend statistics of the monitoring time series of each type of monitoring data at each monitoring point.

4. The method for auxiliary monitoring of settlement displacement in deep foundation pits as described in claim 3, characterized in that, The method for calculating the distance between different monitoring points under the same monitoring data is as follows: ； In the formula, For the same type of monitoring data, the first The monitoring point and the first The metric distance between monitoring points; For each monitoring data sample set, the first The monitoring point and the first Euclidean distance between the eigenvalues ​​of the monitoring points; Indicates the first under the same monitoring data The monitoring point and the first The DTW distance between monitoring points in the time series.

5. The method for auxiliary monitoring of settlement displacement in deep foundation pits as described in claim 1, characterized in that, The clustering of all monitoring points under the same type of monitoring data further includes: For each monitoring point, the metric distance between different monitoring points under the same monitoring data is used as the cluster distance for clustering.

6. The method for auxiliary monitoring of settlement displacement in deep foundation pits as described in claim 1, characterized in that, The method for obtaining the modal components in the similar frequency bands is as follows: The modal components of the same frequency band in the monitoring data at different monitoring points under the same monitoring data are regarded as the modal components of similar frequency bands.

7. The method for auxiliary monitoring of settlement displacement in deep foundation pits as described in claim 1, characterized in that, The filtering process for each modal component of the monitoring data at each monitoring point in each cluster under each type of monitoring data further includes: The standard deviation of the data corresponding to each modal component of the monitoring data at each monitoring point in each cluster under each type of monitoring data, and the mean of the interference modulation value of the monitoring data at each modal component of all monitoring points in each cluster under the same type of monitoring data, are used as the standard deviation parameter of the filter, and then the filtering process is performed on each modal component of the monitoring data at each monitoring point in each cluster under each type of monitoring data.

8. A settlement and displacement auxiliary monitoring device for deep foundation pits, wherein the device stores a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the settlement displacement auxiliary monitoring method for deep foundation pits as described in any one of claims 1-7.

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