Deep foundation pit slope deformation monitoring method and device, storage medium and electronic equipment

Abstract

The invention provides a deep foundation pit slope deformation monitoring method and device, a storage medium and electronic equipment, and relates to the technical field of deep foundation pit monitoring. The method comprises the following steps: acquiring monitoring data of a deep foundation pit slope, wherein the monitoring data comprises data uploaded by a GNSS receiver, a horizontal optical fiber, a settlement optical fiber and a three-dimensional scanner; according to the monitoring data and the timestamps, a three-dimensional deformation field model of the deep foundation pit slope is constructed; segmenting and refining the three-dimensional deformation field model to obtain time sequence deformation information of a deformation unit; predicting through the prediction network model to obtain a predicted deformation amount of each deformation unit; and the risk condition of the deep foundation pit slope is further obtained. Internal and external monitoring data are combined, so that the accuracy of the constructed three-dimensional deformation field model can be ensured; the three-dimensional deformation field model is segmented and refined, the change condition of each deformation unit is monitored, early warning can be carried out at the initial stage when various risk conditions do not occur or just occur, and the timeliness of early warning is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of deep foundation pit monitoring, and in particular to a method, device, storage medium and electronic equipment for monitoring deformation of a deep foundation pit slope. Background Art

[0002] With the increasing number of high-rise buildings, the scale of underground foundation pit excavation has also increased. During the foundation pit excavation and underground engineering construction process, the stability and safety of the structural facade and foundation pit slope have become the key links in engineering monitoring.

[0003] In order to discover problems in time and avoid accidents, deformation monitoring of deep foundation pits is required during building construction and operation. In traditional deformation monitoring, total stations or levels are often used for monitoring.

[0004] A single monitoring method can only monitor the deformation of the deep foundation pit slope in a certain dimension. Therefore, the potential risks of the deep foundation pit slope analyzed based on the data obtained by the traditional deformation monitoring method are also relatively one-sided, resulting in inaccurate deformation prediction and untimely safety warning. Summary of the invention

[0005] The problem to be solved by the present invention is that a single monitoring method can only monitor the deformation of the deep foundation pit slope in a certain dimension, resulting in inaccurate deformation prediction and untimely safety warning.

[0006] In order to solve the above problems, in a first aspect, the present invention provides a method for monitoring deformation of a deep foundation pit slope, comprising:

[0007] Acquiring monitoring data of a deep foundation pit slope, wherein the monitoring data includes slope surface data, slope internal horizontal displacement data, slope internal settlement data, and deep foundation pit slope three-dimensional data;

[0008] Constructing a three-dimensional deformation field model of the deep foundation pit slope according to the monitoring data and timestamp;

[0009] Segmenting each of the three-dimensional deformation field models constructed at multiple time points into multiple deformation units to obtain time-series deformation information of each deformation unit, wherein the deformation unit is a three-dimensional model segmented according to a preset size;

[0010] Inputting the temporal deformation information of the deformation unit into the trained prediction network model to obtain the predicted deformation amount of each deformation unit;

[0011] According to the predicted deformation amounts of the plurality of deformation units, the risk situation of the deep foundation pit slope is obtained.

[0012] Optionally, after acquiring the monitoring data of the deep foundation pit slope, the deep foundation pit slope deformation monitoring method further includes:

[0013] Preprocessing the internal settlement data of the slope in the monitoring data, wherein the internal settlement data of the slope includes settlement amount, and the settlement amount is:

[0014]

[0015] Where D(z1-z2) represents the soil settlement measured by the optical fiber from position z1 to position z2, ε(z) represents the strain of the optical fiber, and f 0 (z) represents the frequency shift of the initial Brillouin scattered light of the optical fiber at the z position, f(z) represents the frequency shift of the Brillouin scattered light of the optical fiber at the z position, C s It represents the proportionality coefficient between the frequency shift of the optical fiber backscattered light and the strain of the optical cable, C T It represents the proportionality coefficient between the frequency shift of the backscattered light of the optical fiber and the temperature of the optical cable. ΔT(z) represents the temperature change of the optical fiber at the z position.

[0016] Optionally, constructing the three-dimensional deformation field model of the deep foundation pit slope according to the monitoring data and the timestamp includes:

[0017] The monitoring data uploaded by different devices at different time points are aligned to a unified time axis by using a timestamp alignment method; the monitoring data uploaded by different devices are aligned to a unified coordinate system by using a coordinate conversion tool.

[0018] Optionally, obtaining the risk condition of the deep foundation pit slope according to the predicted deformation amounts of the plurality of deformation units includes:

[0019] According to the predicted deformations of the plurality of deformation units, a predicted deformation mean of the three-dimensional deformation field model in each direction is obtained, wherein the predicted deformation mean includes a lateral predicted deformation mean, a longitudinal predicted deformation mean and a vertical predicted deformation mean;

[0020] Accumulate the average values ​​of the predicted deformations obtained at multiple consecutive time points in each direction to obtain a predicted deformation cumulative value;

[0021] When any one of the predicted deformation cumulative values ​​is greater than the corresponding cumulative warning value, determining whether the directions of the predicted deformation mean values ​​corresponding to the predicted deformation cumulative value at multiple time points are consistent;

[0022] When the directions of the predicted deformation mean values ​​at multiple time points are consistent, an excessive deformation warning is generated.

[0023] Optionally, obtaining the risk condition of the deep foundation pit slope according to the predicted deformation amounts of the plurality of deformation units includes:

[0024] Determine the difference in deformation between two adjacent deformation units in the selected analysis direction according to the predicted deformation of the two deformation units in the selected analysis direction;

[0025] When the deformation difference is greater than the deformation difference threshold corresponding to the analysis direction, it is determined that there is a deformation risk between two adjacent deformation units in the analysis direction, and the contact surface between the two adjacent deformation units is marked as a deformation surface, and the selected analysis direction is marked as a deformation direction;

[0026] Selecting any direction as a preset deformation direction, controlling a preset selection window to move in the current three-dimensional deformation field model, and counting the number of deformation surfaces in the preset selection window having the same deformation direction as the preset deformation direction to obtain the number of deformation surfaces;

[0027] When the number of deformation surfaces is greater than a preset deformation surface threshold, it is predicted that cracks will be generated in the deep foundation pit slope, and the extension direction of the cracks is perpendicular to the preset deformation direction.

[0028] Optionally, when the number of deformation surfaces is greater than a preset deformation surface threshold, it is predicted that cracks will be generated in the deep foundation pit slope, and the extension direction of the preset range is used as the extension direction of the cracks. The deep foundation pit slope deformation monitoring method further includes:

[0029] When the preset deformation direction is horizontal or vertical, in the preset deformation direction, determining whether the average value of the predicted deformation on one side of the crack close to the center of the three-dimensional deformation field model is greater than the corresponding average value of the predicted deformation on the other side;

[0030] When it is greater than, it is predicted that the deep foundation pit slope will produce extrusion uplift deformation;

[0031] When it is less than, it is predicted that the deep foundation pit slope will undergo fracture deformation.

[0032] Optionally, when the number of deformation surfaces is greater than a preset deformation surface threshold, it is predicted that cracks will be generated in the deep foundation pit slope, and the extension direction of the preset range is used as the extension direction of the cracks. The deep foundation pit slope deformation monitoring method further includes:

[0033] When the preset deformation direction is vertical, in the preset deformation direction, determining whether the average value of the predicted deformation amount of the upper region of the three-dimensional deformation field model is greater than the average value of the predicted deformation amount of the lower region of the three-dimensional deformation field model;

[0034] When it is greater than, it is predicted that the deep foundation pit slope will have a sliding risk;

[0035] When it is less than, it is predicted that the deep foundation pit slope will have the risk of extrusion uplift.

[0036] In a second aspect, the present invention further provides a deep foundation pit slope deformation monitoring device, comprising:

[0037] A monitoring data acquisition module is used to acquire monitoring data of a deep foundation pit slope, wherein the monitoring data includes slope surface data, slope internal horizontal displacement data, slope internal settlement data and deep foundation pit slope three-dimensional data;

[0038] A deformation field construction module, used to construct a three-dimensional deformation field model of the deep foundation pit slope according to the monitoring data and timestamp;

[0039] A deformation field segmentation module, used to segment each of the three-dimensional deformation field models constructed at multiple time points into multiple deformation units, and obtain time-series deformation information of each deformation unit, wherein the deformation unit is a three-dimensional model segmented according to a preset size;

[0040] A deformation amount prediction module, used for inputting the time series deformation information of the deformation unit into the trained prediction network model to obtain the predicted deformation amount of each deformation unit;

[0041] The monitoring and early warning module is used to obtain the risk situation of the deep foundation pit slope according to the predicted deformation amounts of the plurality of deformation units, so as to issue an early warning for the risk of the deep foundation pit slope.

[0042] In a third aspect, the present invention provides a storage medium storing a computer program, wherein the computer program enables a computer to execute the deep foundation pit slope deformation monitoring method as described above.

[0043] In a fourth aspect, the present invention provides an electronic device, comprising:

[0044] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include methods for executing the deep foundation pit slope deformation monitoring method as described above.

[0045] The present invention provides a method, device, storage medium and electronic device for monitoring deformation of deep foundation pit slope. Compared with the prior art, it has the following beneficial effects:

[0046] By obtaining data on multiple aspects of the deep foundation pit slope and using multiple data sources, not only can the surface deformation of the deep foundation pit slope be monitored, but also its internal deformation can be monitored. The combination of internal and external monitoring data can ensure the accuracy of the constructed three-dimensional deformation field model and the accuracy of subsequent predictions; the three-dimensional deformation field model is segmented and refined, and the deformation of each deformation unit is predicted using a prediction network model, so that the changes of each deformation unit can be monitored, the initial stage of deformation of the deep foundation pit slope can be foreseen, and early warning can be issued in the initial stage when various risk situations have not yet appeared or have just appeared, thereby improving the timeliness of the early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 A schematic flow chart of a method for monitoring deformation of a deep foundation pit slope provided by an embodiment of the present invention;

[0049] Figure 2 A schematic diagram of a process for early warning of deformation risk of a deep foundation pit slope provided by an embodiment of the present invention;

[0050] Figure 3 A schematic structural diagram of a deep foundation pit slope deformation monitoring device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0052] The embodiments of the present application provide a deep foundation pit slope deformation monitoring method, device, storage medium and electronic equipment to solve the problem that a single monitoring method can only monitor the deformation of the deep foundation pit slope in a certain dimension, resulting in inaccurate deformation prediction and untimely safety warning, thereby achieving all-round monitoring of the deep foundation pit slope and ensuring the timeliness of deformation monitoring and early warning.

[0053] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows:

[0054] In order to find problems in time and avoid accidents, it is necessary to monitor the deformation of the foundation pit during the construction and operation of the building, pay attention to the changes in the shape and spatial position of the foundation pit under the action of external loads, and judge its stability by analyzing the structural deformation. Therefore, deformation monitoring of the structural facade is of great significance for preventing potential safety hazards, ensuring the structural integrity of the building and the smooth progress of construction.

[0055] In traditional deformation monitoring, methods such as total stations or levels are often used. However, traditional monitoring methods such as total stations or levels often have problems such as long operation time and low efficiency, and a single monitoring method can only monitor the deformation of deep foundation pit slopes in a certain dimension. According to the data obtained by traditional deformation monitoring methods, the potential risks of deep foundation pit slopes analyzed are also relatively one-sided, resulting in inaccurate deformation prediction and untimely safety warnings. Therefore, attempts are made to fuse multi-source heterogeneous data collected by various monitoring equipment to construct a three-dimensional deformation field, so that the deformation of the foundation pit in each direction can be monitored, thereby conducting all-round monitoring of the foundation pit and ensuring the accuracy of deformation monitoring.

[0056] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0057] like Figure 1 As shown, a method for monitoring deformation of a deep foundation pit slope provided in an embodiment of the present application includes:

[0058] S1: Acquire monitoring data of deep foundation pit slope, wherein the monitoring data includes slope surface data uploaded by GNSS receiver (Global Navigation Satellite System Receiver), slope internal horizontal displacement data uploaded by horizontal optical fiber, slope internal settlement data uploaded by settlement optical fiber and deep foundation pit slope three-dimensional data uploaded by three-dimensional scanner.

[0059] S2: Constructing a three-dimensional deformation field model of the deep foundation pit slope according to the monitoring data and timestamp.

[0060] S3: Divide each of the three-dimensional deformation field models constructed at multiple time points into multiple deformation units to obtain time-series deformation information of each deformation unit, wherein the deformation unit is a three-dimensional model obtained by dividing according to a preset size, and the time-series deformation information includes the lateral deformation amount, longitudinal deformation amount and vertical deformation amount of the deformation unit at multiple time points.

[0061] S4: Inputting the temporal deformation information of the deformation unit into the trained prediction network model to obtain the predicted deformation amount of each deformation unit, wherein the predicted deformation amount includes a lateral predicted deformation amount, a longitudinal predicted deformation amount and a vertical predicted deformation amount.

[0062] S5: Obtaining the risk situation of the deep foundation pit slope according to the predicted deformation amounts of the plurality of deformation units.

[0063] In this embodiment, the monitoring data includes slope surface data uploaded by the GNSS receiver, slope internal horizontal displacement data uploaded by the horizontal optical fiber, slope internal settlement data uploaded by the settlement optical fiber, and deep foundation pit slope three-dimensional data uploaded by the three-dimensional scanner. It can be seen that multiple devices are used to collect data to obtain multiple aspects of the deep foundation pit slope. The use of multiple data sources can not only monitor the surface deformation of the deep foundation pit slope, but also monitor its internal deformation. According to the monitoring data and timestamp, a three-dimensional deformation field model of the deep foundation pit slope is constructed. The combination of internal and external monitoring data can ensure the accuracy of the constructed three-dimensional deformation field model and the accuracy of subsequent predictions. Each of the three-dimensional deformation field models constructed at a time point is divided into multiple deformation units to obtain the time series deformation information of each deformation unit, and the time series deformation information of the deformation unit is input into the trained prediction network model to obtain the predicted deformation of each deformation unit, the three-dimensional deformation field model is divided and refined, and the prediction network model is used to predict the deformation of each deformation unit, so that the change of each deformation unit can be monitored, so as to foresee the initial stage of deformation of the deep foundation pit slope, and the risk situation of the deep foundation pit slope is obtained according to the predicted deformation of multiple deformation units, so as to issue an early warning when various risk situations have not appeared or have just appeared, thereby improving the timeliness of the early warning.

[0064] Each step is described in detail below.

[0065] Optionally, S1: after acquiring the monitoring data of the deep foundation pit slope, the deep foundation pit slope deformation monitoring method further comprises:

[0066] The monitoring data is preprocessed.

[0067] Specifically, according to monitoring needs, multiple GNSS receivers can be deployed, the working mode of the GNSS receivers can be configured, and parameters such as sampling rate and data format can be set; ensure that multiple GNSS receivers are synchronized and record accurate timestamps; start the GNSS receiver, start data collection, monitor the status of the GNSS receiver in real time, and regularly check the data storage device to ensure that the data storage is not lost or damaged. For the slope surface data uploaded by the GNSS receiver: import the collected GNSS data into the data processing software for preliminary preprocessing and sorting; use professional GNSS data processing software (such as Trimble Business Center, Leica Infinity, Topcon Magnet, Bernese GNSS Software, etc.) to solve the data and obtain high-precision coordinates and displacement information; use differential technology, precise ephemeris and atmospheric correction methods to correct errors in GNSS data and improve data accuracy.

[0068] For the internal settlement data of the slope collected and uploaded by optical fiber, the micro-strain data is transformed into deformation data, and the settlement (internal settlement data of the slope includes settlement) is calculated as:

[0069]

[0070] Where D(z1-z2) represents the soil settlement measured by the optical fiber from position z1 to position z2, ε(z) represents the strain of the optical fiber, and f 0 (z) represents the initial Brillouin scattered light frequency shift of the optical fiber at position z (MHz), f(z) represents the Brillouin scattered light frequency shift of the optical fiber at position z (MHz), C s It represents the proportionality coefficient between the frequency shift of the optical fiber backscattered light and the strain of the optical cable (MHz / με), which can be provided by the optical cable supplier or determined through the optical cable calibration test. T It represents the proportionality coefficient (MHz / ℃) between the frequency shift of the backscattered light of the optical fiber and the temperature of the optical cable. It can be provided by the optical cable supplier or determined through the optical cable calibration test. ΔT(z) represents the temperature change of the optical fiber at the z position.

[0071] In addition, the monitoring data also includes the internal inclination deformation data of the slope uploaded by the inclinometer. The inclinometer monitors the inclination change of the rock layer inside the slope, which is equivalent to the comprehensive change result of the horizontal displacement change and vertical settlement change inside the slope. Therefore, the internal inclination deformation data of the slope uploaded by the inclinometer can be used to verify the data uploaded by the horizontal optical fiber and the settlement optical fiber.

[0072] For the 3D data of deep foundation pit slope uploaded by 3D scanner: import the collected 3D point cloud data into the data processing software, and use professional data processing software (such as Cyclone, RealWorks, etc.) to pre-process the point cloud data, including denoising, alignment, splicing and modeling, to obtain the 3D boundary model of the deep foundation pit slope.

[0073] Optionally, S2: constructing a three-dimensional deformation field model of the deep foundation pit slope according to the monitoring data and the timestamp includes:

[0074] The monitoring data uploaded by different devices at different time points are aligned to a unified time axis using the timestamp alignment method to ensure the temporal consistency of the data. The monitoring data uploaded by different devices are aligned to a unified coordinate system using the coordinate conversion tool to ensure the spatial consistency of the data.

[0075] Specifically, the same data collection time point or collection cycle is set for multiple devices to ensure that multiple devices collect data at the same time point. When collecting data, the time point of data collection is recorded, and the data collected at the same time point is processed. The relative position relationship between each device or the position relationship between each device and the same reference point is used to convert the data uploaded by multiple devices into the same coordinate system, thereby aligning the data in the time dimension and the space dimension.

[0076] For step S3, a three-dimensional deformation field model can be constructed using the data collected at each time point. In the early stage, data can be collected at multiple collection time points to obtain multiple three-dimensional deformation field models. Each three-dimensional deformation field model is segmented, and only the first three-dimensional deformation field model is segmented. Then, the separation point is fixed as a part of the three-dimensional deformation field model. The separation point moves with the deformation of the three-dimensional deformation field model. Therefore, a corresponding deformation unit can be obtained in the three-dimensional deformation field model obtained later, and the deformation amount of each deformation unit can also be obtained. In order to perform multi-dimensional monitoring of the three-dimensional deformation field model, the deformation of the deformation unit in the horizontal, longitudinal and vertical directions can be focused on. For example, the relative deformation amount of a deformation unit at multiple time points in the horizontal direction is recorded to form the horizontal time series deformation information of the deformation unit. Similarly, the longitudinal time series deformation information and vertical time series deformation information of the deformation unit can also be obtained.

[0077] For step S4, the temporal deformation information of each deformation unit obtained in the early stage can be used to pre-train the ConvLSTM (convolutional long short-term memory network) model to obtain a prediction network model. The deformation prediction network model can also be established using the TensorFlow or PyTorch library. In addition, the actual deformation amount obtained subsequently can be compared with the predicted deformation amount to correct the prediction network model, so that the prediction network model becomes more and more accurate.

[0078] Alternatively, if Figure 2 As shown, S5: according to the predicted deformation amounts of the plurality of deformation units, the risk situation of the deep foundation pit slope is obtained, including:

[0079] S511: According to the predicted deformations of the plurality of deformation units, a predicted deformation mean of the three-dimensional deformation field model in each direction is obtained, wherein the predicted deformation mean includes a lateral predicted deformation mean, a longitudinal predicted deformation mean and a vertical predicted deformation mean.

[0080] Specifically, the lateral deformations of all deformation units are added together and then divided by the number of deformation units to obtain the mean lateral predicted deformation. Similarly, the mean longitudinal predicted deformation and the mean vertical predicted deformation can also be calculated.

[0081] S512: Accumulate the average values ​​of the predicted deformation amounts obtained at multiple consecutive time points in each direction to obtain a predicted deformation cumulative value.

[0082] Specifically, the deformation is the change value of the current three-dimensional deformation field model relative to the three-dimensional deformation field model at the previous time point. Therefore, the predicted deformation accumulation value is the accumulation of the change values ​​of multiple consecutive time points. It can be accumulated from the first monitoring moment or from a certain moment in the middle, so as to obtain the global predicted deformation accumulation value or the predicted deformation accumulation value of a certain period in the middle, which can flexibly analyze the three-dimensional deformation field model. The predicted deformation accumulation value includes the lateral predicted deformation accumulation value, the longitudinal predicted deformation accumulation value and the vertical predicted deformation accumulation value.

[0083] S513: When any one of the predicted deformation cumulative values ​​is greater than the corresponding cumulative warning value, it is determined whether the directions of the predicted deformation mean values ​​corresponding to the predicted deformation cumulative value at multiple time points are consistent.

[0084] S514: When the directions of the predicted deformation mean values ​​at multiple time points are consistent, an excessive deformation warning is generated.

[0085] Specifically, the cumulative value of predicted deformation in each direction corresponds to a cumulative warning value. When a certain cumulative value of predicted deformation is greater than the corresponding cumulative warning value, it means that the deformation of the deep foundation pit slope in the direction corresponding to the cumulative value of predicted deformation is too large. It is necessary to further determine whether the direction of the predicted deformation mean corresponding to the cumulative value of predicted deformation is consistent at multiple time points. If the direction is consistent, it means that multiple time points continuously change in the same direction. In the future, there is a high probability that the deformation will continue to deform in this direction. In the case that the deformation has exceeded the standard, an early warning can be issued; if the direction is inconsistent, it means that there is a reverse deformation in the direction of deformation at multiple time points. If there are many such inconsistencies, it means that even if the current cumulative value of deformation exceeds the standard, it may deform in the opposite direction at the next time point, making the cumulative value smaller and not exceeding the standard. At this time, no early warning can be issued temporarily or a prompt can be issued without early warning. Direction consistency judgment can greatly improve the accuracy of early warning and reduce the probability of false alarms.

[0086] Optionally, S5: obtaining the risk situation of the deep foundation pit slope according to the predicted deformation amounts of the plurality of deformation units includes:

[0087] S521: Determine the difference in deformation between the two adjacent deformation units in the analysis direction according to the predicted deformation of the two adjacent deformation units in the selected analysis direction.

[0088] Specifically, the analysis direction is a direction selected from the horizontal, vertical and vertical directions. Among the two adjacent deformation units, the deformation unit close to the edge of the three-dimensional deformation field model is determined as the first deformation unit, and the other deformation unit is determined as the second deformation unit. The predicted deformation of the first deformation unit is subtracted from the predicted deformation of the second deformation unit to obtain the deformation difference. For example, the horizontal direction is selected as the analysis direction, and the deformation difference between the two adjacent deformation units in the horizontal direction is calculated based on the predicted deformation of the two adjacent deformation units in the horizontal direction. A positive deformation difference indicates that the two deformation units have a tendency to diverge from each other in the horizontal direction, and a negative deformation difference indicates that the two deformation units have a tendency to approach each other in the horizontal direction.

[0089] S522: When the deformation difference is greater than the deformation difference threshold corresponding to the analysis direction, it is determined that there is a deformation risk between two adjacent deformation units in the analysis direction, and the contact surface between the two adjacent deformation units is marked as a deformation surface, and the selected analysis direction is marked as a deformation direction.

[0090] Specifically, when the deformation difference in the lateral direction is greater than the deformation difference threshold in the lateral direction, it is determined that there is a deformation risk in the lateral direction between the two deformation units, and the contact surface perpendicular to the lateral direction is marked as the deformation surface, and the lateral direction is recorded as the deformation direction.

[0091] S523: Select any direction as a preset deformation direction, control the preset selection window to move in the current three-dimensional deformation field model, count the number of deformation surfaces in the preset selection window whose deformation direction is the same as the preset deformation direction, and obtain the number of deformation surfaces.

[0092] S524: When the number of deformation surfaces is greater than a preset deformation surface threshold, it is predicted that cracks will be generated in the deep foundation pit slope, and the extension direction of the cracks is perpendicular to the preset deformation direction.

[0093] Specifically, if the horizontal direction is selected as the preset deformation direction, for example, the preset selection window is a thin rectangular parallelepiped with a width of 30 cm in the longitudinal and vertical directions and a width of 2 cm in the transverse direction. Select a position, move the preset selection window in the transverse direction, the moving step can be 2 cm, so that there is no intersection between the two selection positions, and the moving step can also be 1 cm, so that there can be a cross-selected three-dimensional deformation field model; when the preset selection window is moved in the transverse direction from one side of the three-dimensional deformation field model to the other opposite side at the selected position, move the preset selection window in the longitudinal or vertical direction with a moving step of 30 cm, 20 cm or 10 cm, etc., and then move it step by step in the transverse direction, and repeat the above steps until the preset selection window traverses the entire current three-dimensional deformation field model. After each selection is completed, the number of deformation surfaces with a horizontal deformation direction in the preset selection window is counted. If the number of deformation surfaces is 63 and the preset deformation surface threshold is 30, it means that in this preset selection window, 63 contact surfaces have a horizontal deviation, indicating that horizontal cracks may appear in this narrow preset range, and the extension direction of the cracks is roughly perpendicular to the preset deformation direction. For example, in the above distance analysis, the preset deformation direction is horizontal, and the horizontal deformation surface exceeding the preset deformation surface threshold is analyzed in the preset selection window, indicating that many positions in the preset selection window will have large horizontal deformations, so that cracks that are approximately perpendicular to the horizontal direction will appear in the deformation field model. Based on the knowledge that there is a risk of deformation, the risk type of the deep foundation pit slope is further analyzed. It should be noted that the cracks may be irregular in shape, and their extension direction will not be a straight line, but may be curved or undulating. Therefore, the cracks are approximately perpendicular to the preset deformation direction and can be regarded as being perpendicular to the preset deformation direction.

[0094] In an optional embodiment of the present invention, when the number of deformation surfaces is greater than a preset deformation surface threshold, it is predicted that cracks will be generated in the deep foundation pit slope, and the extension direction of the preset range is used as the extension direction of the cracks. Then, the deep foundation pit slope deformation monitoring method further includes:

[0095] When the preset deformation direction is horizontal or vertical, in the preset deformation direction, it is determined whether the average predicted deformation amount of the crack on one side close to the center of the three-dimensional deformation field model is greater than the corresponding average predicted deformation amount of the other side.

[0096] When it is greater than, it is predicted that the deep foundation pit slope will produce extrusion uplift deformation.

[0097] When it is less than, it is predicted that the deep foundation pit slope will undergo fracture deformation.

[0098] Specifically, when the preset deformation direction is horizontal or vertical, it means that the crack extension direction is perpendicular to the horizontal or vertical extension, the crack will extend vertically, or the crack will extend relatively longitudinally or horizontally, but because the deformation direction is horizontal or vertical, the cracks on the deep foundation pit slope may come from fracture or internal extrusion deformation. Therefore, it is necessary to analyze the average value of the predicted deformation on both sides of the crack. If the preset deformation direction is horizontal, the average values ​​of the horizontal predicted deformation on both sides of the crack are compared; if the preset deformation direction is vertical, the average values ​​of the longitudinal predicted deformation on both sides of the crack are compared. When the predicted deformation value on one side close to the center of the three-dimensional deformation field model is greater than the corresponding predicted deformation value on the other side, it means that the deformation on the side close to the center is larger. However, due to the obstruction of the soil layer with smaller deformation on the periphery, the soil layer with larger deformation can only squeeze the outer layer in the future, and there is a high probability of extrusion uplift deformation. However, when the predicted deformation value on one side close to the center of the three-dimensional deformation field model is less than the corresponding predicted deformation value on the other side, it means that the deformation on the side close to the center is smaller, while the deformation on the side far from the center is larger. At this time, the side far from the center is the outer soil layer area. At this time, the deformation of the outer soil layer will not be blocked, and it will separate from the inner soil layer, resulting in fracture deformation. When the predicted deformation value on one side close to the center of the three-dimensional deformation field model is equal to the corresponding predicted deformation value on the other side, it means that the deformation in the three-dimensional deformation field model is uniform and no cracks will occur. After analyzing that cracks will occur in the slope of the deep foundation pit, the source of the cracks is further located, and a more accurate deformation type is determined, so as to facilitate targeted preventive measures in advance.

[0099] In an optional embodiment of the present invention, when the number of deformation surfaces is greater than a preset deformation surface threshold, it is predicted that cracks will be generated in the deep foundation pit slope, and the extension direction of the preset range is used as the extension direction of the cracks. Then, the deep foundation pit slope deformation monitoring method further includes:

[0100] When the preset deformation direction is vertical, in the preset deformation direction, it is determined whether the average value of the predicted deformation of the upper region of the three-dimensional deformation field model is greater than the average value of the predicted deformation of the lower region of the three-dimensional deformation field model, wherein the three-dimensional deformation field model is divided into an upper and lower part at the height center of the three-dimensional deformation field model, the upper part being used as the upper region of the three-dimensional deformation field model, and the lower part being used as the lower region of the three-dimensional deformation field model.

[0101] When it is greater than, it is predicted that the deep foundation pit slope will have a sliding risk;

[0102] When it is less than, it is predicted that the deep foundation pit slope will have the risk of extrusion uplift.

[0103] Specifically, similar to the above deformation analysis, when the preset deformation direction is vertical, it means that the cracks will extend horizontally or vertically. When the average predicted deformation of the upper area is greater than the average predicted deformation of the lower area, cracks will occur between the upper soil layer and the lower soil layer. When the cracks are large, the upper soil layer will slide along the lower soil layer, especially in the deep foundation pit slope area. The slope itself has a certain slope. When this happens, the risk of sliding is greater. Therefore, it can be predicted in advance that the deep foundation pit slope will have a sliding risk. When the average predicted deformation of the upper area is less than the average predicted deformation of the lower area, the preset deformation direction is vertically upward, and the lower soil layer will squeeze the upper soil layer, forcing the upper soil layer to bulge; however, when the preset deformation direction is vertically downward, it means that both the lower soil layer and the upper soil layer have settled. At this time, the uniform settlement has little effect on the buildings on the foundation pit, but it also needs further attention to avoid a large settlement of the foundation pit.

[0104] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0105] 1. Use a variety of equipment to collect data and obtain data on deep foundation pit slopes from multiple aspects. Not only can the surface deformation of the deep foundation pit slope be monitored, but also its internal deformation. The combination of internal and external monitoring data can ensure the accuracy of the constructed three-dimensional deformation field model and the accuracy of subsequent predictions. The three-dimensional deformation field model is segmented and refined, and the deformation of each deformation unit is predicted using a prediction network model, so that the changes of each deformation unit can be monitored, the initial stage of deformation of the deep foundation pit slope can be foreseen, and early warning can be issued in the initial stage when various risk situations have not yet appeared or have just appeared, thereby improving the timeliness of early warning.

[0106] 2. When a certain predicted deformation cumulative value is greater than the corresponding cumulative warning value, it means that the deformation of the deep foundation pit slope in the direction corresponding to the predicted deformation cumulative value is too large. It is necessary to further determine whether the direction of the predicted deformation mean corresponding to the predicted deformation cumulative value is consistent at multiple time points. If the direction is consistent, it means that multiple time points have continuously changed in the same direction, and there is a high probability that they will continue to deform in this direction in the future. In the case that the deformation has exceeded the standard, an early warning can be issued. Direction consistency judgment can greatly improve the accuracy of the early warning and reduce the probability of false alarms.

[0107] 3. After analyzing that cracks will appear in the slope of the deep foundation pit, further locate the source of the cracks and determine the more accurate deformation type, so as to facilitate targeted preventive measures in advance.

[0108] like Figure 3 As shown, a deep foundation pit slope deformation monitoring device provided in an embodiment of the present application includes:

[0109] The monitoring data acquisition module 100 is used to acquire monitoring data of a deep foundation pit slope, wherein the monitoring data includes slope surface data, slope internal horizontal displacement data, slope internal settlement data and deep foundation pit slope three-dimensional data.

[0110] The deformation field construction module 200 is used to construct a three-dimensional deformation field model of the deep foundation pit slope according to the monitoring data and timestamp.

[0111] The deformation field segmentation module 300 is used to segment each of the three-dimensional deformation field models constructed at multiple time points into multiple deformation units to obtain time-series deformation information of each deformation unit, wherein the deformation unit is a three-dimensional model segmented according to a preset size, and the time-series deformation information includes the lateral deformation amount, longitudinal deformation amount and vertical deformation amount of the deformation unit at multiple time points.

[0112] The deformation prediction module 400 is used to input the temporal deformation information of the deformation unit into the trained prediction network model to obtain the predicted deformation of each deformation unit, wherein the predicted deformation includes a lateral predicted deformation, a longitudinal predicted deformation and a vertical predicted deformation.

[0113] The monitoring and early warning module 500 is used to obtain the risk situation of the deep foundation pit slope according to the predicted deformation amounts of the plurality of deformation units, so as to issue an early warning for the risk of the deep foundation pit slope.

[0114] An optional embodiment of the present application provides a storage medium storing a computer program, wherein the computer program enables a computer to execute the deep foundation pit slope deformation monitoring method as described above.

[0115] An optional embodiment of the present application provides an electronic device, including:

[0116] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include methods for executing the deep foundation pit slope deformation monitoring method as described above.

[0117] The beneficial effects of the deep foundation pit slope deformation monitoring device, storage medium and electronic device in this example are the same as the beneficial effects of the deep foundation pit slope deformation monitoring method mentioned above, and will not be repeated here.

[0118] Now will describe the electronic device that can be used as the server or client of the present application, it is the example of the hardware device that can be applied to various aspects of the present application.Electronic device is intended to represent the computer device of various forms of digital electronics, such as, laptop computer, desktop computer, workbench, personal digital assistant, server, blade server, mainframe computer and other suitable computer.Electronic device can also represent various forms of mobile device, such as, personal digital processing, cellular phone, smart phone, wearable device and other similar computing device.The parts shown in this article, their connection and relationship, and their functions are only as examples, and are not intended to limit the realization of the present application described and / or required herein.

[0119] The electronic device includes a computing unit, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The computing unit, ROM and RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0120] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc. In the present application, the unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme of the present application. In addition, each functional unit in each embodiment of the present application can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0121] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0122] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for monitoring deformation of a deep foundation pit slope, characterized in that: include: Acquiring monitoring data of a deep foundation pit slope, wherein the monitoring data includes slope surface data, slope internal horizontal displacement data, slope internal settlement data, and deep foundation pit slope three-dimensional data; Constructing a three-dimensional deformation field model of the deep foundation pit slope according to the monitoring data and timestamp; Segmenting each of the three-dimensional deformation field models constructed at multiple time points into multiple deformation units to obtain time-series deformation information of each deformation unit, wherein the deformation unit is a three-dimensional model segmented according to a preset size; Inputting the temporal deformation information of the deformation unit into the trained prediction network model to obtain the predicted deformation amount of each deformation unit; According to the predicted deformation amounts of the plurality of deformation units, the risk situation of the deep foundation pit slope is obtained.

2. The deep foundation pit slope deformation monitoring method according to claim 1, characterized in that: After the monitoring data of the deep foundation pit slope is obtained, the method further includes: Preprocessing the internal settlement data of the slope in the monitoring data, wherein the internal settlement data of the slope includes settlement amount, and the settlement amount is: Where D(z1-z2) represents the soil settlement measured by the optical fiber from position z1 to position z2, ε(z) represents the strain of the optical fiber, and f 0 (z) represents the frequency shift of the initial Brillouin scattered light of the optical fiber at the z position, f(z) represents the frequency shift of the Brillouin scattered light of the optical fiber at the z position, C s It represents the proportionality coefficient between the frequency shift of the optical fiber backscattered light and the strain of the optical cable, C T It represents the proportionality coefficient between the frequency shift of the backscattered light of the optical fiber and the temperature of the optical cable. ΔT(z) represents the temperature change of the optical fiber at the z position.

3. The deep foundation pit slope deformation monitoring method according to claim 1, characterized in that: The constructing of the three-dimensional deformation field model of the deep foundation pit slope according to the monitoring data and the timestamp comprises: The monitoring data uploaded by different devices at different time points are aligned to a unified time axis by using a timestamp alignment method; the monitoring data uploaded by different devices are aligned to a unified coordinate system by using a coordinate conversion tool.

4. The method for monitoring deformation of a deep foundation pit slope according to any one of claims 1 to 3, characterized in that: The step of obtaining the risk condition of the deep foundation pit slope according to the predicted deformation amounts of the plurality of deformation units includes: According to the predicted deformations of the plurality of deformation units, a predicted deformation mean of the three-dimensional deformation field model in each direction is obtained, wherein the predicted deformation mean includes a lateral predicted deformation mean, a longitudinal predicted deformation mean and a vertical predicted deformation mean; Accumulate the average values ​​of the predicted deformations obtained at multiple consecutive time points in each direction to obtain a predicted deformation cumulative value; When any one of the predicted deformation cumulative values ​​is greater than the corresponding cumulative warning value, determining whether the directions of the predicted deformation mean values ​​corresponding to the predicted deformation cumulative value at multiple time points are consistent; When the directions of the predicted deformation mean values ​​at multiple time points are consistent, an excessive deformation warning is generated.

5. The method for monitoring deformation of a deep foundation pit slope according to any one of claims 1 to 3, characterized in that: The step of obtaining the risk condition of the deep foundation pit slope according to the predicted deformation amounts of the plurality of deformation units includes: Determine the difference in deformation between two adjacent deformation units in the selected analysis direction according to the predicted deformation of the two deformation units in the selected analysis direction; When the deformation difference is greater than the deformation difference threshold corresponding to the analysis direction, it is determined that there is a deformation risk between two adjacent deformation units in the analysis direction, and the contact surface between the two adjacent deformation units is marked as a deformation surface, and the selected analysis direction is marked as a deformation direction; Selecting any direction as a preset deformation direction, controlling a preset selection window to move within the current three-dimensional deformation field model, and counting the number of deformation surfaces within the preset selection window having the same deformation direction as the preset deformation direction to obtain the number of deformation surfaces; When the number of deformation surfaces is greater than a preset deformation surface threshold, it is predicted that cracks will be generated in the deep foundation pit slope, and the extension direction of the cracks is perpendicular to the preset deformation direction.

6. The method for monitoring deformation of a deep foundation pit slope according to claim 5, characterized in that: When the number of the deformation surfaces is greater than the preset deformation surface threshold, it is predicted that cracks will be generated in the deep foundation pit slope, and the extension direction of the preset range is used as the extension direction of the cracks, and the method further includes: When the preset deformation direction is horizontal or vertical, in the preset deformation direction, determining whether the average value of the predicted deformation on one side of the crack close to the center of the three-dimensional deformation field model is greater than the corresponding average value of the predicted deformation on the other side; When it is greater than, it is predicted that the deep foundation pit slope will produce extrusion uplift deformation; When it is less than, it is predicted that the deep foundation pit slope will undergo fracture deformation.

7. The method for monitoring deformation of a deep foundation pit slope according to claim 5, characterized in that: When the number of the deformation surfaces is greater than the preset deformation surface threshold, it is predicted that cracks will be generated in the deep foundation pit slope, and the extension direction of the preset range is used as the extension direction of the cracks, and the method further includes: When the preset deformation direction is vertical, in the preset deformation direction, determining whether the average value of the predicted deformation amount of the upper region of the three-dimensional deformation field model is greater than the average value of the predicted deformation amount of the lower region of the three-dimensional deformation field model; When it is greater than, it is predicted that the deep foundation pit slope will have a sliding risk; When it is less than, it is predicted that the deep foundation pit slope will have the risk of extrusion uplift.

8. A deep foundation pit slope deformation monitoring device, characterized in that: include: A monitoring data acquisition module is used to acquire monitoring data of a deep foundation pit slope, wherein the monitoring data includes slope surface data, slope internal horizontal displacement data, slope internal settlement data and deep foundation pit slope three-dimensional data; A deformation field construction module, used to construct a three-dimensional deformation field model of the deep foundation pit slope according to the monitoring data and timestamp; A deformation field segmentation module, used to segment each of the three-dimensional deformation field models constructed at multiple time points into multiple deformation units, and obtain time-series deformation information of each deformation unit, wherein the deformation unit is a three-dimensional model segmented according to a preset size; A deformation amount prediction module, used for inputting the time series deformation information of the deformation unit into the trained prediction network model to obtain the predicted deformation amount of each deformation unit; The monitoring and early warning module is used to obtain the risk situation of the deep foundation pit slope according to the predicted deformation of the plurality of deformation units, so as to issue an early warning for the risk of the deep foundation pit slope.

9. A storage medium, characterized in that: It stores a computer program, which enables the computer to execute the deep foundation pit slope deformation monitoring method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include a method for executing the deep foundation pit slope deformation monitoring method as described in any one of claims 1 to 7.

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