A system and method for detecting deformation of subway stations and deep foundation pit support quality

Abstract

The present invention discloses a system and method for detecting deformation of subway stations and quality of deep foundation pit support. The system and method adopt air-ground coordination of 3D laser scanning and oblique photography, and a composite distributed optical fiber measurement method to obtain deformation data of deep foundation pit support. Accurate detection results are obtained by fusion modeling of the collected point cloud data and processing and analysis in combination with the deformation data. The method complements the advantages of 3D laser scanning and oblique photography of unmanned aerial vehicles, and adopts distributed optical fiber to measure the deformation data of deep foundation pit support. The above methods are not restricted by terrain conditions, thus ensuring the detection accuracy of the detection results. The data collected by the method is comprehensive and intuitive, with high detection accuracy, and can realize continuous detection of subway stations and deep foundation pit support structures. The combined analysis of 3D models and deformation data of deep foundation pit support obtained through 3D laser point clouds and oblique image point clouds improves detection accuracy and has good promotion and application value.

Description

Technical Field

[0001] The present invention belongs to the field of deformation detection of close-fitting subway stations, and in particular relates to a system and method for detecting deformation of close-fitting subway stations and deep foundation pit support quality. Background Art

[0002] With economic development and accelerating urbanization, the intensified development of underground space in urban construction has led to an increasing number of foundation pit projects occurring near subway stations. Foundation pit projects near existing subway stations are bound to have a certain impact on the stations, and the deformation of the excavation pit support structure also requires real-time monitoring.

[0003] Current detection methods primarily rely on conventional methods such as chordal-vector traverses, total station corner grids, and precision levels. These methods not only have long observation cycles but are also significantly affected by human factors. These methods are increasingly unable to meet the dynamic monitoring requirements of large underground structures in terms of continuity, real-time performance, and automation. Traditional deformation monitoring methods often require the establishment of high-precision monitoring networks, which are significantly affected by terrain conditions and generally have poor network configurations, significantly impacting the accuracy of monitoring points. Furthermore, traditional methods typically require long observation times and are labor-intensive, making automated monitoring difficult. Furthermore, these methods are unable to implement three-dimensional, holistic inspections and lack a comprehensive analysis of the deformation of subway stations and foundation pit support structures.

[0004] It can be seen that the traditional detection methods for deformation of close-fitting subway stations and the quality of deep foundation pit support have poor detection accuracy due to the limitations of terrain conditions. Summary of the Invention

[0005] In order to overcome the above technical defects, the present invention provides a system and method for detecting deformation of subway stations and deep foundation pit support quality, which can solve the technical problem that existing detection measures are limited by terrain conditions, resulting in poor detection accuracy.

[0006] In order to achieve the above object, the present invention adopts the following technical contents:

[0007] This technology utilizes a combination of 3D laser scanning, drone oblique imaging, and distributed fiber optic measurement. 3D laser scanning rapidly acquires dense, comprehensive, correlated, and continuous 3D coordinate and image data of subway station interior sections. Oblique photography captures complete station interior sections, providing rich texture information and high overlap. Combined with distributed fiber optic sensors, these sensors are deployed on deep foundation pit support structures to generate high-precision, real-time deformation data. Through 3D laser scanning, drone oblique imaging, and distributed fiber optic sensor analysis, the quality of subway station and deep foundation pit support structures can be inspected.

[0008] Compared with the prior art, the present invention has the following beneficial effects:

[0009] The present invention provides a method for detecting deformation of subway stations and quality of deep foundation pit support. The method adopts air-ground coordination of 3D laser scanning and oblique photography, and a composite distributed optical fiber measurement method to obtain deformation data of deep foundation pit support. Accurate detection results are obtained by fusion modeling of the collected point cloud data and processing and analysis in combination with the deformation data. The method complements the advantages of 3D laser scanning and UAV oblique photography, and adopts distributed optical fiber to measure the deformation data of deep foundation pit support. The above methods are not restricted by terrain conditions, thus ensuring the detection accuracy of the detection results. The data collected by the method is comprehensive and intuitive, with high detection accuracy, and can realize continuous detection of subway stations and deep foundation pit support structures. The combined analysis of the 3D model and the deformation data of the deep foundation pit support obtained by the 3D laser point cloud and the oblique image point cloud improves the detection accuracy and has good promotion and application value.

[0010] Preferably, in the present invention, VIS visual tracking technology and IMU inertial navigation sensor are used to automatically stitch the three-dimensional laser point cloud and the oblique image point cloud, thereby ensuring the effect of automatic stitching of the point cloud and further ensuring the detection accuracy.

[0011] Preferably, in the present invention, a radius filtering denoising algorithm is used to denoise the point cloud data; the local complexity of the point cloud is judged based on the curvature combined with the normal vector, and a uniform sampling method is used in the surface area to perform pre-processing operations such as streamlining on the point cloud data. The point cloud data is effectively screened before data fusion, and invalid and highly interfering data are eliminated, thereby ensuring the accuracy of data fusion.

[0012] Preferably, in the present invention, quick-drying glue and epoxy resin are used as adhesives for distributed optical fibers, and the distributed optical fibers are stretched to a taut state before being pasted and laid; this enables the distributed optical fibers to better measure minute strains, thereby ensuring the accuracy of distributed optical fiber measurements.

[0013] The present invention also provides a system for detecting deformation of subway stations and the quality of deep foundation pit support. The system includes a point cloud acquisition device, a distributed sensor and a control unit, and a data processing and analysis unit. It can solve the problems of incomplete data collection, non-intuitive data format, poor accuracy, and inability to achieve continuous detection of subway stations and deep foundation pit support structures in traditional detection measures. The system complements the advantages of three-dimensional laser scanning and drone oblique imaging, and combines the collected data of distributed optical fibers for comprehensive analysis. The above acquisition methods are not restricted by terrain, thus ensuring the accuracy of detection. The system has a simple structure and principle, strong practicality, and good promotion and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic diagram of the arrangement of distributed optical fiber sensors in a system for detecting deformation of a subway station and deep foundation pit support quality, provided by an embodiment of the present invention;

[0015] Figure 2 A flow chart of a method for detecting deformation of a subway station and deep foundation pit support quality provided by an embodiment of the present invention;

[0016] Figure 3 Schematic diagram of the three-dimensional laser scanning principle provided by an embodiment of the present invention;

[0017] Figure 4 Schematic diagram of a distributed optical fiber sensor provided by an embodiment of the present invention;

[0018] Figure 5 The present invention provides a flowchart of a method for detecting deformation of a subway station and quality of deep foundation pit support.

[0019] Reference numerals:

[0020] 1-High-strength steel plate for deep foundation pit support; 2-Corrugated steel plate; 3-Distributed fiber optic sensor. DETAILED DESCRIPTION

[0021] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention more clearly understood, the present invention is further described in detail in the following specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0023] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0024] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0025] In the description of the embodiments of the present invention, it should be noted that if the terms "upper," "lower," "horizontal," "inner," etc. appear, the orientation or positional relationship indicated is based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the inventive product is typically placed when in use. These terms are merely for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. In addition, the terms "first," "second," etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0026] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0027] In the description of the embodiments of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0028] The present invention is described in further detail below with reference to the accompanying drawings:

[0029] Example

[0030] As mentioned in the background technology, the existing water electrolysis hydrogen production technology still has the following problems: the detection means currently used mainly rely on conventional methods such as chord-vector wires, total station corner networks and precision levels. Not only are the observation cycles long, but the accuracy is also greatly affected by human factors. It has become increasingly difficult to meet the dynamic monitoring requirements of large underground structures in terms of continuity, real-time and degree of automation. Traditional deformation monitoring methods often require the establishment of a high-precision monitoring network, which is greatly affected by terrain conditions. The network type of the monitoring network is generally poor, which greatly affects the accuracy of the monitoring points. Moreover, the traditional methods usually have a long observation time and high labor intensity, making it difficult to achieve automated monitoring. In addition, the traditional methods are unable to implement three-dimensional overall detection and lack an overall analysis of the deformation of subway stations and foundation pit support structures.

[0031] In order to solve the above problems, this embodiment provides a system and method for detecting deformation of subway stations and the quality of deep foundation pit support. This method and system adopt three-dimensional laser scanning technology, drone oblique imaging technology and distributed fiber optic measurement technology. Three-dimensional laser scanning technology is an advanced, fully automatic, high-precision stereoscopic scanning technology that can quickly obtain dense, comprehensive, correlated, and continuous three-dimensional coordinate data and image data of the internal cross-sectional surface of the subway station. The use of oblique photography technology can completely capture the internal cross-section of the station, with rich texture information and high overlap. Combined with the distributed fiber optic sensor method, distributed fiber optic sensors are arranged on the deep foundation pit support structure to obtain high-precision, real-time deformation data of the support structure. Through the detection and analysis of three-dimensional laser scanning, drone oblique photography and distributed fiber optic sensors, the quality of subway stations and deep foundation pit support can be detected.

[0032] like Figure 5 As shown, the present invention provides a method for detecting deformation of a subway station and the quality of deep foundation pit support, and the specific steps are as follows:

[0033] S1: Develop laser scanning routes and aerial photography routes based on the survey results inside the subway station;

[0034] S2: Based on the laser scanning route and aerial photography route, the 3D laser point cloud and oblique image point cloud of the subway station interior section are collected, and the point cloud is automatically stitched;

[0035] S3: Fusing the spliced ​​3D laser point cloud with the spliced ​​oblique image point cloud, and performing 3D modeling of the internal section of the subway station based on the fused point cloud data to obtain a 3D model;

[0036] S4: Deformation data of deep foundation pit support is collected based on distributed optical fiber; wherein, distributed optical fiber is pre-laid on the surface of the support structure according to the deep foundation pit support structure;

[0037] S5: Combine and analyze the 3D model with the deformation data of the deep foundation pit support to obtain the detection results of the subway station deformation and the quality of the deep foundation pit support.

[0038] The technical solution of the invention is described in detail below with reference to the accompanying drawings:

[0039] This embodiment provides a system for detecting deformation of closely fitting subway stations and the quality of deep foundation pit support. The system includes a point cloud acquisition device, distributed sensors, a control unit, and a data processing and analysis unit. The control unit is electrically connected to the point cloud acquisition device, the distributed sensors, and the data processing and analysis unit, and is used to control the point cloud acquisition device, the distributed sensors, and the data processing and analysis unit. The point cloud acquisition device is used to collect three-dimensional laser point clouds and oblique image point clouds of the subway station's internal cross-section based on a predetermined laser scanning route and aerial photography route. The distributed sensors are used to collect deformation data of the deep foundation pit support. The data processing and analysis unit is used to process and analyze the collected three-dimensional laser point clouds, oblique image point clouds, and deformation data of the deep foundation pit support, and output detection results of subway station deformation and deep foundation pit support quality.

[0040] Specifically, the point cloud acquisition device uses a three-dimensional laser scanner and an oblique photography drone. Among them, the three-dimensional laser scanner preferably uses the Leica RTC360 laser scanner with high performance in point cloud data acquisition to collect point cloud and panoramic image data; the oblique photography drone preferably uses the DJI M300RTK equipped with a Ruibo five-lens oblique camera, with a single lens pixel of 24 million and a total pixel of 120 million. The camera focal length is 25mm for orthophoto and 35mm for oblique photography.

[0041] like Figure 1 As shown, the distributed sensor consists of continuously distributed optical fiber sensing units of equal length, with no spacing between adjacent sensing units. This sensor can obtain information such as strain, temperature, and pressure along the entire length of the optical fiber. At the top is a high-strength steel plate 1 for deep foundation pit support, with a distributed optical fiber sensor 2 positioned beneath it. To protect the sensor, it is wrapped in a corrugated steel plate.

[0042] Among them, distributed optical fiber sensors are evenly arranged in the deep foundation pit support structure, and the strain information on the entire optical fiber is obtained through continuous optical fiber sensing units.

[0043] In addition, an OFDR data acquisition instrument is required in conjunction with distributed fiber optic sensors. A 4-channel OSI-S high-precision, high-resolution OFDR fiber interrogator with a spatial resolution of 1 cm is used to collect raw fiber strain data. Optical frequency domain reflectometry (OFDR) is a novel monitoring technology that integrates sensing and transmission, using light as a carrier and fiber as a medium. It can achieve a spatial resolution of 1 mm within a 100-meter sensing range. Distributed fiber optic sensors offer advantages such as small size, light weight, corrosion resistance, easy deployment, passive sensing, immunity to electromagnetic interference, and high sensing accuracy.

[0044] The control unit and the data processing and analysis unit are implemented by a computer, a tablet computer or a mobile device, and have control and data analysis processing programs stored therein.

[0045] The above structures were arranged as follows: a 3D laser scanner was pre-installed on a pre-installed station and connected to a computer. The drone used for oblique photography was equipped with a camera lens. Distributed fiber optic sensors were pre-installed on the surface of the deep foundation pit support structure and connected to the OFDR data acquisition system.

[0046] 3D laser scanning is used to conduct field scans of the interior cross-section of subway stations, thereby obtaining 3D point cloud data of the station's interior cross-section. 3D point cloud data primarily consists of a large amount of spatial position coordinate information covering the station's interior cross-section at the time. Based on detection requirements, automatic scanning operations can be performed within specified time intervals to obtain multi-temporal point cloud data of the subway's interior cross-section. The resulting preliminary point cloud data is then processed for modeling and deformation analysis.

[0047] This system utilizes a combination of 3D laser scanning, drone oblique photography, and distributed fiber optic sensor measurement to acquire geometric and deformation data for subway stations. The collected point cloud and strain data are then processed and analyzed using post-processing software. 3D laser scanning boasts high speeds, reaching millions of points per second, and high data acquisition accuracy, reaching ±1mm. The collected point cloud data is more intuitive and less susceptible to external influences, allowing measurements even in the absence of light. Drone oblique photography provides comprehensive coverage of the entire subway station's internal cross-section. The combination of these two methods offers complementary advantages, and both utilize non-contact measurement, far from hazardous areas, fully ensuring the safety of equipment and operators. Distributed fiber optic sensors are pre-placed on the surface of the foundation pit support structure, and deformation data is collected using an OFDR data acquisition system.

[0048] In this embodiment, Figure 2 As shown, a method for detecting deformation of subway stations and deep foundation pit support quality is also provided. The specific steps are as follows:

[0049] Step 1: Conduct an internal survey of the station, arrange control points and image control points, and select an appropriate design flight route (i.e., aerial photography route) and laser scanning route.

[0050] In order to unify the coordinates of the data results, a certain number of control points and image control points are arranged near the internal section of the station, and the coordinates are measured using GPS and RTK, that is, the coordinates are corrected; laser scanning routes and flight routes are formulated based on the corrected control points and image control points.

[0051] In addition, to ensure measurement accuracy, the distance between the control points and image control points and the internal sections of the station is kept within 50 meters.

[0052] Step 2: According to the laser scanning route and flight route, set up the TRC360 3D laser scanner on the pre-arranged station to collect point cloud and image data of the station's internal section, and enable VIS visual tracking technology to automatically stitch the point cloud. Figure 3 As shown, Figure 3 This is the principle diagram of 3D laser scanning.

[0053] Step 3: During field oblique photography, the designed flight route is imported into the task. The drone completes an automatic inspection and, after verification, automatically flies and captures the image point cloud of the station interior. This means that after the image control points are laid out, the triangulation points are sprayed and numbered, and the point coordinates are collected using RTK. The designed flight route is then imported into the task. The drone completes an automatic inspection and, after verification, automatically flies and captures the image point cloud of the station interior.

[0054] Among them, before using a drone for oblique photography, it is necessary to consider the accuracy requirements, camera focal length, ground resolution, altitude, heading overlap, lateral overlap, etc.; the layout of the three-dimensional laser scanning station should ensure that the scanning area overlaps by more than 30% between the stations.

[0055] After data collection is complete, it is exported via a solid-state USB drive and transferred to the Register 360 software. In the Register 360 software, VIS visual tracking technology and IMU inertial navigation sensors can be used to automatically stitch together multi-site clouds.

[0056] Step 4: De-noise and simplify the point cloud data obtained by 3D laser scanning and oblique photography. Specifically, a radius filter denoising algorithm is used to denoise the point cloud data. The local complexity of the point cloud is judged based on the curvature combined with the normal vector, and the uniform sampling method is used in the curved surface area to simplify the point cloud data.

[0057] Step 5: Fuse the processed 3D laser point cloud with the oblique image point cloud, and perform 3D modeling of the interior of the station based on the fused point cloud data.

[0058] The fusion of laser point cloud data and oblique photography point cloud data involves two main stages: coarse registration and fine registration. Coarse registration unifies the coordinate base of the laser point cloud and the image point cloud, and ensures that the two data sources have the same scale. Fine registration of the point cloud data is performed using the neighboring iteration method based on the coarse registration.

[0059] The fused point cloud data is processed by surface fitting, meshing and texture mapping to obtain a three-dimensional point cloud model of the internal section of the subway station.

[0060] Step six: Determine the fiber optic laying plan based on the structure of the deep foundation pit support structure, and pre-lay distributed optical fibers on the surface of the support structure. Here, fiber optic sensors are laid flat on the surface of the deep foundation pit support structure. To ensure the accuracy of the distributed optical fiber measurement, quick-drying glue and epoxy resin are used as adhesives for the surface of the distributed optical fiber. To enable the optical fiber to better test small strains, the optical fiber should be stretched to a taut state before pasting. To protect the sensing optical fiber, the sensor is protected by a casing when the optical fiber is laid below the ground.

[0061] Step 7: The deformation data of the deep foundation pit support structure is measured by the distributed optical fiber sensor. Specifically, the data measured by the distributed optical fiber sensor is collected by the OFDR data acquisition instrument, and the data is output and processed by the software of the OSI-S demodulator, thereby obtaining the deformation information of the deep foundation pit support structure. The specific principle is as follows: Figure 4 shown.

[0062] Step 8: The data measured by the sensor is combined with the point cloud data and the three-dimensional model for analysis to detect the deformation of the subway station and the quality of the deep foundation pit support structure.

[0063] In summary, this embodiment provides a system and method for detecting deformation of subway stations and deep foundation pit support quality, which has the following advantages:

[0064] To address the challenges of traditional subway station and deep foundation pit support structure inspection methods, including incomplete data collection, non-intuitive data formats, poor accuracy, and the inability to continuously inspect stations and foundation pit support structures, a hybrid inspection method combining 3D laser scanning, drone oblique photography, and fiber optic sensors facilitates integration and provides more accurate data collection. Real-time processing of the fused point cloud data from laser scanning and oblique photography allows for the construction of a 3D model. Combined with distributed fiber optic sensor inspection, this method offers high levels of intelligence, automation, and precision. The steps are as follows: First, based on actual inspection requirements, multiple stable control points and image control points are set around the station's internal section, and a laser scanning and flight path is developed. A 3D laser scanner and drone oblique photography are used to inspect the study area and acquire point cloud data. The point cloud data is then subjected to coordinate denoising, simplification, and fusion. The fused point cloud data is then used with third-party software to construct a 3D point cloud model of the station's internal section. Distributed fiber optic sensors are then used to inspect the deep foundation pit support structure, generating deformation data. By combining the three-dimensional point cloud model of the station obtained from the same scanning area data at different time periods with the data obtained from monitoring the foundation pit support structure with the optical fiber sensor, the deformation of the subway station and the foundation pit support structure can be detected; the system and method can implement three-dimensional overall detection and conduct an overall analysis of the deformation of the subway station and the deep foundation pit support structure. The data collected by this method is comprehensive and intuitive, with high detection accuracy, and can realize continuous detection of subway stations and deep foundation pit support structures. The combined analysis of the three-dimensional model and the deformation data of the deep foundation pit support obtained through the three-dimensional laser point cloud and the inclined image point cloud improves the detection accuracy and has good promotion and application value.

[0065] The above embodiment is only one of the implementation methods that can realize the technical solution of the present invention. The scope of protection claimed by the present invention is not limited only to this embodiment, but also includes changes, replacements and other implementation methods that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention.

Claims

1. A method for detecting deformation of subway stations and deep foundation pit support quality, characterized in that: include: S1: Develop laser scanning routes and aerial photography routes based on the survey results inside the subway station; S2: Based on the laser scanning route and aerial photography route, the 3D laser point cloud and oblique image point cloud of the subway station interior section are collected, and the point cloud is automatically stitched; S3: Fusing the spliced ​​3D laser point cloud with the spliced ​​oblique image point cloud, and performing 3D modeling of the internal section of the subway station based on the fused point cloud data to obtain a 3D model; S4: Deformation data of deep foundation pit support is collected based on distributed optical fiber; wherein, distributed optical fiber is pre-laid on the surface of the support structure according to the deep foundation pit support structure; S5: Combine and analyze the 3D model with the deformation data of the deep foundation pit support to obtain the detection results of the subway station deformation and the quality of the deep foundation pit support; In S3, the specific steps of data fusion include coarse registration and fine registration. In the coarse registration stage, the coordinate reference and data source scale of the spliced ​​3D laser point cloud and the spliced ​​oblique image point cloud are unified. Based on the coarse registration, the spliced ​​3D laser point cloud and the spliced ​​oblique image point cloud are processed using the neighboring iteration method to complete the fine registration. In S4, quick-drying glue and epoxy resin are used as adhesives for distributed optical fibers, and the distributed optical fibers are stretched to a taut state before being glued and laid; The 3D laser point cloud and oblique image point cloud are automatically stitched together through VIS visual tracking technology and IMU inertial navigation sensors.

2. A method for detecting deformation of a subway station and deep foundation pit support quality according to claim 1, characterized in that: The specific steps of S1 are as follows: Based on the survey results inside the subway station, multiple control points and image control points are arranged around the internal section of the subway station, and GPS and RTK are used to calibrate the control points and image control points; laser scanning routes and aerial photography routes are formulated based on the calibrated control points and image control points.

3. A method for detecting deformation of a subway station and deep foundation pit support quality according to claim 1, characterized in that: Before S3 starts, data preprocessing is performed on the spliced ​​3D laser point cloud and the spliced ​​oblique image point cloud. The data preprocessing includes: denoising, local complexity judgment, and streamlining the spliced ​​3D laser point cloud and the spliced ​​oblique image point cloud, respectively.

4. A method for detecting deformation of a subway station and deep foundation pit support quality according to claim 1, characterized in that: In S3, the fused point cloud data is subjected to surface fitting, meshing and texture mapping to obtain a three-dimensional model of the internal section of the subway station.

5. The method for detecting deformation of a subway station and the quality of deep foundation pit support according to claim 1 is characterized in that: In S4, the data measured by the distributed optical fiber sensor is collected by the data acquisition instrument and processed by the demodulator to obtain the deformation data of the deep foundation pit support.

6. A method for detecting deformation of a subway station and deep foundation pit support quality according to claim 5, characterized in that: The data measured by the distributed optical fiber sensor includes strain data, pressure data and temperature data.

7. A system for detecting deformation of subway stations and deep foundation pit support quality, characterized in that: include: Point cloud acquisition device, distributed sensor and control unit, and data processing and analysis unit; The control unit is electrically connected to the point cloud acquisition device, the distributed sensor and the data processing and analysis unit, and is used to control the point cloud acquisition device, the distributed sensor and the data processing and analysis unit; The point cloud acquisition device is used to acquire a three-dimensional laser point cloud and an oblique image point cloud of the internal section of the subway station according to the established laser scanning route and aerial photography route; The distributed sensors are used to collect deformation data of deep foundation pit support; The data processing and analysis unit is used to process and analyze the collected three-dimensional laser point cloud, inclined image point cloud, and deformation data of deep foundation pit support, and output the detection results of subway station deformation and deep foundation pit support quality.

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