A foundation pit supporting displacement and settlement auxiliary monitoring device

Abstract

The application discloses a kind of foundation pit support displacement settlement auxiliary monitoring devices, it is related to foundation pit monitoring technical field, to solve the problem of not fine foundation pit monitoring.This foundation pit support displacement settlement auxiliary monitoring device, including support platform, support frame, telescopic leg and auxiliary monitoring system, the support platform bottom end is connected with telescopic leg upper end, support platform one side is connected with support frame one end, support frame bottom end is provided with pressure detector, every camera is calibrated by phase calibration sensor, can effectively monitor out the horizontal offset of retaining wall top, and then can timely prevent the danger caused by deviation, according to the parameter difference of calibration image and original image, whether calibration image finds the risk of settlement and deviation is judged, according to the parameter comparison of original image and calibration image, whether the building on foundation pit image moves can be judged, according to the parameter of movement, the risk of surrounding building settlement is judged.

Description

Technical Field

[0001] This invention belongs to the field of foundation pit monitoring technology, specifically a foundation pit support displacement and settlement auxiliary monitoring device. Background Technology

[0002] Foundation pit monitoring requires the use of relevant instruments to measure and monitor the foundation pit support structure, so as to take timely reinforcement measures in case of potential dangers to construction and the safety of surrounding buildings and structures.

[0003] Chinese patent CN115468534A discloses a real-time monitoring system and method for foundation pit settlement in a smart construction site management platform. It primarily utilizes historical foundation pit monitoring videos collected by each of multiple foundation pit monitoring terminals. Based on these historical monitoring videos, it determines the settlement correlation information between the foundation pits at various construction sites. This allows for the determination of target equipment operating parameters for each of the multiple foundation pit monitoring terminals, ensuring a closer match between the determined target equipment operating parameters and the actual conditions of the foundation pits. This guarantees more reasonable monitoring when the foundation pit monitoring terminals operate based on the corresponding target equipment operating parameters. While this patent solves the settlement monitoring problem, the following issues remain in practical operation:

[0004] 1. After obtaining the monitoring results of the foundation pit, the results are sent directly to the terminal without calculating the parameters of the monitoring results, which makes it impossible to issue an alarm based on the degree of abnormality.

[0005] 2. When monitoring the foundation pit, there was a lack of better monitoring methods to more accurately monitor whether the foundation pit was settling or shifting, which led to a reduction in the protective capacity of the foundation pit.

[0006] 3. Due to the lack of better image calibration in multiple directions of the construction pit and the lack of effective seepage monitoring of the groundwater level in the pit, more accurate monitoring of the pit is impossible. Summary of the Invention

[0007] The purpose of this invention is to provide an auxiliary monitoring device for displacement and settlement of foundation pit support. Each camera is calibrated by a phase calibration sensor, which can effectively monitor the horizontal displacement of the top of the retaining wall, thereby preventing potential dangers caused by displacement. The difference in parameters between the calibrated image and the original image determines whether the calibrated image detects the risk of settlement and displacement. The comparison of parameters between the original image and the calibrated image determines whether the building in the foundation pit image has moved. The risk of settlement of surrounding buildings is determined based on the parameters of the movement. The detection of internal forces during the construction of the foundation pit by the pressure detector can effectively reflect the deformation of the support frame due to pressure. After forward and backward propagation training, the accuracy of the analysis data can be optimized to improve the precision of the analysis results. The abnormal data alarm module marks the data with the largest difference between the level parameters and the standard data as a type of abnormal data and performs alarm processing, which can effectively improve the timeliness and efficiency of monitoring and early warning, and solve the problems in the prior art.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] An auxiliary monitoring device for displacement and settlement of foundation pit support includes a support platform, a support frame, telescopic legs, and an auxiliary monitoring system. The bottom end of the support platform is connected to the upper end of the telescopic legs, one side of the support platform is connected to one end of the support frame, a pressure detector is installed at the bottom end of the support frame, the top end of the support platform is connected to a rotating shaft and the bottom end of a lithium battery plate, a water level sensor is installed on one side of the lithium battery plate, a camera is installed at the upper end of the rotating shaft, and a phase calibration sensor is installed at the upper end of the camera.

[0010] The data collected by the pressure detector, camera, and water level sensor are transmitted via wireless network, and the transmitted data is then used for data-assisted monitoring through the terminal's auxiliary monitoring system.

[0011] Preferably, a solar panel is mounted on the upper end of the lithium battery panel, and the solar panel is connected to the lithium battery panel via a bracket. Both the solar panel and the lithium battery panel are provided with electrical connectors, and the electrical connectors on the solar panel and the lithium battery panel are electrically connected via electrical connecting wires.

[0012] Preferably, the auxiliary monitoring system includes:

[0013] Multi-point calibration module, used for:

[0014] According to the specific requirements of the construction, multiple monitoring points are buried at both ends of the foundation pit. Each horizontal monitoring point is calibrated by a phase calibration sensor.

[0015] After the locations of multiple monitoring points are calibrated, a calibration command is automatically generated and sent to the terminal, which then controls the next step of the operation.

[0016] The monitoring data acquisition module is used for:

[0017] The system acquires construction data collected by pressure detectors, cameras, and water level sensors in the device, and maps different types of construction data to different transmission channels.

[0018] Based on the size of different types of construction data, the transmission capacity of the transmission channel is automatically adjusted, and after the adjustment is completed, the construction data is stored in one category.

[0019] The monitoring data analysis module is used for:

[0020] Based on the construction data collected in the monitoring data acquisition module, different types of construction data are acquired separately, and the construction data are analyzed separately according to the different types of construction data acquired.

[0021] The data analysis and decision-making module is used for:

[0022] Based on the quantity types analyzed in the monitoring data analysis module, parameter model calculations are performed according to the data parameters of different types and standard types of data parameters, and the data are classified into levels according to the model calculation results.

[0023] The abnormal data alarm module is used for:

[0024] Based on the data at different levels in the data analysis decision module, the data with the largest difference between the level parameter and the standard data is marked as Class I abnormal data and an alarm is triggered. The data with the smaller difference between the level parameter and the standard data is marked as Class II abnormal data and a terminal warning is triggered.

[0025] Preferably, the monitoring data acquisition module includes:

[0026] Image data acquisition unit, used for:

[0027] The construction images are collected based on the images captured by the camera. Multiple shots can be taken using the camera, and the number of shots and the duration can be set on the terminal.

[0028] The pressure data acquisition unit is used for:

[0029] Based on the internal force monitoring data detected by the pressure detector, the internal force monitoring data is collected. The pressure detector is equipped with a GIS geolocation device. When the pressure detector detects abnormal data, the GIS geolocation device is used to locate and investigate the specific location.

[0030] The water level data acquisition unit is used for:

[0031] Based on the water level change data detected by the water level sensor, the water level change data is collected, mainly including the water level height and the water flow rate data.

[0032] Preferably, the monitoring data analysis module includes:

[0033] The image analysis unit is calibrated for:

[0034] The image is captured by the camera the first time it is taken, and the first image is marked as the original image;

[0035] Based on the image parameters of the original image, all edge lines in the original image are calculated using the edge calculation method.

[0036] Determine the tangent direction and curvature of each point on the edge line, take the tangent direction as the vector direction, and take the curvature as the vector magnitude to determine the first shape representation vector of the corresponding edge line at the corresponding point.

[0037] Images are captured by the camera except for the first shot, and images other than the first shot are marked as calibration images;

[0038] Based on the calibration image, determine the center coordinates and edge lines of the calibration image, and use the vector pointing from the center coordinates to the corresponding point on the edge line as the second shape representation vector of the corresponding point.

[0039] The final shape representation vector of the corresponding edge line at the corresponding point is determined based on the first shape representation vector and the second shape representation vector.

[0040] Preferably, the calibration image analysis unit is further configured to:

[0041] The final shape representation vectors of all points on the corresponding edge lines are summarized to obtain a set of shape representation vectors. The set of shape representation vectors corresponding to each edge line in the original image is used as the first matching data, and the center coordinates of the calibrated image are used as the second matching data.

[0042] Based on the edge line matching data, the edge lines in the original image and the edge lines in the calibration image are matched to determine the set of successfully matched edge lines;

[0043] The average value of the matching data of all edge lines in the set of successfully matched edge lines is used as the standard matching data of the corresponding edge line set.

[0044] Based on the unmatched edge lines and their matching with the corresponding calibration images, the position coordinates of the target edge lines in the original image are determined, and parameters are calculated based on the coordinates of the original image and the coordinates of the unmatched calibration images.

[0045] The calculated parameters determine whether the construction scenes in the original image and the calibrated image have changed.

[0046] Preferably, the monitoring data analysis module further includes:

[0047] The water level variable data detection unit is used for:

[0048] By using water level sensors to detect groundwater levels, data on temperature, rainfall, and humidity during the construction period will be collected.

[0049] Calculations are performed based on the correlation between temperature, rainfall, humidity data and detected water level and volume data;

[0050] And based on the calculation results, determine whether the water level and water volume data at that time are normal water level and water volume data compared with the corresponding temperature, rainfall and humidity data;

[0051] GIS dynamic detection unit, used for:

[0052] Data calculations are performed using internal force data detected by pressure detectors, combined with construction operations and soil change data during the construction period.

[0053] And based on the calculation results, determine whether the internal force data, construction operation data, and soil change data are normal internal force data.

[0054] Preferably, the data analysis decision module is further used for:

[0055] The analysis data in the monitoring data analysis module is acquired, and the standard type data is set in the terminal after the acquisition is completed;

[0056] After the standard type data is set up, the network model is calculated. The analysis data and standard time are exported separately, and the data are compared after export.

[0057] The parameter data is propagated forward, where the parameter data is propagated from lower levels to higher levels;

[0058] When the data obtained from propagation does not match the expectations, backpropagation is performed. Backpropagation involves propagating the error from higher levels to lower levels for training.

[0059] Based on the training results, the numerical values ​​of the calculated parameters in the analyzed data that are compared with those in the standard type data are classified into different levels.

[0060] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0061] 1. The foundation pit support displacement and settlement auxiliary monitoring device provided by this invention also includes a water level sensor inserted into the foundation pit. A solar panel collects solar energy, converts it into electrical energy, and stores it in a lithium battery panel. The lithium battery panel is electrically connected to the water level sensor, thus providing power. The water level sensor can detect groundwater level and volume, offering advantages such as high frequency and real-time performance. This enables non-contact, high-precision measurement of groundwater level changes in the foundation pit, facilitating the monitoring of seepage in the foundation pit structure. Simultaneously, multiple cameras are installed at different locations on the foundation pit. Each camera undergoes orientation calibration via a phase calibration sensor, effectively monitoring the horizontal offset of the top of the retaining wall. This allows for timely prevention of potential hazards caused by offset, enhancing the accurate monitoring of the foundation pit structure.

[0062] 2. The foundation pit support displacement and settlement auxiliary monitoring device provided by this invention calculates parameters based on the coordinates of the original image and the coordinates of the mismatched calibration image. It can determine whether the parameters of the calibration image and the original image match. Based on the parameter difference between the calibration image and the original image, it can determine whether the calibration image has detected the risk of settlement and displacement. Based on the parameter comparison between the original image and the calibration image, it can determine whether the building on the foundation pit image has moved. Based on the parameters of the movement, it can determine the risk of settlement of surrounding buildings, thereby improving the safety protection of the foundation pit and avoiding the impact on surrounding buildings during construction. At the same time, through the GIS dynamic detection unit, the internal force detection during the foundation pit construction process by the pressure detector can effectively reflect the deformation of the support frame due to pressure, which is conducive to enhancing the monitoring effect of the support frame, thereby improving the safety and stability of foundation pit construction.

[0063] 3. The foundation pit support displacement and settlement auxiliary monitoring device provided by this invention can optimize the accuracy of the analyzed data after forward propagation training and backward propagation training, thereby improving the precision of the analysis results. Furthermore, based on the training results, the values ​​of the calculated parameters in the analyzed data that differ from the standard data are classified into different levels. Then, through the abnormal data alarm module, data with the largest difference between the level parameters and the standard data are marked as Class I abnormal data and trigger an alarm. Data with smaller differences between the level parameters and the standard data are marked as Class II abnormal data and trigger a terminal warning. This effectively improves the timeliness and efficiency of monitoring and early warning, promotes the intelligent and simplified management of construction projects, and saves a significant amount of human and material resources. Attached Figure Description

[0064] Figure 1 This is a schematic diagram of the overall structure of the present invention;

[0065] Figure 2 This is a side view of the structure of the present invention;

[0066] Figure 3 This is a schematic diagram of the auxiliary monitoring system module of the present invention;

[0067] Figure 4 This is a schematic diagram of the monitoring data acquisition module of the present invention;

[0068] Figure 5 This is a schematic diagram of the monitoring data analysis module of the present invention.

[0069] In the diagram: 1. Support platform; 2. Support frame; 3. Telescopic leg; 5. Pressure detector; 6. Rotating shaft; 7. Lithium battery panel; 8. Camera; 9. Phase calibration sensor; 10. Solar panel; 11. Bracket; 12. Electrical connector; 13. Electrical cable; 14. Water level sensor. Implementation

[0070] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] To address the limitations of existing technologies in accurately monitoring foundation pits due to inadequate image calibration across multiple directions and insufficient groundwater level monitoring, please refer to [link to relevant documentation]. Figures 1-2 This embodiment provides the following technical solution:

[0072] A foundation pit support displacement and settlement auxiliary monitoring device includes a support platform 1, a support frame 2, a telescopic leg 3, and an auxiliary monitoring system. The bottom end of the support platform 1 is connected to the upper end of the telescopic leg 3, and one side of the support platform 1 is connected to one end of the support frame 2. A pressure detector 5 is installed at the bottom end of the support frame 2. The top end of the support platform 1 is connected to a rotating shaft 6 and the bottom end of a lithium battery plate 7. A water level sensor 14 is installed on one side of the lithium battery plate 7. A camera 8 is installed at the upper end of the rotating shaft 6, and a phase calibration sensor 9 is installed at the upper end of the camera 8. The data collected by the pressure detector 5, the camera 8, and the water level sensor 14 are transmitted through a wireless network, and the transmitted data is monitored by the auxiliary monitoring system at the terminal. A solar panel 10 is installed on the upper end of the lithium battery plate 7. The solar panel 10 is connected to the lithium battery plate 7 through a bracket 11. Both the solar panel 10 and the lithium battery plate 7 are provided with electrical connectors 12, and the electrical connectors 12 on the solar panel 10 and the lithium battery plate 7 are electrically connected through electrical connecting lines 13.

[0073] Specifically, the bottom ends of the support frame 2 and the telescopic legs 3 are inserted into the foundation pit. The telescopic legs 3 can extend and retract, and the insertion depth can be adjusted according to the specific construction conditions. At the same time, the water level sensor 14 is also inserted into the foundation pit. The solar panel 10 collects solar energy, converts the light energy into electrical energy, and stores the electrical energy in the lithium battery panel 7. The lithium battery panel 7 can be electrically connected to the water level sensor 14, so the lithium battery panel 7 can supply power to the water level sensor 14. The water level sensor 14 can detect the groundwater level and water volume, and has the advantages of high frequency and strong real-time performance. It realizes non-contact high-precision measurement of the changes in the groundwater level of the foundation pit, which promotes the monitoring of water seepage in the foundation pit structure. At the same time, multiple cameras 8 are set at different positions on the foundation pit. Each camera 8 is oriented through a phase calibration sensor 9, which can effectively monitor the horizontal offset of the top of the retaining wall, thereby preventing the danger caused by the offset in time and promoting the accurate monitoring effect of the foundation pit structure.

[0074] To address the problem in existing technologies where monitoring of foundation pits lacks more precise monitoring methods to detect settlement and displacement, thus reducing the pit's protective capacity, please refer to [link to relevant documentation]. Figures 2-4 This embodiment provides the following technical solution:

[0075] The auxiliary monitoring system includes: a multi-point calibration module, used to: bury multiple monitoring points at both ends of the foundation pit according to the specific construction requirements, wherein each horizontal monitoring point is calibrated by a phase calibration sensor 9; after the position calibration of multiple monitoring points is completed, a calibration command is automatically generated and sent to the terminal, which controls the next operation; a monitoring data acquisition module, used to: acquire construction data collected by the pressure detector 5, camera 8 and water level sensor 14 in the device, and map different types of construction data to different transmission channels; automatically adjust the transmission capacity of the transmission channel according to the size of different types of construction data, and store the construction data in one category after adjustment; and a monitoring data analysis module, used to: acquire different types of construction data based on the construction data acquired in the monitoring data acquisition module, and analyze the acquired construction data according to the different types of construction data.

[0076] The monitoring data acquisition module includes: an image data acquisition unit, used to acquire construction images captured by camera 8, wherein camera 8 can take multiple shots, and the number of shots and the time can be set on the terminal; a pressure data acquisition unit, used to acquire internal force monitoring data detected by pressure detector 5, wherein pressure detector 5 is equipped with a GIS geolocation device, and when the pressure detector 5 detects abnormal data, the GIS geolocation device is used to locate and investigate the specific location; and a water level data acquisition unit, used to acquire water level change data detected by water level sensor 14, wherein the main data acquired are the water level height and the water flow rate.

[0077] The monitoring data analysis module includes: a calibration image analysis unit, used for: capturing images from the first shot taken by camera 8 and marking the first shot as the original image; calculating all edge lines in the original image using edge calculation based on the image parameters of the original image; determining the tangent direction and curvature of each point on the corresponding edge line, using the tangent direction as the vector direction and the curvature as the vector magnitude to determine the first shape representation vector of the corresponding edge line at the corresponding point; capturing images from other shots taken by camera 8 and marking images other than the first shot as calibration images; determining the center coordinates and edge lines of the calibration image based on the calibration images, using the vector from the center coordinates to the corresponding point on the edge line as the second shape representation vector of the corresponding point; and determining the final shape representation vector of the corresponding edge line at the corresponding point based on the first shape representation vector and the second shape representation vector. The calibration image analysis unit is further configured to: summarize the final shape representation vectors of all points on the corresponding edge lines to obtain a shape representation vector set; use the shape representation vector set corresponding to each edge line in the original image as the first matching data and the center coordinates of the calibration image as the second matching data; match the edge lines in the original image and the edge lines in the calibration image according to the edge line matching data to determine the set of successfully matched edge lines; use the average value of the matching data of all edge lines in the set of successfully matched edge lines as the standard matching data of the corresponding edge line set; determine the position coordinates of the target edge line in the original image based on the unmatched edge lines and their matching with the corresponding calibration image; calculate parameters based on the coordinates of the original image and the coordinates of the unmatched calibration image; and determine whether the construction scene in the original image and the calibration image has changed based on the calculated parameters.

[0078] The monitoring data analysis module further includes: a water level variable data detection unit, used to: collect temperature, rainfall, and humidity data during the construction period by using the groundwater level detection data from the water level sensor 14; perform data correspondence calculations based on the temperature, rainfall, and humidity data and the detected water level and volume data; and determine whether the water level and volume data at that time are normal water level and volume data based on the calculation results; and a GIS dynamic detection unit, used to: perform data calculations based on the internal force data detected by the pressure detector 5, in conjunction with the construction operations and soil change data during the construction period; and determine whether the internal force data are normal internal force data based on the calculation results.

[0079] Specifically, the calibration image analysis unit determines the tangent direction and curvature of each point on the edge line in the calibration image acquired after the original image, as well as the vector pointing from the center coordinates of the edge line to the corresponding point. This determines the shape representation vector characterizing the shape features of the edge line. Then, by aggregating the center coordinates of the corresponding regions of the edge line, matching data for the corresponding edge line is obtained. Based on this matching data, different edge lines in the calibration image are matched. According to the matching results, all edge lines belonging to the same region can be identified in all calibration images. The matching data of the edge lines in the same region are averaged to determine the standard matching data for that region. The target matching data corresponding to the edge lines in the original image is matched with the standard matching data. Unmatched edge lines are then matched with their corresponding calibration images to confirm the shape representation vector. By determining the position coordinates of the target edge line in the original image, and calculating parameters based on the coordinates of the original image and the mismatched calibration image, it can be determined whether the parameters of the calibration image and the original image match. The difference in parameters between the calibration image and the original image can be used to determine whether the calibration image detects the risk of settlement and displacement. By comparing the parameters of the original image and the calibration image, it can be determined whether the buildings on the foundation pit image have moved. The risk of settlement of surrounding buildings can be determined based on the parameters of the movement, which improves the safety protection of the foundation pit and avoids the impact on surrounding buildings during construction. At the same time, through the GIS dynamic detection unit, the internal force detection of the foundation pit during construction by the pressure detector 5 can effectively reflect the deformation of the support frame 2 due to pressure, which is conducive to enhancing the monitoring effect of the support frame 2, thereby improving the safety and stability of the foundation pit construction.

[0080] To address the issue in existing technologies where monitoring results from foundation pits are directly sent to the terminal without parameter calculations, thus failing to trigger alarms based on the severity of anomalies, please refer to [link to relevant documentation]. Figure 5 This embodiment provides the following technical solution:

[0081] The data analysis decision module is used to: perform parameter model calculations based on the data types analyzed in the monitoring data analysis module, according to the data parameters of different types and the standard data parameters, and classify the data into levels based on the model calculation results; the abnormal data alarm module is used to: mark the data with the largest difference between the level parameters and the standard data as Class I abnormal data and perform alarm processing based on the data of different levels in the data analysis decision module, and mark the data with the smaller difference between the level parameters and the standard data as Class II abnormal data and perform terminal warning processing.

[0082] The data analysis decision module is further configured to: acquire the analysis data from the monitoring data analysis module; set the standard type data on the terminal after acquisition; perform network model calculation after the standard type data is set, wherein the analysis data and standard time are exported separately, and the exported data are compared; perform forward propagation of parameter data, wherein the parameter data is propagated from low level to high level; perform back propagation when the propagated data results do not match the expectations, wherein back propagation propagates the error from high level to low level for training; and classify the values ​​of the calculated parameters in the analysis data and the standard type data according to the training results.

[0083] Specifically, standard type data is used for network model calculations, which improves the accuracy of the calculation results. When parameter data is trained through forward propagation, it passes through each hidden layer, and the final loss data is obtained when passing through the hidden layer. When parameter data is trained through backpropagation, it feeds forward layer by layer according to the gradient reduction formula, forming a backpropagation mechanism that can optimize parameters. After training the analysis data through forward and backpropagation, the accuracy of the analysis data can be optimized, improving the precision of the analysis results. Furthermore, based on the training results, the values ​​of the calculated parameters in the analysis data that differ from the standard type data are classified into levels. Then, the abnormal data alarm module marks the data with the largest difference between the level parameters and the standard data as Class I abnormal data and performs alarm processing. The data with smaller differences between the level parameters and the standard data are marked as Class II abnormal data and perform terminal warning processing. This can effectively improve the timeliness and efficiency of monitoring and early warning, promote the intelligent and simplified management of construction projects, and save a lot of human and material resources.

[0084] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0085] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A foundation pit support displacement and settlement auxiliary monitoring device, comprising a support platform (1), a support frame (2), telescopic legs (3), and an auxiliary monitoring system, characterized in that: The bottom end of the support platform (1) is connected to the top end of the telescopic leg (3), one side of the support platform (1) is connected to one end of the support frame (2), the bottom end of the support frame (2) is equipped with a pressure detector (5), the top end of the support platform (1) is connected to the bottom end of the rotating shaft (6) and the lithium battery plate (7), one side of the lithium battery plate (7) is equipped with a water level sensor (14), the top end of the rotating shaft (6) is equipped with a camera (8), and the top end of the camera (8) is equipped with a phase calibration sensor (9). The data collected by the pressure detector (5), camera (8) and water level sensor (14) are transmitted through a wireless network, and the transmitted data is monitored by the terminal's auxiliary monitoring system. The auxiliary monitoring system includes: Multi-point calibration module, used for: According to the specific requirements of construction, multiple monitoring points are buried at both ends of the foundation pit. Among them, the direction of each horizontal monitoring point is calibrated by a phase calibration sensor (9). After the locations of multiple monitoring points are calibrated, a calibration command is automatically generated and sent to the terminal, which then controls the next step of the operation. The monitoring data acquisition module is used for: The construction data collected by the pressure detector (5), camera (8) and water level sensor (14) in the device are acquired, and different types of construction data are mapped to different transmission channels; Based on the size of different types of construction data, the transmission capacity of the transmission channel is automatically adjusted, and after the adjustment is completed, the construction data is stored in one category. The monitoring data analysis module is used for: Based on the construction data collected in the monitoring data acquisition module, different types of construction data are acquired separately, and the construction data are analyzed separately according to the different types of construction data acquired. The data analysis and decision-making module is used for: Based on the quantity types analyzed in the monitoring data analysis module, parameter model calculations are performed according to the data parameters of different types and standard types of data parameters, and the data are classified into levels according to the model calculation results. The abnormal data alarm module is used for: Based on the data at different levels in the data analysis decision module, the data with the largest difference between the level parameter and the standard data is marked as Class I abnormal data and an alarm is triggered. The data with the smaller difference between the level parameter and the standard data is marked as Class II abnormal data and a terminal warning is triggered. The monitoring data analysis module includes: The image analysis unit is calibrated for: The image is captured by the camera (8) for the first time, and the first image is marked as the original image; Based on the image parameters of the original image, all edge lines in the original image are calculated using the edge calculation method. Determine the tangent direction and curvature of each point on the edge line, take the tangent direction as the vector direction, and take the curvature as the vector magnitude to determine the first shape representation vector of the corresponding edge line at the corresponding point. Images are captured by the camera (8) except for the first shot, and the images other than the first shot are marked as calibration images; Based on the calibration image, determine the center coordinates and edge lines of the calibration image, and use the vector pointing from the center coordinates to the corresponding point on the edge line as the second shape representation vector of the corresponding point. The final shape representation vector of the corresponding edge line at the corresponding point is determined based on the first shape representation vector and the second shape representation vector. The final shape representation vectors of all points on the corresponding edge lines are summarized to obtain a set of shape representation vectors. The set of shape representation vectors corresponding to each edge line in the original image is used as the first matching data, and the center coordinates of the calibrated image are used as the second matching data. Based on the edge line matching data, the edge lines in the original image and the edge lines in the calibration image are matched to determine the set of successfully matched edge lines; The average value of the matching data of all edge lines in the set of successfully matched edge lines is used as the standard matching data of the corresponding edge line set. Based on the unmatched edge lines and their matching with the corresponding calibration images, the position coordinates of the target edge lines in the original image are determined, and parameters are calculated based on the coordinates of the original image and the coordinates of the unmatched calibration images. The calculated parameters determine whether the construction scenes in the original image and the calibrated image have changed.

2. The auxiliary monitoring device for displacement and settlement of foundation pit support according to claim 1, characterized in that: A solar panel (10) is installed on the upper end of the lithium battery panel (7). The solar panel (10) is connected to the lithium battery panel (7) through a bracket (11). Both the solar panel (10) and the lithium battery panel (7) are provided with electrical connectors (12). The electrical connectors (12) provided on the solar panel (10) and the lithium battery panel (7) are electrically connected through an electrical connection line (13).

3. The auxiliary monitoring device for displacement and settlement of foundation pit support according to claim 2, characterized in that: The monitoring data acquisition module includes: Image data acquisition unit, used for: The construction images captured by the camera (8) are collected. Multiple shots can be taken through the camera (8), and the number of shots and the time can be set on the terminal. The pressure data acquisition unit is used for: According to the internal force monitoring data detected by the pressure detector (5), the internal force monitoring data is collected. The pressure detector (5) is equipped with a GIS geographic positioning device. When the pressure detector (5) detects abnormal data, the GIS geographic positioning device is used to locate and investigate the specific location. The water level data acquisition unit is used for: Based on the water level change data detected by the water level sensor (14), the water level change data is collected, including the water level height and the water flow rate data.

4. The auxiliary monitoring device for displacement and settlement of foundation pit support according to claim 3, characterized in that: The monitoring data analysis module also includes: The water level variable data detection unit is used for: The temperature, rainfall, and humidity data during the construction period are collected by using the groundwater level detection data of the water level sensor (14). Calculations are performed based on the correlation between temperature, rainfall, humidity data and detected water level and volume data; And based on the calculation results, determine whether the water level and water volume data at that time are normal water level and water volume data compared with the corresponding temperature, rainfall and humidity data; GIS dynamic detection unit, used for: The internal force data detected by the pressure detector (5) is used to calculate the data in conjunction with the construction operations and soil change data during the construction period. And based on the calculation results, determine whether the internal force data, construction operation data, and soil change data are normal internal force data.

5. The auxiliary monitoring device for displacement and settlement of foundation pit support according to claim 4, characterized in that: The data analysis and decision-making module is also used for: The analysis data in the monitoring data analysis module is acquired, and the standard type data is set in the terminal after the acquisition is completed; After the standard type data is set up, the network model is calculated. The analysis data and standard time are exported separately, and the data are compared after export. The parameter data is propagated forward, where the parameter data is propagated from lower levels to higher levels; When the data obtained from propagation does not match the expectations, backpropagation is performed. Backpropagation involves propagating the error from higher levels to lower levels for training. Based on the training results, the numerical values ​​of the calculated parameters in the analyzed data that are compared with those in the standard type data are classified into different levels.

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