Safety monitoring, prediction and early warning system and method for construction of existing subway structures

Abstract

The present invention discloses a safety monitoring, prediction, and early warning system and method for construction close to existing subway structures, including a distributed monitoring subsystem; a data flow subsystem for storing and exchanging data; a data visualization management subsystem for processing, verifying, calibrating, and storing the whole process monitoring data as formatted data; converting the formatted data into BIM model visualization points or two-dimensional visualization charts; an analysis and prediction subsystem for predicting subsequent subway structure and soil deformation data based on the formatted data; and a linkage deformation control subsystem for comparing the deviations between the prediction results, monitoring data, and thresholds, and issuing an alarm according to a preset level when the deviation exceeds the threshold, and starting corresponding on-site deformation control measures according to the preset level. The present invention meets the relevant requirements for safety monitoring and linkage deformation control of the whole process of construction on both sides of the tunnel and the direct contact surface directly above, and can fully ensure the safety of the construction process close to existing subway structures.

Description

Technical Field

[0001] The present invention belongs to the technical field of safety monitoring of construction close to or in contact with an existing subway structure, and in particular relates to a safety monitoring, prediction and early warning system and method for construction close to an existing subway structure. Background Art

[0002] With the increase of urban population and the advancement of urbanization, subway construction has become an important means to improve urban traffic conditions. Therefore, as subway lines become increasingly dense, construction in cities will often face the problem of impact on existing subway lines. It is particularly important to monitor the structural safety of existing subway lines and the deformation of surrounding soil during construction. However, the existing monitoring system for the impact of construction on the structural safety of existing tunnels is still imperfect. There is a lack of comprehensive, real-time and accurate monitoring and early warning capabilities for the impact of existing subway lines on new construction processes, and it is impossible to effectively deal with potential risks brought about by construction.

[0003] Monitoring data is not comprehensive: At present, the safety monitoring systems involving tunnel structures are mostly used in the tunnel construction phase, mainly for the safety monitoring of the impact of new tunnels on existing structures. The existing monitoring systems can often only obtain single or limited subway structure monitoring data, and cannot fully reflect the deformation of existing subway structures and soil during the construction process.

[0004] Poor real-time performance: Traditional monitoring methods mostly rely on offline observations and are unable to obtain and process monitoring data in real time, resulting in an inability to respond to emergencies in a timely manner.

[0005] Insufficient data analysis and prediction capabilities: The existing system has weak data analysis and prediction capabilities and cannot accurately predict the deformation and displacement that may occur during subway construction, affecting the safety and stability of construction.

[0006] Imperfect early warning mechanism: The current monitoring systems generally lack a complete early warning mechanism. They can only compare monitoring data with standard values ​​and are unable to issue timely alarms when the monitoring data or changing trends deviate greatly from the predicted results, resulting in potential risks not being discovered and handled in a timely manner. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a safety monitoring, prediction and early warning system for construction close to existing subway structures in view of the deficiencies in the above-mentioned prior art. The system has a novel and reasonable design. By integrating distributed automatic monitoring schemes, hybrid heuristic algorithms, finite element numerical simulation analysis and multi-level early warning mechanisms, the monitoring, prediction and early warning capabilities in the construction process close to the subway are comprehensively improved to ensure the safety and stability of the construction process, meet the relevant requirements of full-process construction safety monitoring and linkage deformation control including the direct contact surfaces on both sides of the tunnel and directly above, and can comprehensively ensure the construction safety, structural safety and operational safety in the construction process close to the existing subway structure.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is: a safety monitoring, prediction and early warning system for construction of close-coupled existing subway structures, characterized in that it includes:

[0009] Distributed monitoring subsystem, used to obtain monitoring data around the subway and foundation pit during the construction process;

[0010] Data flow subsystem, used for data storage and exchange;

[0011] The data visualization management subsystem is used to process, verify, calibrate and store the whole process monitoring data as formatted data; convert the formatted data into BIM model visualization points or two-dimensional visualization charts;

[0012] An analysis and prediction subsystem, used to predict subsequent subway structure and soil deformation data based on the formatted data;

[0013] The linkage deformation control subsystem is used to compare the deviations between the prediction results, monitoring data and thresholds. When the deviation exceeds the threshold, an alarm is issued according to a preset level, and corresponding on-site deformation control measures are initiated according to the preset level.

[0014] The above-mentioned safety monitoring, prediction and early warning system for construction of close-connected existing subway structures is characterized in that the distributed monitoring subsystem includes:

[0015] Monitoring layout module, which establishes the monitoring point layout structure based on the unified monitoring benchmark of the construction site;

[0016] Hardware control module, used to control monitoring equipment, information transmission equipment and data receiving equipment;

[0017] The data input module is used to input the whole process deformation monitoring data into the original data module in the data flow rotor system, and provides two input channels: automatic monitoring input and manual input.

[0018] The above-mentioned existing subway structure construction safety monitoring, prediction and early warning system is characterized in that: the monitoring layout module includes a space management component, a measurement point layout component and a hardware layout component; wherein the space management component is used to input a two-dimensional drawing or a three-dimensional space model and provide a space selection function based on the construction stage; the measurement point layout component is used to select the specific location of the monitoring point; the hardware layout component is used to select the monitoring equipment and layout method adopted at the specific monitoring point;

[0019] The hardware control module includes a monitoring device control component, an information transceiver component and a data processing component; wherein the monitoring device control component is used to control the start, stop and condition parameter setting of various monitoring devices; the information transceiver component is used to establish a data transmission channel between the monitoring device and the receiving device and transmit the data of the monitoring device to the receiving device; the data processing component parses, verifies and formats the received raw data, and transmits the processed data to the data input module;

[0020] The data input module includes an automatic input component and a manual input component; wherein the automatic input component is used to automatically transfer the data processed by the hardware control module to the original data module; the manual input component provides an operation interface for manually inputting data into the original data module to supplement or correct the automated monitoring data.

[0021] The above-mentioned existing subway structure construction safety monitoring, prediction and early warning system is characterized in that: the data flow rotor system includes:

[0022] The data management module is used to receive, verify and analyze the commands and data of each subsystem, and operate the original data module, standard data module and system preset data module according to the analysis results, and provide data input, output, modification and deletion;

[0023] The raw data module is used to store the raw data input by the distributed monitoring subsystem;

[0024] Standard data module, used to store data processed by the data visualization management subsystem;

[0025] The system preset data module is used to store multiple types of preset parameters, including preset monitoring layout information, preset data format, preset alarm parameters, and preset linkage control information.

[0026] The above-mentioned proximity existing subway structure construction safety monitoring, prediction and early warning system is characterized in that: the data management module includes a user identity management component, a command analysis and conversion component and a data operation component; wherein the user identity management component is used to verify the identity of the operating user, manage user permissions, add or delete users; the command analysis and conversion component is used to receive and analyze the operation commands of each subsystem, determine the data flow and take operations; the data operation component inputs, outputs, modifies and deletes data according to the results of the command analysis and conversion component.

[0027] The above-mentioned existing subway structure construction safety monitoring, prediction and early warning system is characterized in that the data visualization management subsystem includes:

[0028] The data processing module is used to obtain the original data from the original data module, perform data filtering, noise elimination, and outlier removal, generate formatted data, and store it in the standard data module;

[0029] The format conversion module obtains formatted data from the standard data module and converts point data that conforms to the chart and BIM model data standards;

[0030] The visualization module adds the output data of the format conversion module into the BIM model to achieve real-time visualization rendering of the data in the BIM model, or renders two-dimensional charts separately as needed.

[0031] The above-mentioned safety monitoring, prediction and early warning system for construction of close-to-existing subway structures is characterized in that the analysis and prediction subsystem includes:

[0032] The algorithm prediction module uses a hybrid algorithm prediction method to predict the deformation trend in the future based on the historical monitoring data of the standard data module and outputs the prediction results;

[0033] Numerical simulation module, which establishes a finite element numerical model based on the monitoring data and geological parameters in the standard data module, simulates and analyzes the deformation in the future and outputs the simulation results;

[0034] The result evaluation module compares and evaluates the results of the algorithm prediction module and the numerical simulation module, generates a comprehensive prediction result and transmits it to the linkage deformation control subsystem.

[0035] The above-mentioned safety monitoring, prediction and early warning system for construction of close-to-existing subway structures is characterized in that the linkage deformation control subsystem includes:

[0036] The early warning classification module compares the comprehensive prediction results with the real-time monitoring data in the standard data module. When the deviation between the two exceeds the threshold set in the system preset data module, or when the deviation between the change trend of the two exceeds the threshold set in the system preset data module, an alarm is triggered, and different levels of early warning signals are issued according to the size of the deviation. The early warning can be sent through the early warning module or in the form of mobile phone text messages or apps to achieve remote collaboration;

[0037] The deformation control module, according to the warning level issued by the warning classification module and the current construction stage information, retrieves the corresponding deformation control measures from the system preset data module and starts implementation, and at the same time feeds back the implementation status to the data visualization management subsystem;

[0038] The record and analysis module is used to record the time when the alarm is triggered, the alarm location and the construction location information, and to analyze and determine the cause of the deviation and evaluate the impact of the deviation on construction safety.

[0039] At the same time, the present invention also discloses a method with simple steps, reasonable design, and capable of conducting safety monitoring, prediction, and early warning of construction of existing subway structures. The method is characterized in that the method comprises the following steps:

[0040] Step 1: Based on the unified monitoring benchmark of the construction site, deploy the monitoring layout module around the subway structure and foundation pit;

[0041] Step 2: Collect monitoring data from each monitoring point of the monitoring layout module, process and store them as original monitoring data;

[0042] Step 3: Process the original monitoring data, including data filtering, noise elimination and outlier removal, to generate formatted monitoring data;

[0043] Step 4: Convert the formatted monitoring data into BIM standard data to achieve real-time visualization of the data in the BIM model;

[0044] Step 5: Based on the historical values ​​of formatted monitoring data, a hybrid heuristic algorithm is used to predict the deformation in the future period;

[0045] Step 6: Establish a finite element numerical model to simulate the deformation in the future based on the formatted monitoring data and geological parameters;

[0046] Step 7: Compare and evaluate the prediction results of step 5 and step 6 to generate a comprehensive prediction result, and compare the comprehensive prediction result with the real-time formatted monitoring data. When the deviation between the two exceeds a preset threshold, different levels of warning signals are issued according to the size of the deviation;

[0047] Step 8. According to the level of the early warning signal issued and the current construction stage information, the corresponding measures are retrieved from the preset deformation control measures library and implemented.

[0048] The above-mentioned method for safety monitoring, prediction and early warning of existing subway structure construction is characterized in that: in step 7, according to the formula Calculate the deviation between the two prediction results and the real-time monitoring data under the moving average method Among them, i is the monitoring point number, t is the time, x is the construction process, j is the time discrete point number, n is the total number of discrete points, M i (j,x) is the real-time monitoring data of the i-th monitoring point at time j and construction progress x, P i (j,x) is the algorithm prediction result of the i-th monitoring point at time j and construction progress x, F i (j,x) is the finite element prediction result of the i-th monitoring point at time j and construction progress x;

[0049] According to the formula Calculate the deviation between the two forecast results and the real-time monitoring data under the exponential smoothing method Among them, M i (t,x) is the real-time monitoring data of the i-th monitoring point at time t and construction progress x, M i (t,x) is the exponential smoothing value at time t and construction progress x, α is the smoothing coefficient, P i (t,x) is the algorithm prediction result of the i-th monitoring point at time t and construction progress x, F i (t,x) is the finite element prediction result of the i-th monitoring point at time t and construction progress x;

[0050] According to the formula Calculate the deviation between the two prediction results and the real-time monitoring data under the linear regression method in, M i (t,x) is the constant term in linear regression, M i (t,x) is the coefficient of time t in the linear regression method, M i (t,x) is the coefficient of construction progress x under the linear regression method, P i (t,x) is the constant term in linear regression, P i (t,x) is the coefficient of time t in the linear regression method, P i (t,x) is the coefficient of construction progress x under the linear regression method, F i (t,x) is the constant term in linear regression, F i (t,x) is the coefficient of time t in the linear regression method, F i (t,x) is the coefficient of construction process x under the linear regression method;

[0051] According to the formula Calculate the deviation D between the two prediction results and the real-time monitoring data i (t,x);

[0052] According to the formula Calculate the deviation ΔD between the two prediction results and the change trend of real-time monitoring data i (t,x), where ΔM i (t,x) is the change trend variable of the real-time monitoring data of the ith monitoring point at time t and construction progress x. M i Estimator of (t,x); ΔP i (t,x) is the change trend variable of the algorithm prediction result at the i-th monitoring point at time t and construction progress x. P i Estimator of (t,x); ΔF i (t,x) is the change trend variable of the finite element prediction result of the i-th monitoring point at time t and construction progress x. F i Estimator of (t,x);

[0053] According to the formula Get real-time data and warning classification based on standard thresholds Among them, S DF is the threshold of real-time deformation data, S DF1 is the lower threshold value of the real-time deformation data, S DF2 is the upper threshold value of the real-time deformation data;

[0054] According to the formula Obtain early warning levels for deviations between real-time data and forecast data Among them, S DP1 is the lower threshold value of the deviation between the real-time deformation data and the predicted result, S DP2 is the upper threshold value of the deviation between the real-time deformation data and the predicted results;

[0055] According to the formula Obtain early warning classification of deviations between real-time data and predicted change trends Among them, SDT1 is the lower threshold value of the deviation between the real-time change trend and the predicted change trend, S DT2 It is the upper limit of the threshold for the deviation between the real-time change trend and the predicted change trend.

[0056] The beneficial effects of the technical solution provided by the invention are:

[0057] The present invention provides a safety monitoring, prediction, and early warning system and method for construction close to existing subway structures. Through the application of this system and method, it can provide comprehensive safety monitoring, real-time data acquisition and analysis, deformation prediction, and complete linkage early warning and protection measures for projects whose construction sites are close to or in contact with existing subway structures. At the same time, the visualization function provided by this system enables users to intuitively understand the safety of the subway structure and the surrounding area of ​​the foundation pit during the entire construction process. The present invention meets the relevant requirements of full-process construction safety monitoring and linkage deformation control including the direct contact surfaces on both sides of the tunnel and directly above, and can fully guarantee the construction safety, structural safety, and operational safety during the construction process close to the existing subway structure.

[0058] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a system architecture diagram of the present invention.

[0060] Figure 2 An embodiment of the present invention is a platform design based on a system architecture. DETAILED DESCRIPTION

[0061] like Figure 1 and Figure 2 As shown, the safety monitoring, prediction and early warning system for construction close to existing subway structures of the present invention comprises:

[0062] Distributed monitoring subsystem, used to obtain monitoring data around the subway and foundation pit during the construction process;

[0063] Data flow subsystem, used for data storage and exchange;

[0064] The data visualization management subsystem is used to process, verify, calibrate and store the whole process monitoring data as formatted data; convert the formatted data into BIM model visualization points or two-dimensional visualization charts;

[0065] An analysis and prediction subsystem, used to predict subsequent subway structure and soil deformation data based on the formatted data;

[0066] The linkage deformation control subsystem is used to compare the deviations between the prediction results, monitoring data and thresholds. When the deviation exceeds the threshold, an alarm is issued according to a preset level, and corresponding on-site deformation control measures are initiated according to the preset level.

[0067] In this embodiment, the distributed monitoring subsystem includes:

[0068] Monitoring layout module, which establishes the monitoring point layout structure based on the unified monitoring benchmark of the construction site;

[0069] Hardware control module, used to control monitoring equipment, information transmission equipment and data receiving equipment;

[0070] The data input module is used to input the whole process deformation monitoring data into the original data module in the data flow rotor system, and provides two input channels: automatic monitoring input and manual input.

[0071] In this embodiment, the monitoring layout module includes a space management component, a measurement point layout component and a hardware layout component; wherein the space management component is used to input a two-dimensional drawing or a three-dimensional space model and provide a space selection function based on the construction stage; the measurement point layout component is used to select the specific location of the monitoring point; the hardware layout component is used to select the monitoring equipment and layout method adopted at the specific monitoring point;

[0072] The hardware control module includes a monitoring device control component, an information transceiver component and a data processing component; wherein the monitoring device control component is used to control the start, stop and condition parameter setting of various monitoring devices; the information transceiver component is used to establish a data transmission channel between the monitoring device and the receiving device and transmit the data of the monitoring device to the receiving device; the data processing component parses, verifies and formats the received raw data, and transmits the processed data to the data input module;

[0073] The data input module includes an automatic input component and a manual input component; wherein the automatic input component is used to automatically transfer the data processed by the hardware control module to the original data module; the manual input component provides an operation interface for manually inputting data into the original data module to supplement or correct the automated monitoring data.

[0074] In this embodiment, the data flow rotor system includes:

[0075] The data management module is used to receive, verify and analyze the commands and data of each subsystem, and operate the original data module, standard data module and system preset data module according to the analysis results, and provide data input, output, modification and deletion;

[0076] The raw data module is used to store the raw data input by the distributed monitoring subsystem;

[0077] Standard data module, used to store data processed by the data visualization management subsystem;

[0078] The system preset data module is used to store multiple types of preset parameters, including preset monitoring layout information, preset data format, preset alarm parameters, and preset linkage control information.

[0079] In this embodiment, the data management module includes a user identity management component, a command analysis and conversion component and a data operation component; wherein the user identity management component is used to verify the identity of the operating user, manage user permissions, and add or delete users; the command analysis and conversion component is used to receive and analyze the operation commands of each subsystem, determine the data flow and take actions; the data operation component inputs, outputs, modifies and deletes data according to the results of the command analysis and conversion component.

[0080] In this embodiment, the data visualization management subsystem includes:

[0081] The data processing module is used to obtain the original data from the original data module, perform data filtering, noise elimination, and outlier removal, generate formatted data, and store it in the standard data module;

[0082] The format conversion module obtains formatted data from the standard data module and converts point data that conforms to the chart and BIM model data standards;

[0083] The visualization module adds the output data of the format conversion module into the BIM model to achieve real-time visualization rendering of the data in the BIM model, or renders two-dimensional charts separately as needed.

[0084] In this embodiment, the analysis and prediction subsystem includes:

[0085] The algorithm prediction module uses a hybrid algorithm prediction method to predict the deformation trend in the future based on the historical monitoring data of the standard data module and outputs the prediction results;

[0086] Numerical simulation module, which establishes a finite element numerical model based on the monitoring data and geological parameters in the standard data module, simulates and analyzes the deformation in the future and outputs the simulation results;

[0087] The result evaluation module compares and evaluates the results of the algorithm prediction module and the numerical simulation module, generates a comprehensive prediction result and transmits it to the linkage deformation control subsystem.

[0088] In this embodiment, the linkage deformation control subsystem includes:

[0089] The early warning classification module compares the comprehensive prediction results with the real-time monitoring data in the standard data module. When the deviation between the two exceeds the threshold set in the system preset data module, or when the deviation between the change trend of the two exceeds the threshold set in the system preset data module, an alarm is triggered, and different levels of early warning signals are issued according to the size of the deviation. The early warning can be sent through the early warning module or in the form of mobile phone text messages or apps to achieve remote collaboration;

[0090] The deformation control module, according to the warning level issued by the warning classification module and the current construction stage information, retrieves the corresponding deformation control measures from the system preset data module and starts implementation, and at the same time feeds back the implementation status to the data visualization management subsystem;

[0091] The record and analysis module is used to record the time when the alarm is triggered, the alarm location and the construction location information, and to analyze and determine the cause of the deviation and evaluate the impact of the deviation on construction safety.

[0092] Embodiment 1

[0093] like Figure 2 As shown, this embodiment provides a safety monitoring, prediction and early warning platform for the construction process of an existing subway structure, including an on-site monitoring module, an analysis and prediction module, a visualization module and a linkage deformation control module.

[0094] In this embodiment, the on-site monitoring module provides two modes: automatic monitoring and settlement observation, which are used to monitor the displacement of subway structure and roadbed, soil settlement, and stress of supporting steel support. The monitoring points mainly include subway structure lining, subway roadbed, foundation pits on both sides of the subway, soil on the contact surface directly above the subway, and retaining structures.

[0095] The analysis and prediction module uses BP neural network algorithm and finite element numerical simulation analysis to predict deformation. The deformation prediction specifically includes: deformation prediction during the construction phase (including soil loading and structure unloading process), and deformation prediction in the construction area (including enclosure structure and subway upper loading).

[0096] The visualization module is used to render and display the BIM model with monitoring point data and the deformation displacement coordinate diagram comparing the actual and predicted ones.

[0097] The linkage deformation control module classifies early warnings based on actual and predicted deformation results, and provides suggestions for support and construction process adjustments to prevent or improve deformation in advance. At the same time, based on multi-terminal collaborative technology, relevant personnel are notified through apps, text messages, etc.

[0098] In this embodiment, the data flow and processing flow are as follows:

[0099] Data collection: The on-site monitoring module obtains deformation monitoring data of the subway structure and surrounding soil through automated monitoring and settlement observation.

[0100] Data Input: Monitors the data input into the raw data module in the data flow rotor system.

[0101] Data processing: The data visualization management subsystem obtains data from the original data module, performs data filtering, noise elimination, outlier removal, and other processing, and generates formatted data.

[0102] Data conversion: The format conversion module converts formatted data into point data that conforms to the BIM information model data standard.

[0103] Data visualization: The visualization module adds the converted data into the BIM model to achieve real-time visualization of the data in the BIM model.

[0104] Deformation prediction: The analysis and prediction module uses BP neural network algorithm and finite element numerical simulation based on formatted data to predict deformation and output the prediction results.

[0105] Result verification: The actual monitoring data is compared with the predicted data to verify the prediction accuracy.

[0106] Deformation control: Based on the deviation between the predicted results and the actual monitoring data, the deformation control module provides corresponding control measures and starts implementation.

[0107] Early warning mechanism: When the deviation exceeds the preset threshold, the system will issue an alarm according to the preset level and initiate corresponding on-site deformation control measures.

[0108] Embodiment 2

[0109] A method for monitoring, predicting and warning the construction safety of an existing subway structure, comprising the following steps:

[0110] Step 1: Based on the unified monitoring benchmark of the construction site, deploy the monitoring layout module around the subway structure and foundation pit;

[0111] Step 2: Collect monitoring data from each monitoring point of the monitoring layout module, process and store them as original monitoring data;

[0112] Step 3: Process the original monitoring data, including data filtering, noise elimination and outlier removal, to generate formatted monitoring data;

[0113] Step 4: Convert the formatted monitoring data into BIM standard data to achieve real-time visualization of the data in the BIM model;

[0114] Step 5: Based on the historical values ​​of formatted monitoring data, a hybrid heuristic algorithm is used to predict the deformation in the future period;

[0115] Step 6: Establish a finite element numerical model to simulate the deformation in the future based on the formatted monitoring data and geological parameters;

[0116] Step 7: Compare and evaluate the prediction results of step 5 and step 6 to generate a comprehensive prediction result, and compare the comprehensive prediction result with the real-time formatted monitoring data. When the deviation between the two exceeds a preset threshold, different levels of warning signals are issued according to the size of the deviation;

[0117] Step 8. According to the level of the early warning signal issued and the current construction stage information, the corresponding measures are retrieved from the preset deformation control measures library and implemented.

[0118] In this embodiment, in step seven, according to the formula Calculate the deviation between the two prediction results and the real-time monitoring data under the moving average method Among them, i is the monitoring point number, t is the time, x is the construction process, j is the time discrete point number, n is the total number of discrete points, M i (j,x) is the real-time monitoring data of the i-th monitoring point at time j and construction progress x, P i (j,x) is the algorithm prediction result of the i-th monitoring point at time j and construction progress x, F i (j,x) is the finite element prediction result of the i-th monitoring point at time j and construction progress x;

[0119] In this embodiment, n=3, M i (3,10)=8,M i (4,10)=9,M i (5,10)=10,P i (3,10)=7,P i (4,10)=8,P i (5,10)=9, F i (3,10)=6,F i (4,10)=7,F i (5,10)=8,

[0120] According to the formula Calculate the deviation between the two forecast results and the real-time monitoring data under the exponential smoothing method Among them, M i (t,x) is the real-time monitoring data of the i-th monitoring point at time t and construction progress x, M i (t,x) is the exponential smoothing value at time t and construction progress x, α is the smoothing coefficient, P i(t,x) is the algorithm prediction result of the i-th monitoring point at time t and construction progress x, F i (t,x) is the finite element prediction result of the i-th monitoring point at time t and construction progress x;

[0121] In this embodiment, α=3,

[0122] According to the formula Calculate the deviation between the two prediction results and the real-time monitoring data under the linear regression method in, M i (t,x) is the constant term in linear regression, M i (t,x) is the coefficient of time t in the linear regression method, M i (t,x) is the coefficient of construction progress x under the linear regression method, P i (t,x) is the constant term in linear regression, P i (t,x) is the coefficient of time t in the linear regression method, P i (t,x) is the coefficient of construction progress x under the linear regression method, F i (t,x) is the constant term in linear regression, F i (t,x) is the coefficient of time t in the linear regression method, F i (t,x) is the coefficient of construction process x under the linear regression method;

[0123] In this embodiment, but

[0124] According to the formula Calculate the deviation D between the two prediction results and the real-time monitoring data i (t,x);

[0125] In this embodiment, D i (t,x)=4.22.

[0126] According to the formula Calculate the deviation ΔD between the two prediction results and the change trend of real-time monitoring data i (t,x), where ΔM i (t,x) is the change trend variable of the real-time monitoring data of the ith monitoring point at time t and construction progress x. M i Estimator of (t,x); ΔP i (t,x) is the change trend variable of the algorithm prediction result at the i-th monitoring point at time t and construction progress x. P i Estimator of (t,x); ΔF i (t,x) is the change trend variable of the finite element prediction result of the i-th monitoring point at time t and construction progress x. F i Estimator of (t,x);

[0127] In this embodiment, the Kalman filter method is used to calculate ΔM i (t,x),ΔP i (t,x) and ΔF i (t, x), where the state transfer matrix A = 1, the control matrix B = 0, the observation matrix H = 1, the process noise covariance matrix Q = 0.1, and the measurement noise covariance matrix R = 0.1; then ΔM i (t,x)=0.6,ΔP i (t,x)=0.5,ΔF i (t,x)=0.4; then the deviation of the change trend between the two prediction results and the real-time monitoring data is ΔD i (t,x)=0.22.

[0128] According to the formula Get real-time data and warning classification based on standard thresholds Among them, S DF is the threshold of real-time deformation data, S DF1 is the lower threshold value of the real-time deformation data, S DF2 is the upper threshold value of the real-time deformation data;

[0129] In this embodiment, the lower threshold value S of the real-time deformation data DF1 =5, the upper threshold value S of real-time deformation data DF2 =10,

[0130] According to the formula Obtain early warning levels for deviations between real-time data and forecast data Among them, S DP1 is the lower threshold value of the deviation between the real-time deformation data and the predicted result, S DP2 is the upper threshold value of the deviation between the real-time deformation data and the predicted results;

[0131] In this embodiment, the lower threshold value S of the deviation between the real-time deformation data and the prediction result is DP1 =2, the upper threshold value S of the deviation between real-time deformation data and prediction results DP2 =5,

[0132] According to the formula Obtain early warning classification of deviations between real-time data and predicted change trends Among them, S DT1 is the lower threshold value of the deviation between the real-time change trend and the predicted change trend, S DT2 It is the upper limit of the threshold for the deviation between the real-time change trend and the predicted change trend.

[0133] In this embodiment, the lower limit value S of the threshold of the deviation between the real-time change trend and the predicted change trend is DT1 =1, the upper threshold value S of the deviation between the real-time change trend and the predicted change trend DT2 =2,

[0134] In this embodiment, it is concluded from step 7 that the warning level of the deviation between the current real-time data and the standard threshold is 1, the warning level of the deviation between the real-time and the predicted data is 2, and the warning level of the deviation between the real-time and the predicted change trend is 1. The corresponding measures are retrieved from the preset deformation control measure library and started to be implemented. At the same time, the warning signal is sent through the warning module or mobile phone text messages, apps, etc. to achieve remote collaboration.

[0135] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. Closely connected to the existing subway structure construction safety monitoring, prediction and early warning system, characterized by: include: Distributed monitoring subsystem, used to obtain monitoring data around the subway and foundation pit during the construction process; Data flow subsystem, used for data storage and exchange; Data visualization management subsystem, used to process, verify, calibrate and store the whole process monitoring data as formatted data; Convert the formatted data into BIM model visualization points or two-dimensional visualization charts; An analysis and prediction subsystem, used to predict subsequent subway structure and soil deformation data based on the formatted data; A linkage deformation control subsystem is used to compare the deviations between the prediction results, monitoring data and thresholds. When the deviation exceeds the threshold, an alarm is issued according to a preset level, and corresponding on-site deformation control measures are initiated according to the preset level; The analysis and prediction subsystem includes: The algorithm prediction module uses a hybrid algorithm prediction method to predict the deformation trend in the future based on the historical monitoring data of the standard data module and outputs the prediction results; Numerical simulation module, which establishes a finite element numerical model based on the monitoring data and geological parameters in the standard data module, simulates and analyzes the deformation in the future and outputs the simulation results; The result evaluation module compares and evaluates the results of the algorithm prediction module and the numerical simulation module, generates a comprehensive prediction result and transmits it to the linkage deformation control subsystem.

2. The system for monitoring, predicting and warning the construction safety of an existing subway structure in close proximity to claim 1 is characterized in that: The distributed monitoring subsystem includes: Monitoring layout module, which establishes the monitoring point layout structure based on the unified monitoring benchmark of the construction site; Hardware control module, used to control monitoring equipment, information transmission equipment and data receiving equipment; The data input module is used to input the whole process deformation monitoring data into the original data module in the data flow rotor system, and provides two input channels: automatic monitoring input and manual input.

3. The system for monitoring, predicting and warning the construction safety of an existing subway structure in close proximity to claim 2 is characterized in that: The monitoring layout module includes a space management component, a measurement point layout component and a hardware layout component; wherein the space management component is used to input a two-dimensional drawing or a three-dimensional space model and provide a space selection function based on the construction stage; the measurement point layout component is used to select the specific location of the monitoring point; the hardware layout component is used to select the monitoring equipment and layout method adopted at the specific monitoring point; The hardware control module includes a monitoring device control component, an information transceiver component and a data processing component; wherein the monitoring device control component is used to control the start, stop and condition parameter setting of various monitoring devices; the information transceiver component is used to establish a data transmission channel between the monitoring device and the receiving device and transmit the data of the monitoring device to the receiving device; the data processing component parses, verifies and formats the received raw data, and transmits the processed data to the data input module; The data input module includes an automatic input component and a manual input component; wherein the automatic input component is used to automatically transfer the data processed by the hardware control module to the original data module; the manual input component provides an operation interface for manually inputting data into the original data module to supplement or correct the automated monitoring data.

4. The system for monitoring, predicting and warning the construction safety of an existing subway structure in close proximity to claim 1 is characterized in that: The data flow rotor system comprises: The data management module is used to receive, verify and analyze the commands and data of each subsystem, and operate the original data module, standard data module and system preset data module according to the analysis results, and provide data input, output, modification and deletion; The raw data module is used to store the raw data input by the distributed monitoring subsystem; Standard data module, used to store data processed by the data visualization management subsystem; The system preset data module is used to store multiple types of preset parameters, including preset monitoring layout information, preset data format, preset alarm parameters, and preset linkage control information.

5. The system for monitoring, predicting and warning the construction safety of an existing subway structure in close proximity to claim 4 is characterized by: The data management module includes a user identity management component, a command analysis and conversion component and a data operation component; wherein the user identity management component is used to verify the identity of the operating user, manage user permissions, and add or delete users; the command analysis and conversion component is used to receive and analyze the operation commands of each subsystem, determine the data flow and take actions; the data operation component inputs, outputs, modifies and deletes data according to the results of the command analysis and conversion component.

6. The system for monitoring, predicting and warning the construction safety of an existing subway structure in close proximity to claim 1 is characterized in that: The data visualization management subsystem includes: The data processing module is used to obtain the original data from the original data module, perform data filtering, noise elimination, and outlier removal, generate formatted data, and store it in the standard data module; The format conversion module obtains formatted data from the standard data module and converts point data that conforms to the chart and BIM model data standards; The visualization module adds the output data of the format conversion module into the BIM model to achieve real-time visualization rendering of the data in the BIM model, or renders two-dimensional charts separately as needed.

7. The system for monitoring, predicting and warning the construction safety of an existing subway structure in close proximity to claim 1 is characterized in that: The linkage deformation control subsystem comprises: The early warning classification module compares the comprehensive prediction results with the real-time monitoring data in the standard data module. When the deviation between the two exceeds the threshold set in the system preset data module, or when the deviation between the change trend of the two exceeds the threshold set in the system preset data module, an alarm is triggered, and different levels of early warning signals are issued according to the size of the deviation. The early warning can be sent through the early warning module or in the form of mobile phone text messages or apps to achieve remote collaboration; The deformation control module, according to the warning level issued by the warning classification module and the current construction stage information, retrieves the corresponding deformation control measures from the system preset data module and starts implementation, and at the same time feeds back the implementation status to the data visualization management subsystem; The record and analysis module is used to record the time when the alarm is triggered, the alarm location and the construction location information, and to analyze and determine the cause of the deviation and evaluate the impact of the deviation on construction safety.

8. A method for monitoring, predicting and warning the construction safety of an existing subway structure using the system as claimed in claim 1, characterized in that: The method comprises the following steps: Step 1: Based on the unified monitoring benchmark of the construction site, deploy the monitoring layout module around the subway structure and foundation pit; Step 2: Collect monitoring data from each monitoring point of the monitoring layout module, process and store them as original monitoring data; Step 3: Process the original monitoring data, including data filtering, noise elimination and outlier removal, to generate formatted monitoring data; Step 4: Convert the formatted monitoring data into BIM standard data to achieve real-time visualization of the data in the BIM model; Step 5: Based on the historical values ​​of formatted monitoring data, a hybrid heuristic algorithm is used to predict the deformation in the future period; Step 6: Establish a finite element numerical model to simulate the deformation in the future based on the formatted monitoring data and geological parameters; Step 7: Compare and evaluate the prediction results of step 5 and step 6 to generate a comprehensive prediction result, and compare the comprehensive prediction result with the real-time formatted monitoring data. When the deviation between the two exceeds a preset threshold, different levels of warning signals are issued according to the size of the deviation; Step 8. According to the level of the early warning signal issued and the current construction stage information, the corresponding measures are retrieved from the preset deformation control measures library and implemented.

9. The method for safety monitoring, prediction and early warning of construction close to existing subway structures according to claim 8, characterized in that: In step 7, according to the formula Calculate the deviation between the two prediction results and the real-time monitoring data under the moving average method Among them, i is the monitoring point number, t is the time, x is the construction process, j is the time discrete point number, n is the total number of discrete points, M i (j,x) is the real-time monitoring data of the i-th monitoring point at time j and construction progress x, P i (j,x) is the algorithm prediction result of the i-th monitoring point at time j and construction progress x, F i (j,x) is the finite element prediction result of the i-th monitoring point at time j and construction progress x; According to the formula Calculate the deviation between the two forecast results and the real-time monitoring data under the exponential smoothing method Among them, M i (t,x) is the real-time monitoring data of the i-th monitoring point at time t and construction progress x, M i (t,x) is the exponential smoothing value at time t and construction progress x, α is the smoothing coefficient, P i (t,x) is the algorithm prediction result of the i-th monitoring point at time t and construction progress x, F i (t,x) is the finite element prediction result of the i-th monitoring point at time t and construction progress x; According to the formula Calculate the deviation between the two prediction results and the real-time monitoring data under the linear regression method in, M i (t,x) is the constant term in linear regression, M i (t,x) is the coefficient of time t in the linear regression method, M i (t,x) is the coefficient of construction progress x under the linear regression method, P i (t,x) is the constant term in linear regression, P i (t,x) is the coefficient of time t in the linear regression method, P i (t,x) is the coefficient of construction progress x under the linear regression method, F i (t,x) is the constant term in linear regression, F i (t,x) is the coefficient of time t in the linear regression method, F i (t,x) is the coefficient of construction progress x under the linear regression method; According to the formula Calculate the deviation D between the two prediction results and the real-time monitoring data i (t,x); According to the formula Calculate the deviation ΔD between the two prediction results and the change trend of real-time monitoring data i (t,x), where ΔM i (t,x) is the change trend variable of the real-time monitoring data of the ith monitoring point at time t and construction progress x. M i Estimator of (t,x); ΔP i (t,x) is the change trend variable of the algorithm prediction result at the i-th monitoring point at time t and construction progress x. P i Estimator of (t,x); ΔF i (t,x) is the change trend variable of the finite element prediction result of the i-th monitoring point at time t and construction progress x. F i Estimator of (t,x); According to the formula Get real-time data and warning classification based on standard thresholds Among them, S DF is the threshold of real-time deformation data, S DF1 is the lower threshold value of the real-time deformation data, S DF2 is the upper threshold value of the real-time deformation data; According to the formula Obtain early warning levels for deviations between real-time data and forecast data Among them, S DP1 is the lower threshold value of the deviation between the real-time deformation data and the predicted result, S DP2 is the upper threshold value of the deviation between the real-time deformation data and the predicted results; According to the formula Obtain early warning classification of deviations between real-time data and predicted change trends Among them, S DT1 is the lower threshold value of the deviation between the real-time change trend and the predicted change trend, S DT2 It is the upper limit of the threshold for the deviation between the real-time change trend and the predicted change trend.

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