A tunnel face collapse early warning system and method based on millimeter wave radar

Abstract

The invention discloses a tunnel face collapse early warning system based on millimeter wave radar, which belongs to the technical field of tunnel construction early warning, and comprises a data acquisition and alarm system located near the tunnel face, a data transmission system arranged in the tunnel and at the tunnel entrance, and a remote monitoring platform arranged outside the tunnel; the invention collects microseismic amplitude duration signals through millimeter wave radar, performs clutter filtering processing based on a preset neural network model, and then divides the microseismic amplitude duration curve into a plurality of sections at a preset time interval, converts each section of the microseismic amplitude duration curve into a microseismic amplitude-frequency curve form, obtains the microseismic main frequency in each section of time from each section of the microseismic amplitude-frequency curve, and compares the attenuation amplitude of the microseismic main frequency over time with the preset frequency attenuation amplitude range to realize timely early warning of tunnel face collapse. The invention has high efficiency, convenient implementation, low cost, will not interfere with construction, and can effectively ensure the safety of tunnel face construction.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel construction early warning, and in particular to a tunnel face collapse early warning system and method based on millimeter wave radar. Background Art

[0002] Due to geological conditions, construction disturbances and other reasons, there is a risk of collapse during tunnel excavation. At present, the initial support has not been carried out near the tunnel excavation face, making it less stable and an extremely high-risk construction area. The arch above the face is prone to collapse and injure workers. Excavation and initial support operations near the face are extremely high-risk time periods. However, there is currently a lack of monitoring and early warning of collapse risks near the tunnel excavation face. The traditional method of microseismic monitoring of tunnel rock collapse requires repeated burying and removal of vibration monitoring sensors, which is complicated and affects the progress of tunnel construction. Therefore, it is very necessary to develop an automated monitoring and early warning system and method that can monitor the signs of tunnel face collapse in real time, dynamically and conveniently, and issue timely early warnings, so as to achieve long-term and effective protection of the safety of tunnel face construction workers.

[0003] Therefore, there is an urgent need for a tunnel face collapse early warning system and method to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to solve the defects in the prior art and to propose a tunnel face collapse early warning system and method based on millimeter wave radar.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A tunnel face collapse early warning system based on millimeter wave radar, comprising a data acquisition system and an alarm system located near the tunnel face, a data transmission system arranged in the tunnel and at the tunnel entrance, and a remote monitoring platform arranged outside the tunnel;

[0007] The data acquisition system includes a millimeter wave radar, the alarm system includes an audible and visual alarm; the remote monitoring platform includes a data preprocessing module, a microseismic-collapse relationship analysis module and a face collapse analysis module;

[0008] The millimeter wave radar is used to collect real-time microseismic amplitude duration data of the surrounding rock of the arch above the tunnel face caused by construction disturbance during tunnel construction, except during blasting.

[0009] The data transmission system is used to transmit the microseismic amplitude duration data to the cave entrance through an optical cable, and send it to the remote monitoring platform in the form of a wireless 3G / 4G / 5G signal by a data transmission module arranged at the cave entrance;

[0010] The remote monitoring platform is used to receive the microseismic amplitude duration data, obtain the microseismic main frequency duration data after processing, compare the attenuation amplitude of the microseismic main frequency over time with the preset frequency attenuation amplitude range to obtain an alarm instruction, and display the alarm information in real time through the remote monitoring platform, and feed it back to the alarm system located near the tunnel face;

[0011] The data transmission system is also used to transmit the alarm command to the data transmission module at the tunnel entrance in the form of a wireless 3G / 4G / 5G signal, and then transmit the alarm command to the alarm system near the tunnel construction face through the optical cable from the tunnel entrance to the tunnel;

[0012] The alarm system is also used to receive the alarm instruction and to issue an audible and visual alarm in the form of a flashing light and an alarm sound through the audible and visual alarm to remind construction personnel near the face to evacuate immediately.

[0013] Furthermore, the data transmission system includes two transmission modes: optical cable transmission and wireless transmission, and has a built-in repeater for transmission form conversion. The repeater is also used to perform a signal gain on the microseismic amplitude duration data and alarm instructions.

[0014] Furthermore, the remote monitoring platform also includes an engineering database, which includes a sample database and a historical microseismic database. The sample database is used to store the microseismic amplitude duration data during the millimeter wave radar trial acquisition phase, and the microseismic amplitude duration data during the trial acquisition phase includes clutter waveform data and effective waveform data; the historical microseismic database is used to store microseismic data of various scales of landslide events monitored by traditional embedded vibration sensors.

[0015] Furthermore, the data preprocessing module is used to perform secondary signal gain on the microseismic amplitude duration data, and perform filtering processing based on a pre-trained deep learning model, and finally convert it into the microseismic frequency duration data; the deep learning model is specifically a neural network.

[0016] Furthermore, the microseismic-collapse relationship analysis module is used to extract microseismic data of various scales of landslide events from the historical microseismic database, and perform big data analysis to determine the relationship between the microseismic frequency attenuation amplitude and the triggering of landslides, and based on its preset frequency attenuation amplitude range.

[0017] Furthermore, the tunnel face collapse analysis module is used to obtain the microseismic amplitude duration data and make a judgment on it to analyze whether tunnel face collapse will occur. The specific process includes:

[0018] a. First, obtain the microseismic amplitude duration data and represent it as a microseismic vibration amplitude-time curve;

[0019] b. Then, the microseismic amplitude duration data curve is divided into several sections at a preset time interval, and each section of the microseismic amplitude duration curve is converted into a microseismic amplitude-frequency curve by Fourier transform method, and the frequency corresponding to the maximum value of the microseismic amplitude in each section of the microseismic amplitude-frequency curve is used as the main frequency of the curve, and the main frequency of the microseismic frequency in each time period is obtained;

[0020] c. Compare the attenuation amplitude of the microseismic main frequency over time with the preset frequency attenuation amplitude range to determine whether it is within the preset frequency attenuation amplitude range. If not, it is determined that the tunnel face collapse will not occur. Otherwise, it is determined that the tunnel face collapse will occur and an alarm instruction is generated.

[0021] A tunnel face collapse early warning method based on millimeter wave radar comprises the following steps:

[0022] Step 1: Except during blasting, during tunnel construction, place the millimeter-wave radar in a stable place in front of the tunnel face that is not easily hit by construction machinery. Let the radar antenna aim at the arch surrounding rock above the tunnel face, and move the millimeter-wave radar once during each excavation to collect real-time microseismic amplitude duration data of the arch surrounding rock above the tunnel face caused by construction disturbance;

[0023] Step 2: Transmit the collected microseismic amplitude duration data to the data transmission system via an optical cable, and convert it into a wireless 3G / 4G / 5G signal through a repeater and send it to the remote monitoring processing system;

[0024] Step 3: Filter the microseismic amplitude duration data based on the pre-trained deep learning model, divide the microseismic amplitude duration data curve into several segments at a preset time interval, and convert each segment of the microseismic amplitude duration curve into a microseismic amplitude-frequency curve form by Fourier transform method;

[0025] Step 4: extract the frequency corresponding to the maximum value of the microseismic amplitude in each section of the microseismic amplitude-frequency curve as the main frequency of the curve, obtain the microseismic main frequency in each period of time, compare the attenuation amplitude of the main frequency over time with the preset frequency attenuation amplitude range, judge whether the tunnel face collapse will occur, and obtain the judgment result;

[0026] Step 5: If the judgment result shows that the tunnel face collapse is about to occur, an alarm instruction is generated and sent to an alarm system near the tunnel face based on the data transmission system. The alarm system uses an audible and visual alarm to alert construction personnel near the tunnel face to evacuate immediately.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] (1) The tunnel face collapse early warning system and method based on millimeter wave radar proposed in this application is highly efficient, convenient to implement, and low-cost compared to the traditional method of microseismic monitoring of tunnel rock collapse. It does not require the installation of vibration monitoring sensors or monitoring targets inside or on the surface of the surrounding rock. It can realize the automatic monitoring of the vibration displacement and natural frequency of the surrounding rock of the tunnel face during construction, and realize automatic early warning before collapse occurs.

[0029] (2) The present application proposes a tunnel face collapse early warning system and method based on millimeter wave radar. The monitoring area is the unsupported arch of the face that is prone to collapse. The monitoring and analysis object is the vibration and natural frequency of the surrounding rock of the arch of the face caused by construction disturbance. For brittle materials such as rock, compared with only monitoring the static displacement of the surrounding rock, it can detect the precursors of surrounding rock crushing and collapse in advance. Moreover, the implementation of the present invention will not interfere with the construction. Except for the tunnel blasting period, real-time monitoring and early warning can be carried out in other construction time periods, which reduces the manpower workload and realizes real-time monitoring and early warning when there are people working on the tunnel face. It has good social and economic benefits and can provide a reference for tunnel face collapse monitoring and early warning technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0031] Figure 1 This is an overall structural block diagram of a tunnel face collapse early warning system based on millimeter wave radar proposed by the present invention;

[0032] Figure 2 This is an overall flow chart of a tunnel face collapse early warning method based on millimeter wave radar proposed by the present invention. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0034] In the description of the present invention, it is necessary to understand that the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0035] In one embodiment, referring to Figure 1 , provides a tunnel face collapse early warning system based on millimeter wave radar, including a data acquisition system and an alarm system located near the tunnel face, a data transmission system installed in the tunnel and at the tunnel entrance, and a remote monitoring platform installed outside the tunnel;

[0036] The data acquisition system includes a millimeter wave radar, and the alarm system includes an audible and visual alarm;

[0037] The millimeter wave radar is used to collect real-time microseismic amplitude duration data of the surrounding rock of the arch above the tunnel face caused by construction disturbance during tunnel construction, except during blasting.

[0038] The data transmission system is used to transmit the microseismic amplitude duration data to the cave entrance through an optical cable, and send it to the remote monitoring platform in the form of a wireless 3G / 4G / 5G signal by a data transmission module arranged at the cave entrance;

[0039] Specifically, the data transmission system includes two transmission modes: optical cable transmission and wireless transmission, and has a built-in repeater for transmission form conversion. The repeater is also used to perform a signal gain on the microseismic amplitude duration data and alarm instructions.

[0040] The remote monitoring platform is used to receive the microseismic amplitude duration data, obtain the microseismic main frequency duration data after processing, compare the attenuation amplitude of the microseismic main frequency over time with the preset frequency attenuation amplitude range to obtain an alarm instruction, and display the alarm information in real time through the remote monitoring platform, and feed it back to the alarm system located near the tunnel face;

[0041] The remote monitoring platform includes a data preprocessing module, a microseismic-collapse relationship analysis module and a tunnel face collapse analysis module;

[0042] Specifically, the remote monitoring platform also includes an engineering database, which includes a sample database and a historical microseismic database. The sample database is used to store the microseismic amplitude duration data during the millimeter-wave radar trial acquisition phase, and the microseismic amplitude duration data during the trial acquisition phase includes clutter waveform data and effective waveform data; the historical microseismic database is used to store microseismic data of various scales of landslide events monitored by traditional embedded vibration sensors.

[0043] The data preprocessing module is used to perform secondary signal gain on the microseismic amplitude duration data, and perform filtering processing based on a pre-trained deep learning model, and finally convert it into the microseismic frequency duration data; the deep learning model is specifically a neural network;

[0044] Specifically, the specific process of pre-training the deep learning model includes:

[0045] Obtain the microseismic amplitude duration data during the trial acquisition phase, and annotate the clutter waveform data and valid waveform data therein to generate training sets and test sets;

[0046] Constructing a neural network model, inputting the training set as input data into the neural network model for training, so as to generate a deep learning model with filtering function;

[0047] The deep learning model with filtering function is verified by using a test set to output a deep learning model with filtering function that meets expectations, and then the deep learning model with filtering function is used to filter the microseismic amplitude duration data to extract effective waveform data.

[0048] The microseismic-collapse relationship analysis module is used to extract microseismic data of various scales of collapse events from the historical microseismic database, and perform big data analysis to determine the relationship between the microseismic frequency attenuation amplitude and the triggering of collapse, based on its preset frequency attenuation amplitude range.

[0049] The tunnel face collapse analysis module is used to obtain the microseismic amplitude duration data and make a judgment on it to analyze whether tunnel face collapse will occur. The specific process includes:

[0050] a. First, obtain the microseismic amplitude duration data and represent it as a microseismic vibration amplitude-time curve;

[0051] b. Then, the microseismic amplitude duration data curve is divided into several sections at a preset time interval, and each section of the microseismic amplitude duration curve is converted into a microseismic amplitude-frequency curve by Fourier transform method, and the frequency corresponding to the maximum value of the microseismic amplitude in each section of the microseismic amplitude-frequency curve is used as the main frequency of the curve, and the main frequency of the microseismic frequency in each time period is obtained;

[0052] c. Compare the attenuation amplitude of the microseismic main frequency over time with a preset frequency attenuation amplitude range to determine whether it is within the preset frequency attenuation amplitude range. If not, it is determined that the tunnel face collapse will not occur. Otherwise, it is determined that the tunnel face collapse will occur and an alarm instruction is generated;

[0053] Specifically, when the microseismic frequency of the tunnel face significantly attenuates, that is, the natural vibration frequency of the surrounding rock above the tunnel face decreases, which means that its degree of fragmentation suddenly deepens, an early warning of the risk of collapse of the surrounding rock of the tunnel face will be issued immediately.

[0054] The data transmission system is also used to transmit the alarm command to the data transmission module at the tunnel entrance in the form of a wireless 3G / 4G / 5G signal, and then transmit the alarm command to the alarm system near the tunnel construction face through the optical cable from the tunnel entrance to the tunnel;

[0055] The alarm system is also used to receive the alarm instruction and to issue an audible and visual alarm in the form of a flashing light and an alarm sound through the audible and visual alarm to remind construction personnel near the face to evacuate immediately.

[0056] In one embodiment, referring to Figure 2 A tunnel face collapse early warning method based on millimeter wave radar comprises the following steps:

[0057] Step 1: Except during blasting, during tunnel construction, place the millimeter-wave radar in a stable place in front of the tunnel face that is not easily hit by construction machinery. Let the radar antenna aim at the arch surrounding rock above the tunnel face, and move the millimeter-wave radar once during each excavation to collect real-time microseismic amplitude duration data of the arch surrounding rock above the tunnel face caused by construction disturbance;

[0058] Specifically, the built-in transmitting end of the radar antenna transmits electromagnetic waves with a millimeter wavelength. After the electromagnetic waves are reflected by the surrounding rock in front, they are received by the built-in receiving end of the antenna. The micro-displacement of a certain area on the surrounding rock can be calculated based on the wavelength of the electromagnetic wave, the phase change of the transmitted and received electromagnetic wave signals, and the angle between the transmitted electromagnetic wave direction and the surrounding rock vibration direction. The continuous and high-frequency emission and collection of electromagnetic waves can obtain the micro-displacement changes of a certain area on the surrounding rock in the monitoring area (i.e., microseismic amplitude duration data); the millimeter wave radar can use a single-transmit single-receive antenna (the antenna transmits one beam of electromagnetic waves at a time and receives one beam of reflected electromagnetic waves at a time), or a multi-transmit multi-receive antenna (the antenna transmits multiple beams of electromagnetic waves at a time and receives multiple beams of reflected electromagnetic waves at a time). The monitoring area is the surrounding rock in the arch range above the tunnel face, and the monitoring content is the change of the micro-vibration amplitude of the surrounding rock in the monitoring area over time under the disturbance of the surrounding rock by construction;

[0059] Step 2: Transmit the collected microseismic amplitude duration data to the data transmission system via an optical cable, and convert it into a wireless 3G / 4G / 5G signal through a repeater and send it to the remote monitoring processing system;

[0060] Specifically, to ensure the stability of signal transmission, a wired optical cable transmission form is adopted. The optical cable extends from the tunnel entrance to the face. As the tunnel face excavation progresses, it is gradually extended. The optical cable is fixed along the side wall of the tunnel and protected by a PVC pipe on the outside. As a temporary facility for monitoring during the construction period, it is fixed on the outside of the primary support after the initial support is completed, and fixed on the outside of the secondary lining after the secondary lining is completed. The data is then sent through 3G / 4G / 5G signals and displayed in real time on the monitoring platform of the remote monitoring room. In the process, signal gain is also performed through a repeater;

[0061] Step 3: Filter the microseismic amplitude duration data based on the pre-trained deep learning model, divide the microseismic amplitude duration data curve into several segments at a preset time interval, and convert each segment of the microseismic amplitude duration curve into a microseismic amplitude-frequency curve form by Fourier transform method;

[0062] Step 4: extract the frequency corresponding to the maximum value of the microseismic amplitude in each section of the microseismic amplitude-frequency curve as the main frequency of the curve, obtain the microseismic main frequency in each period of time, compare the attenuation amplitude of the main frequency over time with the preset frequency attenuation amplitude range, judge whether the tunnel face collapse will occur, and obtain the judgment result;

[0063] Step 5: If the judgment result shows that the tunnel face collapse is about to occur, an alarm instruction is generated and sent to an alarm system near the tunnel face based on the data transmission system. The alarm system uses an audible and visual alarm to alert construction personnel near the tunnel face to evacuate immediately.

[0064] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A tunnel face collapse warning system based on millimeter wave radar, characterized in that: It includes a data acquisition system and alarm system located near the tunnel face, a data transmission system installed in the tunnel and at the tunnel entrance, and a remote monitoring platform installed outside the tunnel; The data acquisition system includes a millimeter wave radar, the alarm system includes an acoustic and visual alarm; the remote monitoring platform includes a data preprocessing module, a microseismic-collapse relationship analysis module and a face collapse analysis module; The millimeter wave radar is used to collect real-time microseismic amplitude duration data of the surrounding rock of the arch above the tunnel face caused by construction disturbance during tunnel construction, except during blasting. The data transmission system is used to transmit the microseismic amplitude duration data to the cave entrance through an optical cable, and send it to the remote monitoring platform in the form of a wireless 3G / 4G / 5G signal by a data transmission module arranged at the cave entrance; The remote monitoring platform is used to receive the microseismic amplitude duration data, obtain the microseismic main frequency duration data after processing, compare the attenuation amplitude of the microseismic main frequency over time with the preset frequency attenuation amplitude range to obtain an alarm instruction, and display the alarm information in real time through the remote monitoring platform, and feed it back to the alarm system located near the tunnel face; The data transmission system is also used to transmit the alarm command to the data transmission module at the tunnel entrance in the form of a wireless 3G / 4G / 5G signal, and then transmit the alarm command to the alarm system near the tunnel construction face through the optical cable from the tunnel entrance to the tunnel; The alarm system is also used to receive the alarm instruction and to issue an audible and visual alarm in the form of a flashing light and an alarm sound through the audible and visual alarm to remind construction personnel near the face to evacuate immediately.

2. The millimeter wave radar-based tunnel face collapse early warning system according to claim 1, characterized in that: The data transmission system includes two transmission modes: optical cable transmission and wireless transmission, and has a built-in repeater for transmission form conversion. The repeater is also used to perform a signal gain on the microseismic amplitude duration data and alarm instructions.

3. The millimeter wave radar-based tunnel face collapse early warning system according to claim 1, characterized in that: The remote monitoring platform also includes an engineering database, which includes a sample database and a historical microseismic database. The sample database is used to store the microseismic amplitude duration data during the millimeter wave radar trial acquisition phase, and the microseismic amplitude duration data during the trial acquisition phase includes clutter waveform data and effective waveform data; the historical microseismic database is used to store microseismic data under various scales of landslide events monitored by traditional embedded vibration sensors.

4. The millimeter wave radar-based tunnel face collapse early warning system according to claim 1, characterized in that: The data preprocessing module is used to perform secondary signal gain on the microseismic amplitude duration data, and perform filtering processing based on a pre-trained deep learning model, and finally convert it into microseismic frequency duration data; the deep learning model is specifically a neural network.

5. The millimeter wave radar-based tunnel face collapse early warning system according to claim 1, characterized in that: The microseismic-collapse relationship analysis module is used to extract microseismic data of various scales of collapse events from the historical microseismic database, and perform big data analysis to determine the relationship between the microseismic frequency attenuation amplitude and the triggering of collapse, based on its preset frequency attenuation amplitude range.

6. The millimeter wave radar-based tunnel face collapse early warning system according to claim 1, characterized in that: The tunnel face collapse analysis module is used to obtain the microseismic amplitude duration data and make a judgment on it to analyze whether tunnel face collapse will occur. The specific process includes: a. First, obtain the microseismic amplitude duration data and represent it as a microseismic vibration amplitude-time curve; b. Then, the microseismic amplitude duration data curve is divided into several sections at a preset time interval, and each section of the microseismic amplitude duration curve is converted into a microseismic amplitude-frequency curve form by Fourier transform method, and the frequency corresponding to the maximum value of the microseismic amplitude in each section of the microseismic amplitude-frequency curve is used as the main frequency of each section of the microseismic amplitude-frequency curve to obtain the main frequency of the microseismic amplitude in each time period; c. Compare the attenuation amplitude of the microseismic main frequency over time with the preset frequency attenuation amplitude range to determine whether it is within the preset frequency attenuation amplitude range. If not, it is determined that the tunnel face collapse will not occur. Otherwise, it is determined that the tunnel face collapse will occur and an alarm instruction is generated.

7. A tunnel face collapse early warning method based on millimeter wave radar, characterized in that: The steps include: Step 1: Except during blasting, during tunnel construction, place the millimeter-wave radar in a stable place in front of the tunnel face that is not easily hit by construction machinery. Let the radar antenna aim at the arch surrounding rock above the tunnel face, and move the millimeter-wave radar once during each excavation to collect real-time microseismic amplitude duration data of the arch surrounding rock above the tunnel face caused by construction disturbance; Step 2: Transmit the collected microseismic amplitude duration data to the data transmission system via an optical cable, and convert it into a wireless 3G / 4G / 5G signal through a repeater and send it to the remote monitoring processing system; Step 3: Filter the microseismic amplitude duration data based on the pre-trained deep learning model, divide the microseismic amplitude duration data curve into several segments at a preset time interval, and convert each segment of the microseismic amplitude duration curve into a microseismic amplitude-frequency curve form by Fourier transform method; Step 4: extract the frequency corresponding to the maximum value of the microseismic amplitude in each section of the microseismic amplitude-frequency curve as the main frequency of each section of the microseismic amplitude-frequency curve, obtain the microseismic main frequency in each period of time, compare the attenuation amplitude of the main frequency over time with the preset frequency attenuation amplitude range, judge whether the tunnel face collapse will occur, and obtain the judgment result; Step 5: If the judgment result shows that the tunnel face collapse is about to occur, an alarm instruction is generated and sent to an alarm system near the tunnel face based on the data transmission system. The alarm system uses an audible and visual alarm to alert construction personnel near the tunnel face to evacuate immediately.

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