WO2020199290A1 - 基于岩性与不良地质前兆特征识别的tbm搭载式超前地质预报系统及方法 - Google Patents

基于岩性与不良地质前兆特征识别的tbm搭载式超前地质预报系统及方法 Download PDF

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Publication number
WO2020199290A1
WO2020199290A1 PCT/CN2019/084654 CN2019084654W WO2020199290A1 WO 2020199290 A1 WO2020199290 A1 WO 2020199290A1 CN 2019084654 W CN2019084654 W CN 2019084654W WO 2020199290 A1 WO2020199290 A1 WO 2020199290A1
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Prior art keywords
rock
geological
conveyor belt
tbm
lithology
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PCT/CN2019/084654
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English (en)
French (fr)
Inventor
李术才
许振浩
谢辉辉
余腾飞
石恒
王文扬
潘东东
林鹏
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Shandong University
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Shandong University
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Priority to AU2019439936A priority Critical patent/AU2019439936B2/en
Publication of WO2020199290A1 publication Critical patent/WO2020199290A1/zh
Anticipated expiration legal-status Critical
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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21D—SHAFTS; TUNNELS; GALLERIES; LARGE UNDERGROUND CHAMBERS
    • E21D9/00—Tunnels or galleries, with or without linings; Methods or apparatus for making thereof; Layout of tunnels or galleries
    • E21D9/003—Arrangement of measuring or indicating devices for use during driving of tunnels, e.g. for guiding machines
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21D—SHAFTS; TUNNELS; GALLERIES; LARGE UNDERGROUND CHAMBERS
    • E21D9/00—Tunnels or galleries, with or without linings; Methods or apparatus for making thereof; Layout of tunnels or galleries
    • E21D9/06—Making by using a driving shield, i.e. advanced by pushing means bearing against the already placed lining
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V11/00—Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00

Definitions

  • the present disclosure relates to a TBM mounted advanced geological prediction system and method based on identification of lithology and poor geological precursor features.
  • the TBM construction method should be used first in accordance with international common practice.
  • the internationally recognized TBM construction method has fast excavation speed, low construction disturbance, high quality of hole formation, and comprehensive economic and social benefits. Higher advantage.
  • the TBM construction method has poor adaptability to geological conditions. In the TBM construction, disasters such as water inrush, landslide, and large deformation are often encountered, resulting in major accidents such as jamming, damage, scrapping, and even casualties of the roadheader.
  • the most effective solution is to use advanced geological forecasting technology to detect the bad geological conditions in front of the master, and formulate reasonable disposal measures and construction plans in advance according to the geological conditions ahead to avoid and Prevent the occurrence of major disasters.
  • the present disclosure proposes a TBM-mounted advanced geological prediction system and method based on the identification of lithology and poor geological precursor features.
  • the present disclosure can understand the lithology, minerals and element types and types of surrounding rocks on the TBM tunnel in real time. The change of content, and predict the change of geological conditions in front of the excavation and possible bad geology.
  • the present disclosure adopts the following technical solutions:
  • a TBM-mounted advanced geological prediction system based on the identification of lithology and poor geological precursor features including a base set on the TBM main conveyor belt, on which a retractable mechanical gripper, a conveyor belt system, and a crushing Equipment, control system and data comprehensive analysis platform, including:
  • the telescopic mechanical gripper is used to place the rock mass to be tested on the TBM main conveyor belt and place it on the conveyor belt system.
  • a drying mechanism, an image recognition device, and an X-ray fluorescence analysis device are sequentially arranged above the conveyor belt system, respectively Apply surface drying, lithology identification and X-ray fluorescence analysis to the rock block to be tested;
  • the crushing device is arranged at the end of the conveyor belt system to crush the rock block to be tested, and the powder particles at the outlet of the crushing device can directly fall into the sample chamber of the X-ray diffraction analysis device set below;
  • the control system controls the actions of the retractable mechanical gripper, conveyor belt system, crushing device, image recognition device, X-ray fluorescence analysis device, and X-ray diffraction analysis device;
  • the data comprehensive analysis platform is configured to receive the collection results of the image recognition device, the X-ray fluorescence analysis device, and the X-ray diffraction analysis device, based on the sign images that can reflect the bad geological information, the bad geological sensitive elements, and the bad geological information in the extracted rocks.
  • the enrichment information of geological minerals uses neural network for information fusion and correction, and analysis is performed to obtain the lithological changes and the occurrence characteristics of bad geology in front of the head, and realize the advanced geological prediction of the tunnel based on intelligent identification of lithology and bad geological precursors.
  • the present disclosure is mounted on the TBM, and can analyze the changes of lithology and adverse geological precursor characteristics during the TBM tunneling work in real time.
  • the convolutional neural network is used to extract and characterize the color, texture, and shape of the rock through the convolutional layer and the maximum pooling layer; at the same time, combined with spectral analysis technology, it can identify minerals and minerals in the rock.
  • the change of element content establishes a quantitative characterization relationship between sensitive elements and characteristic minerals and geological precursors of adverse geologically affected areas.
  • the present disclosure uses the base to be set on the TBM, and uses a separate conveyor belt to perform analysis work, does not interfere with the TBM's own work, and realizes the integration and integration of tunneling, analysis, and forecasting.
  • a protective shed is provided on the base.
  • the protective shed can effectively prevent other interference factors.
  • the protective shed is welded and located above the base, supported by four angle steels as the roof.
  • the roof is arched and made of stainless steel plate, which can prevent the falling rock and water seepage of the tunnel vault from damaging the instrument parts on the base.
  • the two sides of the protective shed are covered by toughened glass panels, and are fixed on the supporting angle steel with fastening bolts.
  • the pedestal is composed of angle steel, the outline of the pedestal is rectangular, and it is welded above the TBM conveyor belt near the main control room to provide support for other parts of the system.
  • the telescopic mechanical gripper includes an electro-hydraulic column, a support rod, an electro-hydraulic arm, a mechanical arm, an electro-hydraulic rod, a rocker, a connecting rod, and a grab.
  • the electro-hydraulic column is vertically welded to the base The upper part is used to install the electro-hydraulic arm and support rod, which can be extended up and down to control the lifting of the entire mechanical gripper device.
  • One end of the support rod is vertically fixedly connected to the electro-hydraulic column, and one end is connected to the mechanical arm with a pin shaft.
  • One end of the electro-hydraulic arm is connected to the electro-hydraulic column, and one end is connected to the mechanical arm with a pin.
  • a laser profile measuring instrument is fixedly installed at the front end of the robotic arm and the grab bucket to measure the size of the rock mass on the TBM conveyor belt. When the measured rock mass meets the set range, the signal is transmitted to the control system to control the mechanical gripper to grab the rock block.
  • a high-pressure nozzle is installed in the grab, and the high-pressure nozzle is connected to an external water pump through a water pipe.
  • the high-pressure nozzle will automatically spray water to wash the grabbed rock block. To ensure that the surface of the grabbed rock block is free from other slag powder pollution.
  • the conveyor belt system is installed in the protective shed on the upper part of the base and includes a conveyor belt, a roller, a servo motor and a displacement sensor.
  • a conveyor belt is wound on the roller, and the servo motor
  • the roller is driven to drive the movement of the conveyor belt.
  • the displacement sensor is used to measure the transmission displacement of the rock block to be measured on the conveyor belt.
  • the displacement sensor and servo motor are both communicated with the control system.
  • the drying mechanism is a dryer, which air-drys the picked rock blocks to be tested.
  • the control system starts to control the air dryer Work is carried out, and the hot air is released to the surface of the rock block to be tested. After a certain period of time, the dryer stops working and the surface of the rock block to be tested is dried.
  • the image recognition device includes an image capture device, a lighting device, and an image recognition system.
  • the image capture device is a digital camera configured to take high-definition images of the rock mass to be tested.
  • the lighting device includes several industrial lighting devices.
  • the light source is placed next to the image acquisition device.
  • the image recognition system uses deep learning technology and uses the convolutional neural network intelligent recognition model to train the rock images in the existing rock specimen library, and compare it with the established rock specimen library image for information fusion and The analysis can finally give a preliminary lithology recognition result.
  • the image acquisition device captures an image of the rock block to be tested, it will transmit the acquired image to the data comprehensive analysis platform.
  • the X-ray fluorescence analysis device includes a drive motor, a chassis, a connecting rod, an electro-hydraulic rod, a pin, a spherical hinge, and an X-ray fluorescence analyzer.
  • the drive motor is connected to the chassis to control the rotation of the entire chassis.
  • the chassis and the connecting rod, the chassis and the electro-hydraulic rod are connected by a pin shaft, the connecting rod and the electro-hydraulic rod are fixedly connected, and the X-ray fluorescence analyzer is connected with the other end of the connecting rod by a ball hinge to control
  • the X-ray fluorescence analyzer detects the rock mass under test from different angles.
  • the X-ray fluorescence analyzer emits X-rays and irradiates the rock mass to be tested, and receives the secondary characteristic X-rays generated. The types and contents of elements in the rock block to be tested are obtained, and the results are transmitted to the data comprehensive analysis platform.
  • the crushing device includes a motor, a crushing chamber, a crushing shaft, a crushing knife, a feed port, a compartment, a discharge port, a screen, a valve, and a blower.
  • the motor is installed on the upper part of the entire crushing device and is responsible for providing The power of high-speed rotation
  • the shaft of the servo motor is connected with a crushing shaft
  • the front end of the crushing shaft is equipped with a crushing knife
  • the crushing shaft and the crushing knife are set in the crushing cabin
  • the feeding port and conveyor belt system are provided on one side of the crushing cabin.
  • the sample on the upper part can automatically fall to the feed inlet to enter the crushing chamber.
  • a layer of screen is installed at the lower part of the crushing chamber.
  • the particles filtered by the screen will fall into the bucket set below the crushing chamber.
  • the powder particles at the powder outlet can directly fall into the sample compartment of the X-ray diffraction analysis device below.
  • the waste at the outlet directly falls into the main TBM conveyor belt below, and there is a valve at each of the two discharge ports, and the blower is installed in the upper part of the crushing cabin.
  • the X-ray diffraction analysis device includes an X-ray diffraction analyzer and a sample compartment, the sample compartment has upper and lower openings, and a hair dryer is installed above the upper opening, and both the upper and lower openings are switched on and off. Control the opening and closing of the corresponding opening.
  • the forecast method based on the above system includes the following steps:
  • the convolutional neural network is used to extract and characterize the color, texture and shape of the rock through the convolutional layer and the maximum pooling layer;
  • the extracted and characterized rock image feature information is fused with the collected rock element type and content, rock mineral type and content, and input into the rock image recognition and classification model that has been trained in the intelligent rock recognition system. Analyze and compare, and finally give the rock name, bad geological mark and accuracy rate of the rock block to be tested;
  • the extracted rock can reflect the sign images of bad geological information, bad geological sensitive elements and bad geological mineral enrichment information using neural network for information fusion and correction, and based on data mining technology Comparing and analyzing the established quantitative characterization relational database of adverse geological precursors characteristics, so as to give out the lithological changes and adverse geological occurrence characteristics in front of the tunnels, and realize the advanced geological prediction of tunnels based on the intelligent identification of lithology and adverse geological precursors. .
  • the present disclosure controls the sequence actions of the retractable mechanical gripper, conveyor belt system, crushing device, image recognition device, X-ray fluorescence analysis device and X-ray diffraction analysis device through the control system, which can realize full automation and continuity of work .
  • the present disclosure integrates image recognition technology and spectral analysis technology, which can accurately and quickly identify lithology, use spectral analysis technology to identify changes in minerals and element content in rocks, and establish sensitive elements and characteristic minerals and adverse geological influence areas Quantitative characterization relationship between geological precursor features.
  • the present disclosure is equipped with TBM and uses a separate conveyor belt to perform analysis work. It does not interfere with the TBM's own work, and can analyze the changes in lithology and poor geological precursor characteristics during the TBM tunneling work in real time.
  • the present disclosure can use neural network to integrate the sign images of bad geological information, sensitive elements of bad geology, and enrichment information of bad geological minerals, and then compare it with the quantitative characterization relation database of bad geological precursors established based on data mining technology Comparison and analysis can give out the lithological changes in front of the tunnel and the occurrence characteristics of bad geology, so as to finally realize the advanced geological prediction of the tunnel based on the intelligent identification of lithology and bad geological precursors.
  • Figure 1 is a schematic diagram of the present disclosure
  • Figure 2 is a structural diagram of a retractable mechanical gripper
  • Figure 3 is a three-dimensional structure diagram of the grab
  • Figure 4 is a diagram of an X-ray fluorescence analysis device
  • Figure 5 is a structural diagram of the grinding device
  • Figure 6 is a schematic diagram of the structure of an X-ray diffraction analyzer
  • azimuth or positional relationship is based on the azimuth or positional relationship shown in the drawings, and is only a relationship term determined to facilitate the description of the structural relationship of each component or element in the present disclosure. It does not specifically refer to any component or element in the present disclosure, and cannot be understood as a reference to the present disclosure. Disclosure restrictions.
  • a TBM-mounted advanced geological prediction system and method based on the intelligent recognition of lithology and poor geological precursor features including a base, a protective shed, a retractable mechanical gripper, a conveyor belt system, and a drying Dryer, image acquisition device, X-ray fluorescence analysis device, crushing device, X-ray diffraction analysis device, control system and data comprehensive analysis platform.
  • the base mainly provides support for other components of the system.
  • the protective shed is welded and located above the base to prevent rockfall and water seepage on the tunnel vault from damaging the instrument parts on the base.
  • the retractable mechanical gripper can realize the grabbing of the rock block to be tested and the washing of the rock block to be tested.
  • the dryer is responsible for drying the washed rock block to be tested, and then the image acquisition device completes the collection of the image of the rock block to be tested, and transmits the image information to the rock intelligent identification system in the data comprehensive analysis platform.
  • the X-ray fluorescence analysis device can realize the identification of the type and content of elements in the rock block, and the X-ray diffraction device can realize the diffraction analysis of the powder processed by the crushing device, and give the result of the mineral type and content in the rock block to be tested. Finally, these test results are transmitted to the data comprehensive analysis system for information fusion and correction, and the recognition results of lithological features are given.
  • the data comprehensive analysis platform After continuously measuring rock samples with a certain excavation distance, the data comprehensive analysis platform will use neural network to perform information fusion and correction on the extracted rocks that can reflect the sign images of bad geological information, bad geological sensitive elements, and bad geological mineral enrichment information. Then compare and analyze the relational database based on the quantitative characterization of bad geological precursor features established based on data mining technology, so as to give the lithological changes and the occurrence characteristics of bad geological occurrence in front of the face, and finally realize the characteristics based on lithology and bad geological precursors Advanced geological forecast of tunnels with intelligent identification.
  • the information in the data comprehensive analysis platform is continuously improved, and the final lithology identification results and the tunnel advanced geological forecast results will be continuously revised and become more accurate.
  • a TBM-mounted advanced geological prediction system based on the intelligent recognition of lithology and poor geological precursor features, as shown in Figure 1, specifically includes a base 2, a protective shed 3.
  • Shrinkable mechanical gripper 4 conveyor belt system 5, dryer 6, image acquisition device 7, lighting device 8, X-ray fluorescence analysis device 9, crushing device 10, X-ray diffraction analysis device 11, control system 12 and comprehensive data analysis Platform 13.
  • the base 2 is mainly composed of angle steel, the base is rectangular in outline, and is welded above the TBM conveyor belt near the main control room to provide support for other parts of the system.
  • the protective shed 3 is welded and located above the base, and is supported by four angle steels as the roof.
  • the roof is arched and made of stainless steel, which can prevent falling rocks and water seepage on the tunnel vault from damaging the instrument parts on the base.
  • the front and back of the protective shed 3 are covered by toughened glass plates, and are fixed on the supporting angle steel with fastening bolts.
  • the retractable mechanical gripper 4 is mainly composed of an electro-hydraulic column 15, a support rod 18, an electro-hydraulic arm 16, a mechanical arm 17, an electro-hydraulic rod 19, a rocker 20, a connecting rod 21, and a grab 23 .
  • the electro-hydraulic column 15 is vertically welded on the base 2 to install the electro-hydraulic arm 16 and the support rod 18, which can be extended up and down to control the lifting of the entire mechanical gripper device.
  • One end of the support rod 18 is vertically and fixedly connected to the electro-hydraulic column 15 and one end is connected to the mechanical arm 17 by a pin.
  • One end of the electro-hydraulic arm 16 is connected to the electro-hydraulic column 15 and one end is connected to the mechanical arm 17 by a pin.
  • bucket teeth 24 are installed at the front end of the grab.
  • a laser profile measuring instrument 25 is fixedly installed at the front end of the robotic arm connected to the grab 23 to measure the size of the rock mass on the TBM conveyor belt. When the measured rock mass meets the set range, the The signal is transmitted to the control system to control the mechanical grab to grab the rock block.
  • a high-pressure nozzle 26 is also installed in the grab.
  • the high-pressure nozzle is connected to an external water pump through a water pipe.
  • the high-pressure nozzle 26 will automatically spray water to wash the grabbed rock block to ensure grabbing. There is no pollution from other slag powder on the surface of the rock block.
  • the conveyor belt system 5 is installed in the protective shed on the upper part of the base, and includes a conveyor belt, a roller, a servo motor and a displacement sensor.
  • the dryer 6 is placed at a height of 5 cm above the conveyor belt of the system, and is mainly used for air-drying the grabbed rock blocks to be tested.
  • the servo motor is responsible for driving the movement of the conveyor belt, and the displacement sensor is used to measure the transmission displacement of the rock block to be measured on the conveyor belt. The two are combined with the control system to realize real-time control of the rock block displacement to be measured.
  • the dryer 6 is placed at a height of 5 cm above the conveyor belt of the system, and is mainly used for air-drying the grabbed rock blocks to be tested.
  • the control system starts to control the air dryer to work, and release hot air to blow to the surface of the rock block to be tested. After a certain period of time, the dryer stops working. The surface of the rock block is dried. The conveyor belt system starts to work again and continues to transport the rock blocks to be tested forward.
  • the image recognition device is mainly composed of an image acquisition device 7, a lighting device 8, and an image recognition system.
  • the image acquisition device 7 is a high-definition digital camera, which is mainly responsible for shooting high-definition images of the rock mass to be tested and transmitting the information to the data comprehensive analysis platform 13.
  • the lighting device 8 is mainly composed of two industrial lighting light sources, which are arranged on both sides of the image acquisition device.
  • the image recognition system is a rock intelligent recognition software developed based on the Windows system. The software applies deep learning technology and uses a convolutional neural network intelligent recognition model to train the rock images in the existing rock specimen library, and The established rock specimen library image comparison, information fusion and analysis, can finally give a preliminary lithology recognition results.
  • the image acquisition device captures an image of the rock block to be tested, it will transmit the collected image to the rock intelligent identification system.
  • the conveyor belt continues to work to transfer the rock block to be tested to the X-ray fluorescence analysis device.
  • the X-ray fluorescence analysis device 9 is mainly composed of a servo motor 27, a chassis 29, a connecting rod 31, an electro-hydraulic rod 30, a pin 28, a spherical hinge 32, and an X-ray fluorescence analyzer 33.
  • the servo motor 27 and The chassis 29 is connected to control the rotation of the entire chassis 29.
  • the chassis 29 and the connecting rod 31, the chassis 29 and the electro-hydraulic rod 30 are connected by a pin, the connecting rod 31 and the electro-hydraulic rod 30 are fixedly connected, and the X-ray fluorescence analyzer and the other end of the connecting rod 31 are connected with a ball hinge 32-phase connection, which can control the X-ray fluorescence analyzer to detect the rock mass under test from different angles.
  • the X-ray fluorescence analyzer emits X-rays and irradiates them on the rock block to be tested, and receives the secondary characteristic X-rays generated, gives the element types and content in the rock block to be tested, and transmits the results to the comprehensive data analysis Platform 13.
  • the crushing device 10 is composed of a crusher, which mainly includes a servo motor 34, a crushing cabin 37, a crushing shaft 35, a crushing knife 38, a feed port 36, a bucket 40, and discharge ports 42 and 43,
  • the screen 39, the valve 41 and the blower 44 are composed.
  • the servo motor 34 is installed in the upper part of the whole device and is responsible for providing high-speed rotation power.
  • a pulverizing shaft 35 is connected to the shaft of the servo motor 34, a pulverizing knife 38 is installed at the front end of the pulverizing shaft 35, and both the pulverizing shaft 35 and the pulverizing knife 38 are in the pulverizing cabin 37.
  • a feed port 36 is provided on one side of the crushing cabin 37, and the sample on the conveyor belt system can automatically fall to the feed port 36 to enter the crushing cabin 37.
  • a thicker 200-mesh screen 39 is installed at the lower part of the crushing cabin 37, and particles smaller than 200-mesh are automatically transferred into the lower bucket 40 during the crushing process.
  • the powder particles at the powder outlet can directly fall into the sample compartment of the X-ray diffraction analyzer below. In the middle, the waste at the waste outlet directly falls into the main TBM conveyor belt below, and there is a valve at each of the two outlets.
  • the blower 44 is installed in the upper part of the crushing cabin 37, and is mainly responsible for cleaning the residual waste in the crushing cabin 37 and the screen 39.
  • the rock block to be tested can be ground into powder to provide test materials for the X-ray diffraction device 11.
  • the X-ray diffraction analysis device 11 is mainly composed of an X-ray diffraction analyzer, as shown in FIG. 6, and its main structure includes an X-ray tube, an X-ray detector, a goniometer, a sample chamber, a data acquisition system, and a data processing analysis system.
  • the sample compartment has upper and lower openings, and a small blower is installed above the upper opening. The upper opening is opened when loading samples, and the upper and lower openings are closed when working. After the test is completed, the lower opening is opened, and the blower starts working and blows the rock dust in the sample chamber. Sweep onto the TBM main conveyor belt and transport away.
  • the control system 12 mainly receives control information from the retractable mechanical gripper 4, the conveyor belt system 5, the dryer 6, the image acquisition device 7, the lighting device 8, the X-ray fluorescence analysis device 9 and the crushing device 10, and controls the above devices Work and stop.
  • the data comprehensive analysis platform 13 includes a lithology intelligent recognition system based on image and spectroscopy technology, a bad geological precursor feature database, and an advanced geological prediction system.
  • the platform 13 is installed in the main control room 14 of the TBM and is connected with the above devices by data connection lines.
  • the TBM-mounted advanced geological prediction system Based on the intelligent recognition of lithology and poor geological precursor features, the TBM-mounted advanced geological prediction system based on the recognition of lithology and poor geological precursor features.
  • the specific implementation process includes the following steps:
  • the retractable mechanical gripper 4 starts to work, the front end of the mechanical arm 17, and the laser profile measuring instrument installed at the junction of the grab 23 to measure the size of the rock mass on the TBM conveyor belt, and then select a piece that meets the system requirements Set the size range of rock blocks to grab. After grabbing the rock block, the high-pressure nozzle 25 will automatically spray water to wash the rock block in the grab bucket. After the flushing is completed, the electro-hydraulic arm 16 and electro-hydraulic rod 19 begin to extend and move and place the rock block to be tested on the conveyor belt On system 5.
  • the conveyor belt system 5 continues to work, transports the rock mass to be tested under the X-ray fluorescence analysis device 9 and then stops working again.
  • the X-ray fluorescence analysis device 9 starts to work, firstly controlled by the servo motor 27 and the electro-hydraulic rod 30
  • the irradiation direction of X-ray fluorescence analysis is to irradiate different parts of the rock block under test multiple times, and finally give the element type and content in the rock block to be tested, and transmit the result to the data comprehensive analysis platform 13.
  • the conveyor belt system 5 continues to work, and the rock mass to be tested will be fed into the feed port of the crushing device 10, and then the crusher will start working, firstly the powder discharge port 42 The valve of the waste discharge port 43 is closed.
  • the powder particles falling from the screen 39 are automatically transferred into the sample compartment of the X-ray diffraction analyzer.
  • the pulverizer stops working, the valve of the powder outlet 42 is closed, the valve of the waste outlet 43 is opened, the screen will be drawn out, and the blower 44 will start purging at the same time. The remaining waste is purged from the waste discharge port to the main TBM conveyor 1 below.
  • the opening of the sample chamber is closed, and the X-ray diffraction analysis device 11 starts to work, and then the mineral types and contents in the rock block to be tested are given, and the result is transmitted To the data comprehensive analysis platform 13.
  • the lower opening of the sample compartment is opened, and the blower starts to work and blows the rock dust in the sample compartment to the TBM main conveyor belt 1 for transportation.
  • the data comprehensive analysis platform 13 provides the lithological characteristics according to the image intelligent recognition system, the type and content of each element in the rock sample given by the X-ray fluorescence analysis device, and the mineral types and the mineral types given by the X-ray diffraction analysis device 11. The content result is compared with the established rock image, element and mineral standard database, and finally the accurate lithology identification result of the rock block to be tested is given.
  • the extracted rock can reflect the sign images of bad geological information, bad geological sensitive elements and bad geological mineral enrichment information using neural network for information fusion and correction, and then Comparing and analyzing the relational database based on the quantitative characterization of the bad geological precursor features established based on data mining technology, to give out the lithological changes and the occurrence of bad geological features in the front of the face, and finally realize the intelligence based on the lithology and the bad geological precursor features Advance geological forecast of identified tunnels.

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Abstract

本公开提供了一种基于岩性与不良地质前兆特征识别的TBM搭载式超前地质预报系统及方法，包括基座（2），基座（2）设置于TBM主传送带（1）上，其上依次设置有可伸缩机械抓手（4）、传送带系统（5）、粉碎装置（10）、控制系统（12）和数据综合分析平台（13），传送带系统（5）上方依次设置有烘干机构（6）、图像识别装置和X射线荧光分析装置（9），分别对待测岩块施加表面烘干、岩性识别和X射线荧光分析，依据所提取岩石中能够反映不良地质信息的标志图像、不良地质敏感元素及不良地质矿物富集信息利用神经网络进行信息融合与修正，进行分析，得到掌子面前方岩性变化与不良地质赋存特征，实现基于岩性与不良地质前兆特征智能识别的隧道超前地质预报。

Description

基于岩性与不良地质前兆特征识别的TBM搭载式超前地质预报系统及方法 技术领域
本公开涉及一种基于岩性与不良地质前兆特征识别的TBM搭载式超前地质预报系统及方法。
背景技术
本部分的陈述仅仅是提供了与本公开相关的背景技术信息，不必然构成在先技术。
当深长隧道的长径比达到或超过600～1000时，按照国际通行惯例应优先采用TBM施工方法，国际上公认TBM施工方法具有掘进速度快、施工扰动小、成洞质量高、综合经济社会效益高等优势。但TBM施工方法又对地质条件的适应能力较差，在TBM施工中经常遭遇突水突泥、塌方、大变形等灾害，导致掘进机卡机、损坏、报废甚至人员伤亡的重大事故。为了降低TBM施工中遭遇上述事故的风险，最有效的解决方法就是采用超前地质预报技术提前探明掌子面前方不良地质情况，根据前方的地质情况预先制定合理的处置措施和施工预案，规避和预防重大灾害事故的发生。
隧道施工过程中，在出现断层破碎带、溶洞、暗河以及淤泥带之前，一般都会出现各自的前兆标志，这些标志的出现，常常预示前述不良地质体已经临近。然而，隧道内一线施工人员多缺乏良好的地质知识，不能及时的对不良地质前兆特征进行有效识别，从而酿成一些灾害的发生。同时，TBM施工对地层岩性要求较高，除了关注前方可能存在的不良地质外，还要关注掘进路线上地层岩性的变化，以便及时改进TBM的施工参数，从而保证安全快速施工。
目前，在TBM隧道施工过程实施超前地质预报方法很多，主要是以地球物理探测方法为主，如地震波法、电阻法、红外探水等，且也已经出现了部分TBM搭载设备。但是据发明人了解，从地质分析的角度开展超前地质预报，在识别突水突泥等灾害的不良地质前兆特征方面，现有的预报系统及方法不可以实时了解TBM掘进路线上围岩岩性、矿物及元素种类及含量的变化情况，并预测掘进面前方地质条件变化情况。
发明内容
本公开为了解决上述问题，提出了一种基于岩性与不良地质前兆特征识别的TBM搭载式超前地质预报系统及方法，本公开可实时了解TBM掘进路线上围岩岩性、矿物及元素种类及含量的变化情况，并预测掘进面前方地质条件变化情况及可能存在的不良地质。
根据一些实施例，本公开采用如下技术方案：
一种基于岩性与不良地质前兆特征识别的TBM搭载式超前地质预报系统，包括基座，所述基座设置于TBM主传送带上，其上依次设置有可伸缩机械抓手、传送带系统、粉碎装置、控制系统和数据综合分析平台，其中：
所述可伸缩机械抓手用于将所述TBM主传送带的待测岩块并放置在传送带系统上，所述传送带系统上方依次设置有烘干机构、图像识别装置和X射线荧光分析装置，分别对待测岩块施加表面烘干、岩性识别和X射线荧光分析；
所述粉碎装置设置于传送带系统的末端，对待测岩块进行粉碎，粉碎装置出口的粉末颗粒可直接落入下方设置的X射线衍射分析装置的样品舱中；
所述控制系统控制所述可伸缩机械抓手、传送带系统、粉碎装置、图像识别装置、X射线荧光分析装置及X射线衍射分析装置的动作；
所述数据综合分析平台，被配置为接收图像识别装置、X射线荧光分析装置和X射线衍射分析装置的采集结果，依据所提取岩石中能够反映不良地质信息的标志图像、不良地质敏感元素及不良地质矿物富集信息利用神经网络进行信息融合与修正，进行分析，得到掌子面前方岩性变化与不良地质赋存特征，实现基于岩性与不良地质前兆特征智能识别的隧道超前地质预报。
本公开搭载于TBM上，可以实时分析TBM掘进工作过程中岩性及不良地质前兆特征的变化。基于采集到的岩石图像信息，应用卷积神经网络，通过卷积层和最大池化层对岩石的颜色、纹理和形状等图像特征进行提取与表征；同时结合光谱分析技术可识别岩石中矿物及元素含量的变化，建立起敏感元素与特征矿物和不良地质影响区地质前兆特征之间的定量表征关系。
同时，本公开利用基座设置于TBM上，利用单独的传送带进行分析工作，并不干扰TBM的本身工作，实现掘进、分析和预报的一体化、集成化。
作为进一步的限定，所述基座上面设置有防护棚。防护棚能够有效防止其他干扰因素。
防护棚焊接坐落于基座上方，由四个角钢作为棚顶支撑，棚顶形状为拱形，材质为不锈钢板，可防止隧道拱顶的落石、渗水等破坏基座上的仪器部件。防护棚的两侧由钢化玻璃板遮挡，并用紧固螺栓将其固定在支撑角钢上。
作为进一步的限定，所述基座由角钢构成，基座轮廓为长方形，焊接于主控室附近的TBM传送带上方，主要为该系统的其他部件提供支撑作用。
作为进一步的限定，所述可伸缩式机械抓手，包括电动液压立柱、支撑杆、电动液压臂、机械臂、电动液压杆、摇杆、连杆以及抓斗，电动液压立柱垂直焊接在基座上，用来安装电动液压臂和支撑杆，可上下伸缩，用来控制整个机械抓手装置的升降，所述支撑杆一端垂直固定连接在电动液压立柱上，一端与机械臂用销轴连接，所述电动液压臂一端连接在电动液压立柱上，一端与机械臂用销轴连接，所述抓斗有两个，并用销轴对称安装在机械臂的前端，同时在抓斗前端安装有斗齿，与抓斗相对应，电动液压伸缩杆有两个，分别用摇杆和连杆相连接，用来控制抓斗的张开与闭合。
作为进一步的限定，所述机械臂的前端与抓斗相连处固定安装有激光轮廓测量仪，用来对TBM传送带上的岩块大小进行量测，当测量到的岩块大小符合所设定范围时，将该信号传输至控制系统控制机械抓手对该岩块进行抓取。
作为进一步的限定，所述抓斗中还安装有高压喷头，高压喷头通过水管与外置的水泵相连，当抓斗抓取岩块后，高压喷头会自动喷水对抓取岩块进行冲洗，以确保抓取岩块表面无其他渣粉的污染。
作为进一步的限定，所述传送带系统安装在基座上部的防护棚内，包括传送带、滚筒、伺服电机及位移传感器，所述滚筒有至少两个，所述传送带缠绕在滚筒上，所述伺服电机驱动滚筒，以驱动传送带的运动，位移传感器用来测定传送带上待测岩块的传送位移，位移传感器和伺服电机均与控制系统通讯。
作为进一步的限定，所述烘干机构为烘干机，对所抓取的待测岩块进行风干，当传送带系统将待测岩块传送到烘干机正下方时，控制系统开始控制风干机进行工作，释放出热风吹到待测岩块表面，在一定时间后，烘干机停止工作，待测岩块的表面被烘干。
作为进一步的限定，所述图像识别装置包括图像采集装置、照明装置和图像识别系统，所述图像采集装置为数码相机，被配置为拍摄待测岩块的高清图 像，照明装置包括若干个工业照明光源，安置于图像采集装置旁，图像识别系统应用深度学习技术，使用卷积神经网络智能识别模型训练已有岩石标本库中的岩石图像，与已建立的岩石标本库图像对比，进行信息融合与分析，最终可以给出初步的岩性识别结果，当图像采集装置拍摄到待测岩块图像时，会将采集到的图像传输至数据综合分析平台。
作为进一步的限定，所述X射线荧光分析装置包括驱动电机、底盘、连杆、电动液压杆、销轴、球铰和X射线荧光分析仪，驱动电机与底盘相连接，控制整个底盘的旋转，所述底盘与连杆、底盘与电动液压杆之间用销轴相连接，连杆与电动液压杆之间固定连接，X射线荧光分析仪与连杆的另一端用球铰相连接，从而控制X射线荧光分析仪从不同的角度对下方待测岩块进行检测，所述X射线荧光分析仪会发射出X射线并照射到待测岩块上，并接收产生的次级特征X射线，给出待测岩块中元素种类及其含量，并将该结果传输至数据综合分析平台。
作为进一步的限定，所述粉碎装置包括电机、粉碎舱、粉碎轴、粉碎刀、进料口、斗舱、出料口、筛网、阀门和吹风机，电机安装在整个粉碎装置的上部，负责提供高速旋转的动力，伺服电机轴部连接有粉碎轴，粉碎轴的前端安装有粉碎刀，且粉碎轴和粉碎刀都设置在粉碎舱内，在粉碎舱的一侧设有进料口，传送带系统上的样品可自动掉落到进料口，从而进入粉碎舱，所述粉碎舱的下部安装有一层筛网，粉碎过程中筛网过滤后的颗粒会落入粉碎舱下方设置的斗舱中，所述斗舱下部有两个出料口，一个为粉料出料口，一个为废料出料口，粉料出口的粉末颗粒可直接落入下方的X射线衍射分析装置的样品舱中，废料出口的废料直接掉入下方的TBM主传送带上，且在两个出料口处各有一个阀门，所述吹风机安装在粉碎舱的上部。
作为进一步的限定，所述X射线衍射分析装置包括X射线衍射分析仪和样品舱，所述样品舱具有上、下开口，且在上开口上方安装有吹风机，且上、下开口均有开关进行控制相应开口的开闭。
基于上述系统的预报方法，包括以下步骤：
启动系统，获取TBM掘进过程中采集到的岩石图像信息、岩石中元素种类及含量信息、岩石中矿物种类及含量信息；
基于采集到的岩石图像信息，应用卷积神经网络，通过卷积层和最大池化层对岩石的颜色、纹理和形状等图像特征进行提取与表征；
将提取与表征出的岩石图像特征信息与采集到的岩石元素种类及含量、岩石矿物种类及含量进行信息融合，并输入到岩石智能识别系统中已经训练好的岩石图像自动识别与分类模型中进行分析对比，最后给出待测岩块的岩石名称、不良地质标志以及准确率；
对采集到的大量岩石元素种类及含量进行数据对比与分析，应用数据挖掘技术提取出其不良地质敏感元素，并定量研究其变化规律；
对采集到的大量岩石矿物种类及含量进行数据对比与分析，应用数据挖掘技术提取出不良地质特征矿物，并定量研究其变化规律；
在连续测量一定掘进距离的岩样后，将所提取岩石中能够反映不良地质信息的标志图像、不良地质敏感元素及不良地质矿物富集信息利用神经网络进行信息融合与修正，与基于数据挖掘技术所建立起的不良地质前兆特征定量表征关系数据库相对比、分析，从而给出掌子面前方岩性变化与不良地质赋存特征，实现基于岩性与不良地质前兆特征智能识别的隧道超前地质预报。
与现有技术相比，本公开的有益效果为：
1)本公开通过控制系统控制所述可伸缩机械抓手、传送带系统、粉碎装置、图像识别装置、X射线荧光分析装置及X射线衍射分析装置的次序动作，可实现工作的全自动化和连续性。
2)本公开集合了图像识别技术及光谱分析技术，可对岩性进行准确快速识别，利用光谱分析技术可识别岩石中矿物及元素含量的变化，建立起敏感元素与特征矿物和不良地质影响区地质前兆特征之间的定量表征关系。
3)本公开与TBM搭载，利用单独的传送带进行分析工作，并不干扰TBM的本身工作，可实时分析TBM掘进工作过程中岩性及不良地质前兆特征的变化。
5)本公开能够将不良地质信息的标志图像、不良地质敏感元素及不良地质矿物富集信息利用神经网络进行信息融合，再与基于数据挖掘技术所建立起的不良地质前兆特征定量表征关系数据库相对比、分析，可以给出掌子面前方岩性变化与不良地质赋存特征，从而最终实现基于岩性与不良地质前兆特征智能识别的隧道超前地质预报。
附图说明
构成本申请的一部分的说明书附图用来提供对本申请的进一步理解，本申请的示意性实施例及其说明用于解释本申请，并不构成对本申请的不当限定。
图1是本公开的构示意图；
图2是可收缩机械抓手结构图；
图3是抓斗三维结构图；
图4是X射线荧光分析装置图；
图5是研磨装置结构图；
图6是X射线衍射分析仪结构示意图；
其中1.TBM主传送带；2.基座；3.防护棚；4.可收缩机械抓手；5.传送带系统；6.烘干机；7.图像采集装置；8.照明装置；9.X射线荧光分析装置；10.粉碎装置；11.X射线衍射分析装置；12.控制系统；13.数据综合分析平台；14.TBM主控室；15.电动液压立柱；16.电动液压臂；17.机械臂；18.支撑杆；19.电动液压杆；20.摇杆；21.连杆；22.销轴；23.抓斗；24.斗齿25.高压喷头；26.激光轮廓测量仪；27.伺服电机；28.销轴；29.底盘30.电动液压杆；31.连杆；32.球铰；33.X射线荧光分析仪；34.伺服电机；35.粉碎轴；36.进料口；37.粉碎舱；38.粉碎刀；39.筛网；40.斗舱；41.阀门；42.粉末出料口；43.废料出料口；44.吹风机。
具体实施方式：
下面结合附图与实施例对本公开作进一步说明。
应该指出，以下详细说明都是例示性的，旨在对本申请提供进一步的说明。除非另有指明，本文使用的所有技术和科学术语具有与本申请所属技术领域的普通技术人员通常理解的相同含义。
需要注意的是，这里所使用的术语仅是为了描述具体实施方式，而非意图限制根据本申请的示例性实施方式。如在这里所使用的，除非上下文另外明确指出，否则单数形式也意图包括复数形式，此外，还应当理解的是，当在本说明书中使用术语“包含”和/或“包括”时，其指明存在特征、步骤、操作、器件、组件和/或它们的组合。
在本公开中，术语如“上”、“下”、“左”、“右”、“前”、“后”、“竖直”、“水平”、 “侧”、“底”等指示的方位或位置关系为基于附图所示的方位或位置关系，只是为了便于叙述本公开各部件或元件结构关系而确定的关系词，并非特指本公开中任一部件或元件，不能理解为对本公开的限制。
本公开中，术语如“固接”、“相连”、“连接”等应做广义理解，表示可以是固定连接，也可以是一体地连接或可拆卸连接；可以是直接相连，也可以通过中间媒介间接相连。对于本领域的相关科研或技术人员，可以根据具体情况确定上述术语在本公开中的具体含义，不能理解为对本公开的限制。
一种基于岩性与不良地质前兆特征智能识别的基于岩性与不良地质前兆特征识别的TBM搭载式超前地质预报系统及方法，包括基座、防护棚、可伸缩机械抓手、传送带系统、烘干机、图像采集装置、X射线荧光分析装置、粉碎装置、X射线衍射分析装置、控制系统和数据综合分析平台。基座主要为该系统的其他部件提供支撑作用，防护棚焊接坐落于基座上方，可防止隧道拱顶的落石、渗水等破坏基座上的仪器部件。可伸缩机械抓手能实现对待测岩块的抓取，以及完成对待测岩块的冲洗工作。烘干机负责将冲洗好的待测岩块烘干，随后图像采集装置完成对待测岩块图像的采集工作，并将图像信息传输至数据综合分析平台中的岩石智能识别系统。X射线荧光分析装置能够实现对岩块中元素种类及含量的识别，X射线衍射装置能够实现对由粉碎装置处理后的粉末进行衍射分析，并给出待测岩块中矿物种类及含量结果，最后将这些测试结果传输到数据综合分析系统中进行信息融合与修正，并给出岩性特征的识别结果。
在连续测量一定掘进距离的岩样后，数据综合分析平台将所提取的岩石中能够反映不良地质信息的标志图像、不良地质敏感元素及不良地质矿物富集信息利用神经网络进行信息融合与修正，再与基于数据挖掘技术所建立起的不良地质前兆特征定量表征关系数据库相对比、分析，从而给出掌子面前方岩性变化与不良地质赋存特征，最终实现基于岩性与不良地质前兆特征智能识别的隧道超前地质预报。同时，随着TBM掘进工作的不断推进，该数据综合分析平台中的信息不断完善，最终的岩性识别结果和隧道超前地质预报结果会得到不断的修正，并趋于准确。
具体的，一种基于岩性与不良地质前兆特征智能识别的基于岩性与不良地质前兆特征识别的TBM搭载式超前地质预报系统，如图1所示，具体包括基座 2、防护棚3、可收缩机械抓手4、传送带系统5、烘干机6、图像采集装置7、照明装置8、X射线荧光分析装置9、粉碎装置10、X射线衍射分析装置11、控制系统12和数据综合分析平台13。
基座2主要由角钢构成，基座轮廓为长方形，焊接于主控室附近的TBM传送带上方，主要为该系统的其他部件提供支撑作用。
防护棚3焊接坐落于基座上方，由四个角钢作为棚顶支撑，棚顶形状为拱形，材质为不锈钢板，可防止隧道拱顶的落石、渗水等破坏基座上的仪器部件。防护棚3的前后面由钢化玻璃板遮挡，并用紧固螺栓将其固定在支撑角钢上。
如图2所示，可伸缩式机械抓手4主要由电动液压立柱15、支撑杆18、电动液压臂16、机械臂17、电动液压杆19、摇杆20、连杆21以及抓斗23组成。电动液压立柱15垂直焊接在基座2上，用来安装电动液压臂16和支撑杆18，可上下伸缩，用来控制整个机械抓手装置的升降。支撑杆18一端垂直固定连接在电动液压立柱15上，一端与机械臂17用销轴连接。电动液压臂16一端连接在电动液压立柱15上，一端与机械臂17用销轴连接。如图3所示，在抓斗的前端安装有斗齿24，除了有确保岩块容易抓取外，在抓斗闭合后进行岩块冲洗时，使冲洗后的水和岩渣流出。在机械臂的前端与抓斗23相连处固定安装有激光轮廓测量仪25，用来对TBM传送带上的岩块大小进行量测，当测量到的岩块大小符合所设定范围时，可将该信号传输至控制系统控制机械抓手对该岩块进行抓取。在抓斗中还安装有高压喷头26，高压喷头通过水管与外置的水泵相连，当抓斗抓取岩块后，高压喷头26会自动喷水对抓取岩块进行冲洗，以确保抓取岩块表面无其他渣粉的污染。
传送带系统5安装在基座上部的防护棚内，包括传送带、滚筒、伺服电机及位移传感器。烘干机6放置于系统传送带的上方5cm高度处，主要是对所抓取的待测岩块进行风干。伺服电机负责驱动传送带的运动，位移传感器用来测定传送带上待测岩块的传送位移，两者再与控制系统相结合，可实现对待测岩块位移的实时控制。
烘干机6放置于系统传送带的上方5cm高度处，主要是对所抓取的待测岩块进行风干。当传送带系统将待测岩块传送到烘干机6正下方时，控制系统开始控制风干机进行工作，释放出热风吹到待测岩块表面，在一定时间后，烘干 机停止工作，待测岩块的表面被烘干。传送带系统又开始工作，将待测岩块继续向前传送。
图像识别装置主要由图像采集装置7、照明装置8和图像识别系统组成。图像采集装置7为高清数码相机，主要负责拍摄待测岩块的高清图像，并将信息传输至数据综合分析平台13中。照明装置8主要由两个工业照明光源组成，安置于图像采集装置内部两侧处。在本实施例中，图像识别系统是一个基于Windows系统所开发出的岩石智能识别软件，该软件应用深度学习技术，使用卷积神经网络智能识别模型训练已有岩石标本库中的岩石图像，与已建立的岩石标本库图像对比，进行信息融合与分析，最终可以给出初步的岩性识别结果。当图像采集装置拍摄到待测岩块图像时，会将采集到的图像传输至岩石智能识别系统中，同时，传送带又继续工作，将待测岩块传送到X射线荧光分析装置下方。
如图4所示，X射线荧光分析装置9主要由伺服电机27、底盘29、连杆31、电动液压杆30、销轴28、球铰32、X射线荧光分析仪33构成，伺服电机27与底盘29相连接，可以控制整个底盘29的旋转。底盘29与连杆31、底盘29与电动液压杆30之间用销轴相连接，连杆31与电动液压杆30之间固定连接，X射线荧光分析仪与连杆31的另一端用球铰32相连接，从而可控制X射线荧光分析仪从不同的角度对下方待测岩块进行检测。X射线荧光分析仪发射出X射线并照射到待测岩块上，并接收产生的次级特征X射线，给出待测岩块中元素种类及其含量，并将该结果传输至数据综合分析平台13中。
如图5所示，粉碎装置10由粉碎机构成，粉碎机主要包括伺服电机34、粉碎舱37、粉碎轴35、粉碎刀38、进料口36、斗舱40、出料口42和43、筛网39、阀门41和吹风机44组成，伺服电机34安装在整个装置的上部，负责提供高速旋转的动力。伺服电机34轴部连接有粉碎轴35，粉碎轴35的前端又安装有粉碎刀38，且粉碎轴35和粉碎刀38都在粉碎舱37内。在粉碎舱37的一侧设有进料口36，传送带系统上的样品可自动掉落到进料口36，从而进入粉碎舱37。在粉碎舱37的下部安装有一层较厚的200目筛网39，粉碎过程中小于200目的颗粒会自动调入下方的斗舱40中。在斗舱40下部有两个出料口42、43，一个为粉料出料口，一个为废料出料口，粉料出口的粉末颗粒可直接落入 下方的X射线衍射分析仪的样品舱中，废料出口的废料直接掉入下方的TBM主传送带上，且在两个出料口处各有一个阀门。吹风机44安装在粉碎舱37的上部，主要负责将粉碎舱37及筛网39中的残余废料吹扫干净。可将待测岩块研磨成粉末，为X射线衍射装置11提供试验材料。
X射线衍射分析装置11主要由X射线衍射分析仪构成，如图6所示，其主要结构包括X射线管、X射线检测器、测角仪、样品舱、数据采集系统及数据处理分析系统。样品舱具有上、下开口，且在上开口上方安装有小型吹风机，装样时上开口打开，工作时上下开口闭合，测试完成后下开口打开，吹风机开始工作并将样品舱中的岩粉吹扫到TBM主传送带上运移走。
控制系统12，主要接收来自可伸缩机械抓手4、传送带系统5、烘干机6、图像采集装置7、照明装置8、X射线荧光分析装置9和粉碎装置10的控制信息，控制以上各装置的工作和停止。
数据综合分析平台13包括基于图像及光谱技术的岩性智能识别系统、不良地质前兆特征数据库以及超前地质预报系统。平台13安装在TBM主控室14中，和以上各装置间用数据连接线相连。
基于岩性与不良地质前兆特征智能识别的基于岩性与不良地质前兆特征识别的TBM搭载式超前地质预报系统，具体实施过程包括以下步骤：
(1)在TBM开始掘进且切削下来的岩渣被TBM主传送带1传送到该系统装置的下方后，在TBM主控室14按下整个系统的工作按钮，该系统开始工作。
(2)首先是可伸缩机械抓手4开始工作，机械臂17的前端，与抓斗23连接处安装的激光轮廓测量仪对TBM传送带上的岩块大小进行量测，随后选取一块符合系统所设定大小范围的岩块进行抓取。在抓取岩块后，高压喷头25会自动喷水对抓斗中的岩块进行冲洗，冲洗完成后，电动液压臂16和电动液压杆19开始伸缩和移动并将待测岩块放到传送带系统5上。
(3)当岩块运移到烘干机6的正下方时，传送带系统5停止工作，控制系统12开始控制风干机6进行工作，吹出热风，在2分钟后，待测岩块的表面即被烘干，烘干机停止工作。
(4)当传送带系统5再次开始工作，将待测岩块传送到图像采集装置7的下方时。传送带系统5再次停止工作，照明装置8开始亮灯，图像采集装置对 待测岩块连续拍照，且将所采集到的照片传输至数据综合分析平台13的岩性智能识别系统中。
(5)传送带系统5继续工作，将待测岩块传送到X射线荧光分析装置9下方后再次停止工作，此时X射线荧光分析装置9开始工作，首先是伺服电机27和电动液压杆30控制X射线荧光分析的照射方向，对下方的待测岩块进行不同部位的多次照射，最终给出待测岩块中元素种类及其含量，并将该结果传输至数据综合分析平台13中。
(6)待测岩块完成X射线荧光分析后，传送带系统5继续工作，并将待测岩块会送进粉碎装置10的进料口中，随后粉碎机开始工作，首先是粉末出料口42的阀门打开，废料出料口43的阀门闭合。在粉碎过程中，从筛网39中落下的粉末颗粒会自动调入X射线衍射分析仪中的样品舱中。当样品舱中的粉末达到一定量后，粉碎机停止工作，粉末出料口42的阀门闭合，废料出料口43的阀门打开，筛网会被抽出，吹风机44同时开始进行吹扫工作，将剩余的废料从废料出料口吹扫到下方的TBM主传送带1上。
(7)在落入样品舱的岩粉达到一定量后，样品舱的开口闭合，X射线衍射分析装置11开始工作，随后给出待测岩块中矿物种类及其含量，并将该结果传输至数据综合分析平台13中。测试完成后下，样品舱的下开口打开，吹风机开始工作并将样品舱中的岩粉吹扫到TBM主传送带1上运移走。
(8)数据综合分析平台13根据图像智能识别系统给出岩性特征、X射线荧光分析装置给出的岩样中各元素的种类及含量结果以及X射线衍射分析装置11给出的矿物种类及含量结果，与已建立的岩石图像、元素与矿物标准数据库对比分析，最后给出待测岩块精确的岩性识别结果。
(9)在连续测量一定掘进距离的岩样后，将所提取的岩石中能够反映不良地质信息的标志图像、不良地质敏感元素及不良地质矿物富集信息利用神经网络进行信息融合与修正，再与基于数据挖掘技术所建立起的不良地质前兆特征定量表征关系数据库相对比、分析，从而给出掌子面前方岩性变化与不良地质赋存特征，最终实现基于岩性与不良地质前兆特征智能识别的隧道超前地质预报。
以上所述仅为本申请的优选实施例而已，并不用于限制本申请，对于本领域的技术人员来说，本申请可以有各种更改和变化。凡在本申请的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本申请的保护范围之内。
上述虽然结合附图对本公开的具体实施方式进行了描述，但并非对本公开保护范围的限制，所属领域技术人员应该明白，在本公开的技术方案的基础上，本领域技术人员不需要付出创造性劳动即可做出的各种修改或变形仍在本公开的保护范围以内。

Claims (10)

  1. 一种基于岩性与不良地质前兆特征识别的TBM搭载式超前地质预报系统，其特征是：包括基座，所述基座设置于TBM主传送带上，其上依次设置有可伸缩机械抓手、传送带系统、粉碎装置、控制系统和数据综合分析平台，其中：
    所述可伸缩机械抓手用于将所述TBM主传送带的待测岩块并放置在传送带系统上，所述传送带系统上方依次设置有烘干机构、图像识别装置和X射线荧光分析装置，分别对待测岩块施加表面烘干、岩性识别和X射线荧光分析；
    所述粉碎装置设置于传送带系统的末端，对待测岩块进行粉碎，粉碎装置出口的粉末颗粒可直接落入下方设置的X射线衍射分析装置的样品舱中；
    所述控制系统控制所述可伸缩机械抓手、传送带系统、粉碎装置、图像识别装置、X射线荧光分析装置及X射线衍射分析装置的动作；
    所述数据综合分析平台，被配置为接收图像识别装置、X射线荧光分析装置和X射线衍射分析装置的采集结果，依据所提取岩石中能够反映不良地质信息的标志图像、不良地质敏感元素及不良地质矿物富集信息利用神经网络进行信息融合与修正，进行分析，得到掌子面前方岩性变化与不良地质赋存特征，实现基于岩性与不良地质前兆特征智能识别的隧道超前地质预报。
  2. 如权利要求1所述的一种基于岩性与不良地质前兆特征识别的TBM搭载式超前地质预报系统，其特征是：所述基座上面设置有防护棚，防护棚焊接坐落于基座上方，由角钢作为棚顶支撑，棚顶形状为拱形，材质为不锈钢板，防护棚的两侧由钢化玻璃板遮挡，并用紧固螺栓将其固定在支撑角钢上。
  3. 如权利要求1所述的一种基于岩性与不良地质前兆特征识别的TBM搭载式超前地质预报系统，其特征是：所述基座由角钢构成，基座轮廓为长方形，焊接于主控室附近的TBM传送带上方，主要为该系统的其他部件提供支撑作用。
  4. 如权利要求1所述的一种基于岩性与不良地质前兆特征识别的TBM搭载式超前地质预报系统，其特征是：所述可伸缩式机械抓手，包括电动液压立柱、支撑杆、电动液压臂、机械臂、电动液压杆、摇杆、连杆以及抓斗，电动液压立柱垂直焊接在基座上，用来安装电动液压臂和支撑杆，可上下伸缩，用来控制整个机械抓手装置的升降，所述支撑杆一端垂直固定连接在电动液压立 柱上，一端与机械臂用销轴连接，所述电动液压臂一端连接在电动液压立柱上，一端与机械臂用销轴连接，所述抓斗有两个，并用销轴对称安装在机械臂的前端，同时在抓斗前端安装有斗齿，与抓斗相对应，电动液压伸缩杆有两个，分别用摇杆和连杆相连接，用来控制抓斗的张开与闭合；
    或，所述机械臂的前端与抓斗相连处固定安装有激光轮廓测量仪，用来对TBM传送带上的岩块大小进行量测，当测量到的岩块大小符合所设定范围时，将该信号传输至控制系统控制机械抓手对该岩块进行抓取；
    或，所述抓斗中还安装有高压喷头，高压喷头通过水管与外置的水泵相连，当抓斗抓取岩块后，高压喷头会自动喷水对抓取岩块进行冲洗，以确保抓取岩块表面无其他渣粉的污染。
  5. 如权利要求1所述的一种基于岩性与不良地质前兆特征识别的TBM搭载式超前地质预报系统，其特征是：所述传送带系统安装在基座上部的防护棚内，包括传送带、滚筒、伺服电机及位移传感器，所述滚筒有至少两个，所述传送带缠绕在滚筒上，所述伺服电机驱动滚筒，以驱动传送带的运动，位移传感器用来测定传送带上待测岩块的传送位移，位移传感器和伺服电机均与控制系统通讯。
  6. 如权利要求1所述的一种基于岩性与不良地质前兆特征识别的TBM搭载式超前地质预报系统，其特征是：所述图像识别装置包括图像采集装置、照明装置和图像识别系统，所述图像采集装置为数码相机，被配置为拍摄待测岩块的高清图像，照明装置包括若干个工业照明光源，安置于图像采集装置旁，图像识别系统应用深度学习技术，使用卷积神经网络智能识别模型训练已有岩石标本库中的岩石图像，与已建立的岩石标本库图像对比，进行信息融合与分析，最终可以给出初步的岩性识别结果，当图像采集装置拍摄到待测岩块图像时，会将采集到的图像传输至数据综合分析平台。
  7. 如权利要求1所述的一种基于岩性与不良地质前兆特征识别的TBM搭载式超前地质预报系统，其特征是：所述X射线荧光分析装置包括驱动电机、底盘、连杆、电动液压杆、销轴、球铰和X射线荧光分析仪，驱动电机与底盘相连接，控制整个底盘的旋转，所述底盘与连杆、底盘与电动液压杆之间用销 轴相连接，连杆与电动液压杆之间固定连接，X射线荧光分析仪与连杆的另一端用球铰相连接，从而控制X射线荧光分析仪从不同的角度对下方待测岩块进行检测，所述X射线荧光分析仪会发射出X射线并照射到待测岩块上，并接收产生的次级特征X射线，给出待测岩块中元素种类及其含量，并将该结果传输至数据综合分析平台。
  8. 如权利要求1所述的一种基于岩性与不良地质前兆特征识别的TBM搭载式超前地质预报系统，其特征是：所述烘干机构为烘干机，对所抓取的待测岩块进行风干，当传送带系统将待测岩块传送到烘干机正下方时，控制系统开始控制风干机进行工作，释放出热风吹到待测岩块表面，在一定时间后，烘干机停止工作，待测岩块的表面被烘干。
  9. 如权利要求1所述的一种基于岩性与不良地质前兆特征识别的TBM搭载式超前地质预报系统，其特征是：所述粉碎装置包括电机、粉碎舱、粉碎轴、粉碎刀、进料口、斗舱、出料口、筛网、阀门和吹风机，电机安装在整个粉碎装置的上部，负责提供高速旋转的动力，伺服电机轴部连接有粉碎轴，粉碎轴的前端安装有粉碎刀，且粉碎轴和粉碎刀都设置在粉碎舱内，在粉碎舱的一侧设有进料口，传送带系统上的样品可自动掉落到进料口，从而进入粉碎舱，所述粉碎舱的下部安装有一层筛网，粉碎过程中筛网过滤后的颗粒会落入粉碎舱下方设置的斗舱中，所述斗舱下部有两个出料口，一个为粉料出料口，一个为废料出料口，粉料出口的粉末颗粒可直接落入下方的X射线衍射分析装置的样品舱中，废料出口的废料直接掉入下方的TBM主传送带上，且在两个出料口处各有一个阀门，所述吹风机安装在粉碎舱的上部；
    或，所述X射线衍射分析装置包括X射线衍射分析仪和样品舱，所述样品舱具有上、下开口，且在上开口上方安装有吹风机，且上、下开口均有开关进行控制相应开口的开闭。
  10. 基于权利要求1-9中任一项所述的系统的预报方法，其特征是：包括以下步骤：
    启动系统，获取TBM掘进过程中采集到的岩石图像信息、岩石中元素种类及含量信息、岩石中矿物种类及含量信息；
    基于采集到的岩石图像信息，应用卷积神经网络，通过卷积层和最大池化层对岩石的颜色、纹理和形状等图像特征进行提取与表征；
    将提取与表征出的岩石图像特征信息与采集到的岩石元素种类及含量、岩石矿物种类及含量进行信息融合，并输入到岩石智能识别系统中已经训练好的岩石图像自动识别与分类模型中进行分析对比，最后给出待测岩块的岩石名称、不良地质标志以及准确率；
    对采集到的大量岩石元素种类及含量进行数据对比与分析，应用数据挖掘技术提取出其不良地质敏感元素，并定量研究其变化规律；
    对采集到的大量岩石矿物种类及含量进行数据对比与分析，应用数据挖掘技术提取出不良地质特征矿物，并定量研究其变化规律；
    在连续测量一定掘进距离的岩样后，将所提取岩石中能够反映不良地质信息的标志图像、不良地质敏感元素及不良地质矿物富集信息利用神经网络进行信息融合与修正，与基于数据挖掘技术所建立起的不良地质前兆特征定量表征关系数据库相对比、分析，从而给出掌子面前方岩性变化与不良地质赋存特征，实现基于岩性与不良地质前兆特征智能识别的隧道超前地质预报。
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