AU2019439936A1 - TBM-mounted advanced geological prediction system and method based on identification of lithology and adverse geology precursor characteristics - Google Patents
TBM-mounted advanced geological prediction system and method based on identification of lithology and adverse geology precursor characteristics Download PDFInfo
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- 238000004458 analytical method Methods 0.000 claims abstract description 70
- 238000004876 x-ray fluorescence Methods 0.000 claims abstract description 36
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- 238000013527 convolutional neural network Methods 0.000 claims description 6
- 239000002893 slag Substances 0.000 claims description 5
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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
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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
- E21D9/06—Making by using a driving shield, i.e. advanced by pushing means bearing against the already placed lining
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- 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
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Abstract
The present disclosure provides a TBM-mounted advance geological forecast system and method based on recognition of lithology and unfavorable geological precursor characteristics. Said system comprises a base (2), the base (2) is provided on a TBM main conveyor belt (1), a telescopic mechanical gripper (4), a conveyor belt system (5), a crushing device (10), a control system (12) and a comprehensive data analysis platform (13) are sequentially provided on the base, and a drying mechanism (6), an image recognition device and an X-ray fluorescence analysis device (9) are sequentially provided above the conveyor belt system (5) to apply surface drying, lithology recognition and X-ray fluorescence analysis to a rock to be detected respectively. Information fusion and correction are performed by using a neural network according to an extracted mark image capable of reflecting unfavorable geological information, an unfavorable geological sensitive element and unfavorable geological mineral enrichment information in the rock, and then analysis is performed to obtain the change of lithology and unfavorable geological occurrence characteristics in front of a tunnel face, achieving an advance tunnel geological forecast based on smart recognition of lithology and unfavorable geological precursor characteristics.
Description
Technical Field
The present disclosure relates to a TBM-mounted advanced geological prediction system and method based on recognition of lithology and adverse geology precursor characteristics.
Related Art
The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute the prior art.
When the length-diameter ratio of a deep and long tunnel reaches or exceeds 600 to 1000, a TBM construction method should be preferentially adopted according to international common convention, and it is internationally acknowledged that the TBM construction method has the advantages of a high tunneling speed, small construction disturbance, high tunnel forming quality, high comprehensive economic and social benefits, and the like. However, the adaptability of the TBM construction method to geological conditions is poor. TBM construction often encounters disasters such as water and mud inrush, collapse, and large deformation, resulting in TBM jamming, damage, scrapping, and even major accidents of casualties. To reduce the risk of the above accidents in the TBM construction, the most effective solution is to detect adverse geology conditions ahead of a tunnel face in advance by using an advanced geological prediction technology, and make reasonable treatment measures and construction plans according to the geological conditions ahead, to avoid and prevent the occurrence of major disaster accidents.
In the process of tunnel construction, before a fault fracture zone, a karst cave, an underground river, and a muddy zone appear, their own precursory signs usually appear, and these signs often indicate that the foregoing unfavorable geology is close. However, most of front-line construction workers in the tunnel lack good geological knowledge, and cannot timely and effectively recognize the adverse geology precursor characteristics, resulting in some disasters. Moreover, TBM construction has high requirements on formation lithology. In addition to paying attention to the possible unfavorable geology ahead, it is necessary to pay attention to the change of formation lithology on the tunneling route, to improve the TBM construction parameters in time and ensure the safe and rapid construction.
At present, there are many methods for implementing advanced geological prediction in the TBM tunnel construction process, mainly including geophysical exploration methods, for example, a seismic method, an electrical method, and an infrared water detecting method, and some TBM-mounted devices also appear. However, according to understanding of the inventor, when advanced geological prediction is performed from the perspective of geological analysis, in the aspect of recognizing the adverse geology precursor characteristics of disasters such as water and mud inrush, according to an existing prediction system and method, a change of lithology and mineral and element types and content of surrounding rocks on the TBM tunneling route cannot be obtained in real time, and a change of geological conditions ahead of the tunnel face cannot be predicted.
To resolve the foregoing problems, the present disclosure provides a TBM-mounted advanced geological prediction system and method based on recognition of lithology and adverse geology precursor characteristics. According to the present disclosure, the change of lithology, mineral and element types and content of surrounding rocks on the TBM tunneling route can be obtained in real time and the change of geological conditions ahead of the tunnel face and possibly existing unfavorable geology are predicted.
According to some embodiments, the present disclosure adopts the following technical solutions:
A TBM-mounted advanced geological prediction system based on recognition of lithology and adverse geology precursor characteristics includes a base, where the base is disposed on a TBM main conveyor belt, and a telescopic mechanical gripper, a conveyor belt system, a crushing device, a control system, and a data comprehensive analysis platform are disposed on the base in sequence, the telescopic mechanical gripper is configured to place a to-be-tested rock block of the TBM main conveyor belt on the conveyor belt system, and a drying mechanism, an image recognition device, and an X-ray fluorescence analysis device are disposed above the conveyor belt system in sequence and are respectively configured to perform surface drying, lithology recognition, and element analysis on the to-be-tested rock block; the crushing device is disposed at a tail end of the conveyor belt system and is configured to crush the to-be-tested rock block, and powder particles at an outlet of the crushing device are capable of directly falling into a sample cabin of an X-ray diffraction analysis device disposed below the crushing device; the control system controls actions of the telescopic mechanical gripper, the conveyor belt system, the crushing device, the image recognition device, the X-ray fluorescence analysis device, and the X-ray diffraction analysis device; and the data comprehensive analysis platform is configured to receive acquisition results of the image recognition device, the X-ray fluorescence analysis device, and the X-ray diffraction analysis device, perform information integrating and correction by using a neural network according to an extracted mark image capable of reflecting adverse geology information, and adverse geology sensitive elements and adverse geology mineral enrichment information in rocks, and perform analysis to obtain a lithology change and adverse geology occurrence features ahead of a tunnel face and implement intelligent tunnel advanced geological prediction based on recognition of lithology and adverse geology precursor characteristics.
The present disclosure is mounted on the TBM, so that the change of lithology and adverse geology precursor characteristics in the TBM tunneling process can be analyzed in real time. Based on the acquired rock image information, a convolutional neural network is applied, to extract and characterize image features such as colors, textures and shapes of rocks through a convolutional layer and a maximum pooling layer. At the same time, the changes of content of minerals and elements in rocks may be recognized by means of an optical spectrum analysis technology, and an adverse geology precursor characteristic quantitative characterization relationship of sensitive elements in adverse geology affected areas and an adverse geology precursor characteristic quantitative characterization relationship of diagnostic minerals in the adverse geology affected areas may be established.
Meanwhile, in the present disclosure, the base is disposed on the TBM, an independent conveyor belt is used for analyzing work, the work of the TBM is not interfered, and integration of tunneling, geology analysis, and prediction is realized.
As a further limitation, a protection shed is disposed above the base. The protection shed can effectively prevent other interference factors.
The protection shed is welded and located above the base, the shed roof is supported by four angle steels and is arched in shape and made of stainless steel plates, and may prevent falling stones, water seepage, and the like of a tunnel vault from damaging instrument components on the base. Two sides of the protection shed are shielded by reinforced glass plates, and are fixed on the supporting angle steels through fastening bolts.
As a further limitation, the base is made of an angle steel, is rectangular in outline and welded above a TBM conveyor belt near a main control room, and mainly provides support for other components of the system.
As a further limitation, the telescopic mechanical gripper includes an electric hydraulic column, a support rod, an electric hydraulic arm, a mechanical arm, an electric hydraulic rod, a rocker, a connecting rod, and a grab bucket, the electric hydraulic column is vertically welded on the base and configured to mount the electric hydraulic arm and the support rod, and is capable of stretching up and down to control lifting of the entire mechanical gripper apparatus, one end of the support rod is vertically and fixedly connected to the electric hydraulic column and one end is connected to the mechanical arm through a pin shaft, one end of the electric hydraulic arm is connected to the electric hydraulic column and one end is connected to the mechanical arm through a pin shaft, there are two grab buckets, the two grab buckets are symmetrically mounted on a front end of the mechanical arm through pin shafts, and bucket teeth are mounted on front ends of the grab buckets and correspond to the grab buckets, and there are two electric hydraulic telescopic rods, and the two electric hydraulic telescopic rods are connected respectively through the rocker and the connecting rod and configured to control the opening and closing of the grab buckets.
As a further limitation, a laser profile measuring instrument is fixedly mounted at a position in which the front end of the mechanical arm is connected to the grab bucket and configured to measure a size of the rock block on the TBM conveyor belt, and when the measured size of the rock block meets a set range, a signal is transmitted to the control system to control the mechanical gripper to grab the rock block.
As a further limitation, a high pressure spray nozzle is further mounted in the grab bucket and is connected to an external water pump through a water pipe, and when the grab bucket grabs a rock block, the high pressure spray nozzle automatically sprays water to wash the grabbed rock block, to ensure that a surface of the grabbed rock block is free of other slag powder pollution.
As a further limitation, the conveyor belt system is mounted in a protection shed above the base and includes a conveyor belt, a roller, a servo motor, and a displacement sensor, there are at least two rollers, the conveyor belt is wound on the roller, the servo motor drives the roller to drive movement of the conveyor belt, the displacement sensor is configured to measure a conveying displacement of the to-be-tested rock block on the conveyor belt, and the displacement sensor and the servo motor both communicate with the control system.
As a further limitation, the drying mechanism is a dryer and performs air drying on the grabbed to-be-tested rock block, when the conveyor belt system conveys the to-be-tested rock block to right below the dryer, the control system starts to control the dryer to work, and hot air is released to blow a surface of the to-be-tested rock block, after a specific time, the dryer stops working, and the surface of the to-be-tested rock block is dried.
As a further limitation, the image recognition device includes an image acquisition device, an illumination device, and an image recognition system, the image acquisition device is a digital camera and configured to shoot a high-definition image of the to-be-tested rock block, the illumination device includes a plurality of industrial illumination light sources that are arranged near the image acquisition device, the image recognition system applies a deep learning technology, adopts a convolutional neural network intelligent recognition model to train rock images in an existing rock specimen library, compares the rock image with an established rock specimen library image, and performs information integrating and analysis, to finally provide a preliminary lithology recognition result, and when shooting a to-be-tested rock block image, the image acquisition device transmits the acquired image to the data comprehensive analysis platform.
As a further limitation, the X-ray fluorescence analysis device includes a drive motor, a chassis, a connecting rod, an electric hydraulic rod, a pin shaft, 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 is connected to the connecting rod through the pin shaft, the chassis is connected to the electric hydraulic rod through the pin shaft, and the connecting rod is fixedly connected to the electric hydraulic rod, the X-ray fluorescence analyzer is connected to the other end of the connecting rod through the spherical hinge, so that the X ray fluorescence analyzer is controlled to detect the to-be-tested rock block therebelow from different angles, and the X-ray fluorescence analyzer emits an X-ray and irradiates the to-be tested rock block, receives a generated secondary characteristic X-ray, provides element types and content of the to-be-tested rock block, and transmits the result to the data comprehensive analysis platform.
As a further limitation, the crushing device includes a motor, a crushing cabin, a crushing shaft, a crushing knife, a feed port, a bucket cabin, a discharging port, a sieve, a valve, and an air blower. The motor is mounted on an upper part of the entire crushing device and is responsible for providing power for high-speed rotation. A shaft part of a servo motor is connected to the crushing shaft, the crushing knife is mounted on a front end of the crushing shaft. The crushing shaft and the crushing knife are both disposed in the crushing cabin. One side of the crushing cabin is provided with the feed port, samples on the conveyor belt system are capable of automatically falling into the feed port to enter the crushing cabin. A layer of sieve is mounted on a lower part of the crushing cabin, particles filtered by the sieve fall into the bucket cabin disposed below the crushing cabin in a crushing process. The lower part of the bucket cabin is provided with two discharging ports, one is a useful powder discharging port, and the other is a waste powder discharging port. Powder particles at the useful powder discharging port are capable of directly falling into a sample cabin of the X-ray diffraction analysis device therebelow. Wastes at the waste powder discharging port directly fall into the TBM main conveyor belt therebelow, one valve is disposed at each of the two discharging ports, and the air blower is mounted on an upper part of the crushing cabin.
As a further limitation, the X-ray diffraction analysis device includes an X-ray diffraction analyzer and a sample cabin, the sample cabin is provided with an upper opening and a lower opening, the air blower is mounted above the upper opening, and the upper opening and the lower opening are both provided with switches for controlling opening and closing of the corresponding openings.
A prediction method based on the system includes the following steps:
starting the system, and obtaining rock image information, rock element type and content information, and rock mineral type and content information acquired in a TBM tunneling process;
extracting and characterizing, based on the acquired rock image information and by applying a convolutional neural network, image features such as colors, textures, and shapes of rocks through a convolutional layer and a maximum pooling layer;
performing information integrating on the extracted and characterized rock image feature information and the acquired rock element types and content and rock mineral types and content, inputting the information to a trained rock image automatic recognition and classification model in a rock intelligent recognition system for analysis and comparison, and finally providing a rock name, an adverse geology mark, and an accuracy rate of the to-be tested rock block;
performing data comparison and analysis on the types and the content of a large quantity of acquired rock elements, extracting adverse geology sensitive elements thereof by applying a data mining technology, and quantitatively researching a change rule of the adverse geology sensitive elements; performing data comparison and analysis on the types and the content of a large quantity of acquired rock minerals, extracting adverse geology diagnostic minerals by applying the data mining technology, and quantitatively researching a change rule of the adverse geology diagnostic minerals; and performing information integrating and correction, by using the neural network, on the extracted mark image capable of reflecting the adverse geology information, and the adverse geology sensitive elements and the adverse geology mineral enrichment information in the rocks after continuously measuring rock samples for a specific tunneling distance, comparing the information with a quantitative characterization relationship database of adverse geology precursor characteristics established based on the data mining technology and performing analysis, to provide the lithology change and the adverse geology occurrence features ahead of the tunnel face and implement the intelligent tunnel advanced geological prediction based on recognition of lithology and adverse geology precursor characteristics.
Compared with the existing technology, the present disclosure has the following beneficial effects:
(1) In the present disclosure, the sequence actions of the telescopic mechanical gripper, the conveyor belt system, the crushing device, the image recognition device, the X-ray fluorescence analysis device, and the X-ray diffraction analysis device are controlled through the control system, and full automation and continuity of work can be realized.
(2) In the present disclosure, the image recognition technology and the optical spectrum analysis technology are integrated, lithology may be accurately and rapidly recognized, mineral and element content changes in rocks may be recognized by using the optical spectrum analysis technology, and the quantitative characterization relationship between sensitive elements, diagnostic minerals, and geological precursor characteristics of an adverse geology affected area may be established.
(3) The present disclosure is mounted on the TBM, the analysis work is performed by using an independent conveyor belt, the work of the TBM is not interfered, and changes of lithology and adverse geology precursor characteristics in the TBM tunneling process can be analyzed in real time.
(4) In the present disclosure, information integrating can be performed on the mark image of the adverse geology information, the adverse geology sensitive elements and adverse geology mineral enrichment information by using a neural network, the information is then compared with the quantitative characterization relationship database of adverse geology precursor characteristics established based on the data mining technology, and analysis is performed, to provide the lithology change and the adverse geology occurrence features ahead of the tunnel face and implement the intelligent tunnel advanced geological prediction based on recognition of lithology and adverse geology precursor characteristics.
The accompanying drawings that constitute a part of this application are used to provide a further understanding of this application. Exemplary embodiments of this application and descriptions of the embodiments are used to explain this application, and do not constitute any inappropriate limitation to this application.
FIG. 1 is a schematic structural diagram of the present disclosure
FIG. 2 is a structural diagram of a telescopic mechanical gripper.
FIG. 3 is a three-dimensional structural diagram of a grab bucket.
FIG. 4 is a diagram of an X-ray fluorescence analysis device.
FIG. 5 is a structural diagram of a grinding device.
FIG. 6 is a schematic structural diagram of an X-ray diffraction analyzer.
1. TBM main conveyor belt; 2. Base; 3. Protection shed; 4. Telescopic mechanical gripper; 5. Conveyor belt system; 6. Dryer; 7. Image acquisition device; 8. Illumination device; 9. X-ray fluorescence analysis device; 10. Crushing device; 11. X-ray diffraction analysis device; 12. Control system; 13. Data comprehensive analysis platform; 14. TBM main control room; 15. Electric hydraulic column; 16. Electric hydraulic arm; 17. Mechanical arm; 18. Support rod; 19. Electric hydraulic rod; 20. Rocker; 21. Connecting rod; 22. Pin shaft; 23. Grab bucket; 24. Bucket teeth; 25. High pressure spray nozzle; 26. Laser profile measuring instrument; 27. Servo motor; 28. Pin shaft; 29. Chassis; 30. Electric hydraulic rod; 31. Connecting rod; 32. Spherical hinge; 33. X-ray fluorescence analyzer; 34. Servo motor; 35. Crushing shaft; 36. Feed port; 37. Crushing cabin; 38. Crushing knife; 39. Sieve; 40. Bucket cabin; 41. Valve; 42. Useful powder discharging port; 43. Waste powder discharging port; 44. Air blower.
The present disclosure is further described below with reference to the accompanying drawings and embodiments.
It should be noted that, the following detailed descriptions are exemplary, and are intended to provide a further description to this application. Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by a person of ordinary skill in the field to which this application belongs.
It should be noted that terms used herein are only for the purpose of describing specific implementations and are not intended to limit the exemplary implementations of this application. As used herein, the singular form is also intended to include the plural form unless the context clearly dictates otherwise. In addition, it should be further understood that, terms "comprise" and/or "include" used in this specification indicate that there are features, steps, operations, devices, components, and/or combinations thereof.
In the present disclosure, orientation or position relationships indicated by the terms such as "upper", "lower", "left", "right" "front", "rear", "vertical", "horizontal", "side", and "bottom" are based on orientation or position relationships shown in the accompanying drawings, and are merely relationship words that are determined for ease of describing the structural relationship between components or elements in the present disclosure, and are not intended to specifically refer to any component or element in the present disclosure. Therefore, such terms should not be construed as a limitation on the present disclosure.
In the present disclosure, terms such as "fixedly connected", "interconnection", and
"connection" should be understood in a broad sense. The connection may be a fixed connection, an integral connection or a detachable connection; or the connection may be a direct connection, or an indirect connection by using an intermediary. Relevant scientific research or technical personnel in the art may determine the specific meanings of the foregoing terms in the present disclosure according to specific situations, and such terms should not be construed as a limitation on the present disclosure.
A TBM-mounted advanced geological prediction system and method based on recognition of lithology and adverse geology precursor characteristics are provided. The system includes a base, a protection shed, a telescopic mechanical gripper, a conveyor belt system, a dryer, an image acquisition device, an X-ray fluorescence analysis device, a crushing device, an X-ray diffraction analysis device, a control system, and a data comprehensive analysis platform. The base mainly provides support for other components of the system, the protection shed is welded and located above the base and may prevent falling stones, water seepage, and the like of a tunnel vault from damaging instrument components on the base. The telescopic mechanical gripper can grab a to-be-tested rock block and wash the to-be-tested rock block. The dryer is responsible for drying the washed to-be-tested rock block, and then the image acquisition device acquires a to-be-tested rock block image and transmits image information to a rock intelligent recognition system in the data comprehensive analysis platform. The X-ray fluorescence analysis device can recognize element types and content in a rock block, the X-ray diffraction analysis device can perform diffraction analysis on powder obtained after processing by the crushing device, provide a result of mineral types and content in the to-be-tested rock block, finally transmit the test result to the data comprehensive analysis platform for information integrating and correction, and provide a recognition result of a lithological feature.
After continuously measuring rock samples for a specific tunneling distance, the data comprehensive analysis platform performs, by using a neural network, information integrating and correction on an extracted mark image capable of reflecting adverse geology information, and adverse geology sensitive elements and adverse geology mineral enrichment information in the rocks, and then compares the information with a quantitative characterization relationship database of adverse geology precursor characteristics established based on a data mining technology and performs analysis, to provide a lithology change and adverse geology occurrence features ahead of a tunnel face, and finally implement intelligent tunnel advanced geological prediction based on recognition of lithology and adverse geology precursor characteristics. At the same time, with the continuous advancement of TBM tunneling work, the information in the data comprehensive analysis platform is continuously improved, and a final lithology recognition result and a tunnel advanced geological prediction result are continuously corrected and tend to be accurate.
Specifically, a TBM-mounted advanced geological prediction system based on recognition of lithology and adverse geology precursor characteristics is provided, as shown in FIG. 1, and specifically includes a base 2, a protection shed 3, a telescopic mechanical gripper 4, a conveyor belt system 5, a dryer 6, an image acquisition device 7, an illumination device 8, an X-ray fluorescence analysis device 9, a crushing device 10, an X-ray diffraction analysis device 11, a control system 12, and a data comprehensive analysis platform 13.
The base 2 is mainly made of an angle steel, is rectangular in outline and welded above a TBM conveyor belt near a main control room, and mainly provides support for other components of the system.
The protection shed 3 is welded and located above the base, the shed roof is supported by four angle steels and is arched in shape and made of stainless steel plates, and may prevent falling stones, water seepage, and the like of a tunnel vault from damaging instrument components on the base. Front and rear faces of the protection shed 3 are shielded by reinforced glass plates, and are fixed on the supporting angle steels through fastening bolts.
As shown in FIG. 2, the telescopic mechanical gripper 4 mainly includes an electric hydraulic column 15, a support rod 18, an electric hydraulic arm 16, a mechanical arm 17, an electric hydraulic rod 19, a rocker 20, a connecting rod 21, and a grab bucket 23. The electric hydraulic column 15 is vertically welded on the base 2 and configured to mount the electric hydraulic arm 16 and the support rod 18, and can stretch up and down to control the lifting of the entire mechanical gripper apparatus. One end of the support rod 18 is vertically and fixedly connected to the electric hydraulic column 15, and the other end is connected to the mechanical arm 17 through a pin shaft. One end of the electric hydraulic arm 16 is connected to the electric hydraulic column 15, and the other end is connected to the mechanical arm 17 through a pin shaft. As shown in FIG. 3, bucket teeth 24 are arranged at the front end of the grab bucket. In addition to ensuring that rock blocks are easy to grab, washed water and rock slag flow out when the rock blocks are washed after the grab bucket is closed. A laser profile measuring instrument 25 is fixedly mounted at a position in which the front end of the mechanical arm is connected to the grab bucket 23 and configured to measure a size of the rock block on the TBM conveyor belt, and when the measured size of the rock block meets a set range, a signal may be transmitted to the control system to control the mechanical gripper to grab the rock block. A high pressure spray nozzle 26 is further mounted in the grab bucket and is connected to an external water pump through a water pipe, and when the grab bucket grabs a rock block, the high pressure spray nozzle 26 automatically sprays water to wash the grabbed rock block, to ensure that a surface of the grabbed rock block is free of other slag powder pollution.
The conveyor belt system 5 is mounted in the protection shed above 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 mainly performs air drying on the grabbed to-be-tested rock block. The servo motor is responsible for driving the movement of the conveyor belt, the displacement sensor is configured to measure a conveying displacement of the to-be-tested rock block on the conveyor belt, and the servo motor and the displacement sensor are combined with the control system to implement real time control of the displacement of the to-be-tested rock block.
The dryer 6 is placed at a height of 5 cm above the conveyor belt of the system, and mainly performs air drying on the grabbed to-be-tested rock block. When the conveyor belt system conveys the to-be-tested rock block to right below the dryer 6, the control system starts to control the dryer to work, hot air is released to blow the surface of the to-be-tested rock block, after a specific time, the dryer stops working, and the surface of the to-be-tested rock block is dried. The conveyor belt system starts to work again, and continues to convey the to-be-tested rock block forward.
The image recognition device mainly includes the image acquisition device 7, the illumination device 8, and an image recognition system. The image acquisition device 7 is a high-definition digital camera and is mainly responsible for shooting a high-definition image of the to-be-tested rock block and transmitting information to the data comprehensive analysis platform 13. The illumination device 8 is mainly composed of two industrial illumination light sources that are arranged on two sides inside the image acquisition device. In this embodiment, the image recognition system is rock intelligent recognition software developed based on a Windows system, the software applies a deep learning technology, adopts a convolutional neural network intelligent recognition model to train rock images in an existing rock specimen library, compares the image with an established rock specimen library image, and performs information integrating and analysis, to finally provide a preliminary lithology recognition result. When shooting a to-be-tested rock block image, the image acquisition device transmits the acquired image to a rock intelligent recognition system, and meanwhile, the conveyor belt continues to work again and conveys the to-be tested rock block to below the X-ray fluorescence analysis device.
As shown in FIG. 4, the X-ray fluorescence analysis device 9 mainly includes a servo motor 27, a chassis 29, a connecting rod 31, an electric hydraulic rod 30, a pin shaft 28, a spherical hinge 32, and an X-ray fluorescence analyzer 33, and the servo motor 27 is connected to the chassis 29 to control the rotation of the entire chassis 29. The chassis 29 is connected to the connecting rod 31 through the pin shaft, the chassis 29 is connected to the electric hydraulic rod 30 through the pin shaft, the connecting rod 31 is fixedly connected to the electric hydraulic rod 30, and the X-ray fluorescence analyzer is connected to the other end of the connecting rod 31 through the spherical hinge 32, so that the X-ray fluorescence analyzer may be controlled to detect the to-be-tested rock block therebelow from different angles. The X-ray fluorescence analyzer emits an X-ray and irradiates the to-be-tested rock block, receives a generated secondary characteristic X-ray, provides element types and content in the to-be-tested rock block, and transmits the result to the data comprehensive analysis platform 13.
As shown in FIG. 5, the crushing device 10 is composed of a crushing machine, the crushing machine mainly includes a servo motor 34, a crushing cabin 37, a crushing shaft 35, a crushing knife 38, a feed port 36, a bucket cabin 40, discharging ports 42 and 43, a sieve 39, a valve 41, and an air blower 44, and the servo motor 34 is mounted on the upper part of the entire device and is responsible for providing power for high-speed rotation. A shaft part of the servo motor 34 is connected to the crushing shaft 35, the crushing knife 38 is mounted on a front end of the crushing shaft 35, and the crushing shaft 35 and the crushing knife 38 are both in the crushing cabin 37. One side of the crushing cabin 37 is provided with the feed port 36, and samples on the conveyor belt system may automatically fall into the feed port 36 to enter the crushing cabin 37. A layer of thick sieve 39 with 200 meshes is mounted on the lower part of the crushing cabin 37, and particles smaller than 200 meshes automatically fall into the bucket cabin 40 therebelow in the crushing process. The lower part of the bucket cabin 40 is provided with two discharging ports 42 and 43, one is a useful powder discharging port, and the other is a waste powder discharging port. Powder particles at the useful powder discharging port may directly fall into a sample cabin of the X-ray diffraction analyzer therebelow, wastes at the waste powder discharging port directly fall into the TBM main conveyor belt therebelow, and one valve is disposed at each of the two discharging ports. The air blower 44 is mounted the upper part of the crushing cabin 37 and is responsible for purging residual wastes in the crushing cabin 37 and the sieve 39. The to-be-tested rock block may be ground into powder to provide a test material for the X-ray diffraction analysis device 11.
The X-ray diffraction analysis device 11 is mainly composed of an X-ray diffraction analyzer. As shown in FIG. 6, a main structure of the X-ray diffraction analysis device includes an X-ray tube, an X-ray detector, a goniometer, a sample cabin, a data acquisition system, and a data processing analysis system. The sample cabin is provided with an upper opening and a lower opening, a mini air blower is mounted above the upper opening, the upper opening is opened during sample loading, the upper opening and the lower opening are closed during work, the lower opening is opened after the test is completed, and the air blower starts to work and sweeps rock powder in the sample cabin to the TBM main conveyor belt for removal.
The control system 12 mainly receives control information from the telescopic mechanical gripper 4, the conveyor belt system 5, the dryer 6, the image acquisition device 7, the illumination device 8, the X-ray fluorescence analysis device 9, and the crushing device 10, to control the working and stopping of the devices.
The data comprehensive analysis platform 13 includes an image and optical spectrum technology-based lithology intelligent recognition system, an adverse geology precursor characteristics database, and an advanced geological prediction system. The platform 13 is mounted in the TBM main control room 14 and is connected to the devices through data connection cables.
In the TBM-mounted advanced geological prediction system based on recognition of lithology and adverse geology precursor characteristics, a specific implementation process includes the following steps.
(1) After the TBM starts to tunnel and cut rock slags are conveyed to below the system device by using the TBM main conveyor belt 1, a working button of the entire system is pressed in the TBM main control room 14, and the system starts to work.
(2) First, the telescopic mechanical gripper 4 starts to work, the laser profile measuring instrument mounted at the position in which the front end of the mechanical arm 17 is connected to the grab bucket 23 measures a size of a rock block on the TBM conveyor belt, and then a piece of rock block of which a size meets a set range is selected for grabbing. After the rock block is grabbed, the high pressure spray nozzle 25 automatically sprays water to wash the rock block in the grab bucket, after the washing is completed, the electric hydraulic arm 16 and the electric hydraulic rod 19 start to stretch and move to place the to be-tested rock block on the conveyor belt system 5.
(3) When the rock block is conveyed to right below the dryer 6, the conveyor belt system 5 stops working, the control system 12 starts to control the dryer 6 to work, and hot air is blown out, after 2 minutes, the surface of the to-be-tested rock block is dried, and the dryer stops working.
(4) When the conveyor belt system 5 starts to work again, the to-be-tested rock block is conveyed to below the image acquisition device 7. The conveyor belt system 5 stops working again, the illumination device 8 starts to light up, the image acquisition device continuously shoots the to-be-tested rock block, and the acquired photos are transmitted to a lithology intelligent recognition system of the data comprehensive analysis platform 13.
(5) The conveyor belt system 5 continues to work, the to-be-tested rock block is conveyed to below the X-ray fluorescence analysis device 9 and the conveyor belt system stops working again. In this case, the X-ray fluorescence analysis device 9 starts to work. First, the servo motor 27 and the electric hydraulic rod 30 control an irradiation direction of the X-ray fluorescence analysis device, to perform irradiation on different parts of the to-be tested rock block therebelow for a plurality of times, and finally element types and content in the to-be-tested rock block are provided and the result is transmitted to the data comprehensive analysis platform 13.
(6) After the element analysis is performed on the to-be-tested rock block, the conveyor belt system 5 continues to work, the to-be-tested rock block is conveyed to the feed port of the crushing device 10, and then the crushing machine starts to work. First, the valve of the useful powder discharging port 42 is opened, and the valve of the waste powder discharging port 43 is closed. In the crushing process, powder particles falling from the sieve 39 automatically fall into the sample cabin of the X-ray diffraction analyzer. When the powder in the sample cabin reaches a specific amount, the crushing machine stops working. The valve of the useful powder discharging port 42 is closed, the valve of the waste powder discharging port 43 is opened, and the sieve is removed. The air blower 44 simultaneously starts to purge, and sweeps the residual wastes from the waste powder discharging port to the TBM main conveyor belt 1 therebelow.
(7) After rock powder falling into the sample cabin reaches a specific amount, an opening of the sample cabin is closed. The X-ray diffraction analysis device 11 starts to work, and then provides mineral types and content in the to-be-tested rock block and transmits the result to the data comprehensive analysis platform 13. After the test is completed, the lower opening of the sample cabin is opened, and the air blower starts to work and sweeps the rock powder in the sample cabin to the TBM main conveyor belt 1 for removal.
(8) The data comprehensive analysis platform 13 compares the lithology features provided by the image intelligent recognition system, the element type and content result in the rock samples provided by the X-ray fluorescence analysis device, and the mineral type and content result provided by the X-ray diffraction analysis device 11 with an established rock image, element and mineral standard database, and performs analysis, to finally provide an accurate lithology recognition result of the to-be-tested rock block.
(9) After rock samples for a specific tunneling distance are continuously measured, information integrating and correction are performed, by using a neural network, on an extracted mark image capable of reflecting adverse geology information, and adverse geology sensitive elements and adverse geology mineral enrichment information in the rocks, and then the information is compared with a quantitative characterization relationship database of adverse geology precursor characteristics established based on a data mining technology and analysis is performed, to provide a lithology change and adverse geology occurrence features ahead of a tunnel face, and finally implement intelligent tunnel advanced geological prediction based on recognition of lithology and adverse geology precursor characteristics.
The foregoing descriptions are merely preferred embodiments of this application, but are not intended to limit this application. A person skilled in the art may make various modifications and changes to this application. Any modification, equivalent replacement, or improvement made without departing from the spirit and principle of this application shall fall within the protection scope of this application.
The foregoing specific implementations of the present disclosure are described with reference to the accompanying drawings, but are not intended to limit the protection scope of the present disclosure. A person skilled in the art should understand that various modifications or variations may be made without creative efforts based on the technical solutions of the present disclosure, and such modifications or variations shall fall within the protection scope of the present disclosure.
Claims (10)
1. A TBM-mounted advanced geological prediction system based on recognition of lithology and adverse geology precursor characteristics, comprising a base, wherein the base is disposed on a TBM main conveyor belt, and a telescopic mechanical gripper, a conveyor belt system, a crushing device, a control system, and a data comprehensive analysis platform are disposed on the base in sequence,
the telescopic mechanical gripper is configured to place a to-be-tested rock block of the TBM main conveyor belt on the conveyor belt system, and a drying mechanism, an image recognition device, and an X-ray fluorescence analysis device are disposed above the conveyor belt system in sequence and are respectively configured to perform surface drying, lithology recognition, and element analysis on the to-be-tested rock block;
the crushing device is disposed at a tail end of the conveyor belt system and is configured to crush the to-be-tested rock block, and powder particles at an outlet of the crushing device are capable of directly falling into a sample cabin of an X-ray diffraction analysis device disposed below the crushing device;
the control system controls actions of the telescopic mechanical gripper, the conveyor belt system, the crushing device, the image recognition device, the X-ray fluorescence analysis device, and the X-ray diffraction analysis device; and
the data comprehensive analysis platform is configured to receive acquisition results of the image recognition device, the X-ray fluorescence analysis device, and the X-ray diffraction analysis device, perform information integrating and correction by using a neural network according to an extracted mark image capable of reflecting adverse geology information, and adverse geology sensitive elements and adverse geology mineral enrichment information in rocks, and perform analysis to obtain a lithology change and adverse geology occurrence features ahead of a tunnel face and implement intelligent tunnel advanced geological prediction based on recognition of lithology and adverse geology precursor characteristics.
2. The TBM-mounted advanced geological prediction system based on recognition of lithology and adverse geology precursor characteristics according to claim 1, wherein a protection shed is disposed above the base and is welded and located above the base, a shed roof is supported by angle steels and is arched in shape and made of stainless steel plates, and two sides of the protection shed are shielded by reinforced glass plates and are fixed on the supporting angle steels by using fastening bolts.
3. The TBM-mounted advanced geological prediction system based on recognition of lithology and adverse geology precursor characteristics according to claim 1, wherein the base is made of an angle steel, is rectangular in outline and welded above a TBM conveyor belt near a main control room, and mainly provides support for other components of the system.
4. The TBM-mounted advanced geological prediction system based on recognition of lithology and adverse geology precursor characteristics according to claim 1, wherein the telescopic mechanical gripper comprises an electric hydraulic column, a support rod, an electric hydraulic arm, a mechanical arm, an electric hydraulic rod, a rocker, a connecting rod, and a grab bucket, the electric hydraulic column is vertically welded on the base and configured to mount the electric hydraulic arm and the support rod, and is capable of stretching up and down to control lifting of the entire mechanical gripper apparatus, one end of the support rod is vertically and fixedly connected to the electric hydraulic column and one end is connected to the mechanical arm through a pin shaft, one end of the electric hydraulic arm is connected to the electric hydraulic column and one end is connected to the mechanical arm through a pin shaft, there are two grab buckets, the two grab buckets are symmetrically mounted on a front end of the mechanical arm through pin shafts, and bucket teeth are mounted on front ends of the grab buckets and correspond to the grab buckets, and there are two electric hydraulic telescopic rods, and the two electric hydraulic telescopic rods are connected respectively through the rocker and the connecting rod and configured to control opening and closing of the grab buckets; or
a laser profile measuring instrument is fixedly mounted at a position in which the front end of the mechanical arm is connected to the grab bucket and configured to measure a size of the rock block on the TBM conveyor belt, and when the measured size of the rock block meets a set range, a signal is transmitted to the control system to control the mechanical gripper to grab the rock block; or a high pressure spray nozzle is further mounted in the grab bucket and is connected to an external water pump through a water pipe, and when the grab bucket grabs a rock block, the high pressure spray nozzle automatically sprays water to wash the grabbed rock block, to ensure that a surface of the grabbed rock block is free of other slag powder pollution.
5. The TBM-mounted advanced geological prediction system based on recognition of lithology and adverse geology precursor characteristics according to claim 1, wherein the conveyor belt system is mounted in a protection shed above the base and comprises a conveyor belt, a roller, a servo motor, and a displacement sensor, there are at least two rollers, the conveyor belt is wound on the roller, the servo motor drives the roller to drive movement of the conveyor belt, the displacement sensor is configured to measure a conveying displacement of the to-be-tested rock block on the conveyor belt, and the displacement sensor and the servo motor both communicate with the control system.
6. The TBM-mounted advanced geological prediction system based on recognition of lithology and adverse geology precursor characteristics according to claim 1, wherein the image recognition device comprises an image acquisition device, an illumination device, and an image recognition system, the image acquisition device is a digital camera and configured to shoot a high-definition image of the to-be-tested rock block, the illumination device comprises a plurality of industrial illumination light sources that are arranged near the image acquisition device, the image recognition system applies a deep learning technology, adopts a convolutional neural network intelligent recognition model to train a rock image in an existing rock specimen library, compares the rock image with an established rock specimen library image, and performs information integrating and analysis, to finally provide a preliminary lithology recognition result, and when shooting a to-be-tested rock block image, the image acquisition device transmits the acquired image to the data comprehensive analysis platform.
7. The TBM-mounted advanced geological prediction system based on recognition of lithology and adverse geology precursor characteristics according to claim 1, wherein the X ray fluorescence analysis device comprises a drive motor, a chassis, a connecting rod, an electric hydraulic rod, a pin shaft, a spherical hinge, and an X-ray fluorescence analyzer, the drive motor is connected to the chassis to control rotation of the entire chassis, the chassis is connected to the connecting rod through the pin shaft, the chassis is connected to the electric hydraulic rod through the pin shaft, and the connecting rod is fixedly connected to the electric hydraulic rod, the X-ray fluorescence analyzer is connected to the other end of the connecting rod through the spherical hinge, so that the X-ray fluorescence analyzer is controlled to detect the to-be-tested rock block therebelow from different angles, and the X-ray fluorescence analyzer emits an X-ray and irradiates the to-be-tested rock block, receives a generated secondary characteristic X-ray, provides element types and content of the to-be-tested rock block, and transmits the result to the data comprehensive analysis platform.
8. The TBM-mounted advanced geological prediction system based on recognition of lithology and adverse geology precursor characteristics according to claim 1, wherein the drying mechanism is a dryer and performs air drying on the grabbed to-be-tested rock block, when the conveyor belt system conveys the to-be-tested rock block to right below the dryer, the control system starts to control the dryer to work, and hot air is released to blow a surface of the to-be-tested rock block, after a specific time, the dryer stops working, and the surface of the to-be-tested rock block is dried.
9. The TBM-mounted advanced geological prediction system based on recognition of lithology and adverse geology precursor characteristics according to claim 1, wherein the crushing device comprises a motor, a crushing cabin, a crushing shaft, a crushing knife, a feed port, a bucket cabin, a discharging port, a sieve, a valve, and an air blower, the motor is mounted on an upper part of the entire crushing device and is responsible for providing power for high-speed rotation, a shaft part of a servo motor is connected to the crushing shaft, the crushing knife is mounted on a front end of the crushing shaft, and the crushing shaft and the crushing knife are both disposed in the crushing cabin, one side of the crushing cabin is provided with the feed port, samples on the conveyor belt system are capable of automatically falling into the feed port to enter the crushing cabin, a layer of sieve is mounted on a lower part of the crushing cabin, particles filtered by the sieve fall into the bucket cabin disposed below the crushing cabin in a crushing process, the lower part of the bucket cabin is provided with two discharging ports, one is a useful powder discharging port, and the other is a waste powder discharging port, powder particles at the useful powder discharging port are capable of directly falling into a sample cabin of the X-ray diffraction analysis device therebelow, wastes at the waste powder discharging port directly fall into the TBM main conveyor belt therebelow, one valve is disposed at each of the two discharging ports, and the air blower is mounted on an upper part of the crushing cabin; or the X-ray diffraction analysis device comprises an X-ray diffraction analyzer and a sample cabin, the sample cabin is provided with an upper opening and a lower opening, the air blower is mounted above the upper opening, and the upper opening and the lower opening are both provided with switches for controlling opening and closing of the corresponding openings.
10. A prediction method based on the system according to any one of claims 1 to 9, comprising the following steps:
starting the system, and obtaining rock image information, rock element type and content information, and rock mineral type and content information acquired in a TBM tunneling process;
extracting and characterizing, based on the acquired rock image information and by applying a convolutional neural network, image features such as colors, textures, and shapes of rocks through a convolutional layer and a maximum pooling layer;
performing information integrating on the extracted and characterized rock image feature information and the acquired rock element types and content and rock mineral types and content, inputting the information to a trained rock image automatic recognition and classification model in a rock intelligent recognition system for analysis and comparison, and finally providing a rock name, an adverse geology mark, and an accuracy rate of the to-be tested rock block;
performing data comparison and analysis on the types and the content of a large quantity of acquired rock elements, extracting adverse geology sensitive elements thereof by applying a data mining technology, and quantitatively researching a change rule of the adverse geology sensitive elements; performing data comparison and analysis on the types and the content of a large quantity of acquired rock minerals, extracting adverse geology diagnostic minerals by applying the data mining technology, and quantitatively researching a change rule of the adverse geology diagnostic minerals; and performing information integrating and correction, by using the neural network, on the extracted mark image capable of reflecting the adverse geology information, and the adverse geology sensitive elements and the adverse geology mineral enrichment information in the rocks after continuously measuring rock samples for a specific tunneling distance, comparing the information with a quantitative characterization relationship database of adverse geology precursor characteristics established based on the data mining technology and performing analysis, to provide the lithology change and the adverse geology occurrence features ahead of the tunnel face and implement the intelligent tunnel advanced geological prediction based on recognition of lithology and adverse geology precursor characteristics.
DRAWING
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| PCT/CN2019/084654 WO2020199290A1 (en) | 2019-04-04 | 2019-04-26 | Tbm-mounted advance geological forecast system and method based on recognition of lithology and unfavorable geological precursor characteristics |
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| CN205786402U (en) * | 2016-06-03 | 2016-12-07 | 上海科油石油仪器制造有限公司 | A kind of oil drilling X-ray element well logging analysis and Control system |
| CN206740654U (en) * | 2017-05-15 | 2017-12-12 | 中国地质调查局油气资源调查中心 | Quick comprehensive evaluation device of full-diameter shale sample |
| CN107608005B (en) * | 2017-06-07 | 2019-04-30 | 同济大学 | A kind of the abnormal geological detection analytic method and system |
-
2019
- 2019-04-04 CN CN201910271921.4A patent/CN110043267B/en active Active
- 2019-04-26 AU AU2019439936A patent/AU2019439936B2/en active Active
- 2019-04-26 WO PCT/CN2019/084654 patent/WO2020199290A1/en active Application Filing
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112964854A (en) * | 2021-02-03 | 2021-06-15 | 成都大学 | Intelligent geological disaster monitor |
Also Published As
| Publication number | Publication date |
|---|---|
| AU2019439936B2 (en) | 2021-09-30 |
| WO2020199290A1 (en) | 2020-10-08 |
| CN110043267B (en) | 2020-07-31 |
| CN110043267A (en) | 2019-07-23 |
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