CN112798619A - Rapid detection system and detection method for tunnel defects - Google Patents
Rapid detection system and detection method for tunnel defects Download PDFInfo
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- CN112798619A CN112798619A CN202011562519.0A CN202011562519A CN112798619A CN 112798619 A CN112798619 A CN 112798619A CN 202011562519 A CN202011562519 A CN 202011562519A CN 112798619 A CN112798619 A CN 112798619A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
- G01N21/954—Inspecting the inner surface of hollow bodies, e.g. bores
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/16—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
- G01B21/16—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring distance of clearance between spaced objects
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B5/00—Measuring arrangements characterised by the use of mechanical techniques
- G01B5/02—Measuring arrangements characterised by the use of mechanical techniques for measuring length, width or thickness
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L1/00—Measuring force or stress, in general
- G01L1/10—Measuring force or stress, in general by measuring variations of frequency of stressed vibrating elements, e.g. of stressed strings
Abstract
The invention provides a system and a method for rapidly detecting tunnel defects, which comprise a central control center, a data storage center, a detection hardware system and an alarm system, wherein the detection hardware system is used for acquiring data in a tunnel in real time; the tunnel is internally provided with a special intelligent displacement robot for measuring the settlement displacement condition of the inner wall of the tunnel in real time, other data of the tunnel are monitored in real time through the crack monitoring module, the deformation monitoring module, the vibration measuring module, the stress acquisition module and the vertical displacement monitoring module, the monitored data are modeled into a tunnel model through a three-dimensional GIS model by utilizing vectorization processing, the acquired data variables are made into a broken line statistical graph through the comparison module, and an operator observes the broken line statistical graph in corresponding coordinates in the tunnel model to know the specific position condition in the tunnel, so that the defect condition in the tunnel is conveniently and quickly checked in real time.
Description
Technical Field
The invention relates to the technical field of tunnel detection, in particular to a system and a method for rapidly detecting tunnel defects.
Background
The construction of railways and highways and the construction of urban underground traffic are developed unprecedentedly, the excavation and maintenance of cave tunnels become vital, especially the maintenance work of the cave tunnels in the later period, the maintenance work is very troublesome due to daily use and internal special environment, and how to establish a perfect maintenance scheme of the caves and the tunnels is particularly urgent to ensure that dangerous situations caused by long-term overhaul are found in time;
in the maintenance of the tunnel, the inner wall of the tunnel needs to be detected regularly to prevent the occurrence of defects, in the prior art, workers are generally required to perform patrol, the general patrol is difficult to intuitively and comprehensively reflect the whole situation of the tunnel, detection information cannot be fed back and analyzed quickly, and due to the error of manual operation, the problems of time and area mismatching often occur, so that detection indexes are useless, reliable safety guarantee cannot be provided, and potential safety hazards exist.
Disclosure of Invention
Aiming at the problems, the invention provides a rapid detection system and a detection method for tunnel defects, the rapid detection system is provided with a special intelligent displacement robot in a tunnel, the rapid detection system is used for measuring the settlement displacement condition of the inner wall of the tunnel in real time through the displacement of a hard tube and the identification of an MEMS displacement sensor, the rapid detection system monitors the data such as the crack condition, the deformation data, the vibration measurement module, the stress acquisition module and the vertical displacement monitoring module of the tunnel in real time, the monitored data is modeled into a tunnel model through a three-dimensional GIS model by utilizing vectorization processing, the coordinate position and the time stamp are included, the acquired data variable is made into a broken line statistical graph through a comparison module and displayed in the corresponding coordinate of the tunnel model, an operator observes the broken line statistical graph in the corresponding coordinate in the tunnel model, and can know the specific position condition in the tunnel, the time data is convenient for real-time examination of the defect condition in the tunnel, and is convenient and rapid and high in efficiency.
In order to realize the purpose of the invention, the invention is realized by the following technical scheme: the system for rapidly detecting the tunnel defects comprises a central control center, a data storage center, a detection hardware system and an alarm system, wherein the detection hardware system is used for acquiring data in a tunnel in real time, the central control center comprises a processing chip, a monitoring system and a three-dimensional GIS (geographic information System) model, a data analysis module is arranged in the processing chip, the processing chip is remotely connected with the detection hardware system and receives the data of the detection hardware system, the data analysis module is used for analyzing the data of the detection hardware system, the data storage center is used for storing the data acquired by the processing chip, the three-dimensional GIS model is connected with the data analysis module, and the three-dimensional GIS model models the data analyzed by the data analysis module into the tunnel model through vectorization processing;
the monitoring system comprises a display module and a comparison module, wherein the display module is used for displaying the tunnel model, the comparison module has a data comparison function, and the comparison module is used for comparing and detecting data acquired by the hardware system in real time;
the data storage center comprises a cloud database and a time-space marking system, the cloud database is used for storing data collected by the detection hardware system, the time-space marking system comprises a coordinate marking module and a time stamp module, the coordinate marking module is used for marking coordinate positions on the data collected by the detection hardware system, and the time stamp module is used for marking time on the data collected by the detection hardware system in real time;
the detection hardware system comprises a tunnel detection system and a timing starting module, the tunnel detection system comprises an intelligent displacement robot, a crack monitoring module, a deformation monitoring module, a vibration measurement module, a stress acquisition module, a vertical displacement monitoring module and a CCD (charge coupled device) camera, when the system is used, the intelligent displacement robot is fixedly attached to the inner wall of a tunnel, then a plurality of crack monitoring modules, deformation monitoring modules, vibration measurement modules, stress acquisition modules, vertical displacement monitoring modules and CCD cameras are distributed in the tunnel at equal intervals, the intelligent displacement robot comprises a plurality of sections of hard pipes and a plurality of sections of flexible pipes, the plurality of sections of hard pipes and the plurality of sections of flexible pipes are connected in a staggered mode, each section of hard pipe is provided with an MEMS (micro electro mechanical systems) displacement sensor, and the timing starting module is connected with the tunnel detection system and starts the tunnel detection system to acquire data;
the alarm system comprises a data threshold module and an image and sound display module, wherein the data threshold module is connected with the comparison module and is compared with data acquired by the detection hardware system in real time.
The further improvement lies in that: the comparison module has a broken line statistical graph function, and data variables acquired by the detection hardware system in real time are made into a broken line statistical graph and displayed in coordinates corresponding to the tunnel model.
The further improvement lies in that: the CCD camera is directly connected with the tunnel model, and the CCD camera at the corresponding position can be directly taken to observe the actual situation of the tunnel by selecting the corresponding coordinate point in the tunnel model.
The further improvement lies in that: the crack monitoring module is a crack measuring instrument, the deformation monitoring module is a laser convergence meter, the vibration measuring module is a three-axis vibration measuring instrument, the stress acquisition module is a vibrating string type sensor, and the vertical displacement monitoring module is a non-contact static level.
The further improvement lies in that: and the image sound display module is used for marking the abnormal data compared by the data threshold module in the tunnel model and displaying the abnormal data on the display module, and the mark displays the coordinate position and time and gives an alarm sound at the same time.
The method for rapidly detecting the tunnel defects comprises the following steps:
the method comprises the following steps: the method comprises the following steps that multiple sections of hard pipes and multiple sections of hoses are connected in a staggered mode, an MEMS displacement sensor is arranged in each section of hard pipe, an intelligent displacement robot is manufactured, multiple intelligent displacement robots are attached to the inner wall of a tunnel and used for measuring the settlement displacement condition of the inner wall of the tunnel in real time through displacement of the hard pipes and identification of the MEMS displacement sensors, then multiple crack measuring instruments, laser convergence meters, three-axis vibration measuring instruments, vibrating wire sensors, non-contact static level instruments and CCD cameras are installed in the tunnel at equal intervals, the hardware is connected with a processing chip, and a timing starting module starts a tunnel detection system at regular time to acquire data in the tunnel and transmit the detected data to the processing chip;
step two: the processing chip receives data of the detection hardware system, the data analysis module analyzes the data of the detection hardware system and stores the data in a cloud database, the coordinate marking module marks coordinate positions on the data, the time stamping module marks time on the data, meanwhile, the three-dimensional GIS model is connected with the data analysis module, the data analyzed by the data analysis module is modeled into a tunnel model through vectorization processing, and the tunnel model comprises the coordinate positions and the time stamps;
step three: the tunnel model is on the display module, the comparison module compares data acquired by the detection hardware system in real time, data variables are made into a broken line statistical graph and displayed in coordinates corresponding to the tunnel model, and an operator observes the broken line statistical graph in the corresponding coordinates in the tunnel model to know the specific condition in the tunnel;
step four: in the data acquisition process, the data threshold module is connected with the comparison module and is compared with data acquired by a detection hardware system in real time, when data in a tunnel detected by the tunnel detection system is abnormal, a threshold set in the data threshold module is triggered, the image and sound display module is started, the abnormal data compared by the data threshold module is marked in the tunnel model and displayed on the display module, and the mark displays the coordinate position and time and simultaneously gives out an alarm sound;
step five: and an operator directly calls the CCD camera at the corresponding position to observe the actual condition of the tunnel by selecting the corresponding coordinate point in the tunnel model according to the abnormal data mark in the tunnel model, thereby carrying out positioning and troubleshooting.
The further improvement lies in that: and fifthly, in the positioning and checking process, directly obtaining the time stamp of the abnormal data through the coordinate point of the abnormal position in the motor tunnel model, thereby obtaining the abnormal occurrence time and judging the severity of the tunnel defect.
The invention has the beneficial effects that: the invention arranges a special intelligent displacement robot in the tunnel, which is used for measuring the settlement displacement condition of the inner wall of the tunnel in real time through the displacement of a hard tube and the identification of an MEMS displacement sensor, monitors the data of the crack condition, the deformation data, the vibration stress, the vertical displacement and the like of the tunnel in real time through a crack monitoring module, a deformation monitoring module, a vibration measuring module, a stress acquisition module and a vertical displacement monitoring module, models the monitored data into a tunnel model through a three-dimensional GIS model by utilizing vectorization processing, includes coordinate positions and time stamps, makes the acquired data variables into a broken line statistical diagram through a comparison module, displays the broken line statistical diagram in the corresponding coordinates of the tunnel model, and an operator observes the broken line statistical diagram in the corresponding coordinates of the tunnel model to know the specific position condition and time data in the tunnel, is convenient and rapid to investigate the defect condition in the tunnel in real time, it is efficient, simultaneously, utilize data threshold value module and the data that detect the real-time collection of hardware system to contrast, when the tunnel internal data that tunnel detecting system detected appears unusually, trigger the threshold value, can start the sound display module, the unusual data mark that contrasts out data threshold value module is in tunnel model, send the alarm sound simultaneously, operating personnel starts the CCD camera of relevant position department and observes tunnel actual conditions, can fix a position the investigation, convenient quick location is handled, the cooperation timestamp, can also judge tunnel defect severity, avoid the potential safety hazard.
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FIG. 1 is a schematic structural view of the present invention;
fig. 2 is a schematic diagram of the intelligent displacement robot of the invention.
Detailed Description
In order to further understand the present invention, the following detailed description will be made with reference to the following examples, which are only used for explaining the present invention and are not to be construed as limiting the scope of the present invention.
According to fig. 1 and 2, the embodiment provides a system for rapidly detecting tunnel defects, which includes a central control center, a data storage center, a detection hardware system and an alarm system, wherein the detection hardware system is used for acquiring data in a tunnel in real time, the central control center includes a processing chip, a monitoring system and a three-dimensional GIS model, the processing chip is provided with a data analysis module, the processing chip is remotely connected with the detection hardware system and receives data of the detection hardware system, the data analysis module is used for analyzing data of the detection hardware system, the data storage center is used for storing data acquired by the processing chip, the three-dimensional GIS model is connected with the data analysis module, and the three-dimensional GIS model models the data analyzed by the data analysis module into a tunnel model through vectorization processing;
the monitoring system comprises a display module and a comparison module, wherein the display module is used for displaying the tunnel model, the comparison module has a data comparison function, and the comparison module is used for comparing and detecting data acquired by the hardware system in real time;
the data storage center comprises a cloud database and a time-space marking system, the cloud database is used for storing data collected by the detection hardware system, the time-space marking system comprises a coordinate marking module and a time stamp module, the coordinate marking module is used for marking coordinate positions on the data collected by the detection hardware system, and the time stamp module is used for marking time on the data collected by the detection hardware system in real time;
the detection hardware system comprises a tunnel detection system and a timing starting module, the tunnel detection system comprises an intelligent displacement robot, a crack monitoring module, a deformation monitoring module, a vibration measurement module, a stress acquisition module, a vertical displacement monitoring module and a CCD (charge coupled device) camera, when the system is used, the intelligent displacement robot is fixedly attached to the inner wall of a tunnel, then a plurality of crack monitoring modules, deformation monitoring modules, vibration measurement modules, stress acquisition modules, vertical displacement monitoring modules and CCD cameras are distributed in the tunnel at equal intervals, the intelligent displacement robot comprises a plurality of sections of hard pipes and a plurality of sections of flexible pipes, the plurality of sections of hard pipes and the plurality of sections of flexible pipes are connected in a staggered mode, each section of hard pipe is provided with an MEMS (micro electro mechanical systems) displacement sensor, and the timing starting module is connected with the tunnel detection system and starts the tunnel detection system to acquire data;
the alarm system comprises a data threshold module and an image and sound display module, wherein the data threshold module is connected with the comparison module and is compared with data acquired by the detection hardware system in real time.
The comparison module has a broken line statistical graph function, and data variables acquired by the detection hardware system in real time are made into a broken line statistical graph and displayed in coordinates corresponding to the tunnel model.
The CCD camera is directly connected with the tunnel model, and the CCD camera at the corresponding position can be directly taken to observe the actual situation of the tunnel by selecting the corresponding coordinate point in the tunnel model.
The crack monitoring module is a crack measuring instrument, the deformation monitoring module is a laser convergence meter, the vibration measuring module is a three-axis vibration measuring instrument, the stress acquisition module is a vibrating string type sensor, and the vertical displacement monitoring module is a non-contact static level.
And the image sound display module is used for marking the abnormal data compared by the data threshold module in the tunnel model and displaying the abnormal data on the display module, and the mark displays the coordinate position and time and gives an alarm sound at the same time.
According to fig. 1 and 2, the embodiment proposes a method for rapidly detecting tunnel defects, which includes the following steps:
the method comprises the following steps: the method comprises the following steps that multiple sections of hard pipes and multiple sections of hoses are connected in a staggered mode, an MEMS displacement sensor is arranged in each section of hard pipe, an intelligent displacement robot is manufactured, multiple intelligent displacement robots are attached to the inner wall of a tunnel and used for measuring the settlement displacement condition of the inner wall of the tunnel in real time through displacement of the hard pipes and identification of the MEMS displacement sensors, then multiple crack measuring instruments, laser convergence meters, three-axis vibration measuring instruments, vibrating wire sensors, non-contact static level instruments and CCD cameras are installed in the tunnel at equal intervals, the hardware is connected with a processing chip, and a timing starting module starts a tunnel detection system at regular time to acquire data in the tunnel and transmit the detected data to the processing chip;
step two: the processing chip receives data of the detection hardware system, the data analysis module analyzes the data of the detection hardware system and stores the data in a cloud database, the coordinate marking module marks coordinate positions on the data, the time stamping module marks time on the data, meanwhile, the three-dimensional GIS model is connected with the data analysis module, the data analyzed by the data analysis module is modeled into a tunnel model through vectorization processing, and the tunnel model comprises the coordinate positions and the time stamps;
step three: the tunnel model is on the display module, the comparison module compares data acquired by the detection hardware system in real time, data variables are made into a broken line statistical graph and displayed in coordinates corresponding to the tunnel model, and an operator observes the broken line statistical graph in the corresponding coordinates in the tunnel model to know the specific condition in the tunnel;
step four: in the data acquisition process, the data threshold module is connected with the comparison module and is compared with data acquired by a detection hardware system in real time, when data in a tunnel detected by the tunnel detection system is abnormal, a threshold set in the data threshold module is triggered, the image and sound display module is started, the abnormal data compared by the data threshold module is marked in the tunnel model and displayed on the display module, and the mark displays the coordinate position and time and simultaneously gives out an alarm sound;
step five: an operator directly calls a CCD camera at a corresponding position to observe the actual condition of the tunnel by selecting a corresponding coordinate point in the tunnel model according to the abnormal data mark in the tunnel model, so that positioning and troubleshooting are performed, and in the positioning and troubleshooting process, the coordinate point of the abnormal position in the motor tunnel model directly obtains the timestamp of abnormal data, so that the abnormal occurrence time is obtained, and the severity of tunnel defects is judged.
The tunnel defect rapid detection system and the detection method are characterized in that a special intelligent displacement robot is arranged in a tunnel, the special intelligent displacement robot is used for measuring the settlement displacement condition of the inner wall of the tunnel in real time through displacement of a hard pipe and recognition of an MEMS displacement sensor, the crack condition, deformation data, vibration stress, vertical displacement and other data of the tunnel are monitored in real time through a crack monitoring module, a deformation monitoring module, a vibration measurement module, a stress acquisition module and a vertical displacement monitoring module, the monitored data are modeled into a tunnel model through a three-dimensional GIS model by vectorization treatment, coordinate positions and time stamps are included, the acquired data variables are made into a broken line statistical graph through a comparison module and displayed in corresponding coordinates of the tunnel model, an operator can know the specific position condition and time data in the tunnel by observing the broken line statistical graph in corresponding coordinates in the tunnel model, and conveniently arrange the defect condition in the tunnel in real time, convenient and fast, high efficiency, and simultaneously, utilize data threshold value module to contrast with the data that detect the real-time collection of hardware system, when the tunnel internal data that tunnel detecting system detected appears unusually, trigger the threshold value, can start the image sound display module, the unusual data mark that contrasts out with data threshold value module is in the tunnel model, send the alarm sound simultaneously, operating personnel starts the CCD camera of relevant position department and observes the tunnel actual conditions, can fix a position the investigation, convenient quick location is handled, the cooperation timestamp, can also judge the tunnel defect severity, avoid the potential safety hazard.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. Quick detecting system of tunnel defect, its characterized in that: the tunnel detection system comprises a central control center, a data storage center, a detection hardware system and an alarm system, wherein the detection hardware system is used for collecting data in a tunnel in real time, the central control center comprises a processing chip, a monitoring system and a three-dimensional GIS model, a data analysis module is arranged in the processing chip, the processing chip is remotely connected with the detection hardware system and receives the data of the detection hardware system, the data analysis module is used for analyzing the data of the detection hardware system, the data storage center is used for storing the data collected by the processing chip, the three-dimensional GIS model is connected with the data analysis module, and the three-dimensional GIS model models the data analyzed by the data analysis module into a tunnel model through vectorization processing;
the monitoring system comprises a display module and a comparison module, wherein the display module is used for displaying the tunnel model, the comparison module has a data comparison function, and the comparison module is used for comparing and detecting data acquired by the hardware system in real time;
the data storage center comprises a cloud database and a time-space marking system, the cloud database is used for storing data collected by the detection hardware system, the time-space marking system comprises a coordinate marking module and a time stamp module, the coordinate marking module is used for marking coordinate positions on the data collected by the detection hardware system, and the time stamp module is used for marking time on the data collected by the detection hardware system in real time;
the detection hardware system comprises a tunnel detection system and a timing starting module, the tunnel detection system comprises an intelligent displacement robot, a crack monitoring module, a deformation monitoring module, a vibration measurement module, a stress acquisition module, a vertical displacement monitoring module and a CCD (charge coupled device) camera, when the system is used, the intelligent displacement robot is fixedly attached to the inner wall of a tunnel, then a plurality of crack monitoring modules, deformation monitoring modules, vibration measurement modules, stress acquisition modules, vertical displacement monitoring modules and CCD cameras are distributed in the tunnel at equal intervals, the intelligent displacement robot comprises a plurality of sections of hard pipes and a plurality of sections of flexible pipes, the plurality of sections of hard pipes and the plurality of sections of flexible pipes are connected in a staggered mode, each section of hard pipe is provided with an MEMS (micro electro mechanical systems) displacement sensor, and the timing starting module is connected with the tunnel detection system and starts the tunnel detection system to acquire data;
the alarm system comprises a data threshold module and an image and sound display module, wherein the data threshold module is connected with the comparison module and is compared with data acquired by the detection hardware system in real time.
2. The system for rapid detection of tunnel defects according to claim 1, wherein: the comparison module has a broken line statistical graph function, and data variables acquired by the detection hardware system in real time are made into a broken line statistical graph and displayed in coordinates corresponding to the tunnel model.
3. The system for rapid detection of tunnel defects according to claim 1, wherein: the CCD camera is directly connected with the tunnel model, and the CCD camera at the corresponding position can be directly taken to observe the actual situation of the tunnel by selecting the corresponding coordinate point in the tunnel model.
4. The system for rapid detection of tunnel defects according to claim 1, wherein: the crack monitoring module is a crack measuring instrument, the deformation monitoring module is a laser convergence meter, the vibration measuring module is a three-axis vibration measuring instrument, the stress acquisition module is a vibrating string type sensor, and the vertical displacement monitoring module is a non-contact static level.
5. The system for rapid detection of tunnel defects according to claim 1, wherein: and the image sound display module is used for marking the abnormal data compared by the data threshold module in the tunnel model and displaying the abnormal data on the display module, and the mark displays the coordinate position and time and gives an alarm sound at the same time.
6. The method for rapidly detecting the tunnel defects is characterized by comprising the following steps of:
the method comprises the following steps: the method comprises the following steps that multiple sections of hard pipes and multiple sections of hoses are connected in a staggered mode, an MEMS displacement sensor is arranged in each section of hard pipe, an intelligent displacement robot is manufactured, multiple intelligent displacement robots are attached to the inner wall of a tunnel and used for measuring the settlement displacement condition of the inner wall of the tunnel in real time through displacement of the hard pipes and identification of the MEMS displacement sensors, then multiple crack measuring instruments, laser convergence meters, three-axis vibration measuring instruments, vibrating wire sensors, non-contact static level instruments and CCD cameras are installed in the tunnel at equal intervals, the hardware is connected with a processing chip, and a timing starting module starts a tunnel detection system at regular time to acquire data in the tunnel and transmit the detected data to the processing chip;
step two: the processing chip receives data of the detection hardware system, the data analysis module analyzes the data of the detection hardware system and stores the data in a cloud database, the coordinate marking module marks coordinate positions on the data, the time stamping module marks time on the data, meanwhile, the three-dimensional GIS model is connected with the data analysis module, the data analyzed by the data analysis module is modeled into a tunnel model through vectorization processing, and the tunnel model comprises the coordinate positions and the time stamps;
step three: the tunnel model is on the display module, the comparison module compares data acquired by the detection hardware system in real time, data variables are made into a broken line statistical graph and displayed in coordinates corresponding to the tunnel model, and an operator observes the broken line statistical graph in the corresponding coordinates in the tunnel model to know the specific condition in the tunnel;
step four: in the data acquisition process, the data threshold module is connected with the comparison module and is compared with data acquired by a detection hardware system in real time, when data in a tunnel detected by the tunnel detection system is abnormal, a threshold set in the data threshold module is triggered, the image and sound display module is started, the abnormal data compared by the data threshold module is marked in the tunnel model and displayed on the display module, and the mark displays the coordinate position and time and simultaneously gives out an alarm sound;
step five: and an operator directly calls the CCD camera at the corresponding position to observe the actual condition of the tunnel by selecting the corresponding coordinate point in the tunnel model according to the abnormal data mark in the tunnel model, thereby carrying out positioning and troubleshooting.
7. The method for rapidly detecting tunnel defects according to claim 6, wherein: and fifthly, in the positioning and checking process, directly obtaining the time stamp of the abnormal data through the coordinate point of the abnormal position in the motor tunnel model, thereby obtaining the abnormal occurrence time and judging the severity of the tunnel defect.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113256599A (en) * | 2021-06-09 | 2021-08-13 | 清华四川能源互联网研究院 | Method for detecting hydraulic tunnel defects based on three-dimensional dynamic model |
| CN113450357A (en) * | 2021-09-01 | 2021-09-28 | 南昌市建筑科学研究所（南昌市建筑工程质量检测中心） | Segment image online analysis subsystem and subway shield detection system |
| CN113671982A (en) * | 2021-06-28 | 2021-11-19 | 湖州市公安局特警支队 | Visual leading system applied to indoor outburst combat of unmanned aerial vehicle |
| CN113820328A (en) * | 2021-09-23 | 2021-12-21 | 广州利科科技有限公司 | Tunnel disease detection method and tunnel disease detection device based on laser detection |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002168617A (en) * | 2000-12-01 | 2002-06-14 | Shinei Denshi Keisokki Kk | Device and system for measuring tubular object such as tunnel |
| CN104680579A (en) * | 2015-03-02 | 2015-06-03 | 北京工业大学 | Tunnel construction informatization monitoring system based on three-dimensional scanning point cloud |
| CN106958460A (en) * | 2017-05-16 | 2017-07-18 | 山东大学 | A kind of wisdom sensory perceptual system and method suitable for tunneling and underground engineering monitoring measurement information |
| CN110414532A (en) * | 2019-06-21 | 2019-11-05 | 广州利科科技有限公司 | A kind of commodity image recognition methods based on deep learning |
| CN111272220A (en) * | 2020-02-13 | 2020-06-12 | 中国铁道科学研究院集团有限公司铁道建筑研究所 | Tunnel state detection monitoring management system |
| CN210953849U (en) * | 2019-11-06 | 2020-07-07 | 云南省公路科学技术研究院 | Tunnel secondary lining crack detection auxiliary device |
| CN112039946A (en) * | 2020-07-28 | 2020-12-04 | 金鹏智能家居有限公司 | Intelligent household alarm system |
-
2020
- 2020-12-25 CN CN202011562519.0A patent/CN112798619B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002168617A (en) * | 2000-12-01 | 2002-06-14 | Shinei Denshi Keisokki Kk | Device and system for measuring tubular object such as tunnel |
| CN104680579A (en) * | 2015-03-02 | 2015-06-03 | 北京工业大学 | Tunnel construction informatization monitoring system based on three-dimensional scanning point cloud |
| CN106958460A (en) * | 2017-05-16 | 2017-07-18 | 山东大学 | A kind of wisdom sensory perceptual system and method suitable for tunneling and underground engineering monitoring measurement information |
| CN110414532A (en) * | 2019-06-21 | 2019-11-05 | 广州利科科技有限公司 | A kind of commodity image recognition methods based on deep learning |
| CN210953849U (en) * | 2019-11-06 | 2020-07-07 | 云南省公路科学技术研究院 | Tunnel secondary lining crack detection auxiliary device |
| CN111272220A (en) * | 2020-02-13 | 2020-06-12 | 中国铁道科学研究院集团有限公司铁道建筑研究所 | Tunnel state detection monitoring management system |
| CN112039946A (en) * | 2020-07-28 | 2020-12-04 | 金鹏智能家居有限公司 | Intelligent household alarm system |
Non-Patent Citations (1)
| Title |
|---|
| 赵勇 等: "《中国高速铁路隧道》", 31 October 2016 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113256599A (en) * | 2021-06-09 | 2021-08-13 | 清华四川能源互联网研究院 | Method for detecting hydraulic tunnel defects based on three-dimensional dynamic model |
| WO2022257158A1 (en) * | 2021-06-09 | 2022-12-15 | 清华四川能源互联网研究院 | Three-dimensional dynamic model-based method for detecting hydraulic tunnel defects |
| CN113671982A (en) * | 2021-06-28 | 2021-11-19 | 湖州市公安局特警支队 | Visual leading system applied to indoor outburst combat of unmanned aerial vehicle |
| CN113450357A (en) * | 2021-09-01 | 2021-09-28 | 南昌市建筑科学研究所（南昌市建筑工程质量检测中心） | Segment image online analysis subsystem and subway shield detection system |
| CN113450357B (en) * | 2021-09-01 | 2021-12-17 | 南昌市建筑科学研究所（南昌市建筑工程质量检测中心） | Segment image online analysis subsystem and subway shield detection system |
| CN113820328A (en) * | 2021-09-23 | 2021-12-21 | 广州利科科技有限公司 | Tunnel disease detection method and tunnel disease detection device based on laser detection |
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