CN113532534A - Tunnel multi-information acquisition and detection system - Google Patents
Tunnel multi-information acquisition and detection system Download PDFInfo
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- CN113532534A CN113532534A CN202110822340.2A CN202110822340A CN113532534A CN 113532534 A CN113532534 A CN 113532534A CN 202110822340 A CN202110822340 A CN 202110822340A CN 113532534 A CN113532534 A CN 113532534A
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Abstract
The invention belongs to the technical field of information acquisition and detection systems, and particularly relates to a tunnel multi-information acquisition and detection system. The data processing unit is used for judging whether the intrusion phenomenon exists in the area to be detected according to the first point cloud data transmitted by the completely embedded controlled device unit, and when the intrusion phenomenon exists, the data processing unit also receives the intrusion phenomenon picture data acquired by the completely embedded controlled device unit transmission imaging recording unit, classifies the class of the intrusion object through a deep learning algorithm, and judges the damage level of the intrusion through the class.
Description
Technical Field
The invention belongs to the technical field of information acquisition and detection systems, and particularly relates to a tunnel multi-information acquisition and detection system.
Background
The existing tunnel detection technology can be mainly divided into manual detection and non-manual detection. The manual detection is time-consuming and labor-consuming, and the detection efficiency is low, so that the manual detection is gradually eliminated. The non-manual detection technology is single in system detection function, and cannot comprehensively obtain information such as temperature, humidity, carbon monoxide content, gas content and whether invasion limit harm exists at each position of the tunnel internal environment through one-time detection.
Disclosure of Invention
Aiming at the technical problem that the non-manual detection technology is single in system detection function, the invention provides the tunnel multi-information acquisition detection system which is comprehensive in detection function, good in detection effect and high in efficiency.
In order to solve the technical problems, the invention adopts the technical scheme that:
the utility model provides a many information acquisition detecting system in tunnel, includes laser detection perception unit, formation of image recording unit, temperature perception unit, humidity perception unit, carbon monoxide detector unit, gas detector unit, is used to lead the unit, imbeds controlled device unit and data processing unit completely, laser detection perception unit, formation of image recording unit, temperature perception unit, humidity perception unit, carbon monoxide detector unit, gas detector unit, be used to lead the unit and all connect on imbedding controlled device unit completely, imbed controlled device unit completely and connect on data processing unit.
The imaging recording unit, the temperature sensing unit and the humidity sensing unit are all three, and the imaging recording unit, the temperature sensing unit and the humidity sensing unit are all arranged on the lifting device.
The laser detection sensing unit, the carbon monoxide detector unit and the gas detector unit are all provided with one, and the laser detection sensing unit, the carbon monoxide detector unit and the gas detector unit are all arranged on the lifting device.
The laser detection perception unit adopts two-dimensional laser radar, the formation of image record unit adopts high separated time array camera, the temperature perception unit adopts infrared temperature sensor, the humidity perception unit adopts humidity transducer, carbon monoxide detector unit adopts carbon monoxide sensor, gas detector unit adopts gas sensor, the controlled device unit of embedding completely adopts the raspberry group, data processing unit adopts the computer.
A control method of a tunnel multi-information acquisition detection system comprises the following steps:
s1, the laser detection sensing unit emits laser to sense the to-be-detected area at the current position of the tunnel, collects the laser reflected by the to-be-detected area to generate first point cloud data and sends the first point cloud data to the completely embedded controlled device unit; meanwhile, the temperature sensing unit, the humidity sensing unit, the carbon monoxide detector unit, the gas detector unit and the inertial navigation unit collect corresponding environment information of the tunnel in real time and send the collected data to the completely embedded controlled device unit;
s2, completely embedding the controlled device unit to transmit the first point cloud data generated by the laser to the data processing unit, and judging whether the limit invasion phenomenon exists in the tunnel to-be-detected area or not by the data processing unit according to a standard limit invasion area template; meanwhile, the completely embedded controlled device unit can also transmit data generated by the temperature sensing unit, the humidity sensing unit, the carbon monoxide detector unit, the gas detector unit and the inertial navigation unit to the data processing unit;
s3, if the intrusion phenomenon exists in S2, the imaging recording unit is started by the completely embedded controlled device unit, and the imaging recording unit collects intrusion images and transmits the intrusion images to the data processing unit through the completely embedded controlled device unit;
and S4, analyzing the images acquired by the imaging recording unit through a deep learning algorithm by the data processing unit, judging the category and the hazard level of the intrusion object, and providing temperature information, humidity information, carbon monoxide content information and gas content information of all positions of the tunnel according to the completely embedded controlled device unit by the data processing unit, thereby facilitating the comprehensive analysis of tunnel detection.
The deep learning algorithm employs the YOLOv3 algorithm.
Compared with the prior art, the invention has the following beneficial effects:
the data processing unit is used for judging whether the intrusion phenomenon exists in the area to be detected according to the first point cloud data transmitted by the completely embedded controlled device unit, and when the intrusion phenomenon exists, the data processing unit also receives the intrusion phenomenon picture data acquired by the completely embedded controlled device unit transmission imaging recording unit, classifies the class of the intrusion object through a deep learning algorithm, and judges the damage level of the intrusion through the class. Meanwhile, the data processing unit can provide temperature information, humidity information, carbon monoxide content information and gas content information of all positions of the whole tunnel according to the completely embedded controlled device unit, so that comprehensive analysis of tunnel detection is facilitated.
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FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a flow chart of the steps of the present invention.
Wherein: 10 is a laser detection sensing unit, 20 is an imaging recording unit, 30 is a temperature sensing unit, 40 is a humidity sensing unit, 50 is a carbon monoxide detector unit, 60 is a gas detector unit, 70 is an inertial navigation unit, 80 is a completely embedded controlled device unit, and 90 is a data processing unit.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "connected" and "connected" are to be interpreted broadly, e.g., as being fixed or detachable or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The utility model provides a many information acquisition detecting system in tunnel, including laser detection sensing element 10, formation of image recording unit 20, temperature sensing element 30, humidity sensing element 40, carbon monoxide detector unit 50, gas detector unit 60, inertial navigation unit 70, totally embed controlled device unit 80 and data processing unit 90, laser detection sensing element 10, formation of image recording unit 20, temperature sensing element 30, humidity sensing element 40, carbon monoxide detector unit 50, gas detector unit 60, inertial navigation unit 70 all connect on totally embedding controlled device unit 80, totally embed controlled device unit 80 and connect on data processing unit 90. The laser detection sensing unit 10 is configured to emit laser to sense the current position intrusion area of the tunnel, collect laser reflected by the current position intrusion area to generate first point cloud data, and send the first point cloud data to the completely embedded controlled device unit 30. The imaging recording unit 20 is configured to call the imaging recording unit 20 to shoot and collect an intrusion phenomenon through the fully-embedded controlled device unit 30 and send collected intrusion image data to the fully-embedded controlled device unit 30 when the intrusion phenomenon is analyzed according to the first point cloud data. The temperature sensing unit 30 is used for collecting temperature information of the current position of the tunnel in real time and sending the collected temperature information to the fully embedded controlled device unit 80. The humidity sensing unit 40 is used for collecting humidity information of the current position of the tunnel in real time and sending the collected humidity information to the fully embedded controlled device unit 80. The carbon monoxide detector unit 50 is used for detecting the carbon monoxide content of the current position of the acquisition tunnel in real time and sending the detected and acquired carbon monoxide content information to the fully embedded controlled device unit 80. The gas detector unit 60 is used for detecting and collecting the gas content of the current position of the tunnel in real time and sending the detected and collected gas content information to the completely embedded controlled device unit 80. The inertial navigation unit 70 is used to provide current tunnel position information and send the position information to the fully embedded controlled device unit 80. The fully embedded controlled device unit 80 is used for controlling the on and off of the laser detection sensing unit 10, the imaging recording unit 20, the temperature sensing unit 30, the humidity sensing unit 40, the carbon monoxide detector unit 50, the gas detector unit 60 and the inertial navigation unit 70, and the receiving and transmitting of signals. The data processing unit 90 is configured to determine whether the intrusion phenomenon exists in the area to be detected according to the first point cloud data transmitted by the completely embedded controlled device unit 80.
Further, the imaging recording unit 20, the temperature sensing unit 30 and the humidity sensing unit 40 are all provided in three, and the imaging recording unit 20, the temperature sensing unit 30 and the humidity sensing unit 40 are all arranged on the lifting device.
Further, the laser detection sensing unit 10, the carbon monoxide detector unit 50 and the gas detector unit 60 are all provided with one, and the laser detection sensing unit 10, the carbon monoxide detector unit 50 and the gas detector unit 60 are all arranged on the lifting device.
Further, preferably, the laser detection sensing unit 10 employs a two-dimensional laser radar, the imaging recording unit 20 employs a high-resolution linear array camera, the temperature sensing unit 30 employs an infrared temperature sensor, the humidity sensing unit 40 employs a humidity sensor, the carbon monoxide detector unit 50 employs a carbon monoxide sensor, the gas detector unit 60 employs a gas sensor, the completely embedded controlled device unit 80 employs a raspberry pi, and the data processing unit 90 employs a computer.
A control method of a tunnel multi-information acquisition detection system comprises the following steps:
s1, the laser detection sensing unit emits laser to sense the to-be-detected area at the current position of the tunnel, collects the laser reflected by the to-be-detected area to generate first point cloud data and sends the first point cloud data to the completely embedded controlled device unit; meanwhile, the temperature sensing unit, the humidity sensing unit, the carbon monoxide detector unit, the gas detector unit and the inertial navigation unit collect corresponding environment information of the tunnel in real time and send the collected data to the completely embedded controlled device unit;
s2, completely embedding the controlled device unit to transmit the first point cloud data generated by the laser to the data processing unit, and judging whether the limit invasion phenomenon exists in the tunnel to-be-detected area or not by the data processing unit according to a standard limit invasion area template; meanwhile, the completely embedded controlled device unit can also transmit data generated by the temperature sensing unit, the humidity sensing unit, the carbon monoxide detector unit, the gas detector unit and the inertial navigation unit to the data processing unit;
s3, if the intrusion phenomenon exists in S2, the imaging recording unit is started by the completely embedded controlled device unit, and the imaging recording unit collects intrusion images and transmits the intrusion images to the data processing unit through the completely embedded controlled device unit;
and S4, analyzing the images acquired by the imaging recording unit through a deep learning algorithm by the data processing unit, judging the category and the hazard level of the intrusion object, and providing temperature information, humidity information, carbon monoxide content information and gas content information of all positions of the tunnel according to the completely embedded controlled device unit by the data processing unit, thereby facilitating the comprehensive analysis of tunnel detection.
Further, preferably, the deep learning algorithm adopts the YOLOv3 algorithm.
Although only the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art, and all changes are encompassed in the scope of the present invention.
Claims (6)
1. The utility model provides a many information acquisition detecting system in tunnel which characterized in that: including laser detection perception unit (10), formation of image recording unit (20), temperature perception unit (30), humidity perception unit (40), carbon monoxide detector unit (50), gas detector unit (60), be used to lead unit (70), embed controlled device unit (80) and data processing unit (90) completely, laser detection perception unit (10), formation of image recording unit (20), temperature perception unit (30), humidity perception unit (40), carbon monoxide detector unit (50), gas detector unit (60), be used to lead unit (70) and all connect on embedding controlled device unit (80) completely, it connects on data processing unit (90) to embed controlled device unit (80) completely.
2. The tunnel multi-information acquisition detection system according to claim 1, characterized in that: the imaging recording unit (20), the temperature sensing unit (30) and the humidity sensing unit (40) are all three, and the imaging recording unit (20), the temperature sensing unit (30) and the humidity sensing unit (40) are all arranged on the lifting device.
3. The tunnel multi-information acquisition detection system according to claim 1, characterized in that: the laser detection sensing unit (10), the carbon monoxide detector unit (50) and the gas detector unit (60) are all provided with one, and the laser detection sensing unit (10), the carbon monoxide detector unit (50) and the gas detector unit (60) are all arranged on the lifting device.
4. The tunnel multi-information acquisition detection system according to claim 1, characterized in that: laser detection perception unit (10) adopts two-dimensional laser radar, high separated time array camera is adopted in formation of image record unit (20), infrared temperature sensor is adopted in temperature perception unit (30), humidity perception unit (40) adopts humidity transducer, carbon monoxide detector unit (50) adopt carbon monoxide sensor, gas detector unit (60) adopt gas sensor, controlled device unit of embedding (80) adopts the raspberry group completely, data processing unit (90) adopt the computer.
5. The control method of the tunnel multi-information acquisition detection system according to any one of claims 1 to 4, characterized in that: comprises the following steps:
s1, the laser detection sensing unit emits laser to sense the to-be-detected area at the current position of the tunnel, collects the laser reflected by the to-be-detected area to generate first point cloud data and sends the first point cloud data to the completely embedded controlled device unit; meanwhile, the temperature sensing unit, the humidity sensing unit, the carbon monoxide detector unit, the gas detector unit and the inertial navigation unit collect corresponding environment information of the tunnel in real time and send the collected data to the completely embedded controlled device unit;
s2, completely embedding the controlled device unit to transmit the first point cloud data generated by the laser to the data processing unit, and judging whether the limit invasion phenomenon exists in the tunnel to-be-detected area or not by the data processing unit according to a standard limit invasion area template; meanwhile, the completely embedded controlled device unit can also transmit data generated by the temperature sensing unit, the humidity sensing unit, the carbon monoxide detector unit, the gas detector unit and the inertial navigation unit to the data processing unit;
s3, if the intrusion phenomenon exists in S2, the imaging recording unit is started by the completely embedded controlled device unit, and the imaging recording unit collects intrusion images and transmits the intrusion images to the data processing unit through the completely embedded controlled device unit;
and S4, analyzing the images acquired by the imaging recording unit through a deep learning algorithm by the data processing unit, judging the category and the hazard level of the intrusion object, and providing temperature information, humidity information, carbon monoxide content information and gas content information of all positions of the tunnel according to the completely embedded controlled device unit by the data processing unit, thereby facilitating the comprehensive analysis of tunnel detection.
6. The tunnel multi-information acquisition detection system according to claim 5, characterized in that: the deep learning algorithm employs the YOLOv3 algorithm.
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| CN205748485U (en) * | 2016-06-24 | 2016-11-30 | 成都慧途科技有限公司 | A kind of vehicle-mounted tunnel defect diagnostic equipment |
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Application publication date: 20211022 |