CN115903779A - Intelligent early warning system and method for shield tunnel electric locomotive - Google Patents
Intelligent early warning system and method for shield tunnel electric locomotive Download PDFInfo
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Abstract
The invention discloses an intelligent early warning system and method for shield tunnel electric locomotives, relating to the technical field of safety protection and comprising the following steps: the obstacle information acquisition module is used for acquiring different types of obstacle information around each trolley of the electric locomotive to be pre-warned, which runs in the shield tunnel; the electric locomotive state information acquisition module is used for acquiring the speed information and the position information of each trolley of the electric locomotive and acquiring the position information of the electric locomotive relative to the tunnel portal; the barrier identification module is used for dynamically fusing various types of barrier information by utilizing a combined algorithm to obtain the position and the type of the barrier; the positioning module is used for fusing the position information of each trolley and the obtained position information of the electric locomotive relative to the tunnel portal by using a combination algorithm to determine the position of the electric locomotive; and the early warning module is used for starting early warning according to the acquired data information and braking the electric locomotive. The invention realizes the guarantee of the safe running of the electric locomotive in the tunnel.
Description
Technical Field
The invention relates to the technical field of mine roadway and subway tunnel construction technology and safety protection, in particular to an intelligent early warning system and method for a shield tunnel electric locomotive.
Background
The electric locomotive is widely applied to the construction process of mine roadways and subway tunnels, is a special traction locomotive for transporting sand, stone, earth, materials and equipment, is simultaneously suitable for narrow-gauge railway ground in industries such as coal, metallurgy, mine, railway, highway tunnel construction and the like, is used as a transportation hub of an excavation surface and the outside, and is one of core equipment of construction.
There are some problems and potential risks in the electric locomotive application process. Firstly, a supervision blind area exists, no network or signal exists during the running of the electric locomotive, and the supervision blind area of the running state of the electric locomotive exists; secondly, a driver has a visual blind area, the driver of the battery car is difficult to observe the conditions behind the car and the conditions on the two sides, and the collision and the scratch are main reasons of personal safety accidents; secondly, the trolley is difficult to drive in the direction, and the driver can visually not know whether to advance or retreat according to the situation behind the paddle car, so that command receiving delay of the interphone exists; finally, the degree of automation of the information is low, illegal operation is not warned, no warning is provided through a risk source, no digitalization is provided, the speed and the braking distance of a driver are difficult to accurately control when driving, and even the situation of vehicle sliding is caused by operation errors. Potential high-risk risks such as overspeed, vehicle slipping, visual blind areas and the like caused by the electric locomotive are easy to cause impact to cause damage to the trolley, and construction accidents such as casualties and the like occur occasionally.
Disclosure of Invention
The invention provides an intelligent early warning system and method for an electric locomotive in a shield tunnel, which can adopt an active brake control strategy before determining accident risk and provide guarantee for the safe running of the electric locomotive in the tunnel.
The invention provides an intelligent early warning system of a shield tunnel electric locomotive, which comprises:
the obstacle information acquisition module is used for acquiring different types of obstacle information around each trolley of the electric locomotive to be pre-warned running in the shield tunnel;
the electric locomotive state information acquisition module is used for acquiring the speed information and the position information of each trolley of the electric locomotive and acquiring the position information of the electric locomotive relative to the tunnel portal;
the barrier identification module is used for dynamically fusing various types of barrier information by utilizing a combined algorithm to obtain the position and the type of the barrier;
the positioning module is used for fusing the position information of each trolley and the obtained position information of the electric locomotive relative to the tunnel portal by using a combination algorithm to determine the position of the electric locomotive;
the early warning module is used for starting early warning or braking the electric locomotive according to the distance between the position of the electric locomotive and the barrier in the shield tunnel; and when the speed of the electric locomotive exceeds the preset speed, starting early warning and braking the electric locomotive.
Further, the obstacle information acquiring module includes:
the system comprises a plurality of groups of radars, a scanning unit, a data acquisition unit and a data processing unit, wherein the radars are respectively arranged on each trolley of the electric locomotive and are respectively used for scanning millimeter wave point cloud data of obstacles around each trolley;
the system comprises a plurality of groups of double-light fusion vision sensors, a plurality of groups of double-light fusion vision sensors and a controller, wherein the double-light fusion vision sensors are respectively arranged on each trolley of the electric locomotive and are respectively used for scanning video data information of obstacles around each trolley;
the double-light fusion vision sensor comprises a visible light vision sensor and an infrared light vision sensor.
Further, the electric locomotive state information acquisition module includes:
the system comprises a plurality of positioning base stations, a plurality of positioning base stations and a plurality of positioning base stations, wherein two positioning base stations are respectively arranged at the tunnel portal of the shield tunnel and the front end of the shield machine and are used for acquiring the position information of the electric locomotive relative to the tunnel portal; the other positioning base stations are respectively arranged on the roofs of all trolleys of the electric locomotive and are respectively used for acquiring the position information of all trolleys;
the laser velocimeters are respectively arranged on the side surfaces of the trolleys of the electric locomotive and are respectively used for acquiring the speed information of the trolleys.
Further, the obstacle identification module includes:
the first data processing module is used for performing time domain filtering, spatial domain filtering and K-mean clustering on the millimeter wave point cloud data respectively acquired by the plurality of groups of radars to obtain the information of obstacles in front of and at the side of each trolley of the electric locomotive;
the second data processing module is used for classifying and screening the obstacle information acquired by the multiple groups of visible light vision sensors by using a TensorFlowYOLO v3 deep learning framework to obtain the obstacle information in front of and at the side of each trolley of the electric locomotive;
the third data processing module is used for carrying out image processing on the barrier information acquired by the infrared light vision sensors and dividing the ROI according to the heat gray value to obtain the barrier information in front of and at the side of each trolley of the electric locomotive;
and the data fusion module is used for training a classifier by using historical obstacle information, and dynamically fusing the obstacle information acquired by the first data processing module, the second data processing module and the third data processing module according to the classifier and the confidence coefficient to obtain the position and the type of the obstacle.
Further, the positioning module includes:
the positioning information processing module is used for receiving wireless positioning information of each trolley from a plurality of positioning base stations by using ultra-wideband carrier UWB and carrying out smooth time domain filtering processing on the wireless positioning information;
the positioning information fusion module is used for correcting mileage data acquired by the odometer of the electric locomotive by using the wireless positioning information after the smooth time domain filtering processing when the electric locomotive is positioned at the shield tunnel portal or each trolley is positioned in the positioning range of the ultra-wideband carrier UWB; and when the position of each trolley exceeds the positioning range of the UWB, positioning the electric locomotive by using the mileage data acquired by the odometer of the electric locomotive.
Further, the early warning module includes:
the plurality of audible and visual alarms are respectively arranged on each trolley of the electric locomotive, and are used for giving an alarm when the electric locomotive is close to an obstacle;
and the vehicle braking device is used for braking the electric locomotive when the speed of the electric locomotive exceeds a preset speed value.
Further, still include:
the two bidirectional positioning units are respectively arranged on a tail area of the shield machine trolley and a tunnel portal and are used for acquiring the distance between the electric locomotive and the tunnel portal;
and when the distance of the electric locomotive relative to the tunnel portal is gradually reduced, the plurality of audible and visual alarms give an alarm.
Further, still include:
and the manual monitoring module is used for carrying out intelligent early warning and risk control intervention monitoring on the electric locomotive by adopting a manual method according to the acquired position and type of the obstacle and the speed information and position of the electric locomotive.
The invention also provides an intelligent early warning method of the shield tunnel electric locomotive, which comprises the following steps:
acquiring different types of obstacle information around each trolley of an electric locomotive to be early-warned running in a shield tunnel;
acquiring the speed information and the position information of each trolley of the electric locomotive, and acquiring the position information of the electric locomotive relative to a tunnel portal;
dynamically fusing various different types of obstacle information by using a combination algorithm to obtain the position and the type of the obstacle;
fusing the position information of each trolley and the obtained position information of the electric locomotive relative to the tunnel portal by using a combination algorithm to determine the position of the electric locomotive;
the early warning module is used for starting early warning or braking the electric locomotive according to the distance between the position of the electric locomotive and the barrier in the shield tunnel; and when the speed of the electric locomotive exceeds a preset speed value, starting early warning and braking the electric locomotive.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides information such as locomotive position, state, road condition, potential risk and the like for the driver end and the ground monitoring end of the electric locomotive in real time based on technologies such as wireless positioning and communication, visual perception measurement, laser measurement, panoramic video monitoring, active braking, deep learning, artificial intelligence and the like, can adopt an active braking control strategy before determining accident risk and provides guarantee for safe running of the electric locomotive in a tunnel.
According to the invention, by researching wireless positioning, monitoring and early warning, obstacle target identification and active brake control strategies in the tunnel, wherein the wireless positioning in the tunnel uses an ultra-wideband carrier and odometer combined algorithm, ultra-wideband carrier base stations are respectively arranged at the tunnel portal and the front end of the shield, and the position is corrected in real time by using an odometer in the midway; the monitoring and early warning comprises common video monitoring to avoid sight blind areas and vehicle sliding monitoring, and accidents such as injury of people and damage of mechanical equipment are effectively avoided due to vehicle sliding caused by load change when a rail with a slope stops.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic structural diagram of an intelligent early warning system of a shield tunnel electric locomotive according to the present invention;
FIG. 2 is a diagram of an apparatus layout in an embodiment of the present invention;
FIG. 3 is a block diagram of an instrument connection configuration in an embodiment of the invention;
fig. 4 is a schematic flow chart of a method in an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, but it should be understood that the scope of the present invention is not limited by the specific embodiments.
Example 1
As shown in fig. 1, the present invention provides an intelligent early warning system for shield tunnel electric locomotive, which comprises:
the obstacle information acquisition module is used for acquiring different types of obstacle information around each trolley of the electric locomotive to be pre-warned running in the shield tunnel;
the electric locomotive state information acquisition module is used for acquiring the speed information and the position information of each trolley of the electric locomotive and acquiring the position information of the electric locomotive relative to the tunnel portal;
the barrier identification module is used for dynamically fusing barrier information of different types by utilizing a combined algorithm to obtain the position and the type of the barrier;
the positioning module is used for fusing the position information of each trolley and the obtained position information of the electric locomotive relative to the tunnel portal by using a combination algorithm to determine the position of the electric locomotive;
the early warning module is used for starting early warning or braking the electric locomotive according to the distance between the position of the electric locomotive and the barrier in the shield tunnel; when the speed of the electric locomotive exceeds a preset speed value, starting early warning and braking the electric locomotive;
and the vehicle braking device is used for starting early warning on the electric locomotive and obstacles around the electric locomotive when the speed of the electric locomotive exceeds a preset speed value, and braking the electric locomotive.
The obstacle information acquisition module in the invention comprises:
the system comprises a plurality of groups of radars, a plurality of groups of sensors, a plurality of sensors and a plurality of sensors, wherein the radars are respectively arranged on each trolley of the electric locomotive and are respectively used for scanning millimeter wave point cloud data of obstacles around each trolley;
the system comprises a plurality of groups of double-light fusion vision sensors, a plurality of groups of double-light fusion vision sensors and a plurality of image processing units, wherein the double-light fusion vision sensors are respectively arranged on each trolley of the electric locomotive and are respectively used for scanning video data information of obstacles around each trolley;
the double-light fusion vision sensor comprises a visible light vision sensor and an infrared light vision sensor.
The electric locomotive state information acquisition module comprises:
the system comprises a plurality of positioning base stations, a plurality of positioning base stations and a plurality of positioning base stations, wherein two positioning base stations are respectively arranged at the tunnel portal of the shield tunnel and the front end of the shield machine and are used for acquiring the position information of the electric locomotive relative to the tunnel portal; the other positioning base stations are respectively arranged on the roofs of all trolleys of the electric locomotives and are respectively used for acquiring the position information of all trolleys;
the laser velocimeters are respectively arranged on the side surfaces of the trolleys of the electric locomotive and are respectively used for acquiring the speed information of the trolleys.
The obstacle recognition module of the invention comprises:
the first data processing module is used for performing time domain filtering, spatial domain filtering and K-mean clustering on millimeter wave point cloud data respectively acquired by a plurality of groups of radars to obtain barrier information in front of and on the side of each trolley of the electric locomotive;
the second data processing module is used for classifying and screening the obstacle information acquired by the multiple groups of visible light vision sensors by using a TensorFlowYOLO v3 deep learning framework to obtain the obstacle information in front of and at the side of each trolley of the electric locomotive;
the third data processing module is used for carrying out image processing on the barrier information acquired by the infrared light visual sensors and dividing the ROI according to the heat gray value to obtain the barrier information in front of and at the side of each trolley of the electric locomotive;
and the data fusion module is used for training a classifier by using the historical obstacle information, and dynamically fusing the obstacle information acquired by the first data processing module, the second data processing module and the third data processing module according to the classifier and the confidence coefficient to obtain the position and the type of the obstacle.
In the obstacle identification process:
(1) The confidence coefficient of the obstacle information in front of and at the side of each trolley of the electric locomotive is higher by performing time domain filtering, space domain filtering and K-mean clustering on the millimeter wave point cloud data respectively acquired by the plurality of groups of radars;
(2) Classifying and screening the obstacle information acquired by the multiple groups of visible light visual sensors by utilizing a TensorFlow YoLO v3 deep learning framework to obtain the obstacle information in front of and at the side of each trolley of the electric locomotive, wherein the obtained obstacle information has high recognition degree and can recognize the obstacle information with certain false alarm;
(3) The method mainly identifies the human body obstacles by processing images of the obstacle information acquired by the infrared visual sensors and dividing ROI areas according to the heat gray value to obtain the obstacle information in front of and beside each trolley of the electric locomotive.
The obstacle target recognition method has the core that scene information acquired by the millimeter wave radar and the double-light fusion vision sensor is analyzed based on a deep learning algorithm, a complete algorithm classifier is trained by combining a field obstacle target, the obstacle can be recognized accurately in a long distance, the false alarm probability is low, and self-learning optimization is continuously performed.
The positioning module in the invention comprises:
the positioning information processing module is used for receiving wireless positioning information of each trolley from a plurality of positioning base stations by using ultra-wideband carrier UWB and carrying out smooth time domain filtering processing on the wireless positioning information;
the positioning information fusion module is used for correcting mileage data acquired by the odometer of the electric locomotive by using the wireless positioning information after the smoothing time domain filtering treatment when the electric locomotive is positioned at the shield tunnel portal or each trolley is positioned in the positioning range of the ultra-wideband carrier UWB; and when the position of each trolley exceeds the positioning range of the UWB, positioning the electric locomotive by using the mileage data acquired by the odometer of the electric locomotive.
When the UWB is used for receiving wireless positioning information, the fluctuation is large occasionally, smooth time domain filtering is needed, and meanwhile, the positioning range of the UWB is only D (the typical value is 200 m), and the use is limited by the distance; the odometer of the electric locomotive is a contact sensor, accumulated errors can be generated after long-time running, and particularly in the environment with much water and mud on the spot, the confidence coefficient of the data after long-time running is not high. Therefore, a fusion protocol is used, which is specifically as follows:
and (3) correcting odometer data by using ultra-wideband carrier UWB filtering data in the range D of the shield tunnel portal and the rack of the electric locomotive, completely positioning by using the odometer when the range D is exceeded, correcting the odometer by using the ultra-wideband carrier UWB data by using construction information when the range D is entered next time, and gradually circulating to obtain high-precision positioning in the full tunnel range.
The early warning module in the invention comprises:
the plurality of audible and visual alarms are respectively arranged on each trolley of the electric locomotive, and are used for alarming when the electric locomotive is close to an obstacle in a tail area of the shield tunneling machine trolley and a tunnel portal;
and the vehicle braking device is used for braking the electric locomotive when the speed of the electric locomotive exceeds a preset speed value.
The invention also comprises:
the two bidirectional positioning units are respectively arranged on the tail area of the shield machine trolley and the tunnel portal and are used for acquiring the distance between the electric locomotive and the tunnel portal;
and when the distance of the electric locomotive relative to the tunnel portal is gradually reduced, the plurality of audible and visual alarms give an alarm.
The invention also comprises:
and the manual monitoring module is used for carrying out intelligent early warning and risk control intervention monitoring on the electric locomotive by adopting a manual method according to the acquired position and type of the obstacle and the speed information and position of the electric locomotive.
The invention also provides an intelligent early warning method of the shield tunnel electric locomotive, which comprises the following steps:
acquiring different types of obstacle information around each trolley of an electric locomotive to be early-warned running in a shield tunnel;
acquiring the speed information and the position information of each trolley of the electric locomotive, and acquiring the position information of the electric locomotive relative to a tunnel portal;
dynamically fusing various types of obstacle information by using a combined algorithm to obtain the position and the type of the obstacle;
fusing the position information of each trolley and the obtained position information of the electric locomotive relative to the tunnel portal by using a combined algorithm to determine the position of the electric locomotive;
the early warning module is used for starting early warning or braking the electric locomotive according to the distance between the position of the electric locomotive and the barrier in the shield tunnel; and when the speed of the electric locomotive exceeds the preset speed, starting early warning and braking the electric locomotive.
The following description will be made with reference to specific examples.
1. As shown in fig. 2-3, in the embodiment, the parameter indexes are as follows:
the external dimension of the electric locomotive head is 8020 × 1500 × 2300mm;
the maximum hourly speed of the electric locomotive is 12km/h;
the normal running speed of the electric locomotive is 10km/h;
maximum traction 148KN of the electric locomotive;
the total length and the diameter of the shield tunnel in the implementation case are 2000m and 5m respectively;
the total length of four carriages of the car head, the slag car, the mortar car and the flat car is 35m;
the terrain is highest at the center of the tunnel, and the gradient from the tunnel entrance to the center of the tunnel and the gradient from the center of the tunnel to the tail of the shield machine station are both 1.5 percent.
For detecting the intelligent early warning and risk control intervention capability of the system, arranging a 500 x 500mm paper box as a first barrier on a motor vehicle track 500m away from the interior of the tunnel portal;
one person is arranged on the track of the electric locomotive at the position 1000m away from the center of the tunnel and serves as a second barrier;
and (3) manually setting a 500 x 500mm paper box for dropping the side wall of the tunnel as a third barrier when the distance is 500m away from the tail of the shield machine station.
The shield tunnel electric locomotive is integrally divided into six areas, namely a locomotive head area, a slag car area, a mortar car area, a flat car area, a tunnel portal area and a shield tunnel trolley tail area, and equipment in different areas is arranged as follows:
a locomotive head area: the first radar, the first laser velocimeter, the first positioning base station and the first video monitor to the fourth video monitor are respectively connected with the first wireless communication module through leads; the first audible and visual alarm is connected with the second wireless communication module through a lead;
a slag car area: the fifth radar reaches an eighth radar, the second laser velocimeter, the second positioning base station, the fifth video monitor and the eighth video monitor, and the fifth radar, the second laser velocimeter, the second positioning base station and the fifth video monitor are respectively connected with the third wireless communication module through leads; the second audible and visual alarm is connected with the fourth wireless communication module through a lead;
a mortar vehicle area: the ninth radar reaches a twelfth radar, the third laser velocimeter, the third positioning base station and the ninth video monitoring to the twelfth video monitoring are respectively connected with the fifth wireless communication module through leads; the third audible and visual alarm is connected with the sixth wireless communication module through a wire;
a flat car area: the thirteenth to sixteenth radar, the fourth laser velocimeter, the fourth positioning base station, the thirteenth to sixteenth video monitoring and seventh wireless communication module are connected through conducting wires; the fourth audible and visual alarm is connected with the eighth wireless communication module through a wire;
the tail area of the shield machine trolley: the fifth positioning base station is connected with the ninth wireless communication module through a lead, and the fifth audible and visual alarm is connected with the tenth wireless communication module through a lead;
tunnel portal area: the sixth positioning base station is connected with the eleventh wireless communication module through a lead; the sixth audible and visual alarm is connected with the twelfth wireless communication module through a wire;
the vehicle braking device is connected with the centralized wireless communication module through a lead; the centralized wireless communication module is connected with the central processing unit through a lead
The radar adopts FLS-CH30 millimeter wave radars which are arranged at the vehicle head, the vehicle tail and two sides of the vehicle body;
the video monitoring adopts MV-CE200-10UC industrial cameras which are arranged at the head, the tail and two sides of the vehicle body;
the laser velocimeter adopts a MicroCenti Doppler velocimeter with the velocimetry precision of 0.3km/h, and is arranged at two sides of the vehicle body;
the positioning base station adopts an FU-GA-OD-01 type UWB outdoor positioning base station with the positioning precision of 0.5m and is arranged on the roof of the vehicle;
the wireless communication modules adopt wireless digital signal transceiver stations and are arranged on the roof and the top of the tunnel opening;
the central processing unit is a single chip microcomputer which is independently developed and has deep learning and artificial intelligence, and is arranged in a locomotive carriage;
the audible and visual alarm is arranged on the vehicle roof;
the vehicle braking device is arranged on the locomotive wheels.
2. Receiving state signals of the electric locomotive through a radar, a video monitoring device, a laser velocimeter and a positioning base station; the state signal of the electric locomotive is transmitted to the central processing unit through the wireless communication module; the central processing unit obtains an intelligent early warning and risk control intervention strategy of the electric locomotive through a combination algorithm according to the state signals of the electric locomotive;
the system comprises a radar, a video monitoring unit, a central processing unit, an audible and visual alarm, a vehicle braking device, a vehicle speed monitoring unit and a vehicle speed monitoring unit, wherein the radar and the video monitoring unit are used for monitoring, detecting and acquiring barrier and personnel signals in a scanning range of the radar and the video monitoring unit, and when the central processing unit judges that a set value is exceeded, the central processing unit sends a starting signal to the audible and visual alarm to perform early warning and activates the vehicle braking device to control the vehicle speed;
the central processing unit sends a starting signal to the audible and visual alarm to perform early warning when judging that the speed exceeds a set speed value or a risk source through a combined algorithm, and activates a vehicle braking device to control the speed and prevent sliding;
when the central processing unit sets the range through a combination algorithm, the electric locomotive normally runs, and the audible and visual alarm and the vehicle braking device are not started.
3. As shown in fig. 4, the specific implementation method:
step S1: before the intelligent early warning system is used, whether all equipment of the system is normal or not is observed through a cab monitoring screen, the power supply condition of a battery pack is checked, and whether a signal transmission channel is smooth or not is judged. After the detection is finished, the electric locomotive is started from a muck and mortar unloading plant after loading fins and runs to the tunnel entrance at a constant speed of 10 km/h.
Monitoring and detecting information of personnel and obstacles around the electric locomotive from the first radar to the twelfth radar and from the first video monitoring to the sixteenth video;
detecting the position information of the electric locomotive from the first positioning base station to the sixteenth positioning base station;
the first velocimeter to the fourth velocimeter detect the speed information of the electric locomotive;
the acquired information is transmitted to the centralized wireless communication module through the first wireless communication module, the twelfth wireless communication module and the vehicle intelligent braking module, finally the information reaches the central processing unit, and the state of the electric locomotive is judged through a comprehensive algorithm;
step S2: when the distance between the fourth positioning base station and the sixth positioning base station is less than 300m, the electric locomotive runs to the tail of the flat car close to the tunnel entrance area, signals are transmitted to the centralized wireless communication module through the seventh wireless communication module, the eighth wireless communication module, the eleventh wireless communication module and the twelfth wireless communication module, and the signals enter the central processing unit to be comprehensively calculated and judged:
starting a preset program, controlling the speed of the vehicle to be reduced to 6km/h, the speed of the vehicle to be reduced to 3km/h at a position away from 200m and the speed of the vehicle to be reduced to 1km/h at a position away from 100m through a vehicle braking device, and turning on a fourth audible and visual alarm and a sixth audible and visual alarm;
when the distance between the first positioning base station and the sixth positioning base station is more than 300m, transmitting signals to the centralized wireless communication module through the first wireless communication module, the second wireless communication module, the eleventh wireless communication module and the twelfth wireless communication module, reaching the central processing unit for comprehensive calculation and judgment, controlling the vehicle speed to gradually recover to 10km/h through the vehicle braking device, and turning off the audible and visual alarm;
when the distance between the fifth positioning base station and the sixth positioning base station is less than 300m, the electric locomotive runs to the tail of the flat car close to the tail of the shield machine, signals are transmitted to the centralized wireless communication module through the seventh wireless communication module, the eighth wireless communication module, the ninth wireless communication module and the tenth wireless communication module, the signals enter the central processing unit to be comprehensively calculated and judged, a preset program is started, the speed of the electric locomotive is controlled to be reduced to 6km/h through the vehicle braking device, the speed of the electric locomotive is reduced to 3km/h from a position 200m, the speed of the electric locomotive is reduced to 1km/h from a position 100m, the fourth audible and visual alarm and the fifth audible and visual alarm are turned on, the electric locomotive is braked and stopped when the distance is 5m, and the audible and visual alarm is turned off;
and step S3: when the electric locomotive runs to a distance of 50m from the first obstacle, a sixteenth radar and a sixteenth video monitor and scan that the obstacle exists on the track of the electric locomotive, the seventh wireless communication module and the eighth wireless communication module transmit signals to the centralized wireless communication module, the signals reach the central processing unit, the obstacle is determined after comprehensive judgment through a preset program, the speed of the electric locomotive is controlled to be reduced to 2km/h through a vehicle braking device, a fourth acousto-optic alarm is started, when the electric locomotive brakes and stops at a position of 1m from the obstacle, the electric locomotive is manually removed, the electric locomotive gradually recovers the speed of the electric locomotive to normally run for 10km/h, and the acousto-optic alarm is turned off;
the detection flow and method of the second barrier are completely consistent with the barrier, and the database needs to be continuously learned and updated only when the central processing unit comprehensively judges, so that more types of barriers can be judged;
when the electric locomotive runs to a distance of 1m from a third obstacle, the obstacle is thrown into the electric locomotive, in the embodiment, the collision point of the obstacle is in a slag car area, an eighth radar and an eighth video monitor scan that the obstacle exists at a position smaller than 1m of the electric locomotive, signals are transmitted to a centralized wireless communication module through a third wireless communication module and a fourth wireless communication module, the signals reach a central processing unit, the obstacle is determined to be the obstacle after comprehensive judgment through a preset program, the distance is smaller than 1m, emergency braking and parking are carried out through a vehicle braking device, a second acousto-optic alarm is started, after the obstacle is manually removed, the electric locomotive gradually recovers the speed of 10km/h to normally run, and the acousto-optic alarm is turned off;
and step S4: the electric locomotive is a climbing section from a tunnel entrance to a tunnel center, stops in the climbing section, manually simulates the sliding condition, a first laser velocimeter measures information that the speed of the electric locomotive is a negative value, a first positioning base station and a sixth positioning base station measure information that the distance is reduced, the two information are transmitted to a centralized wireless communication module through a first wireless communication module and a second wireless communication module, the two information are comprehensively calculated by a central processing unit, the speed information is judged, the combined algorithm of broadband carrier waves and a mileometer is utilized, the electric locomotive is in the sliding condition, the electric locomotive is emergently braked and stopped through a vehicle braking device, a first audible and visual alarm is started, the electric locomotive is restarted, and the audible and visual alarm is turned off after the speed of the electric locomotive is recovered to 10km/h and the distance measurement is normal; the electric locomotive is a downhill section from the center of the tunnel to the tail of the shield machine platform, the speed is artificially increased to 12km/h, the overspeed condition is simulated, the speed measured by the first laser velocimeter exceeds the normal speed by 10km/h, information is transmitted to the centralized wireless communication module through the first wireless communication module and the second wireless communication module, the central processing unit judges that the electric locomotive is in the overspeed condition, the first audible and visual alarm is started, the vehicle is braked by the vehicle brake device and the speed is controlled to 10km/h, and the audible and visual alarm is closed after the speed is recovered;
step S5: besides the intelligent braking strategy of the central processing unit of the shield tunnel electric locomotive, the strategy of monitoring and correcting by matching manpower is as follows:
the electric locomotive driving screen can show the position, the speed, the front and rear images and the obstacle detection condition of the electric locomotive, and uploads all state information of the electric locomotive to a real-time efficient data transmission link of a ground monitoring center.
A driver is required to be arranged in the operation of the electric locomotive, a monitoring screen is arranged in a cab of the locomotive head of the electric locomotive, the driver can play a role in monitoring an intelligent early warning and risk control intervention system, and the electric locomotive is operated by adopting a manual method under an emergency condition.
Software and hardware of the intelligent early warning and risk control intervention system need to be checked and maintained every day, damaged hardware is found and replaced in time, and bugs and defects of the software are upgraded.
Finally, the description is as follows: the above disclosure is only one specific embodiment of the present invention, however, the present invention is not limited thereto, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present invention.
Claims (9)
1. The utility model provides a shield tunnel electric locomotive's intelligent early warning system which characterized in that includes:
the obstacle information acquisition module is used for acquiring different types of obstacle information around each trolley of the electric locomotive to be pre-warned, which runs in the shield tunnel;
the electric locomotive state information acquisition module is used for acquiring the speed information and the position information of each trolley of the electric locomotive and acquiring the position information of the electric locomotive relative to the tunnel portal;
the barrier identification module is used for dynamically fusing barrier information of different types by utilizing a combined algorithm to obtain the position and the type of the barrier;
the positioning module is used for fusing the position information of each trolley and the obtained position information of the electric locomotive relative to the tunnel portal by using a combination algorithm to determine the position of the electric locomotive;
the early warning module is used for starting early warning or braking the electric locomotive according to the obtained position and type of the barrier; and when the speed of the electric locomotive exceeds a preset speed value or the electric locomotive is close to the tunnel portal, starting early warning and braking the electric locomotive.
2. The intelligent early warning system of the shield tunnel electric locomotive according to claim 1, characterized in that: the obstacle information acquisition module includes:
the system comprises a plurality of groups of radars, a plurality of groups of sensors, a plurality of sensors and a plurality of sensors, wherein the radars are respectively arranged on each trolley of the electric locomotive and are respectively used for scanning millimeter wave point cloud data of obstacles around each trolley;
the system comprises a plurality of groups of double-light fusion vision sensors, a plurality of groups of double-light fusion vision sensors and a plurality of image processing units, wherein the double-light fusion vision sensors are respectively arranged on each trolley of the electric locomotive and are respectively used for scanning video data information of obstacles around each trolley;
the double-light fusion vision sensor comprises a visible light vision sensor and an infrared light vision sensor.
3. The intelligent early warning system of the shield tunnel electric locomotive according to claim 1, characterized in that: the electric locomotive state information acquisition module comprises:
the system comprises a plurality of positioning base stations, a plurality of positioning base stations and a plurality of positioning base stations, wherein two positioning base stations are respectively arranged at a shield tunnel portal and the front end of a shield machine and are used for acquiring the position information of an electric locomotive relative to the tunnel portal; the other positioning base stations are respectively arranged on the roofs of all trolleys of the electric locomotive and are respectively used for acquiring the position information of all trolleys;
the laser velocimeters are respectively arranged on the side surfaces of the trolleys of the electric locomotive and are respectively used for acquiring the speed information of the trolleys.
4. The intelligent early warning system of the shield tunnel electric locomotive according to claim 2, characterized in that: the obstacle identification module includes:
the first data processing module is used for performing time domain filtering, spatial domain filtering and K-mean clustering on the millimeter wave point cloud data respectively acquired by the plurality of groups of radars to obtain the information of obstacles in front of and at the side of each trolley of the electric locomotive;
the second data processing module is used for classifying and screening the obstacle information acquired by the multiple groups of visible light vision sensors by using a TensorFlowYOLO v3 deep learning framework to obtain the obstacle information in front of and at the side of each trolley of the electric locomotive;
the third data processing module is used for carrying out image processing on the barrier information acquired by the infrared light vision sensors and dividing the ROI according to the heat gray value to obtain the barrier information in front of and at the side of each trolley of the electric locomotive;
and the data fusion module is used for training a classifier by using historical obstacle information, and dynamically fusing the obstacle information acquired by the first data processing module, the second data processing module and the third data processing module according to the classifier and the confidence coefficient to obtain the position and the type of the obstacle.
5. The intelligent early warning system of the shield tunnel electric locomotive according to claim 3, characterized in that: the positioning module comprises:
the positioning information processing module is used for receiving wireless positioning information of each trolley by a plurality of positioning base stations by using ultra-wideband carrier UWB and carrying out smooth time domain filtering processing on the wireless positioning information;
the positioning information fusion module is used for correcting mileage data acquired by the odometer of the electric locomotive by using wireless positioning information after smooth time domain filtering when the electric locomotive is positioned at a shield tunnel portal or each trolley is positioned in a positioning range of ultra wide band carrier UWB; and when the position of each trolley exceeds the positioning range of the UWB, positioning the electric locomotive by using the mileage data acquired by the odometer of the electric locomotive.
6. The intelligent early warning system of the shield tunnel electric locomotive according to claim 1, characterized in that: the early warning module includes:
the plurality of audible and visual alarms are respectively arranged on each trolley of the electric locomotive, and are used for giving an alarm when the electric locomotive is close to an obstacle;
and the vehicle braking device is used for braking the electric locomotive when the speed of the electric locomotive exceeds a preset speed value.
7. The intelligent early warning system of the shield tunnel electric locomotive according to claim 6, characterized in that: further comprising:
the two bidirectional positioning units are respectively arranged on a tail area of the shield machine trolley and a tunnel portal and are used for acquiring the distance between the electric locomotive and the tunnel portal;
and when the distance of the electric locomotive relative to the tunnel portal is gradually reduced, the plurality of audible and visual alarms give an alarm.
8. The intelligent early warning system of the shield tunnel electric locomotive according to claim 1, characterized in that: further comprising:
and the manual monitoring module is used for carrying out intelligent early warning and risk control intervention monitoring on the electric locomotive by adopting a manual method according to the acquired position and type of the obstacle and the speed information and position of the electric locomotive.
9. An intelligent early warning method for shield tunnel electric locomotives is characterized by comprising the following steps: the method comprises the following steps:
acquiring different types of obstacle information around each trolley of an electric locomotive to be early-warned running in a shield tunnel;
acquiring the speed information and the position information of each trolley of the electric locomotive, and acquiring the position information of the electric locomotive relative to a tunnel portal;
dynamically fusing various different types of obstacle information by using a combination algorithm to obtain the position and the type of the obstacle;
fusing the position information of each trolley and the obtained position information of the electric locomotive relative to the tunnel portal by using a combination algorithm to determine the position of the electric locomotive;
starting early warning or braking the electric locomotive according to the distance between the position of the electric locomotive and the barrier in the shield tunnel; and when the speed of the electric locomotive exceeds a preset speed value, starting early warning and braking the electric locomotive.
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| CN117734683A (en) * | 2024-02-19 | 2024-03-22 | 中国科学院自动化研究所 | Underground vehicle anti-collision safety early warning decision-making method |
| CN117734683B (en) * | 2024-02-19 | 2024-05-24 | 中国科学院自动化研究所 | Underground vehicle anti-collision safety early warning decision-making method |
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