CN113501029A - Urban railway barrier and derailment detection device and method - Google Patents
Urban railway barrier and derailment detection device and method Download PDFInfo
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- CN113501029A CN113501029A CN202110904278.1A CN202110904278A CN113501029A CN 113501029 A CN113501029 A CN 113501029A CN 202110904278 A CN202110904278 A CN 202110904278A CN 113501029 A CN113501029 A CN 113501029A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L23/00—Control, warning or like safety means along the route or between vehicles or trains
- B61L23/04—Control, warning or like safety means along the route or between vehicles or trains for monitoring the mechanical state of the route
- B61L23/041—Obstacle detection
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
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Abstract
The invention provides a device and a method for detecting urban railway obstacles and derailment, which comprises the following steps: the system comprises a sensing module, a storage module, a communication module and a passive obstacle avoidance device, wherein the communication module transmits early warning signals provided by the sensing module to a train control system in time; the passive obstacle avoidance device is arranged on a bogie at the front end of the train and comprises a mechanical system and an electrical system, wherein the mechanical system is used for converting energy in the collision process into elastic potential energy and converting collision displacement generated in the collision process into an electric signal to be transmitted to the electrical system to control a braking system of the train; the sensing module and the signal output end of the passive obstacle avoidance device are connected with the signal input end of the sensing module, the signal output end of the sensing module is connected with the signal input ends of the storage module and the communication module, and therefore the accuracy of collision risk and derailment risk judgment is greatly improved.
Description
Technical Field
The invention relates to the technical field of train operation safety detection and control, in particular to a device and a method for detecting urban railway obstacles and derailment.
Background
With the continuous development of science and technology, the railway construction of China also develops rapidly, and a large number of express trains and express trains start to run after the speed is increased for a plurality of times in recent years. After the sixth speed increase in 2007, the harmonious motor train unit is driven by the railway department, and the speed per hour can reach more than 200 kilometers. Especially, high-speed railways such as Jingguang high-speed railway, Jinghu high-speed railway and Hukun high-speed railway are built successively, and the maximum operation speed reaches 350 km. In addition to wheel tracks, maglev trains are also used as part of inter-city rail traffic. Under the condition that the running speed of a train is higher and higher, the running safety problem of the train is also more and more emphasized, and based on the rapid development of an automatic driving technology, the detection of obstacles and derailment in the running process of the train is more important.
Because different sensors acquire different information dimensions, the information needs to be unified through multi-sensor calibration, and the mapping relation of data among the sensors is determined through calibrating parameters of the sensors. And sensor information acquisition does not take place instantaneously, and within a certain time, train and barrier can produce relative displacement, therefore, the asynchronous of sensor data collection can lead to barrier and derailment risk judgement result deviation great if not handling.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a device and a method for detecting obstacles and derailment of an urban railway, which optimize the structure of the obstacle and derailment detection device, optimize and improve the collision and derailment risk detection mode by adopting the device, and greatly improve the accuracy of collision risk and derailment risk judgment by combining a deep learning technology and a data fusion technology, thereby ensuring the high-speed stable operation of a train.
According to a first aspect of the present invention, there is provided an urban railway obstacle and derailment detection apparatus, comprising:
a sensing module, a storage module, a communication module and a passive obstacle avoidance device,
the sensing module comprises a millimeter wave radar, a laser radar, a visual camera, an infrared camera and a plurality of data acquisition sensors, and is respectively used for detecting the information of obstacles in front of the train and the parameters required by derailment judgment;
the sensing module is used for completing barrier detection, barrier tracking and track line detection functions by carrying out computer vision and a deep learning algorithm on the vehicle-mounted host, analyzing and processing data and providing an early warning signal;
the storage module stores data information in the driving process so as to facilitate the review of train running conditions in early warning history, and provides training data for the detection device for deep learning;
the communication module transmits the early warning signal provided by the sensing module to a train control system in time;
the passive obstacle avoidance device is arranged on a bogie at the front end of the train and comprises a mechanical system and an electrical system, wherein the mechanical system is used for converting energy in the collision process into elastic potential energy and converting collision displacement generated in the collision process into an electric signal to be transmitted to the electrical system to control a braking system of the train;
the signal output ends of the sensing module and the passive obstacle avoidance device are connected with the signal input end of the sensing module, and the signal output end of the sensing module is connected with the signal input ends of the storage module and the communication module.
On the basis of the technical scheme, the invention can be improved as follows.
Optionally, the system further comprises an active obstacle avoidance device, a signal output end of the active obstacle avoidance device is connected with a signal input end of the sensing module, and the active obstacle avoidance device and the passive obstacle avoidance device operate independently.
Optionally, the mechanical system of the passive obstacle avoidance device includes a detection beam, a detection assembly, and a sliding assembly; the detection assembly is arranged on a detection cross beam, and the detection cross beam is connected with the sliding assembly, so that the detection cross beam can slide relative to the sliding assembly; the detection assembly comprises a connecting frame, a sealed box body and a fixed support, the connecting frame fixedly connects the sealed box body to the detection cross beam, and the fixed support connects the sealed box body with the sliding assembly; the sealed box body is internally provided with a rack assembly, a plate spring, a shifting needle limiting frame, a switch bracket, a proximity switch, a limiting switch and a displacement sensor, the plate spring and the shifting needle limiting frame are fixed on the side surface of the upper end of the rack assembly, one end of the plate spring penetrates through the switch bracket to be connected with the rack assembly, the other end of the plate spring is connected with a fixed bracket or the inner wall of the sealed box body, the proximity switch and the limiting switch are fixed on the upper end of the switch bracket through a mounting bracket, the shifting needle of the limiting switch penetrates through the switch bracket and is limited through the shifting needle limiting frame, and a detection rod of the displacement sensor is in contact with the plate spring; and a steel wire connecting piece is arranged between the outer side of the sealing box body and the detection cross beam.
According to a second aspect of the present invention, there is provided a method for detecting an obstacle and derailment of an urban railway, comprising the steps of:
the method comprises the following steps: the detection device is adopted to simultaneously carry out collision detection and derailment detection on the train in the running process;
step two: preprocessing the acquired information, namely performing image target segmentation on data information generated in the collision detection and derailment detection processes, separating objects in the image from a background environment through a deep learning algorithm, determining contour information of different objects, and simultaneously performing track line segmentation to determine the direction of a track;
step three: carrying out data fusion and feature extraction on the preprocessed data information, removing the influence of noise by adopting a Kalman filtering algorithm and utilizing the dynamic information of a target, unifying the coordinates of the segmented image data and the point cloud data obtained from a laser radar and a millimeter wave radar, and determining the coordinates and the relative speed of an object in the image in space;
step four: judging obstacles and derailment risks according to the data information processed in the third step, calculating whether the object has the risk of collision with the train body according to the coordinates and the relative speed of the object and the direction of the track, simultaneously acquiring train acceleration data provided by derailment detection by the vehicle-mounted host, and judging whether the train body has the risk of derailment or not by comparing the acceleration with a set threshold;
step five: and based on the judgment of the fourth step, if the train has collision or derailment risk, the vehicle-mounted host sends an alarm signal to the train control system so as to perform emergency braking on the train.
Optionally, during derailment detection, an acceleration signal of a train axle is collected, whether the running speed of the train is greater than a set value is detected, the acceleration signal is analyzed, whether the acceleration signal needs to be bypassed is judged, and an alarm signal is output to the TCMS, the VCU and the EB-LOOP.
Optionally, the derailment detection function is configured with a plurality of derailment detection control units, when the derailment detection fails, each derailment detection control unit and the train emergency safety isolation loop bypass are bypassed, and the derailment detection bypass function can be stopped by manual operation on the cab.
Optionally, the alarm signal sent in the fifth step includes parameter information of the obstacles, where the parameter information includes the number, size, shape, type, relative speed, and collision time of the obstacles.
Optionally, the risk level of the obstacle is evaluated according to the parameter information of the obstacle, the risk level parameter is output, the risk level parameter is compared with a set threshold value, whether an emergency braking measure needs to be taken or not is judged, and if the emergency braking measure does not need to be taken, the procedure returns to the procedure entry to wait for processing of next frame data.
Optionally, after the obstacle is detected to disappear at a certain time, the normal state information is output, and the train returns to the train state before the obstacle is detected.
Optionally, when the acceleration signal of the train axle is collected, the acceleration signals in the X-axis direction and the Z-axis direction of the axle are collected at the same time, and the derailment coefficient of the train is calculated according to the acceleration signals so as to determine the derailment risk subsequently.
Drawings
Fig. 1 is a schematic block diagram of an urban railway obstacle and derailment detection device provided by the invention;
FIG. 2 is a schematic structural diagram of an urban railway obstacle and derailment detection device according to the present invention;
FIG. 3 is a schematic cross-sectional view of a detecting assembly of the urban railway obstacle and derailment detecting apparatus according to the present invention;
FIG. 4 is a top view of an urban railway barrier and derailment detection device according to the present invention;
FIG. 5 is a flowchart illustrating the operation of a method for detecting obstacles and derailments in an urban railway according to the present invention;
FIG. 6 is a block avoidance detection flow chart of the method for detecting an obstacle and derailment of an urban railway according to the present invention;
FIG. 7 is a derailment detection flow chart of the urban railway barrier and the derailment detection method according to the present invention;
fig. 8 is a logic diagram of derailment detection of an urban railway obstacle and a method for detecting derailment according to the present invention.
The detection device comprises a detection beam 1, a steel wire connecting piece 2, a detection assembly 3, a fixed support 4, a proximity switch 5, a limit switch 6, a shifting needle limit frame 7, a rack assembly 8, a shifting needle 9, a plate spring 10, a switch support 11, a sealing box body 12 and a connecting frame 13.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Fig. 1 is a device for detecting an obstacle and derailment in an urban railway according to the present invention, as shown in fig. 1, which includes:
a sensing module, a storage module, a communication module and a passive obstacle avoidance device,
the sensing module comprises a millimeter wave radar, a laser radar, a visual camera, an infrared camera and a plurality of data acquisition sensors, and is respectively used for detecting the information of obstacles in front of the train and the parameters required by derailment judgment;
the sensing module is used for completing barrier detection, barrier tracking and track line detection functions by carrying out computer vision and a deep learning algorithm on the vehicle-mounted host, analyzing and processing data and providing an early warning signal;
the storage module stores data information in the driving process so as to facilitate the review of train running conditions in early warning history, and provides training data for the detection device for deep learning;
the communication module transmits the early warning signal provided by the sensing module to a train control system in time;
the passive obstacle avoidance device is arranged on a bogie at the front end of the train and comprises a mechanical system and an electrical system, wherein the mechanical system is used for converting energy in the collision process into elastic potential energy and converting collision displacement generated in the collision process into an electric signal to be transmitted to the electrical system to control a braking system of the train;
the signal output ends of the sensing module and the passive obstacle avoidance device are connected with the signal input end of the sensing module, and the signal output end of the sensing module is connected with the signal input ends of the storage module and the communication module.
It can be understood that, based on the defects in the background art, the embodiment of the invention provides an urban railway barrier and derailment detection device, wherein a sensing module acquires road condition signals by using various integrated sensors. Common sensors are integrated including: infrared, millimeter wave radar, laser radar, vision cameras, and the like. Because a single sensor can not independently complete the sensing task in a complex scene, the integration of multiple sensors is necessary, the advantages and the disadvantages between the sensors are made up, and the whole road condition sensing is carried out. In addition, the data acquisition sensor is used for acquiring other parameters of the train, such as acceleration, speed and the like.
In a possible embodiment, the sensing module further comprises an active obstacle avoidance device, a signal output end of the active obstacle avoidance device is connected with a signal input end of the sensing module, and the active obstacle avoidance device and the passive obstacle avoidance device operate independently.
It can be understood that the active obstacle avoidance device and the passive obstacle avoidance device operate relatively independently, so that the operation of the active obstacle avoidance device and the passive obstacle avoidance device does not affect each other.
In a possible embodiment, the mechanical system of the passive obstacle avoidance device includes a detection beam 1, a detection assembly 3 and a sliding assembly; the detection assembly 3 is arranged on the detection beam 1, and the detection beam 1 is connected with the sliding assembly, so that the detection beam 1 and the sliding assembly can slide relatively; the detection assembly 3 comprises a connecting frame 13, a sealed box body 12 and a fixed support 4, the connecting frame 13 fixedly connects the sealed box body 12 to the detection cross beam 1, and the fixed support 4 connects the sealed box body 12 with the sliding assembly; a rack assembly 8, a plate spring 10, a shifting needle limiting frame 7, a switch bracket 11, a proximity switch 5, a limiting switch 6 and a displacement sensor are arranged in the sealed box body 12, the plate spring 10 and the shifting needle limiting frame 7 are fixed on the side surface of the upper end of the rack assembly 8, one end of the plate spring 10 penetrates through the switch bracket 11 to be connected with the rack assembly 8, the other end of the plate spring is connected with the inner wall of the fixed bracket 4 or the sealed box body 12, the proximity switch 5 and the limiting switch 6 are fixed on the upper end of the switch bracket 11 through mounting brackets, a shifting needle 9 of the limiting switch 6 penetrates through the switch bracket 11 and is limited through the shifting needle limiting frame 7, and a detection rod of the displacement sensor is in contact with the plate spring 10; and a steel wire connecting piece 2 is arranged between the outer side of the sealed box body 12 and the detection beam 1.
It can be understood that the passive obstacle avoidance device is mounted on bogies at both ends of the train, as shown in fig. 2-4. In the running process of the train, when the detection beam 1 of the device collides with an obstacle on a train moving line, the detection beam 1 swings in the opposite direction of the running of the train, and the energy in the collision is converted into elastic potential energy. According to the parameters designed by the detection device, after the elastic potential energy reaches a certain value, a sensor switch is triggered, and the train is emergently braked through a vehicle-mounted control unit of the device. Meanwhile, the emergency braking signal is uploaded to a train control system, and then the emergency braking signal is forwarded to a control center by the train control system.
The electrical system is a vehicle-mounted case arranged in the train electrical cabinet; the systems are connected by cables, and the sensor data in the mechanical system is transmitted to the electrical system.
The energy transmission of the collision of the obstacles is completed through a mechanical system, and after the train collides with the obstacles on the track in the running process, a part of kinetic energy is converted into elastic potential energy of the spring through the detection device. And meanwhile, the spring generates displacement opposite to the movement direction, the displacement is detected by the sensor, when the displacement exceeds a set value, the sensor is triggered to generate a signal and transmit the signal to an electric system, and the electric system disconnects a train braking safety loop to enable the train to generate emergency braking. And simultaneously, the signal is uploaded to a control center through a mine car control system.
In the examples, obstacle detection capability:
triggering conditions are as follows: 1/2mv2>1kx2；
m is barrier mass (Kg), V is relative running speed (km/h) of the train;
the coefficient of stiffness K of the plate spring is 100KN/m, and x is the displacement of the plate spring;
detecting a trigger condition:
the height of the barrier is higher than the lowest height of the cross beam;
the height of the barrier can not be higher than the height of the bottom of the vehicle.
Fig. 5 is a detection flowchart of the urban railway obstacle and derailment detection method provided by the present invention, as shown in fig. 5, which includes the following steps:
the method comprises the following steps: the detection device is adopted to simultaneously carry out collision detection and derailment detection on the train in the running process;
step two: preprocessing the acquired information, namely performing image target segmentation on data information generated in the collision detection and derailment detection processes, separating objects in the image from a background environment through a deep learning algorithm, determining contour information of different objects, and simultaneously performing track line segmentation to determine the direction of a track;
step three: carrying out data fusion and feature extraction on the preprocessed data information, removing the influence of noise by adopting a Kalman filtering algorithm and utilizing the dynamic information of a target, unifying the coordinates of the segmented image data and the point cloud data obtained from a laser radar and a millimeter wave radar, and determining the coordinates and the relative speed of an object in the image in space;
step four: judging obstacles and derailment risks according to the data information processed in the third step, calculating whether the object has the risk of collision with the train body according to the coordinates and the relative speed of the object and the direction of the track, simultaneously acquiring train acceleration data provided by derailment detection by the vehicle-mounted host, and judging whether the train body has the risk of derailment or not by comparing the acceleration with a set threshold;
step five: and based on the judgment of the fourth step, if the train has collision or derailment risk, the vehicle-mounted host sends an alarm signal to the train control system so as to perform emergency braking on the train.
It can be understood that the derailment detection monitors the derailment state of the train through the output change of the acceleration sensor, and directly transmits a derailment signal to the vehicle-mounted host computer once the derailment occurs. After the logic judgment, the vehicle-mounted host directly controls the emergency braking of the train, thereby ensuring the driving safety.
When the system operates, the obstacle avoidance detection part and the derailment detection part operate, detection parameters are calculated through the vehicle-mounted host computer, judgment on obstacles and the driving width of the train is formed, and an early warning signal is sent to the train control system.
The obstacle avoidance detection flowchart is shown in fig. 6.
Because different sensors acquire different information dimensions, the information needs to be unified through multi-sensor calibration, and the mapping relation of data among the sensors is determined through calibrating parameters of the sensors. Firstly, calibrating a camera and a laser radar, searching objects and markers with obvious edges in a generated point cloud projection image, and comparing the objects and markers with an image acquired by the camera. The millimeter wave radar needs to introduce a laser radar as a bridge, the external parameters of the millimeter wave radar and the laser radar are obtained through calculation through the external parameters of the millimeter wave radar and the camera in the same system and the external parameters of the camera and the laser radar, the millimeter wave radar data are projected into a laser radar coordinate system to be fused with the laser point cloud, and a corresponding aerial view is drawn for auxiliary verification.
Synchronization of sensor information is also necessary because sensor information acquisition does not occur instantaneously, but within a certain time, the train and the obstacle will produce relative displacement. We do this by algorithmically controlling temporal synchronization and motion compensation. For the fusion of multi-sensor data, a Kalman filtering algorithm is adopted, the dynamic information of a target is utilized, the influence of noise is removed, and a good estimation about the position of the target is obtained. The Kalman filtering algorithm is a dynamic iterative loop process for predicting and estimating the target detection state at the next moment of the target according to the detection state of the target at the current moment.
The detailed operation process is as follows:
firstly, data acquisition is carried out through a vision camera, a laser radar and a millimeter wave radar, and data required by detection is transmitted to a vehicle-mounted host. The vision camera provides image data in front of the vehicle; the laser radar provides 3D point cloud data in front of the vehicle; the millimeter wave radar provides 2D point cloud data in front of the train. And then, carrying out image target segmentation on the image data, separating the object in the image from the background environment through a depth learning algorithm, and determining the contour information of different objects. In this flow, the track line division is performed at the same time to determine the track direction. And next, performing sensor feature fusion, unifying the coordinates of the segmented image data and the point cloud data obtained from the laser radar and the millimeter wave radar, and determining the coordinates and the relative speed of the object in the image in the space. Through the data processing of the previous step, obstacle judgment can be carried out, and whether the object has the risk of collision with the vehicle body or not is calculated through the coordinates and the relative speed of the object and the direction of the track. The vehicle-mounted host simultaneously acquires train acceleration data provided by the derailment detection module, and judges whether the train body has a risk of derailment or not by comparing the acceleration with a threshold value. If collision/derailment risks exist, the vehicle-mounted host sends an alarm signal to a train control system to brake the train.
For the perception module, it mainly implements the following functions:
target detection: possible obstacles such as pedestrians, animals, stones, nearby trains and the like encountered during driving are identified through a deep learning technology, and spatial position information of the object relative to the vehicle is accurately provided.
Obstacle tracking: and through the collected information of different time sequences, the identified dynamic barrier is dynamically tracked and identified, and information such as relative speed, advancing direction and the like is provided. Providing the necessary data for collision risk assessment.
Track line detection: and accurately identifying the track line by adopting a deep learning algorithm, judging the driving track of the vehicle, and distinguishing whether the normal driving of the object is influenced by combining with the obstacle information.
And integrating the results provided by the functions, evaluating the potential collision risk, and transmitting an early warning signal to the control system through the communication module.
In a possible embodiment, as shown in fig. 8, during derailment detection, an acceleration signal of a train axle is collected, whether the running speed of a train is greater than a set value is detected, and the acceleration signal is analyzed, and whether the acceleration signal needs to be bypassed is determined and an alarm signal is output to the TCMS, the VCU, and the EB-LOOP.
It can be understood that if a train derailment is detected or there is a great risk of train derailment during derailment prevention detection, the derailment control unit will immediately send a "derailment detection" signal to The (TCMS) train control system, and then the terminal emergency safety circuit emergently brakes the whole train.
In one possible embodiment, the derailment detection function is provided with a plurality of derailment detection control units, each derailment detection control unit is bypassed with the train emergency safety isolation loop when the derailment detection fails, and the derailment detection bypass function can be manually operated from the cab to stop the derailment detection function.
It will be appreciated that each derailment detection control unit may be bypassed with the train emergency safety isolation loop if the derailment prevention detection fails. Furthermore, the bypass function of derailment detection may also be manually operated on the cab to activate the derailment detection means to a stop detection function.
In a possible embodiment mode, the alarm signal sent out in the fifth step comprises parameter information of the obstacles, wherein the parameter information comprises the number, the size, the shape, the type, the relative speed and the collision time of the obstacles.
It can be understood that, first, the safety influence of the obstacle on the train running can be determined according to the parameters such as the number, size, shape, type and relative speed of the obstacle, and therefore, when the alarm information is output, the above parameter information is attached for the viewing and understanding thereof as well as the calculation and feedback control.
In a possible embodiment, the risk level of the obstacle is evaluated according to the parameter information of the obstacle, the risk level parameter is output, the risk level parameter is compared with a set threshold value, whether an emergency braking measure needs to be taken or not is judged, and if the emergency braking measure does not need to be taken, the program returns to a program entrance to wait for processing of next frame data.
It can be understood that based on the above information, the influence of the barrier on the train operation can be evaluated, and by setting an evaluation rule, the level of the collision risk caused by the barrier is output according to the basic parameter information, so that the collision risk can be intuitively known, and the system can set different feedback controls to be directly output according to the difference of the risk levels so as to deal with the collision risk, and therefore, the intelligent degree of the reaction can be improved.
In a possible embodiment, after the obstacle is detected to disappear at a certain time, the normal state information is output, and the train returns to the train state before the obstacle is detected.
It can be understood that when the obstacle disappears before the collision occurs, the parameter information of normal operation is output, and all the early warning and alarm of the train and the corresponding control generated based on the alarm state are restored to the train state before the obstacle is detected by the train until the next obstacle is detected.
In a possible embodiment mode, when the acceleration signals of the train axle are collected, the acceleration signals in the X-axis direction and the Z-axis direction of the axle are collected at the same time, and the derailment coefficient of the train is calculated according to the acceleration signals so as to be used for judging the derailment risk subsequently.
It can be understood that the derailment detection control unit is a master control plate for derailment detection, and all derailment detection control units in the train are connected through a train line. The derailment detection sensor module is arranged on a wheel or a gear box large gear shaft of a train, the working state of the main control board is the working state of the vehicle-mounted case, and the vehicle-mounted case has the following operating states according to the functions of the main control board:
self-checking state: the system is electrified and initialized, and whether the relay can normally act needs to be verified.
The working state is as follows: after the system self-checking is finished, the main control board normally operates
Emergency braking state: after the rail surface is determined to have obstacles or the train is derailed, the main control board controls the EB relay to act, the EB loop is disconnected, and the train is emergently braked.
And (3) fault state: when the system is self-checked, the relay can not be normally connected and disconnected or the communication is failed, the main control board reports the system failure to the TCMS and stops working, and a fault lamp of the front panel of the vehicle-mounted case is lightened.
The logic block diagram of the main control board is shown in fig. 7.
And measuring acceleration signals of the axle in the X direction and the Z direction, and calculating the derailment coefficient of the train. The working logic is shown in fig. 8.
It should be noted that, in the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to relevant descriptions of other embodiments for parts that are not described in detail in a certain embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. An urban railway obstacle and derailment detection device is characterized by comprising a sensing module, a storage module, a communication module and a passive obstacle avoidance device,
the sensing module comprises a millimeter wave radar, a laser radar, a visual camera, an infrared camera and a plurality of data acquisition sensors, and is respectively used for detecting the information of obstacles in front of the train and the parameters required by derailment judgment;
the sensing module is used for completing barrier detection, barrier tracking and track line detection functions by carrying out computer vision and a deep learning algorithm on the vehicle-mounted host, analyzing and processing data and providing an early warning signal;
the storage module stores data information in the driving process so as to facilitate the review of train running conditions in early warning history, and provides training data for the detection device for deep learning;
the communication module transmits the early warning signal provided by the sensing module to a train control system in time;
the passive obstacle avoidance device is arranged on a bogie at the front end of the train and comprises a mechanical system and an electrical system, wherein the mechanical system is used for converting energy in the collision process into elastic potential energy and converting collision displacement generated in the collision process into an electric signal to be transmitted to the electrical system to control a braking system of the train;
the signal output ends of the sensing module and the passive obstacle avoidance device are connected with the signal input end of the sensing module, and the signal output end of the sensing module is connected with the signal input ends of the storage module and the communication module.
2. The device for detecting the obstacle and derailment of an urban railway according to claim 1, further comprising an active obstacle avoidance device, wherein a signal output end of the active obstacle avoidance device is connected with a signal input end of the sensing module, and the active obstacle avoidance device and the passive obstacle avoidance device operate independently.
3. The device for detecting the obstacle and derailment of an urban railway according to claim 1, wherein a mechanical system of the passive obstacle avoidance device comprises a detection beam, a detection assembly and a sliding assembly; the detection assembly is arranged on a detection cross beam, and the detection cross beam is connected with the sliding assembly, so that the detection cross beam can slide relative to the sliding assembly; the detection assembly comprises a connecting frame, a sealed box body and a fixed support, the connecting frame fixedly connects the sealed box body to the detection cross beam, and the fixed support connects the sealed box body with the sliding assembly; the sealed box body is internally provided with a rack assembly, a plate spring, a shifting needle limiting frame, a switch bracket, a proximity switch, a limiting switch and a displacement sensor, the plate spring and the shifting needle limiting frame are fixed on the side surface of the upper end of the rack assembly, one end of the plate spring penetrates through the switch bracket to be connected with the rack assembly, the other end of the plate spring is connected with a fixed bracket or the inner wall of the sealed box body, the proximity switch and the limiting switch are fixed on the upper end of the switch bracket through a mounting bracket, the shifting needle of the limiting switch penetrates through the switch bracket and is limited through the shifting needle limiting frame, and a detection rod of the displacement sensor is in contact with the plate spring; and a steel wire connecting piece is arranged between the outer side of the sealing box body and the detection cross beam.
4. A method for detecting urban railway obstacles and derailment is characterized by comprising the following steps:
the method comprises the following steps: the urban railway barrier and derailment detection device of any one of claims 1 to 3 is adopted to simultaneously carry out collision detection and derailment detection on a train in the process of operation;
step two: preprocessing the acquired information, namely performing image target segmentation on data information generated in the collision detection and derailment detection processes, separating objects in the image from a background environment through a deep learning algorithm, determining contour information of different objects, and simultaneously performing track line segmentation to determine the direction of a track;
step three: carrying out data fusion and feature extraction on the preprocessed data information, removing the influence of noise by adopting a Kalman filtering algorithm and utilizing the dynamic information of a target, unifying the coordinates of the segmented image data and the point cloud data obtained from a laser radar and a millimeter wave radar, and determining the coordinates and the relative speed of an object in the image in space;
step four: judging obstacles and derailment risks according to the data information processed in the third step, calculating whether the object has the risk of collision with the train body according to the coordinates and the relative speed of the object and the direction of the track, simultaneously acquiring train acceleration data provided by derailment detection by the vehicle-mounted host, and judging whether the train body has the risk of derailment or not by comparing the acceleration with a set threshold;
step five: and based on the judgment of the fourth step, if the train has collision or derailment risk, the vehicle-mounted host sends an alarm signal to the train control system so as to perform emergency braking on the train.
5. The method as claimed in claim 4, wherein during derailment detection, the method collects acceleration signals of train axles, detects whether the running speed of the train is greater than a set value, analyzes the acceleration signals, judges whether the acceleration signals need to be bypassed, and outputs alarm signals to TCMS, VCU, EB-LOOP.
6. The urban railway barrier and derailment detection method according to claim 5, wherein the derailment detection function is configured with a plurality of derailment detection control units, each derailment detection control unit is bypassed with the emergency safety isolation loop of the train when the derailment detection fails, and the bypass function of the derailment detection can be manually operated from the cab to stop the derailment detection function.
7. The method as claimed in claim 4, wherein the alarm signal issued in the course of step five includes parameter information of obstacles, and the parameter information includes number, size, shape, type, relative speed and collision time of the obstacles.
8. The method as claimed in claim 7, wherein the risk level of the obstacle is evaluated according to the parameter information of the obstacle, and the risk level parameter is outputted, and the risk level parameter is compared with a set threshold value to determine whether an emergency braking measure is required, and if the emergency braking measure is not required, the method returns to a program entry to wait for processing of the next frame of data.
9. The method as claimed in claim 8, wherein after the obstacle disappears at a certain time, outputting normal state information, and the train returns to the train state before the obstacle is detected.
10. The method as claimed in claim 5, wherein when the acceleration signals of the train axle are collected, the acceleration signals in the X-axis direction and the Z-axis direction of the axle are collected at the same time, and the derailment coefficient of the train is calculated to determine the derailment risk subsequently.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114275004A (en) * | 2022-01-07 | 2022-04-05 | 长沙润伟机电科技有限责任公司 | Contact type obstacle and derailment detection device |
| CN114572276A (en) * | 2022-01-18 | 2022-06-03 | 东南数字经济发展研究院 | Track obstacle early warning device |
| CN115626187A (en) * | 2022-10-31 | 2023-01-20 | 南京苏莱瑞新技术有限公司 | Contact type obstacle and derailment detection system and method |
| CN116612603A (en) * | 2023-07-17 | 2023-08-18 | 微网优联科技(成都)有限公司 | Mobile object monitoring system and method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103303338A (en) * | 2013-06-19 | 2013-09-18 | 北京交通大学 | Detection device for contact-type obstacle of railway vehicle |
| CN105658488A (en) * | 2013-10-24 | 2016-06-08 | 丰田自动车株式会社 | Brake apparatus |
| CN110422151A (en) * | 2019-07-18 | 2019-11-08 | 浙江吉利新能源商用车集团有限公司 | A kind of vehicle auxiliary braking method, device, system and terminal |
| CN211642165U (en) * | 2019-12-31 | 2020-10-09 | 青岛宏达赛耐尔科技股份有限公司 | Train obstacle and derailment detection system, detection device and rail train |
| CN112406960A (en) * | 2020-11-08 | 2021-02-26 | 青岛融创信为技术有限公司 | Active anti-collision system and method for fusion of multiple sensors for subway |
| CN112606831A (en) * | 2019-09-19 | 2021-04-06 | 中车时代电动汽车股份有限公司 | Anti-collision warning information external interaction method and system for passenger car |
-
2021
- 2021-08-06 CN CN202110904278.1A patent/CN113501029A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103303338A (en) * | 2013-06-19 | 2013-09-18 | 北京交通大学 | Detection device for contact-type obstacle of railway vehicle |
| CN105658488A (en) * | 2013-10-24 | 2016-06-08 | 丰田自动车株式会社 | Brake apparatus |
| CN110422151A (en) * | 2019-07-18 | 2019-11-08 | 浙江吉利新能源商用车集团有限公司 | A kind of vehicle auxiliary braking method, device, system and terminal |
| CN112606831A (en) * | 2019-09-19 | 2021-04-06 | 中车时代电动汽车股份有限公司 | Anti-collision warning information external interaction method and system for passenger car |
| CN211642165U (en) * | 2019-12-31 | 2020-10-09 | 青岛宏达赛耐尔科技股份有限公司 | Train obstacle and derailment detection system, detection device and rail train |
| CN112406960A (en) * | 2020-11-08 | 2021-02-26 | 青岛融创信为技术有限公司 | Active anti-collision system and method for fusion of multiple sensors for subway |
Non-Patent Citations (4)
| Title |
|---|
| 张强等: "列车智能障碍物检测系统在北京新机场线全自动运行中应用的研究", 《铁道机车车辆》 * |
| 王晓飞: "《二手车鉴定与评估》", 31 January 2016 * |
| 邓伟文: "《汽车智能驾驶模拟仿真技术》", 30 April 2021 * |
| 隋起胜: "《太空安全防御技术》", 30 June 2018 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114275004A (en) * | 2022-01-07 | 2022-04-05 | 长沙润伟机电科技有限责任公司 | Contact type obstacle and derailment detection device |
| CN114572276A (en) * | 2022-01-18 | 2022-06-03 | 东南数字经济发展研究院 | Track obstacle early warning device |
| CN114572276B (en) * | 2022-01-18 | 2024-07-12 | 东南数字经济发展研究院 | Rail barrier early warning device |
| CN115626187A (en) * | 2022-10-31 | 2023-01-20 | 南京苏莱瑞新技术有限公司 | Contact type obstacle and derailment detection system and method |
| CN116612603A (en) * | 2023-07-17 | 2023-08-18 | 微网优联科技(成都)有限公司 | Mobile object monitoring system and method |
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