CN112660207A - Wearable subway intelligent auxiliary driving system - Google Patents
Wearable subway intelligent auxiliary driving system Download PDFInfo
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- CN112660207A CN112660207A CN202011506191.0A CN202011506191A CN112660207A CN 112660207 A CN112660207 A CN 112660207A CN 202011506191 A CN202011506191 A CN 202011506191A CN 112660207 A CN112660207 A CN 112660207A
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Abstract
A wearable subway intelligent assistant driving system utilizes an internet of things sensing device to achieve basic information acquisition, analyzes and processes the acquired information in a mode of combining edge computing and cloud computing, pushes various analysis evaluation indexes to the front end to achieve multi-scale, multi-dimension and multi-form digital presentation, synchronizes all monitoring and analyzing data in a command center, and can visually present fatigue states and vehicle health states of train drivers and perform visual emergency command if necessary; the invention can monitor and early warn the states of drivers, passengers, vehicles, environment and the like, provides intelligent assistance for the aspects of daily routing inspection before the subway train starts, state monitoring in the driving process, safety confirmation when the subway train arrives at a departure station, rapid handling of emergency and the like, greatly lightens the workload of the subway drivers, improves the intelligent degree of subway operation management, and improves the safe operation index of the subway train.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of rail transit, in particular to a wearable intelligent driving assistance system for a subway.
[ background of the invention ]
With the development of subway signal systems, most of the existing newly-built subway systems have realized automatic driving, but this does not mean that the subway drivers can be completely liberated.
Drivers need to keep vigilance all the time, observe the working states of the signal display screen and the train at any time, and always observe the forward route in the running process without interrupting lookout; if an emergency situation endangering the driving safety occurs, such as an obstacle exists in an interval, a person enters an orbit area without permission, and other abnormal situations, the vehicle should be immediately stopped emergently, and the traffic regulation of a control center is reported; after the train arrives at the station, subway drivers can execute the operation steps of door opening, getting off, door closing, driving and the like under the condition of confirming absolute safety; this puts high demands on subway drivers, and the working condition of the subway drivers directly determines the safe operation index of the subway train.
At present, no existing technology can solve the problem, and an intelligent auxiliary means is urgently needed to improve the working efficiency of subway drivers, reduce the working strength and improve the safe operation index of subway trains.
[ summary of the invention ]
In order to solve the problems, the invention provides a wearable subway intelligent auxiliary driving system, which utilizes an internet of things sensing device to realize basic information acquisition, analyzes and processes the acquired information in a mode of combining edge computing and cloud computing, pushes various analysis evaluation indexes to the front end to realize multi-scale, multi-dimension and multi-form digital presentation, provides real-time monitoring for the states of personnel, vehicles and external environments, provides timely early warning prompt for dangerous conditions, subway drivers can see important information concerned by themselves through wearable devices, and uses a portable mobile panel to quickly inquire and process through a humanized man-machine interaction interface when necessary, all monitoring and analyzing data are synchronized in a command center, and the command center can visually present the fatigue state, the fatigue state and the fatigue state of each train driver, When early warning occurs, the command center large screen can highlight and flash the prompt; when the cockpit has an emergency, the command center can immediately switch the field video through the built-in forward camera of the driver wearable device to carry out visual emergency command.
Further, the information collection comprises information collection based on a wearable device and information collection based on a vehicle-mounted device.
A wearable subway intelligent auxiliary driving system is disclosed, wherein the system architecture of the intelligent auxiliary driving system comprises a base layer, a resource layer, a data layer, a supporting layer, an application layer and a user layer;
the base layer comprises a wearable device and an in-vehicle system device;
the resource layer expansion application server, the data server, the analysis server, the communication network and the operating system, wherein the data layer comprises various relevant basic data information, service data, data information, real-time data, historical data, various theme data, storage of data resources and a unified data bus interface;
the supporting layer provides core technical support for the construction of an upper application system, and comprises a data sharing exchange platform, a mobile internet platform, a data visualization platform and a data analysis supporting platform;
the application layer comprises driving assistance, safety early warning, intelligent maintenance, emergency command and teaching training;
the user layer comprises a command center and subway drivers.
Further, the driving assistance mainly comprises state monitoring in the driving process, safety confirmation when the vehicle arrives at a station and caution item voice reminding, and the state monitoring in the driving process comprises target identification display of front obstacles, signal lamp states, fire sources and the like and abnormal prompt of running parameters of vehicle-mounted main electromechanical equipment.
Further, the shunting is supplementary to rely on the embedded preceding view information that gathers to the camera of wearing formula helmet, utilizes the degree of depth learning technique to carry out image enhancement, detects pedestrian's invasion limit action, shunting indicator lamp in the track region to directly show the testing result at the display lens.
Further, the safety early warning comprises fatigue driving early warning, vehicle-mounted electromechanical equipment abnormal operation early warning, night auxiliary viewing early warning, track foreign matter early warning and shield door abnormal early warning.
Furthermore, the night auxiliary vision early warning utilizes a deep learning algorithm to automatically identify and analyze the fire source, and when a dark fire is found, the related abnormal information is immediately displayed on the display lens of the intelligent helmet, and voice reminding is performed.
Further, the track foreign matter early warning utilizes a deep learning algorithm to automatically extract a track area, and when an illegal person or an obstacle target is identified, relevant abnormal information is displayed on a display lens of the intelligent helmet at once, and voice reminding is performed.
Furthermore, the built-in front-mounted intelligent helmet is used for the emergency command to acquire field audio data in real time by the camera, the miniature industrial personal computer transmits the data to the large screen of the command center, and the commander visually knows the field situation at the first time through a field video image, talkbacks in a two-way mode in real time and issues instructions.
A wearable intelligent subway auxiliary driving system comprises wearable equipment, wherein the wearable equipment comprises an intelligent helmet, a miniature industrial personal computer and a portable mobile tablet; the intelligent helmet is responsible for information acquisition and result display; the miniature industrial personal computer is responsible for lightweight task analysis processing and data forwarding; the portable mobile tablet can inquire various evaluation indexes and statistical analysis data.
Compared with the prior art, the invention has the following beneficial effects:
the invention can monitor and early warn the states of drivers, passengers, vehicles, environment and the like, provides intelligent assistance for the aspects of daily routing inspection before the subway train starts, state monitoring in the driving process, safety confirmation when the subway train arrives at a departure station, rapid handling of emergency and the like, greatly lightens the workload of the subway drivers, improves the intelligent degree of subway operation management, and improves the safe operation index of the subway train.
[ description of the drawings ]
Fig. 1 is a technical route diagram of the wearable intelligent driving assistance system for a subway according to the invention.
Fig. 2 is a system architecture diagram of the wearable subway intelligent assistant driving system of the invention.
Fig. 3 is a schematic logical structure diagram of the wearable subway intelligent assistant driving system of the invention.
[ detailed description ] embodiments
The directional terms of the present invention, such as "up", "down", "front", "back", "left", "right", "inner", "outer", "side", etc., are only directions in the drawings, and are only used to explain and illustrate the present invention, but not to limit the scope of the present invention.
The invention utilizes advanced technologies such as internet of things sensing, mobile interconnection, artificial intelligence, big data, cloud computing, deep learning and the like, and provides a wearable subway intelligent auxiliary driving system, which monitors and warns states of drivers, passengers, vehicles, environment and the like, solves the problem that manual operation of subway drivers is seriously depended on in aspects of train routing inspection before subway departure, state monitoring in driving, safety confirmation when arriving at and leaving a station, rapid processing of emergency and the like, reduces the working pressure of the subway drivers, ensures the safe operation of subway trains, and realizes intelligent auxiliary driving.
Referring to fig. 1, the technical route of the wearable intelligent driving assistance system for the subway comprises the following steps:
the method comprises the following steps: basic information acquisition is realized by using advanced internet of things sensing equipment;
respectively completing the acquisition of view information of a track area in front of a subway train and the physiological characteristic information of a subway driver by utilizing a built-in forward camera and a built-in biosensor of the wearable intelligent helmet; the method comprises the steps of utilizing vehicle-mounted video monitoring equipment, temperature and humidity sensing equipment and electromechanical acquisition equipment to respectively finish acquisition of view information of a subway carriage, environment information of the carriage and operation parameter information of the electromechanical equipment.
Step two: analyzing and processing the acquired information in a mode of combining edge computing and cloud computing;
the method comprises the following steps that edge calculation is adopted for lightweight tasks with low processing performance requirements and high real-time performance, primary processing is achieved through a miniaturization algorithm embedded in a wearable industrial personal computer, and the rear-end resource pressure is reduced while the high real-time performance requirements are met; cloud computing is adopted for computing tasks with high processing performance requirements, acquired basic information and front-end primary processing data are transmitted to a rear-end cloud platform, and accurate analysis is achieved through technologies of convolutional neural network feature extraction, poly-yolo-based target detection, image enhancement, data statistics, correlation comparison and analysis and the like.
Step three: various analysis and evaluation indexes are pushed to the front end to realize multi-scale, multi-dimension and multi-form digital presentation, real-time monitoring is provided for states of personnel, vehicles and external environments, timely early warning prompts are provided for possible dangerous conditions, subway drivers can see important information concerned by the subway drivers through built-in display lenses of the wearable helmets, and the subway drivers can use the portable mobile tablet to perform rapid query processing through a humanized human-computer interaction interface when necessary.
In addition, all monitoring and analyzing data are synchronized in the command center, the fatigue state and the vehicle health state of each train driver can be visually presented on a large screen of the command center, and when early warning occurs, the large screen of the command center can highlight and flash the prompt; when the cockpit has an emergency, the command center can immediately switch the field video through the built-in forward camera of the driver wearable device to carry out visual emergency command.
The information acquisition comprises information acquisition based on wearable equipment and information acquisition based on vehicle-mounted equipment;
the information acquisition based on the wearable equipment comprises driver physiological characteristic information acquisition and train front track area view information acquisition; the driver physiological characteristic information acquisition relies on a built-in biosensor of the wearable helmet to acquire physiological characteristic values of the driver, such as body temperature, heart rate and the like, and provides a data source for analyzing the fatigue state of the driver; the view information acquisition of the rail area in front of the train is realized by taking a scene image through a built-in front camera of a wearable helmet, and a data source is provided for rail obstacle identification, rail traffic area unauthorized personnel identification, fire source identification and visual emergency command.
The information acquisition based on the vehicle-mounted equipment is realized by collecting information of vehicle-mounted important electromechanical equipment, passengers, a shielding door and the like by means of vehicle-mounted video monitoring equipment, a position sensor, a temperature and humidity sensor, electromechanical acquisition equipment and the like, wherein the information comprises temperature and humidity in a cockpit, train main equipment operation data, carriage passenger view information, shielding door view information and the like, and a data source is provided for analyzing the state of a train.
Referring to fig. 2, the system architecture of the wearable subway intelligent auxiliary driving system includes a base layer, a resource layer, a data layer, a support layer, an application layer, and a user layer;
the base layer comprises wearable equipment and vehicle-mounted system equipment, wherein the wearable equipment comprises an intelligent helmet (an embedded camera, a display lens, a biosensor and other accessory equipment), a portable mobile flat plate and a miniature industrial personal computer; the vehicle-mounted system equipment comprises rear-end video monitoring equipment, various sensor equipment and electromechanical acquisition equipment.
The resource layer packet expansion application server, the data server, the analysis server, the communication network and the operating system.
The data layer comprises various relevant basic data information, service data, data information, real-time data, historical data, various theme data, storage of data resources and a unified data bus interface, the data unification, the interface specification unification and the data management and maintenance unification are guaranteed, efficient basic support is provided for the construction of various application systems based on data, and meanwhile a foundation is provided for the sharing and exchange of the data.
The supporting layer provides core technical support for the construction of an upper application system, and comprises a data sharing exchange platform, a mobile internet platform, a data visualization platform and a data analysis supporting platform.
The application layer comprises driving assistance, safety early warning, intelligent maintenance, emergency command and teaching training.
The user layer comprises a command center and subway drivers.
Wherein the auxiliary driving of the application layer comprises driving assistance and shunting assistance;
the driving assistance mainly comprises state monitoring in the driving process, safety confirmation when the vehicle arrives at a station and attention voice reminding, wherein the state monitoring in the driving process comprises target identification display of front obstacles, signal lamp states, fire sources and the like, abnormal prompt of running parameters of vehicle-mounted main electromechanical equipment and the like; the safety confirmation when the passenger arrives at the station and leaves the station comprises the conditions of getting on and off the vehicle of the passenger, whether the passenger stops illegally, whether the shielding door is normally opened or closed and the like; the notice reminding mainly carries out voice reminding on operation steps before starting, during driving and after arriving at a station, so that the driver is prevented from missing operation.
The supplementary view information that relies on the embedded preceding camera of wearing formula helmet to gather of shunting utilizes the deep learning technique to carry out the image enhancement, invade the limit action to the pedestrian in the track region, the shunting instructs the signal lamp to detect, and directly show the testing result on the display lens, solve because shunting personnel operating time is long, energy can't concentrate on for a long time, shunting personnel can't differentiate switch direction or see mixed signal lamp signal, or shunting personnel often is interrupted to watch out, can't in time discover train the place ahead foreign matter and break into the scheduling problem.
The safety early warning of the application layer comprises fatigue driving early warning, vehicle-mounted electromechanical equipment operation abnormity early warning, night auxiliary viewing early warning, track foreign matter early warning, shield door abnormity early warning and the like, when abnormal conditions are found, relevant abnormal information is displayed on a display lens of the intelligent helmet at once, voice reminding is carried out, and meanwhile highlight flicker reminding is carried out on a large screen of a command center.
Fatigue driving early warning: and after the physiological characteristic value of the subway driver approaches or exceeds a preset fatigue threshold range, the equipment sends out voice prompt.
And (4) auxiliary night vision early warning: the fire source is automatically identified and analyzed by utilizing a deep learning algorithm, and when a dark fire is found, relevant abnormal information is immediately displayed on a display lens of the intelligent helmet, and voice reminding is performed.
Early warning of track foreign matters: and automatically extracting the track area by using a deep learning algorithm, and when identifying the illegal personnel or the obstacle target, immediately displaying related abnormal information on a display lens of the intelligent helmet and carrying out voice reminding.
The vehicle-mounted electromechanical equipment abnormal operation early warning: when the running parameters of the vehicle-mounted important electromechanical equipment exceed the set threshold value, relevant abnormal information is displayed on the display lens of the intelligent helmet immediately, and voice reminding is carried out.
Abnormal early warning of a shielding door: by means of video detection and analysis, when people are clamped by the shielding door, people remain between the shielding door and the vehicle door, and illegal people stay in the yellow line, relevant abnormal information is displayed on the display lens of the intelligent helmet at once, voice reminding is carried out, and voice communication is carried out with station staff until the shielding door is opened or closed safely.
The intelligent maintenance of the application layer comprises train inspection before starting and fault maintenance in the driving process; if abnormity is found in the routing inspection process before starting, the technical manual is inquired through the portable mobile flat panel to complete maintenance processing, and when complex technical problems are encountered, the problems are quickly, timely and effectively processed through remote expert guidance by means of a built-in forward camera of the wearable helmet; when the electromechanical equipment of the train is abnormal in the driving process, the system can prompt a subway driver to execute relevant processing steps in a voice mode or be assisted by a remote expert to process.
The emergency command of the application layer utilizes the built-in front of the intelligent helmet to acquire the field audio data in real time by the camera, the miniature industrial personal computer transmits the data to the large screen of the command center, and the commander visually knows the field situation at the first time through the field video image, talkbacks in two ways in real time and issues instructions.
Wherein the teaching training of application layer utilizes "AR/VR + 5G" technique through the embedded display lens of wearable helmet, realizes the virtual teaching, the training to the student.
Referring to fig. 3, a schematic diagram of a logic structure of the wearable subway intelligent assistant driving system is provided.
The wearable device comprises an intelligent helmet, a miniature industrial personal computer and a portable mobile panel; the intelligent helmet is responsible for information acquisition and result display; the miniature industrial personal computer is responsible for lightweight task analysis processing and data forwarding; the portable mobile tablet can inquire various evaluation indexes and statistical analysis data.
The intelligent helmet equipment comprises a built-in forward camera, a display lens, a built-in biosensor and other accessory equipment; the display lens adopts a high-resolution screen and a micro-projection technology, has an AR/VR and view display function, and receives and displays important information focused by subway drivers in real time through a wireless communication technology; the built-in biosensor comprises a temperature sensor and a heart rate sensor and is used for detecting the temperature and heart rate characteristic value information of a subway driver; the built-in forward camera is used for collecting view information and providing a data source for identifying and analyzing targets such as signal lamp states, obstacles, flames and the like; the accessory devices mainly include wireless connections, storage, batteries, etc.
The miniature industrial personal computer is composed of a communication module, a data interface module and an embedded algorithm module, network communication is carried out between the miniature industrial personal computer and the intelligent helmet and the cloud platform by utilizing communication technologies such as 4G/5G/Bluetooth, computing tasks with high real-time performance and low processing performance requirements are completed, the computing tasks are uploaded to the cloud platform, index data analyzed and processed by the cloud platform are received, and finally, important attention information of subway drivers is pushed to the intelligent helmet display lens.
Portable removal is dull and stereotyped has installed subway application APP, provides convenient daily operation management for the subway driver.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (10)
1. A wearable subway intelligent assistant driving system is characterized in that the intelligent assistant driving system utilizes an internet of things sensing device to realize basic information acquisition, analyzes and processes the acquired information in a mode of combining edge computing and cloud computing, pushes various analysis and evaluation indexes to the front end to realize multi-scale, multi-dimension and multi-form digital presentation, provides real-time monitoring for personnel, vehicles and external environment states, provides timely early warning prompt for possible dangerous conditions, subway drivers can see important information concerned by themselves through wearable devices, and uses a portable mobile panel to quickly inquire and process through a humanized man-machine interaction interface if necessary, all monitoring and analyzing data are synchronous in a command center, and the command center can visually present fatigue states and vehicle health states of train drivers, when early warning occurs, a command center large screen can highlight and flash a prompt; when the cockpit has an emergency, the command center can immediately switch the field video through the built-in forward camera of the driver wearable device to carry out visual emergency command.
2. The wearable subway intelligent assistant driving system of claim 1, wherein the information collection comprises wearable device-based information collection and vehicle-mounted device-based information collection.
3. A wearable subway intelligent auxiliary driving system is characterized in that a system architecture of the intelligent auxiliary driving system comprises a base layer, a resource layer, a data layer, a supporting layer, an application layer and a user layer;
the base layer comprises a wearable device and an in-vehicle system device;
the resource layer expansion application server, the data server, the analysis server, the communication network and the operating system, wherein the data layer comprises various relevant basic data information, service data, data information, real-time data, historical data, various theme data, storage of data resources and a unified data bus interface;
the supporting layer provides core technical support for the construction of an upper application system, and comprises a data sharing exchange platform, a mobile internet platform, a data visualization platform and a data analysis supporting platform;
the application layer comprises driving assistance, safety early warning, intelligent maintenance, emergency command and teaching training;
the user layer comprises a command center and subway drivers.
4. The wearable intelligent driving system for the subway according to claim 3, wherein the driving assistance mainly comprises state monitoring during driving, safety confirmation during arrival and departure and notice voice reminding, and the state monitoring during driving comprises target recognition display of front obstacles, signal lamp states, fire sources and the like and abnormal prompt of running parameters of vehicle-mounted main electromechanical equipment.
5. The wearable intelligent assistant driving system for the subway according to claim 3, wherein the shunting assistant relies on the view information collected by a forward camera embedded in the wearable helmet, utilizes a deep learning technology to perform image enhancement, detects pedestrian intrusion behaviors and shunting indicator lights in the rail area, and directly displays the detection result on the display lens.
6. The wearable intelligent driving assistance system for the subway according to claim 3, wherein the safety early warning comprises a fatigue driving early warning, an abnormal operation early warning of vehicle-mounted electromechanical devices, a night vision early warning, a rail foreign matter early warning and a shield door abnormal early warning.
7. The wearable intelligent assistant driving system for the subway according to claim 6, wherein the night vision early warning utilizes a deep learning algorithm to automatically recognize and analyze a fire source, and when a dark fire is found, relevant abnormal information is immediately displayed on a display lens of the intelligent helmet, and a voice prompt is performed.
8. The wearable intelligent driving assistance system for the subway according to claim 6, wherein the track foreign matter early warning utilizes a deep learning algorithm to automatically extract a track area, and when an offending person or an obstacle target is identified, relevant abnormal information is immediately displayed on a display lens of the intelligent helmet, and a voice prompt is performed.
9. The wearable intelligent assistant driving system for the subway, as claimed in claim 6, wherein said emergency command utilizes a built-in forward facing camera of an intelligent helmet to collect the live audio data in real time, and a micro industrial personal computer transmits the data to a large screen of a command center, so that the commander can directly know the live situation at the first time through the live video image, and can talk back and give instructions in real time and in two directions.
10. A wearable intelligent subway auxiliary driving system is characterized by comprising wearable equipment, wherein the wearable equipment comprises an intelligent helmet, a miniature industrial personal computer and a portable mobile tablet; the intelligent helmet is responsible for information acquisition and result display; the miniature industrial personal computer is responsible for lightweight task analysis processing and data forwarding; the portable mobile tablet can inquire various evaluation indexes and statistical analysis data.
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