CN112331034A - High-speed train driver monitoring and assisting system - Google Patents
High-speed train driver monitoring and assisting system Download PDFInfo
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Abstract
The invention discloses a high-speed train driver monitoring and assisting system, which comprises a ground monitoring module, a vehicle-mounted driving system, an environment sensing module, a first deep learning module and a second deep learning module, wherein the ground monitoring module is used for monitoring a train; the vehicle-mounted driving system is in communication connection with the ground monitoring module; the environment sensing module is connected with the ground monitoring module; the deep learning module is connected with the ground monitoring module; when a new driver operates on the real vehicle, the deep learning module synchronously performs virtual driving operation, and when an error condition is met, the ground monitoring module broadcasts an empirical driving instruction output by the deep learning module to the driver through the vehicle-mounted driving system to assist the operation of the new driver; the beneficial technical effects of the invention are as follows: the utility model provides a high speed train driver monitoring, auxiliary system, this scheme can effectively improve new driver's real efficiency of instructing, reduces artifical the occupation, but also can play the driver assistance effect to new driver.
Description
Technical Field
The invention relates to a high-speed train auxiliary driving technology, in particular to a high-speed train driver monitoring and auxiliary system.
Background
Although the automation degree of a high-speed train is higher and higher, a driver is still the core of train driving, and the higher automation degree also puts higher demands on the personal quality of the driver, and the driver needs to be familiar with various electrical properties of the train and also needs to be skilled in various operation skills according to the electrical characteristics of the train, and particularly needs to have certain capacity of handling the emergency in emergencies or severe weather conditions.
For the new driver, the fluency of the operation action is not good due to the novice, the command of the control center may be delayed, especially in bad weather, the vision and the mind of the new driver are affected inevitably, and the fluency of the operation action is difficult to be ensured.
Under the prerequisite of guaranteeing driving safety, in order to make new driver can obtain actual exercise, prior art generally adopts old new mode of taking to instruct the new driver in fact, controls the train together by experienced old driver with new driver promptly, and this kind of training mode needs old driver to accompany many times, and efficiency is lower, the cycle is longer, and the manual work occupies great.
Disclosure of Invention
Aiming at the problems in the background technology, the invention provides a high-speed train driver monitoring and assisting system, which is innovative in that: the high-speed train driver monitoring and assisting system comprises a ground monitoring module, a vehicle-mounted driving system, an environment sensing module, a first deep learning module and a second deep learning module;
the vehicle-mounted driving system is in communication connection with the ground monitoring module, a driver drives the train to run through the vehicle-mounted driving system, and in the running process, the vehicle-mounted driving system can send train running parameters and the operation action of the driver to the ground monitoring module in real time;
the environment sensing module is connected with the ground monitoring module; the environment sensing module can monitor the current environment parameters through various sensors and transmit the monitored environment parameters to the ground monitoring module and the vehicle-mounted driving system in real time; after receiving the environmental parameters, the vehicle-mounted driving system broadcasts the environmental parameters to the driver in an acoustic and optical mode; the environmental parameters comprise rainfall parameters, visibility parameters, temperature parameters, humidity parameters, wind direction parameters and wind power parameters;
the first deep learning module and the second deep learning module are both connected with the ground monitoring module; the ground monitoring module can periodically identify the current weather condition according to the current environmental parameters: if the current weather condition is identified as normal weather, the ground monitoring module continuously transmits the train operation parameters and the environment parameters to the first deep learning module in real time; if the current weather condition is identified as severe weather, the ground monitoring module stops transmitting data to the first deep learning module, and meanwhile, the ground monitoring module continuously transmits train operation parameters and environment parameters to the second deep learning module in real time;
the first deep learning module can generate a first driving instruction in real time according to the train operation parameters and the environmental parameters and transmit the first driving instruction to the ground monitoring module in real time; the first driving instruction comprises a gear adjusting instruction, corresponding gear running time information and a train speed curve;
the second deep learning module can generate a second driving instruction in real time according to the train operation parameters and the environmental parameters and transmit the second driving instruction to the ground monitoring module in real time; the second driving instruction comprises a gear adjusting instruction, corresponding gear running time information and a train speed curve;
recording the operation action of a driver as an actual driving instruction, and recording a first driving instruction and a second driving instruction as an empirical driving instruction; after receiving an actual driving instruction and an empirical driving instruction, the ground monitoring module stores the actual driving instruction and the empirical driving instruction, compares the actual driving instruction and the empirical driving instruction matched in time sequence, does not act if the difference value between the actual driving instruction and the empirical driving instruction is within an allowable range, transmits the empirical driving instruction to the vehicle-mounted driving system if the difference value between the actual driving instruction and the empirical driving instruction exceeds the allowable range, and broadcasts the empirical driving instruction to a driver in a sound and light mode by the vehicle-mounted driving system;
the first deep learning module and the second deep learning module are both realized by adopting a deep learning neural network; the deep learning neural network corresponding to the first deep learning module is marked as a first network, and the deep learning neural network corresponding to the second deep learning module is marked as a second network; the network I is trained according to the driving data of the excellent driver under the normal weather condition, and the network II is trained according to the driving data of the excellent driver under the severe weather condition; the driving data comprises train operation parameters and operation actions of a driver.
The principle of the invention is as follows: when a new driver operates on a real vehicle, the two deep learning modules are equivalent to training, namely under the condition of the same train operation parameters and environmental parameters, the new driver and the deep learning modules synchronously carry out driving operation (only the deep learning modules carry out virtual operation), if the difference value between the operation action of the new driver and the empirical driving instruction is within an allowable range, the new driver can independently carry out operation, and if the difference value between the operation action of the new driver and the empirical driving instruction exceeds the allowable range, the ground monitoring module broadcasts the empirical driving instruction to the driver through the vehicle-mounted driving system to assist the driver in correcting the operation; after the invention is adopted, when a new driver is on duty and actually operates, the old driver does not need to accompany the new driver, and the manual occupation can be effectively avoided; and because the ground monitoring module stores the actual driving instruction and the experience driving instruction, after the driving operation is finished, the actual driving instruction and the experience driving instruction under any moment and same condition can be known through data query, so that the operation of the new driver can be analyzed in detail, the problem can be efficiently found by combining the personal experience of the new driver, and the new driver can be trained in a targeted manner in the later stage, thereby improving the training efficiency.
Preferably, when training the first network, the following 15 features are used as the input features of the first network: the method comprises the following steps of (1) changing the position of a train working condition point, the historical average speed of a train before a certain position point, the running speed of the train at a current sampling point, the grade value of the train at the certain position point, the starting position of a grade section of the train at the point, the ending position of the grade section of the train at the point, the residual length of the train from the point to the current grade section, the average speed of the train at the grade section of the point, the maximum speed limit value of the train at the point, the starting position of the maximum speed limit value of the train at the point, the ending position of the maximum speed limit value of the train at the point, the total running time between train stations, the train model, the train length and the train weight;
taking the gear adjusting instruction, the corresponding gear running time information and the train speed curve as output characteristics of the first network;
when training the second network, adding 3 features on the basis of the 15 features, wherein a total of 18 features are used as input features of the second network, and the added 3 features are respectively as follows: the temporary speed limit value, the temporary speed limit starting position and the temporary speed limit ending position; and taking the gear adjusting instruction, the corresponding gear running time information and the train speed curve as the output characteristics of the second network.
In specific implementation, the driving data of the first ten percent of excellent drivers in normal weather and severe weather can be used as training samples, and the data can be acquired from a train operation monitoring device LKJ and a train control and management system TCMS (train control and management system) device; and respectively preprocessing the driving data, and making the driving data into samples required by respective training of two deep learning neural networks after operations such as feature selection, data imbalance processing, data sample normalization and the like.
The beneficial technical effects of the invention are as follows: the utility model provides a high speed train driver monitoring, auxiliary system, this scheme can effectively improve new driver's real efficiency of instructing, reduces artifical the occupation, but also can play the driver assistance effect to new driver.
Drawings
Fig. 1 is a schematic diagram of the principle of the present invention.
Detailed Description
The utility model provides a high speed train driver monitoring, auxiliary system which innovation lies in: the high-speed train driver monitoring and assisting system comprises a ground monitoring module, a vehicle-mounted driving system, an environment sensing module, a first deep learning module and a second deep learning module;
the vehicle-mounted driving system is in communication connection with the ground monitoring module, a driver drives the train to run through the vehicle-mounted driving system, and in the running process, the vehicle-mounted driving system can send train running parameters and the operation action of the driver to the ground monitoring module in real time;
the environment sensing module is connected with the ground monitoring module; the environment sensing module can monitor the current environment parameters through various sensors and transmit the monitored environment parameters to the ground monitoring module and the vehicle-mounted driving system in real time; after receiving the environmental parameters, the vehicle-mounted driving system broadcasts the environmental parameters to the driver in an acoustic and optical mode; the environmental parameters comprise rainfall parameters, visibility parameters, temperature parameters, humidity parameters, wind direction parameters and wind power parameters;
the first deep learning module and the second deep learning module are both connected with the ground monitoring module; the ground monitoring module can periodically identify the current weather condition according to the current environmental parameters: if the current weather condition is identified as normal weather, the ground monitoring module continuously transmits the train operation parameters and the environment parameters to the first deep learning module in real time; if the current weather condition is identified as severe weather, the ground monitoring module stops transmitting data to the first deep learning module, and meanwhile, the ground monitoring module continuously transmits train operation parameters and environment parameters to the second deep learning module in real time;
the first deep learning module can generate a first driving instruction in real time according to the train operation parameters and the environmental parameters and transmit the first driving instruction to the ground monitoring module in real time; the first driving instruction comprises a gear adjusting instruction, corresponding gear running time information and a train speed curve;
the second deep learning module can generate a second driving instruction in real time according to the train operation parameters and the environmental parameters and transmit the second driving instruction to the ground monitoring module in real time; the second driving instruction comprises a gear adjusting instruction, corresponding gear running time information and a train speed curve;
recording the operation action of a driver as an actual driving instruction, and recording a first driving instruction and a second driving instruction as an empirical driving instruction; after receiving an actual driving instruction and an empirical driving instruction, the ground monitoring module stores the actual driving instruction and the empirical driving instruction, compares the actual driving instruction and the empirical driving instruction matched in time sequence, does not act if the difference value between the actual driving instruction and the empirical driving instruction is within an allowable range, transmits the empirical driving instruction to the vehicle-mounted driving system if the difference value between the actual driving instruction and the empirical driving instruction exceeds the allowable range, and broadcasts the empirical driving instruction to a driver in a sound and light mode by the vehicle-mounted driving system;
the first deep learning module and the second deep learning module are both realized by adopting a deep learning neural network; the deep learning neural network corresponding to the first deep learning module is marked as a first network, and the deep learning neural network corresponding to the second deep learning module is marked as a second network; the network I is trained according to the driving data of the excellent driver under the normal weather condition, and the network II is trained according to the driving data of the excellent driver under the severe weather condition; the driving data comprises train operation parameters and operation actions of a driver.
Further, when training the first network, the following 15 features are used as input features of the first network: the method comprises the following steps of (1) changing the position of a train working condition point, the historical average speed of a train before a certain position point, the running speed of the train at a current sampling point, the grade value of the train at the certain position point, the starting position of a grade section of the train at the point, the ending position of the grade section of the train at the point, the residual length of the train from the point to the current grade section, the average speed of the train at the grade section of the point, the maximum speed limit value of the train at the point, the starting position of the maximum speed limit value of the train at the point, the ending position of the maximum speed limit value of the train at the point, the total running time between train stations, the train model, the train length and the train weight;
taking the gear adjusting instruction, the corresponding gear running time information and the train speed curve as output characteristics of the first network;
when training the second network, adding 3 features on the basis of the 15 features, wherein a total of 18 features are used as input features of the second network, and the added 3 features are respectively as follows: the temporary speed limit value, the temporary speed limit starting position and the temporary speed limit ending position; and taking the gear adjusting instruction, the corresponding gear running time information and the train speed curve as the output characteristics of the second network.
Claims (2)
1. The utility model provides a high speed train driver monitoring, auxiliary system which characterized in that: the high-speed train driver monitoring and assisting system comprises a ground monitoring module, a vehicle-mounted driving system, an environment sensing module, a first deep learning module and a second deep learning module;
the vehicle-mounted driving system is in communication connection with the ground monitoring module, a driver drives the train to run through the vehicle-mounted driving system, and in the running process, the vehicle-mounted driving system can send train running parameters and the operation action of the driver to the ground monitoring module in real time;
the environment sensing module is connected with the ground monitoring module; the environment sensing module can monitor the current environment parameters through various sensors and transmit the monitored environment parameters to the ground monitoring module and the vehicle-mounted driving system in real time; after receiving the environmental parameters, the vehicle-mounted driving system broadcasts the environmental parameters to the driver in an acoustic and optical mode; the environmental parameters comprise rainfall parameters, visibility parameters, temperature parameters, humidity parameters, wind direction parameters and wind power parameters;
the first deep learning module and the second deep learning module are both connected with the ground monitoring module; the ground monitoring module can periodically identify the current weather condition according to the current environmental parameters: if the current weather condition is identified as normal weather, the ground monitoring module continuously transmits the train operation parameters and the environment parameters to the first deep learning module in real time; if the current weather condition is identified as severe weather, the ground monitoring module stops transmitting data to the first deep learning module, and meanwhile, the ground monitoring module continuously transmits train operation parameters and environment parameters to the second deep learning module in real time;
the first deep learning module can generate a first driving instruction in real time according to the train operation parameters and the environmental parameters and transmit the first driving instruction to the ground monitoring module in real time; the first driving instruction comprises a gear adjusting instruction, corresponding gear running time information and a train speed curve;
the second deep learning module can generate a second driving instruction in real time according to the train operation parameters and the environmental parameters and transmit the second driving instruction to the ground monitoring module in real time; the second driving instruction comprises a gear adjusting instruction, corresponding gear running time information and a train speed curve;
recording the operation action of a driver as an actual driving instruction, and recording a first driving instruction and a second driving instruction as an empirical driving instruction; after receiving an actual driving instruction and an empirical driving instruction, the ground monitoring module stores the actual driving instruction and the empirical driving instruction, compares the actual driving instruction and the empirical driving instruction matched in time sequence, does not act if the difference value between the actual driving instruction and the empirical driving instruction is within an allowable range, transmits the empirical driving instruction to the vehicle-mounted driving system if the difference value between the actual driving instruction and the empirical driving instruction exceeds the allowable range, and broadcasts the empirical driving instruction to a driver in a sound and light mode by the vehicle-mounted driving system;
the first deep learning module and the second deep learning module are both realized by adopting a deep learning neural network; the deep learning neural network corresponding to the first deep learning module is marked as a first network, and the deep learning neural network corresponding to the second deep learning module is marked as a second network; the network I is trained according to the driving data of the excellent driver under the normal weather condition, and the network II is trained according to the driving data of the excellent driver under the severe weather condition; the driving data comprises train operation parameters and operation actions of a driver.
2. The high-speed train driver monitoring and assistance system according to claim 1, characterized in that: when training the first network, taking the following 15 features as input features of the first network: the method comprises the following steps of (1) changing the position of a train working condition point, the historical average speed of a train before a certain position point, the running speed of the train at a current sampling point, the grade value of the train at the certain position point, the starting position of a grade section of the train at the point, the ending position of the grade section of the train at the point, the residual length of the train from the point to the current grade section, the average speed of the train at the grade section of the point, the maximum speed limit value of the train at the point, the starting position of the maximum speed limit value of the train at the point, the ending position of the maximum speed limit value of the train at the point, the total running time between train stations, the train model, the train length and the train weight;
taking the gear adjusting instruction, the corresponding gear running time information and the train speed curve as output characteristics of the first network;
when training the second network, adding 3 features on the basis of the 15 features, wherein a total of 18 features are used as input features of the second network, and the added 3 features are respectively as follows: the temporary speed limit value, the temporary speed limit starting position and the temporary speed limit ending position; and taking the gear adjusting instruction, the corresponding gear running time information and the train speed curve as the output characteristics of the second network.
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