CN115050180A - Knowledge learning-based command scheduling system and operation method - Google Patents
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Abstract
The invention belongs to the technical field of intelligent traffic, and discloses a command scheduling system based on knowledge learning and an operation method thereof, which comprise a traffic management platform unit, a traffic command station unit, a dispatcher unit and the like, wherein an alarm receiving module in the traffic management platform unit is used for receiving accident alarm, recording the occurrence time, the accident profile and the alarm information of an accident in a whole process through an accident recording module, transmitting the accident record to the traffic command station unit nearest to an accident point through a scheduling module, an information receiving module is used for receiving the accident information transmitted by the accident recording module, searching the nearest dispatcher according to a positioning information display module, transmitting the accident information to an incident state reporting module, transmitting the optimal route reaching the accident point to a navigation module through a route transmitting module, and simultaneously, recording the route reaching the accident point through a deep learning unit, and optimizing the route.
Description
Technical Field
The invention belongs to the technical field of intelligent traffic, and particularly relates to a knowledge learning-based command and scheduling system and an operation method.
Background
The intelligent traffic is based on Intelligent Traffic (ITS), technologies such as Internet of things, cloud computing, internet, artificial intelligence, automatic control and mobile internet are fully applied in the traffic field, traffic information is collected through high and new technologies, all aspects of the traffic fields such as traffic management, traffic transportation and public trip and the whole process of traffic construction management are managed and supported, so that the traffic system has the capabilities of perception, interconnection, analysis, prediction, control and the like in regions and cities or even larger space-time ranges, the traffic safety is fully guaranteed, the efficiency of traffic infrastructure is exerted, the operation efficiency and the management level of the traffic system are improved, and the traffic system serves smooth public trip and sustainable economic development.
The command and dispatch is an important component of an intelligent traffic system, and meanwhile, how to more reasonably command and dispatch is also one of key problems to be solved by intelligent traffic. The emergency command site has burst, emergency, short period, short time and short place. The type of the event is uncertain, and the development situation of the emergency needs to be known by a command center in time.
At present, a traffic platform issues a scheduling instruction and an executive member arrives at an accident site to perform command scheduling, but the current road is complex and various, and if the executive member is not well done with the road, the time of arriving at the accident site is influenced, and the accident handling efficiency is influenced.
Disclosure of Invention
The invention aims to provide a command scheduling system based on knowledge learning and an operation method thereof, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention adopts the following technical scheme:
a command scheduling system based on knowledge learning comprises a traffic management platform unit, wherein a deep learning unit is arranged in the traffic management platform unit;
the traffic command station unit is in bidirectional butt joint with the traffic management platform unit, and the dispatcher unit is in bidirectional butt joint with the traffic command station unit;
the dispatcher unit includes: the system comprises a positioning module for determining the position of a dispatcher, an event state reporting module for acquiring an accident situation and a navigation module for displaying an optimal route to an accident point;
the deep learning unit is used for recording routes reaching accident points and optimizing the routes.
Preferably, the pipe-handling platform unit includes: the system comprises an alarm receiving module, an accident recording module and a scheduling module;
the alarm receiving module is used for receiving accident alarm and recording the occurrence time of the accident, the general accident situation and the alarm information of the alarm person in the whole process through the accident recording module;
the dispatching module is used for transmitting the accident record to the traffic guidance station unit closest to the accident point.
Preferably, the traffic conductor station unit includes: the system comprises an information receiving module, a positioning information display module and a route sending module;
the information receiving module is used for receiving accident information sent by the accident recording module, searching a nearest dispatcher according to the positioning information display module, sending the accident information to the event reporting module, and simultaneously transmitting an optimal route reaching an accident point to the navigation module through the route sending module.
Preferably, the deep learning unit includes: the system comprises a block dividing module, a route collecting module, an initial point recording module and an end point recording module;
the block division module is used for carrying out grid division on the district and recording the route in the grid through the route collection module;
the initial point recording module and the terminal point recording module are used for recording the initial point and the terminal point of each scheduling.
Preferably, the block division module performs boundary learning on the region of the dispatcher, divides the whole environment region into grids in a rectangular grid form, then maps the whole actual environment with each grid region, finally realizes discretization of the actual environment, generates an environment map, interacts with the dynamically changing environment, judges the obstacle grids in the course of the path and processes the obstacle grids.
Preferably, the route collection module generates an initial path through the end position of the accident information, preprocesses the path, plans the path of the intelligent driving vehicle according to the preprocessed path, grids the planned range area, and establishes a network topology.
Preferably, the deep learning unit is used for reinforcement learning priori knowledge training, a dispatcher selects a route to interact with a known route to obtain priori knowledge, and the training process is recorded as a learning process; meanwhile, through continuous learning, the reference estimated value in parameter setting is updated regularly, the parameter can be the shortest driving distance, the minimum driving time or the minimum comprehensive cost as a standard, the standard can be converted according to different weights by all the costs, and the weight regulation needs to be strengthened and learned to carry out multiple dynamic adjustments in the continuous learning process until the change is kept in the set threshold range.
Preferably, an operation method of the command scheduling system based on knowledge learning includes the following steps:
the dispatching information is sent to a traffic command station unit through a traffic management platform unit;
sending accident information to a dispatcher unit through a traffic command station unit;
meanwhile, the optimal route reaching the accident point is transmitted to a navigation module of a dispatcher unit;
and in the process of arriving at the accident point by the dispatcher, recording the route of the accident point by the deep learning unit and optimizing the route.
The invention has the technical effects and advantages that: compared with the prior art, the command scheduling system based on knowledge learning and the operation method thereof provided by the invention have the following advantages:
the invention mainly comprises a traffic management platform unit, a traffic command station unit, a dispatcher unit and the like, wherein an alarm receiving module in the traffic management platform unit is used for receiving accident alarm, recording the occurrence time, the accident general situation and the alarm information of an accident in a whole process through an accident recording module, transmitting the accident record to the traffic command station unit nearest to an accident point through a dispatching module, an information receiving module is used for receiving the accident information sent by the accident recording module, searching the nearest dispatcher according to a positioning information display module, transmitting the accident information to an incident reporting module, transmitting the optimal route reaching the accident point to a navigation module through a route transmitting module, simultaneously, recording the route reaching the accident point by a deep learning unit, optimizing the route, effectively providing the optimal route for the dispatcher so as to facilitate the dispatcher to reach the accident point at the fastest speed, the efficiency of accident treatment is improved.
Drawings
FIG. 1 is a block diagram of a knowledge learning based command and dispatch system of the present invention;
FIG. 2 is a block diagram of a dispatcher unit of the present invention;
FIG. 3 is a block diagram of a traffic management platform unit according to the present invention;
FIG. 4 is a block diagram of a traffic conductor station unit of the present invention;
FIG. 5 is a block diagram of a deep learning unit of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The specific embodiments described herein are merely illustrative of the invention and do not delimit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
The invention provides a command scheduling system based on knowledge learning, which comprises a traffic management platform unit, wherein a deep learning unit is arranged in the traffic management platform unit; and the traffic command station unit is in bidirectional butt joint with the traffic management platform unit, and the dispatcher unit is in bidirectional butt joint with the traffic command station unit.
The dispatcher unit includes: the system comprises a positioning module for determining the position of a dispatcher, an event state reporting module for acquiring an accident situation and a navigation module for displaying an optimal route to an accident point; the deep learning unit is used for recording routes reaching accident points and optimizing the routes.
The traffic pipe platform unit includes: the system comprises an alarm receiving module, an accident recording module and a scheduling module; the alarm receiving module is used for receiving accident alarm and recording the occurrence time of the accident, the accident general situation and the alarm information in the whole process through the accident recording module; and the dispatching module is used for transmitting the accident record to the traffic guidance station unit closest to the accident point.
The traffic guidance station unit includes: the system comprises an information receiving module, a positioning information display module and a route sending module; the information receiving module is used for receiving accident information sent by the accident recording module, searching a nearest dispatcher according to the positioning information display module, sending the accident information to the event reporting module, and simultaneously transmitting an optimal route reaching an accident point to the navigation module through the route sending module.
The deep learning unit includes: the system comprises a block dividing module, a route collecting module, an initial point recording module and an end point recording module; the block division module is used for carrying out grid division on the district and recording the route in the grid through the route collection module; the initial point recording module and the terminal point recording module are used for recording the initial point and the terminal point of each scheduling.
The block division module is used for learning the boundary of the region of the dispatcher, dividing the whole environment region into grids in a rectangular grid mode, mapping the whole actual environment with each grid region, finally realizing the discretization of the actual environment, generating an environment map, interacting with the dynamically changed environment, judging the obstacle grids in the course of the path and processing the obstacle grids.
The route collection module generates an initial path through the end point position of the accident information, preprocesses the path, plans the path of the intelligent driving vehicle according to the preprocessed path, grids the planned range area, and establishes a network topology.
The deep learning unit is used for reinforcement learning priori knowledge training, a dispatcher selects a route to interact with a known route to obtain priori knowledge, and the training process is recorded as a learning process; meanwhile, through continuous learning, the reference estimated value in parameter setting is updated regularly, the parameter can be the shortest driving distance, the minimum driving time or the minimum comprehensive cost as a standard, the standard can be converted according to different weights by all the costs, and the weight regulation needs to be strengthened and learned to carry out multiple dynamic adjustments in the continuous learning process until the change is kept in the set threshold range.
Example 2
In this embodiment, an operation method of a command scheduling system based on knowledge learning is provided, which includes the following steps:
the dispatching information is sent to a traffic command station unit through a traffic management platform unit;
sending accident information to a dispatcher unit through a traffic command station unit;
simultaneously transmitting the optimal route to the accident point to a navigation module of a dispatcher unit;
and in the process of arriving at the accident point by the dispatcher, recording the route of the accident point by the deep learning unit and optimizing the route.
An alarm receiving module in the traffic management platform unit is used for receiving accident alarm, recording the occurrence time of an accident, the accident general situation and alarm information in a whole process through an accident recording module, transmitting the accident record to the traffic command station unit closest to an accident point through a scheduling module, receiving the accident information transmitted by the accident recording module, searching a nearest dispatcher according to a positioning information display module, transmitting the accident information to an incident state reporting module, transmitting the optimal route reaching the accident point to a navigation module through a route transmitting module, recording the route reaching the accident point through a deep learning unit, optimizing the route, and effectively providing the optimal route for the dispatcher so as to facilitate the dispatcher to reach the accident point and improve the efficiency of accident treatment.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still make modifications to the technical solutions described in the foregoing embodiments, or make equivalent substitutions and improvements to part of the technical features of the foregoing embodiments, and any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A command scheduling system based on knowledge learning, characterized in that:
the system comprises a traffic management platform unit, wherein a deep learning unit is arranged in the traffic management platform unit;
the traffic command station unit is in bidirectional butt joint with the traffic management platform unit, and the dispatcher unit is in bidirectional butt joint with the traffic command station unit;
the dispatcher unit includes: the system comprises a positioning module for determining the position of a dispatcher, an event state reporting module for acquiring accident scenarios and a navigation module for displaying an optimal route to an accident point;
the deep learning unit is used for recording routes reaching accident points and optimizing the routes.
2. The command scheduling system based on knowledge learning of claim 1, wherein: the traffic pipe platform unit includes: the system comprises an alarm receiving module, an accident recording module and a scheduling module;
the alarm receiving module is used for receiving accident alarm and recording the occurrence time of the accident, the general accident situation and the alarm information of the alarm person in the whole process through the accident recording module;
the dispatching module is used for transmitting the accident record to the traffic guidance station unit closest to the accident point.
3. The command scheduling system based on knowledge learning of claim 2, wherein: the traffic guidance station unit includes: the system comprises an information receiving module, a positioning information display module and a route sending module;
the information receiving module is used for receiving the accident information sent by the accident recording module, searching a nearest dispatcher according to the positioning information display module, sending the accident information to the event reporting module, and simultaneously transmitting the optimal route reaching an accident point to the navigation module through the route sending module.
4. The command scheduling system based on knowledge learning of claim 3, wherein: the deep learning unit includes: the system comprises a block dividing module, a route collecting module, an initial point recording module and an end point recording module;
the block division module is used for carrying out grid division on the district and recording the route in the grid through the route collection module;
the initial point recording module and the terminal point recording module are used for recording the initial point and the terminal point of each scheduling.
5. The command scheduling system based on knowledge learning of claim 4, wherein: the block division module is used for learning the boundary of the region of the dispatcher, dividing the whole environment region into grids in a rectangular grid mode, mapping the whole actual environment with each grid region, finally realizing the discretization of the actual environment, generating an environment map, interacting with the dynamically changed environment, judging the obstacle grids in the course of the path and processing the obstacle grids.
6. The system of claim 5, wherein the command scheduling system based on knowledge learning comprises: the route collection module generates an initial path through the end point position of the accident information, preprocesses the path, plans the path of the intelligent driving vehicle according to the preprocessed path, grids the planned range area, and establishes a network topology.
7. The command scheduling system based on knowledge learning of claim 6, wherein: the deep learning unit is used for reinforcement learning priori knowledge training, a dispatcher selects a route to interact with a known route to obtain priori knowledge, and the training process is recorded as a learning process; meanwhile, through continuous learning, the reference estimated value in parameter setting is updated regularly, the parameter can be the shortest driving distance, the minimum driving time or the minimum comprehensive cost is taken as a standard, the standard can be converted according to different weights by all costs, and the weight is specified by carrying out dynamic adjustment for many times in the continuous learning process of reinforcement learning until the change is kept within the range of the set threshold value.
8. A method for operating a command and dispatch system based on knowledge learning, comprising the command and dispatch system according to any one of claims 1 to 7, comprising the steps of:
the dispatching information is sent to a traffic command station unit through a traffic management platform unit;
the accident information is sent to a dispatcher unit through a traffic command station unit;
simultaneously transmitting the optimal route to the accident point to a navigation module of a dispatcher unit;
and in the process of arriving at the accident point by the dispatcher, recording the route of the accident point by the deep learning unit and optimizing the route.
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