CN112433539A - Unmanned aerial vehicle scheduling method and system for processing high-speed traffic accidents and readable storage medium - Google Patents
Unmanned aerial vehicle scheduling method and system for processing high-speed traffic accidents and readable storage medium Download PDFInfo
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- CN112433539A CN112433539A CN202011364605.0A CN202011364605A CN112433539A CN 112433539 A CN112433539 A CN 112433539A CN 202011364605 A CN202011364605 A CN 202011364605A CN 112433539 A CN112433539 A CN 112433539A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
- G05D1/104—Simultaneous control of position or course in three dimensions specially adapted for aircraft involving a plurality of aircrafts, e.g. formation flying
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/0047—Navigation or guidance aids for a single aircraft
- G08G5/0069—Navigation or guidance aids for a single aircraft specially adapted for an unmanned aircraft
Abstract
The invention relates to an unmanned aerial vehicle dispatching method, a system and a readable storage medium for processing high-speed traffic accidents, wherein the method comprises the following steps: collecting historical traffic data of a highway, establishing an accident model, obtaining vehicle parameter information, extracting a characteristic vector, and generating position information of a vehicle and a lane; establishing an observation point zone bit, generating a detection mode, and generating unmanned aerial vehicle formation according to the detection mode to obtain formation information; and generating scheduling information according to the formation information, performing position control on the unmanned aerial vehicle according to the scheduling information, and transmitting the control information to the terminal.
Description
Technical Field
The invention relates to an unmanned aerial vehicle dispatching method, in particular to an unmanned aerial vehicle dispatching method, a system and a readable storage medium for processing high-speed traffic accidents.
Background
The traffic jam is a negative external product of urbanization overflow, and the dealing and governing of the traffic jam are always the focus fields of all social circles in the global range. Intersection signal control and route guidance are becoming more and more widely used as important components in intelligent traffic systems in increasingly severe traffic environments. However, while hardware facilities are rapidly developed and improved, the problem of low intelligentization degree of a traffic management system still exists, and a traffic management department faces the problems of acquiring hidden intrinsic information from mass traffic flow data, particularly data with non-repetitive characteristics generated by sudden traffic congestion, making full use of information advantages to formulate a scientific dredging strategy and the like. Therefore, on the basis of massive traffic data, dynamic traffic flow information processing and congestion control and guidance strategy research is developed, the method has important significance for relieving urban traffic congestion, sudden traffic accidents are easily caused in the traffic congestion process, and the prior art relates to the field of some basic management, particularly certain hysteresis is provided for field management, such as traffic congestion and traffic accidents (reaching the field requires certain time and is difficult to accurately judge the direction of traffic flow in a bidirectional lane of a highway, for example, no matter an alarm person or a police person, so that the difficulty is increased for accident handling.
In order to realize accurate control in the unmanned aerial vehicle scheduling that can supervise in the expressway accident handling process, need develop a section and control rather than assorted system, this system acquires vehicle parameter information, generate vehicle and lane position information, establish observation point zone bit, generate the detection mode, form the formation of unmanned aerial vehicle according to the detection mode, generate scheduling information, carry out the management and control of position management and control and unmanned aerial vehicle attitude of taking photo by plane to unmanned aerial vehicle, but in carrying out control process, when how to realize accurate control, realize that the accurate unmanned aerial vehicle of road observation all is the urgent problem that can not wait to solve.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a method and a system for dispatching an unmanned aerial vehicle for processing high-speed traffic accidents and a readable storage medium.
In order to achieve the purpose, the invention adopts the technical scheme that: an unmanned aerial vehicle dispatching method for processing high-speed traffic accidents comprises the following steps:
collecting historical traffic data of the highway, establishing an accident model,
acquiring vehicle parameter information, extracting a characteristic vector, and generating vehicle and lane position information;
establishing observation point zone bits, generating a detection mode,
generating unmanned aerial vehicle formation according to a detection mode to obtain formation information;
generating scheduling information according to the formation information,
and carrying out position control on the unmanned aerial vehicle according to the scheduling information, and transmitting the control information to the terminal.
In a preferred embodiment of the invention, vehicle parameter information is obtained, a characteristic vector is extracted, and vehicle and lane position information is generated; the method specifically comprises the following steps:
acquiring vehicle position information and extracting a feature vector;
comparing the characteristic vector with a preset vector to generate included angle information;
comparing the included angle information with a preset included angle threshold value to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if so, generating vehicle offset information;
and generating vehicle and lane position information according to the vehicle offset information.
In a preferred embodiment of the present invention, the method further comprises:
collecting traffic image information, and carrying out thresholding treatment on a traffic image, wherein a non-lane line area is treated into black, and a lane line area is treated into white;
removing image noise points by a local minimum method, and performing compensation processing on a white area by a local maximum method;
setting the elimination width, carrying out fixed width processing on the white area, establishing polar angle constraint conditions according to the polar angle characteristics of the lane lines,
and generating compensation information according to the polar angle constraint condition, and adjusting the aerial photography attitude angle of the unmanned aerial vehicle according to the compensation information.
In a preferred embodiment of the invention, unmanned aerial vehicle formation is generated according to a detection mode to obtain formation information; the method specifically comprises the following steps:
establishing a three-dimensional scene, extracting the location information of the virtual unmanned aerial vehicle, establishing an unmanned aerial vehicle formation model,
generating virtual unmanned aerial vehicle formation keeping information according to the unmanned aerial vehicle formation model;
generating a virtual unmanned aerial vehicle formation mode according to the formation keeping information of the virtual unmanned aerial vehicles;
forming unmanned aerial vehicles according to the virtual unmanned aerial vehicle forming mode to obtain result information;
comparing the result information with actual detection information; obtaining unmanned aerial vehicle formation deviation information;
judging whether the deviation information is larger than a preset threshold value,
and if so, generating correction information to correct the formation mode of the virtual unmanned aerial vehicles.
In a preferred embodiment of the present invention, the method further comprises:
establishing a constraint model through big data, and generating a navigation time constraint condition through the constraint model;
acquiring an initial position and a target position of the unmanned aerial vehicle;
calculating standard navigation time from the starting position of the unmanned aerial vehicle to the target position according to the reference flight path;
collecting dynamic track information and calculating and predicting navigation time;
comparing the standard voyage time with the predicted voyage time to obtain a deviation ratio;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if the unmanned aerial vehicle node is larger than the target node, the navigation speed of the unmanned aerial vehicle node is adjusted.
In a preferred embodiment of the present invention, the position control of the unmanned aerial vehicle according to the scheduling information and the transmission of the control information to the terminal specifically include:
acquiring initial state information of the unmanned aerial vehicle, and generating position information of the unmanned aerial vehicle;
receiving a control instruction, and generating a flight mode of the unmanned aerial vehicle;
carrying out formation aggregation according to the flight mode of the unmanned aerial vehicle to generate aggregation time information;
comparing the aggregation time information with preset time to obtain a deviation rate;
judging whether the deviation rate is larger than the deviation rate threshold value,
if the number of the unmanned aerial vehicles in the different zone bits is larger than the preset number, generating formation reconstruction information, and re-forming the unmanned aerial vehicles in the different zone bits;
and if the number of the queue holding messages is less than the preset value, generating the queue holding messages and transmitting the queue holding messages to the terminal.
The second aspect of the present invention also provides an unmanned aerial vehicle dispatching system for handling high-speed traffic accidents, comprising: the storage comprises an unmanned aerial vehicle dispatching method program for processing high-speed traffic accidents, and the unmanned aerial vehicle dispatching method program for processing high-speed traffic accidents realizes the following steps when being executed by the processor: collecting historical traffic data of the highway, establishing an accident model,
acquiring vehicle parameter information, extracting a characteristic vector, and generating vehicle and lane position information;
establishing observation point zone bits, generating a detection mode,
generating unmanned aerial vehicle formation according to a detection mode to obtain formation information;
generating scheduling information according to the formation information,
and carrying out position control on the unmanned aerial vehicle according to the scheduling information, and transmitting the control information to the terminal.
In a preferred embodiment of the invention, vehicle parameter information is obtained, a characteristic vector is extracted, and vehicle and lane position information is generated; the method specifically comprises the following steps:
acquiring vehicle position information and extracting a feature vector;
comparing the characteristic vector with a preset vector to generate included angle information;
comparing the included angle information with a preset included angle threshold value to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if so, generating vehicle offset information;
and generating vehicle and lane position information according to the vehicle offset information.
In a preferred embodiment of the invention, unmanned aerial vehicle formation is generated according to a detection mode to obtain formation information; the method specifically comprises the following steps:
establishing a three-dimensional scene, extracting the location information of the virtual unmanned aerial vehicle, establishing an unmanned aerial vehicle formation model,
generating virtual unmanned aerial vehicle formation keeping information according to the unmanned aerial vehicle formation model;
generating a virtual unmanned aerial vehicle formation mode according to the formation keeping information of the virtual unmanned aerial vehicles;
forming unmanned aerial vehicles according to the virtual unmanned aerial vehicle forming mode to obtain result information;
comparing the result information with actual detection information; obtaining unmanned aerial vehicle formation deviation information;
judging whether the deviation information is larger than a preset threshold value,
and if so, generating correction information to correct the formation mode of the virtual unmanned aerial vehicles.
The third aspect of the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes a program for a method for scheduling an unmanned aerial vehicle for handling a high-speed traffic accident, and when the program for the method for scheduling an unmanned aerial vehicle for handling a high-speed traffic accident is executed by a processor, the method for scheduling an unmanned aerial vehicle for handling a high-speed traffic accident as described in any one of the above is implemented.
The invention solves the defects in the background technology, and has the following beneficial effects:
(1) through unmanned aerial vehicle aerial photography road image information, acquire traffic accident information, make corresponding decision-making according to the traffic accident classification of difference, the unmanned aerial vehicle node carries out real-time picture transmission to the accident area, the on-the-spot condition in monitoring road accident emergence place that can be accurate.
(2) By creating a three-dimensional scene of an area near the accident occurrence, the observation scene and the observation effect of the accident area can be simulated and observed on line through the simulated unmanned aerial vehicle, the problems found in the accident handling process can be adjusted on line, the occurrence of emergency in the accident handling process is reduced, and the accident handling efficiency is improved.
(3) When traffic accidents happen to roads or casualties of people exist, the number of the optimal unmanned aerial vehicle nodes is selected according to the standby state of the unmanned aerial vehicles in the nearby areas to fly to a preset area, the unmanned aerial vehicle nodes are matched and jointly photographed by adjusting the aerial photographing angles or the aerial photographing postures of the unmanned aerial vehicle nodes, the scene pictures transmitted by the unmanned aerial vehicles are clear, the scene pictures transmitted by the unmanned aerial vehicles can be further interconnected with a rescue system of a hospital, and the remote guidance temporary self-rescue service is carried out.
(4) The system also has a learning function, after the accident handling is completed, the handling data in the handling mode and the handling process are automatically generated, the data are stored in the database, different accident handling data are stored in different databases according to different accident types, the accident data are updated and replaced at intervals, the validity of the accident data is ensured, when the accident happens next time, an accident handling scheme can be automatically generated through the system, and the accident can be quickly handled.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a flow chart illustrating a method of unmanned aerial vehicle dispatch for handling high speed traffic accidents in accordance with the present invention;
FIG. 2 shows a flow chart of a method of obtaining vehicle and lane position information;
FIG. 3 shows a flowchart of a method for adjusting an attitude angle of an unmanned aerial vehicle during aerial photography;
fig. 4 shows a flowchart of a method for drone formation correction;
FIG. 5 shows a flow chart of a method for adjusting the node voyage speed of an unmanned aerial vehicle;
fig. 6 shows a block diagram of an unmanned aerial vehicle dispatching system for processing high-speed traffic accidents.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
Fig. 1 shows a flow chart of an unmanned aerial vehicle scheduling method for handling high-speed traffic accidents according to the invention.
As shown in fig. 1, a first aspect of the present invention provides an unmanned aerial vehicle scheduling method for handling a high-speed traffic accident, including:
s102, collecting historical traffic data of the highway, establishing an accident model,
s104, obtaining vehicle parameter information, extracting a characteristic vector, and generating vehicle and lane position information;
s106, establishing an observation point zone bit, generating a detection mode,
s108, generating unmanned aerial vehicle formation according to the detection mode to obtain formation information;
s110, generating scheduling information according to the formation information,
and S112, carrying out position control on the unmanned aerial vehicle according to the scheduling information, and transmitting the control information to the terminal.
It should be noted that, through the unmanned aerial vehicle aerial photography road image information, traffic accident information is obtained, corresponding decisions are made according to different traffic accident categories, real-time picture transmission is carried out on an accident area by an unmanned aerial vehicle node, the site situation of a road accident occurrence place can be accurately monitored, the system also has a learning function, after accident processing is completed, processing data in a processing mode and a processing process are automatically generated, data are stored in a database, different accident processing data are stored in different databases according to different accident types, the accident data are updated and replaced at intervals, the validity of the accident data is ensured, when an accident occurs next time, an accident processing scheme can be automatically generated through the system, and the accident can be rapidly processed.
As shown in FIG. 2, the present invention discloses a flow chart of a method for obtaining vehicle and lane position information;
according to the embodiment of the invention, vehicle parameter information is obtained, the characteristic vector is extracted, and the position information of the vehicle and the lane is generated; the method specifically comprises the following steps:
s202, obtaining vehicle position information and extracting a feature vector;
s204, comparing the characteristic vector with a preset vector to generate included angle information;
s206, comparing the included angle information with a preset included angle threshold value to obtain a deviation rate;
s208, judging whether the deviation rate is greater than a preset deviation rate threshold value;
s210, if the vehicle offset information is larger than the preset threshold value, generating vehicle offset information;
and S212, generating vehicle and lane position information according to the vehicle offset information.
As shown in fig. 3, the invention discloses a flow chart of an unmanned aerial vehicle aerial photography attitude angle adjusting method;
according to the embodiment of the invention, the method further comprises the following steps:
s302, collecting traffic image information, and carrying out thresholding on a traffic image, wherein a non-lane line area is processed into black, and a lane line area is processed into white;
s304, removing image noise by a local minimum method, and performing compensation processing on a white area by a local maximum method;
s306, setting the elimination width, carrying out fixed width processing on the white area, establishing polar angle constraint conditions according to the polar angle characteristics of the lane line,
and S308, generating compensation information according to the polar angle constraint condition, and adjusting the aerial photography attitude angle of the unmanned aerial vehicle according to the compensation information.
As shown in fig. 4, the invention discloses a flow chart of a method for correcting formation of unmanned aerial vehicles;
according to the embodiment of the invention, unmanned aerial vehicle formation is generated according to a detection mode to obtain formation information; the method specifically comprises the following steps:
s402, establishing a three-dimensional scene, extracting location information of the virtual unmanned aerial vehicles, establishing an unmanned aerial vehicle formation model, and S404, generating formation holding information of the virtual unmanned aerial vehicles according to the unmanned aerial vehicle formation model;
s406, generating a virtual unmanned aerial vehicle formation mode according to the formation keeping information of the virtual unmanned aerial vehicles;
s408, unmanned aerial vehicle formation is carried out according to the virtual unmanned aerial vehicle formation mode to obtain result information;
s410, comparing the result information with the actual detection information; obtaining unmanned aerial vehicle formation deviation information;
s412, judging whether the deviation information is larger than a preset threshold value,
and S414, if the number of the unmanned aerial vehicles is larger than the preset number, generating correction information and correcting the formation mode of the virtual unmanned aerial vehicles.
It should be noted that by creating a three-dimensional scene of an area near an accident, an observation scene and an observation effect of an accident area can be simulated and observed on line by simulating an unmanned aerial vehicle, problems found in an accident handling process can be adjusted on line, occurrence of emergency in the accident handling process is reduced, and the accident handling efficiency is improved.
As shown in fig. 5, the invention discloses a flow chart of a method for adjusting the node navigation speed of an unmanned aerial vehicle;
according to the embodiment of the invention, the method further comprises the following steps:
s502, establishing a constraint model through big data, and generating a navigation time constraint condition through the constraint model;
s504, acquiring an initial position and a target position of the unmanned aerial vehicle;
s506, calculating standard navigation time from the starting position of the unmanned aerial vehicle to the target position according to the reference track;
s508, collecting dynamic track information and calculating and predicting navigation time;
s510, comparing the standard voyage time with the predicted voyage time to obtain a deviation rate;
s512, judging whether the deviation rate is greater than a preset deviation rate threshold value;
and S514, if the navigation speed is larger than the target navigation speed, adjusting the navigation speed of the unmanned aerial vehicle node.
According to the embodiment of the invention, the position control of the unmanned aerial vehicle is carried out according to the scheduling information, and the control information is transmitted to the terminal, which specifically comprises the following steps:
acquiring initial state information of the unmanned aerial vehicle, and generating position information of the unmanned aerial vehicle;
receiving a control instruction, and generating a flight mode of the unmanned aerial vehicle;
carrying out formation aggregation according to the flight mode of the unmanned aerial vehicle to generate aggregation time information;
comparing the aggregation time information with preset time to obtain a deviation rate;
judging whether the deviation rate is larger than the deviation rate threshold value,
if the number of the unmanned aerial vehicles in the different zone bits is larger than the preset number, generating formation reconstruction information, and re-forming the unmanned aerial vehicles in the different zone bits;
and if the number of the queue holding messages is less than the preset value, generating the queue holding messages and transmitting the queue holding messages to the terminal.
As shown in fig. 6, the present invention discloses a block diagram of an unmanned aerial vehicle dispatching system for handling high-speed traffic accidents;
the second aspect of the present invention also provides an unmanned aerial vehicle dispatching system for handling high-speed traffic accidents, wherein the system 6 comprises: the memory 61 and the processor 62, the memory includes a program of the unmanned plane dispatching method for processing the high-speed traffic accident, and the program of the unmanned plane dispatching method for processing the high-speed traffic accident realizes the following steps when being executed by the processor: collecting historical traffic data of the highway, establishing an accident model,
acquiring vehicle parameter information, extracting a characteristic vector, and generating vehicle and lane position information;
establishing observation point zone bits, generating a detection mode,
generating unmanned aerial vehicle formation according to a detection mode to obtain formation information;
generating scheduling information according to the formation information,
and carrying out position control on the unmanned aerial vehicle according to the scheduling information, and transmitting the control information to the terminal.
It should be noted that, through the unmanned aerial vehicle aerial photography road image information, traffic accident information is obtained, corresponding decisions are made according to different traffic accident categories, real-time picture transmission is carried out on an accident area by an unmanned aerial vehicle node, the site situation of a road accident occurrence place can be accurately monitored, the system also has a learning function, after accident processing is completed, processing data in a processing mode and a processing process are automatically generated, data are stored in a database, different accident processing data are stored in different databases according to different accident types, the accident data are updated and replaced at intervals, the validity of the accident data is ensured, when an accident occurs next time, an accident processing scheme can be automatically generated through the system, and the accident can be rapidly processed.
According to the embodiment of the invention, vehicle parameter information is obtained, the characteristic vector is extracted, and the position information of the vehicle and the lane is generated; the method specifically comprises the following steps:
acquiring vehicle position information and extracting a feature vector;
comparing the characteristic vector with a preset vector to generate included angle information;
comparing the included angle information with a preset included angle threshold value to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if so, generating vehicle offset information;
and generating vehicle and lane position information according to the vehicle offset information.
According to the embodiment of the invention, unmanned aerial vehicle formation is generated according to a detection mode to obtain formation information; the method specifically comprises the following steps:
establishing a three-dimensional scene, extracting the location information of the virtual unmanned aerial vehicle, establishing an unmanned aerial vehicle formation model,
generating virtual unmanned aerial vehicle formation keeping information according to the unmanned aerial vehicle formation model;
generating a virtual unmanned aerial vehicle formation mode according to the formation keeping information of the virtual unmanned aerial vehicles;
forming unmanned aerial vehicles according to the virtual unmanned aerial vehicle forming mode to obtain result information;
comparing the result information with actual detection information; obtaining unmanned aerial vehicle formation deviation information;
judging whether the deviation information is larger than a preset threshold value,
and if so, generating correction information to correct the formation mode of the virtual unmanned aerial vehicles.
According to the embodiment of the invention, the method further comprises the following steps:
collecting traffic image information, and carrying out thresholding treatment on a traffic image, wherein a non-lane line area is treated into black, and a lane line area is treated into white;
removing image noise points by a local minimum method, and performing compensation processing on a white area by a local maximum method;
setting the elimination width, carrying out fixed width processing on the white area, establishing polar angle constraint conditions according to the polar angle characteristics of the lane lines,
and generating compensation information according to the polar angle constraint condition, and adjusting the aerial photography attitude angle of the unmanned aerial vehicle according to the compensation information.
According to the embodiment of the invention, the method further comprises the following steps:
establishing a constraint model through big data, and generating a navigation time constraint condition through the constraint model;
acquiring an initial position and a target position of the unmanned aerial vehicle;
calculating standard navigation time from the starting position of the unmanned aerial vehicle to the target position according to the reference flight path;
collecting dynamic track information and calculating and predicting navigation time;
comparing the standard voyage time with the predicted voyage time to obtain a deviation ratio;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if the unmanned aerial vehicle node is larger than the target node, the navigation speed of the unmanned aerial vehicle node is adjusted.
According to the embodiment of the invention, the position control of the unmanned aerial vehicle is carried out according to the scheduling information, and the control information is transmitted to the terminal, which specifically comprises the following steps:
acquiring initial state information of the unmanned aerial vehicle, and generating position information of the unmanned aerial vehicle;
receiving a control instruction, and generating a flight mode of the unmanned aerial vehicle;
carrying out formation aggregation according to the flight mode of the unmanned aerial vehicle to generate aggregation time information;
comparing the aggregation time information with preset time to obtain a deviation rate;
judging whether the deviation rate is larger than the deviation rate threshold value,
if the number of the unmanned aerial vehicles in the different zone bits is larger than the preset number, generating formation reconstruction information, and re-forming the unmanned aerial vehicles in the different zone bits;
and if the number of the queue holding messages is less than the preset value, generating the queue holding messages and transmitting the queue holding messages to the terminal.
The third aspect of the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes a program for a method for scheduling an unmanned aerial vehicle for handling a high-speed traffic accident, and when the program for the method for scheduling an unmanned aerial vehicle for handling a high-speed traffic accident is executed by a processor, the method for scheduling an unmanned aerial vehicle for handling a high-speed traffic accident as described in any one of the above is implemented.
In conclusion, traffic accident information is acquired through the aerial image information of the road of the unmanned aerial vehicle, corresponding decisions are made according to different traffic accident categories, real-time picture transmission is carried out on the unmanned aerial vehicle nodes aiming at accident areas, and the scene condition of the road accident site can be accurately monitored.
By creating a three-dimensional scene of an area near the accident occurrence, the observation scene and the observation effect of the accident area can be simulated and observed on line through the simulated unmanned aerial vehicle, the problems found in the accident handling process can be adjusted on line, the occurrence of emergency in the accident handling process is reduced, and the accident handling efficiency is improved.
When traffic accidents happen to roads or casualties of people exist, the number of the optimal unmanned aerial vehicle nodes is selected according to the standby state of the unmanned aerial vehicles in the nearby areas to fly to a preset area, the unmanned aerial vehicle nodes are matched and jointly photographed by adjusting the aerial photographing angles or the aerial photographing postures of the unmanned aerial vehicle nodes, the scene pictures transmitted by the unmanned aerial vehicles are clear, the scene pictures transmitted by the unmanned aerial vehicles can be further interconnected with a rescue system of a hospital, and the remote guidance temporary self-rescue service is carried out.
The system also has a learning function, after the accident handling is completed, the handling data in the handling mode and the handling process are automatically generated, the data are stored in the database, different accident handling data are stored in different databases according to different accident types, the accident data are updated and replaced at intervals, the validity of the accident data is ensured, when the accident happens next time, an accident handling scheme can be automatically generated through the system, and the accident can be quickly handled.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of a unit is only one logical function division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An unmanned aerial vehicle dispatching method for processing high-speed traffic accidents is characterized by comprising the following steps:
collecting historical traffic data of the highway, establishing an accident model,
acquiring vehicle parameter information, extracting a characteristic vector, and generating vehicle and lane position information;
establishing observation point zone bits, generating a detection mode,
generating unmanned aerial vehicle formation according to a detection mode to obtain formation information;
generating scheduling information according to the formation information,
and carrying out position control on the unmanned aerial vehicle according to the scheduling information, and transmitting the control information to the terminal.
2. The unmanned aerial vehicle dispatching method for processing high-speed traffic accidents according to claim 1, wherein vehicle parameter information is obtained, feature vectors are extracted, and vehicle and lane position information is generated; the method specifically comprises the following steps:
acquiring vehicle position information and extracting a feature vector;
comparing the characteristic vector with a preset vector to generate included angle information;
comparing the included angle information with a preset included angle threshold value to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if so, generating vehicle offset information;
and generating vehicle and lane position information according to the vehicle offset information.
3. The method for dispatching unmanned aerial vehicle for processing high-speed traffic accident according to claim 1, further comprising:
collecting traffic image information, and carrying out thresholding treatment on a traffic image, wherein a non-lane line area is treated into black, and a lane line area is treated into white;
removing image noise points by a local minimum method, and performing compensation processing on a white area by a local maximum method;
setting the elimination width, carrying out fixed width processing on the white area, establishing polar angle constraint conditions according to the polar angle characteristics of the lane lines,
and generating compensation information according to the polar angle constraint condition, and adjusting the aerial photography attitude angle of the unmanned aerial vehicle according to the compensation information.
4. The unmanned aerial vehicle dispatching method for processing high-speed traffic accidents according to claim 1, wherein unmanned aerial vehicle formation is generated according to a detection mode, and formation information is obtained; the method specifically comprises the following steps:
establishing a three-dimensional scene, extracting the location information of the virtual unmanned aerial vehicle, establishing an unmanned aerial vehicle formation model,
generating virtual unmanned aerial vehicle formation keeping information according to the unmanned aerial vehicle formation model;
generating a virtual unmanned aerial vehicle formation mode according to the formation keeping information of the virtual unmanned aerial vehicles;
forming unmanned aerial vehicles according to the virtual unmanned aerial vehicle forming mode to obtain result information;
comparing the result information with actual detection information; obtaining unmanned aerial vehicle formation deviation information;
judging whether the deviation information is larger than a preset threshold value,
and if so, generating correction information to correct the formation mode of the virtual unmanned aerial vehicles.
5. The method for dispatching unmanned aerial vehicle for processing high-speed traffic accident according to claim 1, further comprising:
establishing a constraint model through big data, and generating a navigation time constraint condition through the constraint model;
acquiring an initial position and a target position of the unmanned aerial vehicle;
calculating standard navigation time from the starting position of the unmanned aerial vehicle to the target position according to the reference flight path;
collecting dynamic track information and calculating and predicting navigation time;
comparing the standard voyage time with the predicted voyage time to obtain a deviation ratio;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if the unmanned aerial vehicle node is larger than the target node, the navigation speed of the unmanned aerial vehicle node is adjusted.
6. The unmanned aerial vehicle scheduling method for processing high-speed traffic accidents according to claim 1, wherein position control is performed on the unmanned aerial vehicle according to the scheduling information, and the control information is transmitted to the terminal, and specifically comprises:
acquiring initial state information of the unmanned aerial vehicle, and generating position information of the unmanned aerial vehicle;
receiving a control instruction, and generating a flight mode of the unmanned aerial vehicle;
carrying out formation aggregation according to the flight mode of the unmanned aerial vehicle to generate aggregation time information;
comparing the aggregation time information with preset time to obtain a deviation rate;
judging whether the deviation rate is larger than the deviation rate threshold value,
if the number of the unmanned aerial vehicles in the different zone bits is larger than the preset number, generating formation reconstruction information, and re-forming the unmanned aerial vehicles in the different zone bits;
and if the number of the queue holding messages is less than the preset value, generating the queue holding messages and transmitting the queue holding messages to the terminal.
7. An unmanned aerial vehicle dispatch system for handling high speed traffic accidents, the system comprising: the storage comprises an unmanned aerial vehicle dispatching method program for processing high-speed traffic accidents, and the unmanned aerial vehicle dispatching method program for processing high-speed traffic accidents realizes the following steps when being executed by the processor: collecting historical traffic data of the highway, establishing an accident model,
acquiring vehicle parameter information, extracting a characteristic vector, and generating vehicle and lane position information;
establishing observation point zone bits, generating a detection mode,
generating unmanned aerial vehicle formation according to a detection mode to obtain formation information;
generating scheduling information according to the formation information,
and carrying out position control on the unmanned aerial vehicle according to the scheduling information, and transmitting the control information to the terminal.
8. The unmanned aerial vehicle dispatching system for processing high-speed traffic accidents of claim 7, wherein vehicle parameter information is obtained, feature vectors are extracted, and vehicle and lane position information is generated; the method specifically comprises the following steps:
acquiring vehicle position information and extracting a feature vector;
comparing the characteristic vector with a preset vector to generate included angle information;
comparing the included angle information with a preset included angle threshold value to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if so, generating vehicle offset information;
and generating vehicle and lane position information according to the vehicle offset information.
9. The unmanned aerial vehicle dispatching system for processing high-speed traffic accidents of claim 7, wherein the formation of the unmanned aerial vehicles is generated according to a detection mode, and formation information is obtained; the method specifically comprises the following steps:
establishing a three-dimensional scene, extracting the location information of the virtual unmanned aerial vehicle, establishing an unmanned aerial vehicle formation model,
generating virtual unmanned aerial vehicle formation keeping information according to the unmanned aerial vehicle formation model;
generating a virtual unmanned aerial vehicle formation mode according to the formation keeping information of the virtual unmanned aerial vehicles;
forming unmanned aerial vehicles according to the virtual unmanned aerial vehicle forming mode to obtain result information;
comparing the result information with actual detection information; obtaining unmanned aerial vehicle formation deviation information;
judging whether the deviation information is larger than a preset threshold value,
and if so, generating correction information to correct the formation mode of the virtual unmanned aerial vehicles.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium includes therein a program of a method for scheduling unmanned aerial vehicles for handling high-speed traffic accidents, and when the program of the method for scheduling unmanned aerial vehicles for handling high-speed traffic accidents is executed by a processor, the steps of the method for scheduling unmanned aerial vehicles for handling high-speed traffic accidents according to any one of claims 1 to 6 are implemented.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113701718A (en) * | 2021-07-06 | 2021-11-26 | 宁波市海策测绘有限公司 | Surveying and mapping map data acquisition method, system, storage medium and intelligent terminal |
| CN117075515A (en) * | 2023-09-05 | 2023-11-17 | 江苏芯安集成电路设计有限公司 | Singlechip control system for adjusting flight attitude based on visual analysis |
| CN117315934A (en) * | 2023-09-25 | 2023-12-29 | 阜阳交通能源投资有限公司 | Expressway traffic flow real-time monitoring and congestion prediction system based on unmanned aerial vehicle |
-
2020
- 2020-11-27 CN CN202011364605.0A patent/CN112433539A/en not_active Withdrawn
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113701718A (en) * | 2021-07-06 | 2021-11-26 | 宁波市海策测绘有限公司 | Surveying and mapping map data acquisition method, system, storage medium and intelligent terminal |
| CN113701718B (en) * | 2021-07-06 | 2024-03-19 | 海策信息科技(浙江)有限公司 | Mapping map data acquisition method, mapping map data acquisition system, storage medium and intelligent terminal |
| CN117075515A (en) * | 2023-09-05 | 2023-11-17 | 江苏芯安集成电路设计有限公司 | Singlechip control system for adjusting flight attitude based on visual analysis |
| CN117075515B (en) * | 2023-09-05 | 2024-04-16 | 江苏芯安集成电路设计有限公司 | Singlechip control system for adjusting flight attitude based on visual analysis |
| CN117315934A (en) * | 2023-09-25 | 2023-12-29 | 阜阳交通能源投资有限公司 | Expressway traffic flow real-time monitoring and congestion prediction system based on unmanned aerial vehicle |
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