CN108198413A - Blocking method is delayed in the intelligent transportation of a kind of big data and autonomous deep learning - Google Patents
Blocking method is delayed in the intelligent transportation of a kind of big data and autonomous deep learning Download PDFInfo
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Abstract
Blocking method is delayed in a kind of intelligent transportation the present invention provides big data and autonomous deep learning, and this method includes：Obtain the route planning information of each user in the current car flow information and city in city Nei Ge sections；Current car flow information and route planning information are input in the congestion points decision model pre-established, the location information of output cur-rent congestion point and following congestion points；Congestion points decision model is trained by neural network by the way of autonomous deep learning；By the location information prompt of congestion points to user, route planning information is updated for user, to alleviate traffic congestion.The car flow information and route planning information in city are obtained the present invention is based on big data, neural network model is built based on autonomous deep learning, and then export the location information of congestion points in city, and then guide user's reasonably avoiding congestion points, route planning information is updated, can significantly alleviate traffic congestion.
Description
Technical field
The present invention relates to traffic to delay plugging technique field, is handed over more particularly, to the intelligence of a kind of big data and autonomous deep learning
Logical slow blocking method.
Background technology
Existing traffic is delayed stifled mode and is manually dredged by police, association police etc. mostly；Partial navigation system can root
The car flow information in each section is obtained according to satellite, and then reminds the position of user's reasonably avoiding traffic congestion, however, this mode
Since data information more lags, traffic is caused to delay stifled mode effect poor.
Invention content
In view of this, stifled side is delayed in the intelligent transportation the purpose of the present invention is to provide a kind of big data and autonomous deep learning
Method and device, significantly to alleviate traffic congestion.
In a first aspect, stifled side is delayed in the intelligent transportation an embodiment of the present invention provides a kind of big data and autonomous deep learning
Method, method are applied to cloud server；Method includes：It obtains each in the current car flow information and city in city Nei Ge sections
The route planning information of user；Current car flow information and route planning information are input to the congestion points decision model pre-established
In, export the location informations of congestion points；Congestion points include cur-rent congestion point and following congestion points；Congestion points decision model passes through god
Through network, trained by the way of autonomous deep learning；By the location information prompt of congestion points to user, updated for user
Route planning information, to alleviate traffic congestion.
With reference to first aspect, an embodiment of the present invention provides the first possible embodiment of first aspect, wherein, on
Congestion points decision model is stated to establish by following manner：Obtain the car flow information of setting regions, multiple route planning informations and
Corresponding markup information；Markup information includes the location information of congestion points；Establish the network structure of neural network；Wagon flow is believed
Breath, multiple route planning informations and corresponding markup information are input in network structure, by the way of autonomous deep learning
It is trained, generates congestion points decision model.
The possible embodiment of with reference to first aspect the first, an embodiment of the present invention provides second of first aspect
Possible embodiment, wherein, the above method further includes：The location information of congestion points is input to network structure, to congestion points
Decision model is updated training, generates updated congestion points decision model.
With reference to first aspect, an embodiment of the present invention provides the third possible embodiment of first aspect, wherein, on
The step of stating the route planning information of each user in the current car flow information for obtaining city Nei Ge sections and city, including：It is logical
Cross the current car flow information that intelligent transportation supervisory systems obtains city Nei Ge sections；Each user in city is obtained by navigation system
Route planning information.
With reference to first aspect, an embodiment of the present invention provides the 4th kind of possible embodiment of first aspect, wherein, on
The step of stating the location information prompt of congestion points to user, including：By the location information of congestion points by being back to navigation system
System, so that the location information of congestion points is added on the route planning information of user by navigation system.
Stifled dress is delayed in second aspect, the intelligent transportation an embodiment of the present invention provides a kind of big data and autonomous deep learning
It puts, device is set to cloud server；Device includes：Data obtaining module, for obtaining the current wagon flow in city Nei Ge sections
The route planning information of each user in information and city；Message output module, for by current car flow information and path planning
Information is input in the congestion points decision model pre-established, exports the location information of congestion points；Congestion points include cur-rent congestion
Point and following congestion points；Congestion points decision model is trained by neural network by the way of autonomous deep learning；Prompting
Module, for user, the location information prompt of congestion points to be updated route planning information for user, to alleviate traffic congestion shape
Condition.
With reference to second aspect, an embodiment of the present invention provides the first possible embodiment of second aspect, wherein, on
Congestion points decision model is stated to establish by following manner：Obtain the car flow information of setting regions, multiple route planning informations and
Corresponding markup information；Markup information includes the location information of congestion points；Establish the network structure of neural network；Wagon flow is believed
Breath, multiple route planning informations and corresponding markup information are input in network structure, by the way of autonomous deep learning
It is trained, generates congestion points decision model.
With reference to second aspect, an embodiment of the present invention provides second of possible embodiment of second aspect, wherein, on
Data obtaining module is stated, is additionally operable to：The current car flow information in city Nei Ge sections is obtained by intelligent transportation supervisory systems；Pass through
Navigation system obtains the route planning information of each user in city.
With reference to second aspect, an embodiment of the present invention provides the third possible embodiment of second aspect, wherein, on
Reminding module is stated, is additionally operable to：By the location information of congestion points by being back to navigation system, so that navigation system is by congestion points
Location information is added on the route planning information of user.
Stifled realization is delayed in the third aspect, the intelligent transportation an embodiment of the present invention provides a kind of big data and autonomous deep learning
Device, including processor and machine readable storage medium, machine readable storage medium is stored with the machine that can be executed by processor
Device executable instruction, processor perform machine-executable instruction to realize the above method.
The embodiment of the present invention brings following advantageous effect：
Blocking method and device are delayed in the intelligent transportation of a kind of big data provided in an embodiment of the present invention and autonomous deep learning, gather around
Stifled point decision model is trained by neural network by the way of autonomous deep learning；By will be in the city that got
The route planning information of each user in the current car flow information in each section and city is input to the congestion points pre-established and sentences
In cover half type, cur-rent congestion point and the location information of following congestion points can be obtained；The location information prompt of congestion points is given again
User updates route planning information, to alleviate traffic congestion for user.Which obtains the vehicle in city based on big data
Stream information and route planning information build neural network model based on autonomous deep learning, and then export congestion points in city
Location information, and then user's reasonably avoiding congestion points are guided, route planning information is updated, can significantly alleviate traffic congestion shape
Condition.
Other features and advantages of the present invention will illustrate in the following description, alternatively, Partial Feature and advantage can be with
Deduce from specification or unambiguously determine or by implement the present invention above-mentioned technology it can be learnt that.
For the above objects, features and advantages of the present invention is enable to be clearer and more comprehensible, better embodiment cited below particularly, and match
Attached drawing appended by conjunction, is described in detail below.
Description of the drawings
It, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical solution of the prior art
Embodiment or attached drawing needed to be used in the description of the prior art are briefly described, it should be apparent that, in being described below
Attached drawing is some embodiments of the present invention, for those of ordinary skill in the art, before not making the creative labor
It puts, can also be obtained according to these attached drawings other attached drawings.
Fig. 1 delays the stream of blocking method for the intelligent transportation of a kind of big data provided in an embodiment of the present invention and autonomous deep learning
Cheng Tu；
Fig. 2 delays blocking method for the intelligent transportation of another big data provided in an embodiment of the present invention and autonomous deep learning
Flow chart；
Fig. 3 delays the knot of block apparatus for the intelligent transportation of a kind of big data provided in an embodiment of the present invention and autonomous deep learning
Structure schematic diagram；
Fig. 4 delays stifled realization device for the intelligent transportation of a kind of big data provided in an embodiment of the present invention and autonomous deep learning
Structure diagram.
Specific embodiment
Purpose, technical scheme and advantage to make the embodiment of the present invention are clearer, below in conjunction with attached drawing to the present invention
Technical solution be clearly and completely described, it is clear that described embodiment be part of the embodiment of the present invention rather than
Whole embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not making creative work premise
Lower all other embodiments obtained, shall fall within the protection scope of the present invention.
Delay the problem of stifled mode effect is poor in view of existing traffic, an embodiment of the present invention provides a kind of big data and
Blocking method and device are delayed in the intelligent transportation of autonomous deep learning；The technology can be applied to urban transport demanding system, navigation system
In the related systems such as system or equipment；Relevant software or hardware realization may be used in the technology, is retouched below by embodiment
It states.
The flow chart of blocking method is delayed in a kind of intelligent transportation of big data and autonomous deep learning shown in Figure 1；The party
Method is applied to cloud server；This method comprises the following steps：
Step S102 obtains the path planning letter of each user in the current car flow information and city in city Nei Ge sections
Breath；
Current car flow information and route planning information are input to the congestion points decision model pre-established by step S104
In, export the location informations of congestion points；The congestion points include cur-rent congestion point and following congestion points；The congestion points decision model leads to
Neural network is crossed, is trained by the way of autonomous deep learning；
Step S106 by the location information prompt of congestion points to user, updates route planning information for user, is handed over alleviating
Logical congestion.
It for example, can be by being arranged on the electronic image pickup device or other car flow information harvesters in city Nei Ge sections
Obtain current car flow information；User in the city is obtained by the corresponding server of application program of each map, navigation to drive
The route planning information of trip.Above-mentioned congestion points decision model can export the position of cur-rent congestion point according to current car flow information
Information predicts congestion points according to current car flow information and route planning information, exports the position letter of following congestion points
Breath；The information such as the congestion time that the congestion points decision model can also be predicted and export each congestion points.
The location information of congestion points can also be sent to the user for including these congestion points in path planning by server, with
User is prompted to update route planning information.
Blocking method is delayed in the intelligent transportation of a kind of big data provided in an embodiment of the present invention and autonomous deep learning, and congestion points are sentenced
Cover half type is trained by neural network by the way of autonomous deep learning；Pass through the city Nei Ge sections that will be got
Current car flow information and city in each user route planning information, be input to the congestion points decision model pre-established
In, cur-rent congestion point and the location information of following congestion points can be obtained；The location information prompt of congestion points is supplied to user again
User updates route planning information, to alleviate traffic congestion.Which obtains the car flow information in city based on big data
And route planning information, neural network model is built based on autonomous deep learning, and then export the position letter of congestion points in city
Breath, and then user's reasonably avoiding congestion points are guided, route planning information is updated, can significantly alleviate traffic congestion.
The flow chart of blocking method is delayed in the intelligent transportation of another big data and autonomous deep learning shown in Figure 2；It should
It is realized on the basis of method method shown in Fig. 1, this method is applied to cloud server；In this method, it is necessary first to which foundation is gathered around
Stifled point decision model, can specifically be realized by following step S202-S206：
Step S202 obtains car flow information, multiple route planning informations and the corresponding markup information of setting regions；
The markup information includes the location information of congestion points；
Step S204 establishes the network structure of neural network；
Car flow information, multiple route planning informations and corresponding markup information are input to network structure by step S206
In, it is trained by the way of autonomous deep learning, generates congestion points decision model.
Specifically, which can be labeled in car flow information, according to markup information and car flow information, neural network
Learn the feature of congestion points；In actual implementation, car flow information can be updated according to route planning information, when obtaining following setting
The car flow information at quarter, neural network to updated car flow information and the congestion point feature learnt, are further learned again
It practises, obtains final congestion points decision model.
Step S208 obtains the current car flow information in city Nei Ge sections by intelligent transportation supervisory systems；Pass through navigation
System obtains the route planning information of each user in city；
Wherein, which can be navigation application supplier, for example, Baidu map, Amap or vehicle-mounted leading
Navigate device systems etc..
Current car flow information and route planning information are input to the congestion points decision model pre-established by step S210
In, export the location informations of congestion points；The congestion points include cur-rent congestion point and following congestion points；
Step S212, by the location information of congestion points by being back to navigation system, so that navigation system is by congestion points
Location information is added on the route planning information of user, route planning information is updated for user, to alleviate traffic congestion.
For example, navigation system can be marked the location information of congestion points on navigation map with conspicuous color or mark；
Meanwhile the navigation system can also regenerate path planning after the location information of congestion points is received, to evade the congestion
The location information of point, pushes to navigation page, for selection by the user by updated route planning information.
Step S214, network structure is input to by the location information of congestion points, and instruction is updated to congestion points decision model
Practice, generate updated congestion points decision model.
In actual implementation, may be used generation congestion points location information markup information the most, periodically to the model into
Row update training, to improve the judgement accuracy of congestion points decision model.
Which obtains car flow information and route planning information in city based on big data, is taken based on autonomous deep learning
Neural network model is built, and then exports the location information of congestion points in city, and then guides user's reasonably avoiding congestion points, update
Route planning information can significantly alleviate traffic congestion.
Corresponding to above method embodiment, the intelligent transportation of a kind of big data shown in Figure 3 and autonomous deep learning
The structure diagram of slow block apparatus；The device is set to cloud server；The device includes：
Data obtaining module 30, for obtaining each user in the current car flow information in city Nei Ge sections and city
Route planning information；
Message output module 31, for current car flow information and route planning information to be input to the congestion points pre-established
In decision model, the location information of congestion points is exported；Congestion points include cur-rent congestion point and following congestion points；Congestion points judge mould
Type is trained by neural network by the way of autonomous deep learning；
Reminding module 32, for user, the location information prompt of congestion points to be updated route planning information for user, with
Alleviate traffic congestion.
In the device, congestion points decision model is established by following manner：Obtain car flow information, the Duo Gelu of setting regions
Diameter planning information and corresponding markup information；Markup information includes the location information of congestion points；Establish the network of neural network
Structure；Car flow information, multiple route planning informations and corresponding markup information are input in network structure, using autonomous
The mode of deep learning is trained, and generates congestion points decision model.
Further, above- mentioned information acquisition module is additionally operable to：City Nei Ge sections are obtained by intelligent transportation supervisory systems
Current car flow information；The route planning information of each user in city is obtained by navigation system.
Further, above-mentioned reminding module, is additionally operable to：By the location information of congestion points by being back to navigation system, with
Navigation system is made to be added to the location information of congestion points on the route planning information of user.
Block apparatus is delayed in the intelligent transportation of a kind of big data provided in an embodiment of the present invention and autonomous deep learning, based on big number
According to the car flow information and route planning information obtained in city, neural network model is built, and then defeated based on autonomous deep learning
Go out the location information of congestion points in city, and then guide user's reasonably avoiding congestion points, update route planning information, it can be apparent
Alleviate traffic congestion in ground.
The structural representation of stifled realization device is delayed in a kind of intelligent transportation of big data and autonomous deep learning shown in Figure 4
Figure；The equipment includes memory 100 and processor 101；Wherein, memory 100 is used to store one or more computer instruction,
One or more computer instruction is executed by processor, and is delayed with the intelligent transportation for realizing above-mentioned big data and autonomous deep learning stifled
Method, blocking method is delayed in the intelligent transportation of the big data and autonomous deep learning can be including one or more in above method.
Further, Network Management Equipment shown in Fig. 4 further includes bus 102 and communication interface 103, processor 101, communication interface
103 and memory 100 connected by bus 102.
Wherein, memory 100 may include high-speed random access memory (RAM, Random Access Memory),
Non-labile memory (non-volatile memory), for example, at least a magnetic disk storage may be further included.By extremely
A few communication interface 103 (can be wired or wireless) is realized logical between the system network element and at least one other network element
Letter connection can use internet, wide area network, local network, Metropolitan Area Network (MAN) etc..Bus 102 can be isa bus, pci bus or
Eisa bus etc..The bus can be divided into address bus, data/address bus, controlling bus etc..For ease of representing, only used in Fig. 4
One four-headed arrow represents, it is not intended that an only bus or a type of bus.
Processor 101 may be a kind of IC chip, have the processing capacity of signal.It is above-mentioned during realization
Each step of method can be completed by the integrated logic circuit of the hardware in processor 101 or the instruction of software form.On
The processor 101 stated can be general processor, including central processing unit (Central Processing Unit, abbreviation
CPU), network processing unit (Network Processor, abbreviation NP) etc.；It can also be digital signal processor (Digital
Signal Processing, abbreviation DSP), application-specific integrated circuit (Application Specific Integrated
Circuit, abbreviation ASIC), ready-made programmable gate array (Field-Programmable Gate Array, abbreviation FPGA) or
Person other programmable logic device, discrete gate or transistor logic, discrete hardware components.It can realize or perform sheet
Disclosed each method, step and logic diagram in invention embodiment.General processor can be microprocessor or this at
It can also be any conventional processor etc. to manage device.The step of method with reference to disclosed in embodiment of the present invention, can direct body
Now completion is performed for hardware decoding processor or perform completion with the hardware in decoding processor and software module combination.It is soft
Part module can be located at random access memory, and flash memory, read-only memory, programmable read only memory or electrically erasable programmable are deposited
In the storage medium of this fields such as reservoir, register maturation.The storage medium is located at memory 100, and processor 101 reads storage
Information in device 100, with reference to its hardware complete aforementioned embodiments method the step of.
Further, embodiment of the present invention additionally provides a kind of machine readable storage medium, the machine readable storage medium
Machine-executable instruction is stored with, when being called and being performed by processor, machine-executable instruction promotees the machine-executable instruction
The intelligent transportation that processor realizes above-mentioned big data and autonomous deep learning is made to delay blocking method, the realization of pulmonary lesions identification can
It is one or more in above method to include.
The meter of blocking method and device is delayed in the intelligent transportation of big data and autonomous deep learning that the embodiment of the present invention is provided
Calculation machine program product, the computer readable storage medium including storing program code, the instruction that said program code includes can
For performing the method described in previous methods embodiment, specific implementation can be found in embodiment of the method, and details are not described herein.
If the function is realized in the form of SFU software functional unit and is independent product sale or in use, can be with
It is stored in a computer read/write memory medium.Based on such understanding, technical scheme of the present invention is substantially in other words
The part contribute to the prior art or the part of the technical solution can be embodied in the form of software product, the meter
Calculation machine software product is stored in a storage medium, is used including some instructions so that a computer equipment (can be
People's computer, server or network equipment etc.) perform all or part of the steps of the method according to each embodiment of the present invention.
And aforementioned storage medium includes：USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), arbitrary access are deposited
The various media that can store program code such as reservoir (RAM, Random Access Memory), magnetic disc or CD.
Finally it should be noted that：Embodiment described above, only specific embodiment of the invention, to illustrate the present invention
Technical solution, rather than its limitations, protection scope of the present invention is not limited thereto, although with reference to the foregoing embodiments to this hair
It is bright to be described in detail, it will be understood by those of ordinary skill in the art that：Any one skilled in the art
In the technical scope disclosed by the present invention, it can still modify to the technical solution recorded in previous embodiment or can be light
It is readily conceivable that variation or equivalent replacement is carried out to which part technical characteristic；And these modifications, variation or replacement, do not make
The essence of corresponding technical solution is detached from the spirit and scope of technical solution of the embodiment of the present invention, should all cover the protection in the present invention
Within the scope of.Therefore, protection scope of the present invention described should be subject to the protection scope in claims.
Claims (10)
1. blocking method is delayed in the intelligent transportation of a kind of big data and autonomous deep learning, which is characterized in that the method is applied to cloud
Hold server；The method includes：
Obtain the route planning information of each user in the current car flow information and the city in city Nei Ge sections；
The current car flow information and the route planning information are input in the congestion points decision model pre-established, exported
The location information of congestion points；The congestion points include cur-rent congestion point and following congestion points；The congestion points decision model passes through
Neural network is trained by the way of autonomous deep learning；
By the location information prompt of the congestion points to user, the route planning information is updated for the user, is handed over alleviating
Logical congestion.
2. according to the method described in claim 1, it is characterized in that, the congestion points decision model is established by following manner：
Obtain car flow information, multiple route planning informations and the corresponding markup information of setting regions；The markup information packet
Include the location information of congestion points；
Establish the network structure of neural network；
The car flow information, multiple route planning informations and corresponding markup information are input in the network structure, adopted
It is trained with the mode of autonomous deep learning, generates the congestion points decision model.
3. according to the method described in claim 2, it is characterized in that, the method further includes：
The location information of the congestion points is input to the network structure, instruction is updated to the congestion points decision model
Practice, generate updated congestion points decision model.
4. according to the method described in claim 1, it is characterized in that, it is described obtain city Nei Ge sections current car flow information,
And in the city the step of route planning information of each user, including：
The current car flow information in city Nei Ge sections is obtained by intelligent transportation supervisory systems；The city is obtained by navigation system
The route planning information of each user in area.
5. according to the method described in claim 1, it is characterized in that, the location information prompt by the congestion points is to user
The step of, including：
By the location information of the congestion points by being back to the navigation system, so that the navigation system is by the congestion points
Location information be added to the user route planning information on.
6. block apparatus is delayed in the intelligent transportation of a kind of big data and autonomous deep learning, which is characterized in that described device is set to cloud
Hold server；Described device includes：
Data obtaining module, for obtaining the road of each user in the current car flow information in city Nei Ge sections and the city
Diameter planning information；
Message output module, for the current car flow information and the route planning information to be input to the congestion pre-established
In point decision model, the location information of congestion points is exported；The congestion points include cur-rent congestion point and following congestion points；It is described to gather around
Stifled point decision model is trained by neural network by the way of autonomous deep learning；
Reminding module, for user, the location information prompt of the congestion points to be updated the path planning for the user
Information, to alleviate traffic congestion.
7. device according to claim 6, which is characterized in that the congestion points decision model is established by following manner：
Obtain car flow information, multiple route planning informations and the corresponding markup information of setting regions；The markup information packet
Include the location information of congestion points；
Establish the network structure of neural network；
The car flow information, multiple route planning informations and corresponding markup information are input in the network structure, adopted
It is trained with the mode of autonomous deep learning, generates the congestion points decision model.
8. device according to claim 6, which is characterized in that described information acquisition module is additionally operable to：
The current car flow information in city Nei Ge sections is obtained by intelligent transportation supervisory systems；The city is obtained by navigation system
The route planning information of each user in area.
9. device according to claim 6, which is characterized in that the reminding module is additionally operable to：
By the location information of the congestion points by being back to the navigation system, so that the navigation system is by the congestion points
Location information be added to the user route planning information on.
10. stifled realization device is delayed in the intelligent transportation of a kind of big data and autonomous deep learning, which is characterized in that including processor and
Machine readable storage medium, the machine readable storage medium, which is stored with, can perform finger by the machine that the processor performs
It enables, the processor performs the machine-executable instruction to realize method described in any one of claim 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711383423.6A CN108198413A (en) | 2017-12-20 | 2017-12-20 | Blocking method is delayed in the intelligent transportation of a kind of big data and autonomous deep learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711383423.6A CN108198413A (en) | 2017-12-20 | 2017-12-20 | Blocking method is delayed in the intelligent transportation of a kind of big data and autonomous deep learning |
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| Publication Number | Publication Date |
|---|---|
| CN108198413A true CN108198413A (en) | 2018-06-22 |
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