CN110460667A - The processing method of road big data based on block chain - Google Patents
The processing method of road big data based on block chain Download PDFInfo
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- CN110460667A CN110460667A CN201910753944.9A CN201910753944A CN110460667A CN 110460667 A CN110460667 A CN 110460667A CN 201910753944 A CN201910753944 A CN 201910753944A CN 110460667 A CN110460667 A CN 110460667A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
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Abstract
The present invention discloses a kind of processing method of road big data based on block chain, comprising the following steps: to the same area and multiple nodes in same level construct block chain, determine that a node in multiple nodes is core node by competition mechanism；Vehicle obtains intelligent contract according to region, and the block chain of region is added by intelligent contract, and wherein the intelligent contract of the block chain of different zones is different；Vehicle is by core node by location information real-time broadcast on each vehicle for being located at same block chain；Vehicle constructs vehicle distribution node figure according to the location information of acquisition；According to vehicle distribution node figure, determine whether target vehicle is safety traffic.By the method for the invention, the vehicle driving situation of region can be learnt in real time by vehicle distribution node figure；Even if can learn driving situation by vehicle distribution node figure under the lower environment of the visibility such as dense fog, driving risk is reduced.
Description
Technical field
The present invention relates to block chain technical field more particularly to a kind of processing sides of the road big data based on block chain
Method.
Background technique
Block chain technology is a kind of collective's right-safeguarding database technology, is safeguarded by collective, distributed recording, the feature of storage
It realizes decentralization, encrypts mathematics by asymmetric techniques and authentic data library technology completes letter endorsement, ensure block catenary system
It increases income, is transparent, safe.As the continuous development of block chain technology, such as one kind disclosed in publication number CN109361588A are based on
The block chain network construction method and its system of Star Network, can be improved common recognition speed, have very high expansibility, improve
The capacity of the whole network.But how to combine block chain technology with car networking technology, it is the technical issues of increasingly concern.
Summary of the invention
The present invention discloses a kind of processing method of road big data based on block chain, for realizing block chain and car networking
Effective integration.
To solve the above-mentioned problems, the present invention adopts the following technical solutions:
A kind of processing method of block chain road big data is provided, comprising the following steps:
To the same area and multiple nodes in same level construct block chain, determine multiple nodes by competition mechanism
In a node be core node；
Vehicle obtains intelligent contract according to region, the block chain of region is added by intelligent contract, wherein not
It is different with the intelligent contract of the block chain in region；
Vehicle is by core node by location information real-time broadcast on each vehicle for being located at same block chain；
Vehicle constructs vehicle distribution node figure according to the location information of acquisition；
According to vehicle distribution node figure, the driving states of target vehicle are determined.
Optionally, vehicle obtains intelligent contract according to region, and the block chain of region is added by intelligent contract,
Include:
Vehicle is pre-stored with the intelligent contract of all areas；
Vehicle obtains corresponding intelligent contract according to region；
Vehicle passes through intelligent contract, and the block chain of region is added.
Optionally, vehicle is by location information real-time broadcast on each vehicle for being located at same block chain, comprising:
Vehicle is by license plate number and the location point real-time broadcast being made of latitude, longitude in each vehicle for being located at same block chain
On.
Optionally, vehicle location is shown with dot on the vehicle distribution node figure, license plate number is shown with license plate number format,
Lane is shown with lines.
Optionally, according to vehicle distribution node figure, determine whether target vehicle is safety traffic, comprising:
According to vehicle distribution node figure, the location point of the fore-aft vehicle before and after target vehicle is obtained；
According to the location point of the location point of target vehicle and fore-aft vehicle, target vehicle is calculated at a distance from fore-aft vehicle；
If target vehicle is more than safe distance at a distance from fore-aft vehicle, target vehicle is safety traffic, wherein safety
Apart from the distance by being travelled when vehicle emergency brake；
If target vehicle is less than safe distance at a distance from fore-aft vehicle, target vehicle is dangerous traveling, is carried out
Alarm.
Optionally, according to the location point of the location point of target vehicle and fore-aft vehicle, target vehicle and fore-aft vehicle are calculated
Distance, before further include:
The camera of target vehicle obtains the license plate number of front vehicles and the license plate number of front vehicle in real time；
The license plate number of corresponding position point compares in the license plate number and vehicle distribution node figure that target vehicle obtains camera；
If comparison is consistent, according to the location point of the location point of target vehicle and fore-aft vehicle, target vehicle is calculated with before
The distance of rear vehicle；
If comparison is inconsistent, other nodes in same level redefine core node by competition mechanism, by
The core node redefined broadcasts the location information of each vehicle.
Optionally, if target vehicle is more than safe distance at a distance from fore-aft vehicle, target vehicle is safety traffic, it
Afterwards further include:
Target vehicle obtains the vehicle trouble messages of fault car broadcast in real time；
If target vehicle gets vehicle trouble messages, according to vehicle distribution node figure determine fault car whether with mesh
Mark vehicle is located at same lane and is located at front；
If so, target vehicle redefines driving states.
Optionally, target vehicle redefines driving states, comprising:
Target vehicle is determined to simultaneously vehicle lane；
Target vehicle is according to vehicle number, target vehicle and the disabled vehicle between current vehicle speed, target vehicle and fault car
The distance between adjustment running speed；
It is incorporated to behind simultaneously vehicle lane after the vehicle between target vehicle and fault car, target vehicle is incorporated to simultaneously vehicle vehicle
Road.
Optionally, target vehicle according between current vehicle speed, target vehicle and fault car vehicle number, target vehicle with
The distance between fault car adjusts running speed, meets following formula:
Wherein, x2Indicate that target vehicle running speed adjusted, unit are thousand ms/h；
x1Indicate the present speed of target vehicle, unit is thousand ms/h；
L indicates the distance between target vehicle and fault car, and unit is rice；
N indicates the vehicle number between target vehicle and fault car, and unit is a.
Optionally, to, at interval of a vehicle, be incorporated to a vehicle, wherein a meets following formula on simultaneously vehicle lane:
A=n2-n
Wherein, n indicates the vehicle number between target vehicle and fault car, and unit is a.
The technical solution adopted by the present invention can reach it is following the utility model has the advantages that
It can learn the vehicle driving situation of region in real time by vehicle distribution node figure；Even if can see in dense fog etc.
It spends under lower environment, driving situation can be learnt by vehicle distribution node figure, reduce driving risk.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, required use in being described below to embodiment
Attached drawing be briefly described, constitute a part of the invention, illustrative embodiments and their description of the invention explain the present invention,
It does not constitute improper limitations of the present invention.In the accompanying drawings:
Fig. 1 is the flow chart of the processing method of the road big data disclosed by the embodiments of the present invention based on block chain；
Fig. 2 is the schematic diagram of vehicle distribution node figure disclosed by the embodiments of the present invention；
Fig. 3 be fault car rear disclosed by the embodiments of the present invention first car and car state figure；
Fig. 4 be fault car rear disclosed by the embodiments of the present invention second car and car state figure；
Fig. 5 be fault car rear area target vehicle disclosed by the embodiments of the present invention and car state figure.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with the specific embodiment of the invention and
Technical solution of the present invention is clearly and completely described in corresponding attached drawing.Obviously, described embodiment is only the present invention one
Section Example, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not doing
Every other embodiment obtained under the premise of creative work out, shall fall within the protection scope of the present invention.
Below in conjunction with attached drawing, technical solution disclosed in each embodiment that the present invention will be described in detail.
The processing method of road big data based on block chain of the invention, as shown in Figure 1, including the following steps.
Step S1, to the same area and multiple nodes in same level construct block chain, are determined by competition mechanism
A node in multiple nodes is core node.
In this step, multiple regions can be divided into, each region is by being in same level as unit of province or city etc.
Multiple nodes construct block chain.Then determine that a node in multiple nodes is core for example, by Byzantine failure tolerance mechanism
Node improves the transmittability of road big data so that the node of higher operational capability can be selected as core node.
Step S2, vehicle obtain intelligent contract according to region, the block chain of region are added by intelligent contract.
In this step, the intelligent contract that the block chain of different zones uses is different.Intelligent contract is a kind of particular protocol,
It is intended to provide, verifies and executes contract.Intelligent contract is write and is operated according to logic, vehicle can be made to be limited by binding
Digitlization agreement, the concrete form of intelligent contract can set according to demand.
The intelligent contract of all areas (i.e. all block chain core nodes) can be pre-stored in each vehicle, vehicle obtains
The approach of intelligent contract can there are many, such as from the website of public security department downloading etc..After vehicle driving is to a region,
Vehicle obtains intelligent contract corresponding with region, and vehicle passes through the block chain that region is added in intelligent contract.Vehicle can
To learn the region at place according to GPS positioning, the region where can also otherwise learning certainly, such as by driver people
Work input etc..
Step S3, vehicle is by core node by location information real-time broadcast on each vehicle for being located at same block chain.
In this step, location information includes license plate number and the location point that is collectively formed by latitude, longitude, and location point can be with
It is obtained from GPS, license plate number can be pre-stored in the car.The block chain core node in each region can be common recognition node, vehicle
Location point and license plate number are subjected to real-time broadcast, are transmitted to location point and license plate number that the vehicle is broadcasted by common recognition node other
Vehicle makes other vehicle real-time reception location points and license plate number.
Step S4, vehicle construct vehicle distribution node figure according to the location information of acquisition.
In this step, as shown in Fig. 2, vehicle distribution node figure can show that lane (can be obtained by GPS by lines
Take, wire body is constructed according to practical application scene, including solid line, dotted line etc.), then display position point and license plate on corresponding lane
Number, location point can be characterized by dot, and license plate number is characterized in the form of license plate number, such as Ji A11111.In vehicle distribution node figure
On, the distance between two dots can be reduced by the actual range between vehicle with certain proportion.
Step S5 determines the driving states of target vehicle 1 according to vehicle distribution node figure.In this step, it specifically includes
Following sub-step.
Sub-step S51, according to vehicle distribution node figure, target vehicle 1 obtains the fore-aft vehicle before and after target vehicle 1
Location point.
In the sub-step, target vehicle 1 can drive for oneself or the vehicle of seating.On vehicle distribution node figure,
The dot for characterizing target vehicle 1 can be shown in red, and the dot for characterizing other vehicles is shown as example blue, can hold
Easily distinguish target vehicle 1.
Fore-aft vehicle refers to the vehicle for being located at same lane with target vehicle 1 and being located at 1 front and back of target vehicle.Mesh
Mark vehicle 1 obtain fore-aft vehicle location point mode can there are many, such as target vehicle 1 finds out according to vehicle distribution node figure
It is located at the vehicle that on same lane and distance objective vehicle 1 is nearest with target vehicle 1.
After the sub-step, the license plate number of front vehicles can also be obtained in real time by the camera of target vehicle 1 with after
The license plate number of square vehicle, the license plate number that then target vehicle 1 obtains camera and corresponding position point in vehicle distribution node figure
License plate number comparison carry out sub-step S42 if comparison is consistent.In this way, can be with vehicle in real time correction vehicle distribution node figure
Location information it is whether accurate.
Sub-step S52 calculates target vehicle 1 and front and back according to the location point of the location point of target vehicle 1 and fore-aft vehicle
The distance of vehicle.
In the sub-step, target vehicle 1 can be linear distance at a distance from fore-aft vehicle.In this way, convenient for calculating, it can
Directly to pass through longitude, latitude calculates；Lane is mostly straight turning road, the negligible amounts in turn lane；Even if on turn lane,
The value of linear distance and arc distance difference also can be ignored.
Sub-step S53, if target vehicle 1 is more than safe distance at a distance from fore-aft vehicle, target vehicle 1 is security row
It sails.
In the sub-step, safe distance is by the distance that travels when vehicle emergency brake.A kind of calculating of safe distance
In method, using 4 seconds methods, when Vehicle Speed is 100 kilometers/hour, safe distance was 100 meters.
Sub-step S54, if target vehicle 1 is less than safe distance at a distance from fore-aft vehicle, target vehicle 1 is uneasiness
Full traveling, alarms, with can be with the speed of suitable control target vehicle 1.
Can also include before sub-step S52, the camera of target vehicle 1 obtain in real time front vehicles license plate number and
The license plate number of front vehicle；Corresponding position point in the license plate number and vehicle distribution node figure that target vehicle 1 obtains camera
License plate number comparison；If comparison is consistent, according to the location point of the location point of target vehicle 1 and fore-aft vehicle, target vehicle 1 is calculated
At a distance from fore-aft vehicle；If comparison is inconsistent, illustrate that the data-handling capacity of core node weakens, therefore by being in same layer
Other nodes (the other nodes outside core node used before removing) of grade redefine core node by competition mechanism,
The location information of each vehicle is broadcasted by the core node redefined.
It can also include: that target vehicle 1 obtains the vehicle trouble letter that fault car 2 is broadcasted in real time after sub-step S53
Breath；If target vehicle 1 gets vehicle trouble messages, according to vehicle distribution node figure determine fault car 2 whether with target
Vehicle 1 is located at same lane and is located at front；If so, target vehicle 1 determines driving states.
Wherein, when there is vehicle to break down, then the vehicle is referred to as fault car 2.Fault car 2 is by vehicle trouble messages
Broadcast, makes other vehicles (including target vehicle 1) get vehicle trouble messages.Vehicle trouble messages include fault car 2
Location point and fault type, fault type include that vehicle such as casts anchor, collides at the failure that makes vehicle parking and cannot exercise.
After target vehicle 1 gets the location point of fault car 2, (such as dodged in vehicle distribution node figure with highlighted
Bright yellow) dot shows 2 position of fault car, and target vehicle 1 is according to the lane line in vehicle distribution node figure
Determine fault car 2 whether with target vehicle 1 in same lane, whether fault car 2 is determined according to location point and direction of traffic
Positioned at 1 front of target vehicle.If it is determined that fault car 2 and target vehicle 1 are in same lane and fault car 2 is located at target carriage
1 front, then target vehicle 1 determines driving states.
Target vehicle 1 determines that driving states include: that target vehicle 1 is determined to simultaneously vehicle lane 3；Target vehicle 1 is according to current
The adjustment driving of the distance between vehicle number, target vehicle 1 and fault car 2 between speed, target vehicle 1 and fault car 2
Speed；It is incorporated to behind simultaneously vehicle lane 3 after the vehicle between target vehicle 1 and fault car 2, target vehicle 1 is incorporated to simultaneously vehicle
Lane 3.
Wherein, when the two sides of target vehicle 1 have can and vehicle lane when, target vehicle 1 can according to need selection
The lane of any side is used as to simultaneously vehicle lane 3.
Target vehicle 1 is according to vehicle number, target vehicle 1 and the event between current vehicle speed, target vehicle 1 and fault car 2
Hinder the distance between vehicle 2 adjustment running speed, meet following formula:
Wherein, x2Indicate that the running speed adjusted of target vehicle 1, unit are thousand ms/h；
x1Indicate the present speed of target vehicle 1, unit is thousand ms/h；
L indicates the distance between target vehicle 1 and fault car 2, and unit is rice；
N indicates the vehicle number between target vehicle 1 and fault car 2, and unit is a.
By above-mentioned formula, as shown in Figure 3-Figure 5, when having vehicle between target vehicle 1 and fault car 2, target carriage
1 first can travel a period of time in current lane, when the vehicle in 1 front of target vehicle enter after and target carriage behind vehicle lane 3
1 enter back into and vehicle lane 3, make the vehicle after fault car 2 be successively incorporated to and vehicle lane 3, reduce traffic congestion risk.
It is multiple when the simultaneously vehicle of vehicle when having, it is incorporated to a vehicle at interval of a vehicle to simultaneously vehicle lane 3, wherein a vehicle
Be to and vehicle lane 3 on normally travel a vehicle.Wherein a meets following formula:
A=n2-n
Wherein, n indicates the vehicle number between target vehicle 1 and fault car 2, and unit is a.
Pass through the formula, it is possible to reduce to the traffic congestion risk in simultaneously vehicle lane 3, and guarantee that vehicle orderly travels, reduction is touched
The risks such as vehicle.
The processing method of block chain road big data through the invention can learn institute by vehicle distribution node figure in real time
Vehicle fleet size, travel situations etc. in region, it is especially lower in visibility such as greasy weather, nights and in the environment of influence sight, can
To learn the driving condition of surrounding vehicles, and the running distance with front vehicles can be learnt by vehicle distribution node figure,
Relative to only being estimated by driver, driving risk can be effectively reduced.
The embodiment of the present invention is described with above attached drawing, but the invention is not limited to above-mentioned specific
Embodiment, the above mentioned embodiment is only schematical, rather than restrictive, those skilled in the art
Under the inspiration of the present invention, without breaking away from the scope protected by the purposes and claims of the present invention, it can also make very much
Form belongs within protection of the invention.
Claims (10)
1. a kind of road big data processing method based on block chain, which comprises the following steps:
To the same area and multiple nodes in same level construct block chain, are determined in multiple nodes by competition mechanism
One node is core node；
Vehicle obtains intelligent contract according to region, the block chain of region is added by intelligent contract, wherein not same district
The intelligent contract of the block chain in domain is different；
Vehicle is by core node by location information real-time broadcast on each vehicle for being located at same block chain；Vehicle is according to acquisition
Location information construct vehicle distribution node figure；
According to vehicle distribution node figure, the driving states of target vehicle are determined.
2. the processing method of the road big data according to claim 1 based on block chain, which is characterized in that vehicle according to
Region obtains intelligent contract, and the block chain of region is added by intelligent contract, comprising:
Vehicle is pre-stored with the intelligent contract of all areas；
Vehicle obtains corresponding intelligent contract according to region；
Vehicle passes through intelligent contract, and the block chain of region is added.
3. the processing method of the road big data according to claim 1 based on block chain, which is characterized in that vehicle is by position
Confidence ceases real-time broadcast on each vehicle for being located at same block chain, comprising:
Vehicle is by license plate number and the location point real-time broadcast being made of latitude, longitude on each vehicle for being located at same block chain.
4. the processing method of the road big data according to claim 3 based on block chain, which is characterized in that the vehicle
Vehicle location is shown with dot on distribution node figure, license plate number is shown with license plate number format, shows lane with lines.
5. the processing method of the road big data according to claim 1 based on block chain, which is characterized in that according to vehicle
Distribution node figure determines the driving states of target vehicle, comprising:
According to vehicle distribution node figure, the location point of the fore-aft vehicle before and after target vehicle is obtained；
According to the location point of the location point of target vehicle and fore-aft vehicle, target vehicle is calculated at a distance from fore-aft vehicle；
If target vehicle is more than safe distance at a distance from fore-aft vehicle, target vehicle is safety traffic, wherein safe distance
By the distance travelled when vehicle emergency brake；
If target vehicle is less than safe distance at a distance from fore-aft vehicle, target vehicle is dangerous traveling, is alarmed.
6. the processing method of the road big data according to claim 5 based on block chain, which is characterized in that according to target
The location point of vehicle and the location point of fore-aft vehicle calculate target vehicle at a distance from fore-aft vehicle, before further include:
The camera of target vehicle obtains the license plate number of front vehicles and the license plate number of front vehicle in real time；
The license plate number of corresponding position point compares in the license plate number and vehicle distribution node figure that target vehicle obtains camera；
If comparison is consistent, according to the location point of the location point of target vehicle and fore-aft vehicle, target vehicle and front and back vehicle are calculated
Distance；
If comparison is inconsistent, other nodes in same level redefine core node by competition mechanism, by again
Determining core node broadcasts the location information of each vehicle.
7. the processing method of the road big data according to claim 5 based on block chain, which is characterized in that if target carriage
It is more than safe distance at a distance from fore-aft vehicle, then target vehicle is safety traffic, later further include:
Target vehicle obtains the vehicle trouble messages of fault car broadcast in real time；
If target vehicle gets vehicle trouble messages, according to vehicle distribution node figure determine fault car whether with target carriage
Be located at same lane and be located at front；
If so, target vehicle redefines driving states.
8. the processing method of the road big data according to claim 7 based on block chain, which is characterized in that target vehicle
Redefine driving states, comprising:
Target vehicle is determined to simultaneously vehicle lane；
Target vehicle according to vehicle number, target vehicle and the fault car between current vehicle speed, target vehicle and fault car it
Between distance adjust running speed；
It is incorporated to behind simultaneously vehicle lane after the vehicle between target vehicle and fault car, target vehicle is incorporated to simultaneously vehicle lane.
9. the processing method of the road big data according to claim 8 based on block chain, which is characterized in that target vehicle
It is adjusted according to the distance between vehicle number, target vehicle and the fault car between current vehicle speed, target vehicle and fault car
Running speed meets following formula:
Wherein, x2Indicate that target vehicle running speed adjusted, unit are thousand ms/h；
x1Indicate the present speed of target vehicle, unit is thousand ms/h；
L indicates the distance between target vehicle and fault car, and unit is rice；
N indicates the vehicle number between target vehicle and fault car, and unit is a.
10. the processing method of the road big data according to claim 8 based on block chain, which is characterized in that simultaneously vehicle
At interval of a vehicle on lane, it is incorporated to a vehicle, wherein a meets following formula:
A=n2-n
Wherein, n indicates the vehicle number between target vehicle and fault car, and unit is a.
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Application publication date: 20191115 |