CN116321351A - Internet of vehicles optimization method and system based on regional routing algorithm and vehicle - Google Patents
Internet of vehicles optimization method and system based on regional routing algorithm and vehicle Download PDFInfo
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Abstract
The application discloses a vehicle networking optimization method, a system and a vehicle based on a regional routing algorithm, on one hand, the method divides the vehicle into a plurality of clusters according to the state parameters of the vehicle, and sets a core vehicle and a common vehicle in each cluster so as to realize the communication of all vehicles only by communicating each core vehicle in the vehicle communication process; on the other hand, the clusters corresponding to the vehicles are adjusted in real time according to the target state similarity prediction model, so that the accuracy of information communication between the vehicles can be effectively ensured, and disorder is avoided. In conclusion, the clustering processing is carried out on the vehicles, so that information connection between all vehicles is avoided, and the network connectivity of the vehicles is improved; furthermore, only the core vehicles in each cluster can communicate information to the outside, so that the phenomenon of communication overlapping is effectively reduced.
Description
Technical Field
The invention relates to the technical field of routing and Internet of vehicles communication, in particular to an Internet of vehicles optimization method, an Internet of vehicles optimization system and a vehicle based on a regional routing algorithm.
Background
The vehicle-mounted self-organizing network aims to reduce the harmful influence of vehicle-mounted communication equipment on the natural environment, grouping vehicles for communication in the VANET can remarkably improve network efficiency and reduce infrastructure cost, and as a dynamic network system, maintaining network connectivity and reducing communication overlap are two key challenges of a network cluster.
However, the existing clustering algorithms all aim at realizing clustering construction, and cluster maintenance is considered as an indispensable clustering process in VANET because the original network structure is seriously affected by the highly dynamic network topology and the communication overhead is high due to uneven distribution of vehicles. For network connectivity, most existing research focuses on optimization based on vehicle mobility, however, complex urban traffic can severely impact vehicle mobility and network topology, resulting in excessive communication overlap and network congestion.
Therefore, in the prior art, in the process of networking communication of vehicles, the problems of poor network connectivity and overlapping communication exist.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a method, a system and a vehicle for optimizing the internet of vehicles based on a regional routing algorithm, so as to solve the problems of low energy utilization rate and low safety coefficient in the internet of vehicles communication in the prior art.
In order to solve the problems, the invention provides a vehicle networking optimization method based on a regional routing algorithm, which comprises the following steps:
acquiring state parameters and track information of a vehicle;
dividing the vehicle into a plurality of clusters according to the state parameters, wherein each cluster at least comprises a core vehicle and a common vehicle;
setting a target state similarity prediction model, and adaptively adjusting clusters corresponding to the vehicles according to the track information;
when a first vehicle transmits information to a second vehicle, information communication is performed through a routing scheme based on a core vehicle.
Further, the state parameters include at least a vehicle node, a vehicle type, a vehicle speed, a vehicle acceleration, a travel direction, a travel time index.
Further, the expression of the state parameter is:
wherein for a vehicle node
i represents a unique identifier, k represents a vehicle type, and γ i A section indicating a section where the vehicle stays at time t; />
Representing a vehicle speed; />
Representing acceleration of the vehicle; θ represents a relative value of the traveling direction of the vehicle and the traveling direction of the core vehicle, if the vehicle travels in the same direction as the core vehicle, θ=1, otherwise, θ= -1; for travel time index delta TTI The travel time index is calculated according to the following formula:
further, according to the state parameter, the vehicle is divided into a plurality of clusters, each cluster at least comprises a core vehicle and a common vehicle, and the method comprises the following steps:
acquiring the running direction of a vehicle and a vehicle node;
dividing vehicles which are in the same driving direction and within a preset node range into a cluster, and determining a plurality of clusters;
and selecting a core vehicle from each cluster, wherein the rest vehicles are common vehicles.
Further, setting a target state similarity prediction model, and adaptively adjusting a cluster corresponding to the vehicle according to the track information, wherein the method comprises the following steps:
constructing an initial state similarity prediction model, respectively taking core vehicle track information and common vehicle track information as input, taking the association degree of the common vehicle as output, and training the initial state similarity prediction model so as to determine a complete training target state similarity prediction model;
according to the association degree of the common vehicles, determining the core vehicles corresponding to the common vehicles;
and adaptively adjusting the clusters corresponding to the vehicles according to the common vehicles and the corresponding core vehicles thereof.
Further, when the first vehicle transmits information to the second vehicle, information communication is performed through a routing scheme based on the core vehicle, including:
judging whether the first vehicle and the second vehicle belong to the same cluster;
if yes, the first vehicle and the second vehicle directly communicate information;
if not, the first vehicle sends the information to the corresponding first core vehicle, and the first core vehicle forwards the information to other core vehicles to determine the second core vehicle; the second vehicle obtains information from the second core vehicle.
Further, the first core vehicle forwards the information to other core vehicles, including:
the first core vehicle forwards the route request packet, the route reply packet, and the data packet to the other core vehicles.
Further, the first core vehicle forwards the information to other core vehicles, determines a second core vehicle, comprising:
acquiring a first transmission direction vector of a first core vehicle;
judging whether adjacent core vehicles exist or not according to the first transmission direction vector;
if yes, determining a second core vehicle according to the first transmission direction vector and the running direction of the adjacent core vehicle;
if not, the information is temporarily stored in the first core vehicle.
In order to solve the above problems, the present invention further provides an internet of vehicles optimization system based on a regional routing algorithm, including:
the vehicle information acquisition module is used for acquiring state parameters and track information of the vehicle;
the vehicle cluster dividing module is used for dividing the vehicles into a plurality of clusters according to the state parameters, wherein each cluster at least comprises a core vehicle and a common vehicle, and part of the clusters also comprise gateway vehicles;
the vehicle cluster adjustment module is used for setting a target state similarity prediction model and adaptively adjusting clusters corresponding to the vehicles according to the track information;
and the information transmission module is used for carrying out information communication through a routing scheme based on the core vehicle when the first vehicle sends information to the second vehicle.
In order to solve the above problems, the present invention also provides a vehicle including a vehicle networking optimization system based on a regional routing algorithm as described above;
or;
comprising a memory and a processor, wherein,
a memory for storing a program;
and a processor coupled to the memory for executing the program stored in the memory to implement the steps in the area routing algorithm based internet of vehicles optimization method as described above.
The beneficial effects of adopting above-mentioned technical scheme are: the invention provides a vehicle networking optimization method, a system and a vehicle based on a regional routing algorithm, on one hand, the method divides the vehicle into a plurality of clusters according to the state parameters of the vehicle, and sets a core vehicle and a common vehicle in each cluster so as to realize the communication of all vehicles only by communicating each core vehicle in the vehicle communication process; on the other hand, the clusters corresponding to the vehicles are adjusted in real time according to the target state similarity prediction model, so that the accuracy of information communication between the vehicles can be effectively ensured, and disorder is avoided. In conclusion, the clustering processing is carried out on the vehicles, so that information connection between all vehicles is avoided, and the network connectivity of the vehicles is improved; furthermore, only the core vehicles in each cluster can communicate information to the outside, so that the phenomenon of communication overlapping is effectively reduced.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of an optimization method of Internet of vehicles based on a regional routing algorithm provided by the invention;
FIG. 2 is a flow chart of an embodiment of the present invention for dividing a vehicle into a plurality of clusters;
FIG. 3 is a schematic flow chart of an embodiment of an adaptive adjustment vehicle cluster according to the present invention;
FIG. 4 is a flow chart of an embodiment of information communication according to the present invention;
FIG. 5 is a flow chart illustrating an embodiment of determining a second core vehicle according to the present invention;
fig. 6 is a schematic structural diagram of an embodiment of an optimization system of internet of vehicles based on a regional routing algorithm provided by the invention.
Detailed Description
Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form a part hereof, and together with the description serve to explain the principles of the invention, and are not intended to limit the scope of the invention.
Before the embodiments are set forth, a vehicle-mounted random mobile network, a vehicle networking, a routing algorithm, a trunking communication system and a dynamic clustering method are set forth:
a Vehicular ad-hoc network (VANET), which is also called a Vehicular mobile communication network, uses a moving vehicle and a traffic facility as nodes, and forms a mobile network by using a wireless communication technology.
The internet of vehicles mainly refers to that vehicle-mounted equipment on a vehicle effectively utilizes all vehicle dynamic information in an information network platform through a wireless communication technology, and provides different functional services in the running process of the vehicle. Specifically, the car networking table exhibits the following characteristics: the internet of vehicles can provide a guarantee for the distance between vehicles, and reduce the probability of collision accidents of vehicles; the internet of vehicles can help the car owners to navigate in real time, and the traffic running efficiency is improved through communication with other vehicles and network systems.
Routing algorithms, also known as routing algorithms, can be distinguished by a number of characteristics. The purpose of the algorithm is to find a "good" path (i.e., the path with the lowest cost) from the source router to the destination router. The particular goals of the algorithm designer affect the operation of the routing protocol; in particular, there are a number of routing algorithms, each having a different impact on network and router resources; since the routing algorithm uses a variety of metrics, it affects the computation of the best path.
The trunking communication system is a mobile communication system for group dispatch command communication, and is mainly applied to the field of professional mobile communication. The system has available channels which can be shared by all users of the system, and has the function of automatically selecting channels, and is a multipurpose and high-efficiency wireless dispatch communication system which shares resources, cost and shared channel equipment and services.
Dynamic clustering (dynamical clustering methods) is also known as stepwise clustering. A clustering method belongs to a large sample clustering method, and specifically comprises the following steps: the classification method has the advantages of smaller calculated amount, less occupied computer memory unit, simple method and the like compared with the systematic clustering method, so the classification method is more suitable for the clustering analysis of large samples. The clustering process of the dynamic clustering method can be described by using a block diagram, and various methods can be adopted for each part of the block diagram, and various dynamic clustering methods can be obtained by combining the methods according to the block diagram.
Currently, the existing clustering algorithms are all dedicated to realizing clustering construction, and because the original network structure is seriously affected by the highly dynamic network topology structure and the communication overhead is high due to uneven distribution of vehicles, cluster maintenance is considered as an indispensable cluster process in the VANET. For network connectivity, most existing research focuses on optimization based on vehicle mobility, however, complex urban traffic can severely impact vehicle mobility and network topology, resulting in excessive communication overlap and network congestion.
Therefore, in the prior art, in the process of networking communication of vehicles, the problems of poor network connectivity and overlapping communication exist.
In order to solve the problems, the invention provides a vehicle networking optimization method, a system and a vehicle based on a regional routing algorithm, and the method, the system and the vehicle are respectively described in detail below.
As shown in fig. 1, fig. 1 is a schematic flow chart of an embodiment of an optimization method of internet of vehicles based on a regional routing algorithm, provided by the invention, including:
step S101: and acquiring state parameters and track information of the vehicle.
Step S102: according to the state parameters, the vehicles are divided into a plurality of clusters, and each cluster at least comprises a core vehicle and a common vehicle.
Step S103: and setting a target state similarity prediction model, and adaptively adjusting the clusters corresponding to the vehicles according to the track information.
Step S104: when a first vehicle transmits information to a second vehicle, information communication is performed through a routing scheme based on a core vehicle.
In this embodiment, first, state parameters and track information of a vehicle are acquired to determine a current state of the vehicle and a running target track; then, dividing the vehicles into a plurality of clusters according to the state parameters, wherein each cluster at least comprises a core vehicle and a common vehicle, namely, in order to reduce the network communication requirement, carrying out cluster planning on the vehicles; further, a target state similarity prediction model is set, and the clusters corresponding to the vehicles are adaptively adjusted according to the track information, so that the clusters of the vehicles are adjusted in real time, and communication interruption and other conditions are avoided; finally, when the first vehicle transmits information to the second vehicle, information communication is performed by a routing scheme based on the core vehicle, that is, communication between a plurality of clusters is achieved with the core vehicle as a medium when information transmission is performed.
It can be understood that in this embodiment, on one hand, the vehicles are divided into a plurality of clusters according to the state parameters of the vehicles, and the core vehicles and the common vehicles are set in each cluster, so that in the vehicle communication process, communication of all the vehicles can be realized only by communicating each core vehicle; on the other hand, the clusters corresponding to the vehicles are adjusted in real time according to the target state similarity prediction model, so that the accuracy of information communication between the vehicles can be effectively ensured, and disorder is avoided. In conclusion, the clustering processing is carried out on the vehicles, so that information connection between all vehicles is avoided, and the network connectivity of the vehicles is improved; furthermore, only the core vehicles in each cluster can communicate information to the outside, so that the phenomenon of communication overlapping is effectively reduced.
In a specific embodiment, the routing scheme includes a routing algorithm, a core vehicle replacement, and an area maintenance. That is, by setting a routing scheme to realize information communication, not only an optimal information communication path can be calculated through a routing algorithm, but also the core vehicle can be adaptively adjusted in time to ensure the stability of subsequent information communication, and finally, the reliability of the information communication of the area where the whole vehicle is located is ensured through area maintenance.
In one embodiment, the first core vehicle selects another core vehicle to forward the data packet based on its direction of movement. When the vehicle is connected to the virtual local area network, a routing algorithm is triggered. The vehicles will select the appropriate area nearby or form their own area, divide the vehicles with the same direction of movement into clusters, and then select one core vehicle in each cluster. The region construction criteria are based on the similarity of mobility metrics and trajectory characteristics.
As a preferred embodiment, in step S101, the state parameters include at least a vehicle node, a vehicle type, a vehicle speed, a vehicle acceleration, a traveling direction, a travel time index.
In one embodiment, the state parameter is expressed as:
wherein for a vehicle node
i represents a unique identifier, k represents a vehicle type, and γ i A section indicating a section where the vehicle stays at time t; />
Representing a vehicle speed; />
Representing acceleration of the vehicle; θ represents a relative value of the traveling direction of the vehicle and the traveling direction of the core vehicle, if the vehicle travels in the same direction as the core vehicle, θ=1, otherwise, θ= -1; for travel time index delta TTI The travel time index is calculated according to the following formula:
further, the track information of each vehicle is defined as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
indicating vehicle->
Track, state i (t n ) Time t is represented n Vehicle at->
Status of the device.
As a preferred embodiment, in step S102, in order to divide a vehicle into a plurality of clusters, as shown in fig. 2, fig. 2 is a schematic flow chart of an embodiment of dividing a vehicle into a plurality of clusters according to the present invention, including:
step S121: the driving direction of the vehicle and the vehicle node are obtained.
Step S122: and dividing vehicles which are in the same driving direction and are in a preset node range into a cluster, and determining a plurality of clusters.
Step S123: and selecting a core vehicle from each cluster, wherein the rest vehicles are common vehicles.
In the embodiment, all vehicles are clustered according to the running direction of the vehicles and the vehicle nodes, so that aggregation data processing is carried out on massive vehicles, and the overall data processing amount is reduced.
As a preferred embodiment, in step S103, in order to adaptively adjust a cluster corresponding to a vehicle, as shown in fig. 3, fig. 3 is a schematic flow chart of an embodiment of adaptively adjusting a cluster corresponding to a vehicle according to the present invention, which includes:
step S131: and constructing an initial state similarity prediction model, respectively taking the core vehicle track information and the common vehicle track information as inputs, taking the association degree of the common vehicle as output, and training the initial state similarity prediction model so as to determine a complete training target state similarity prediction model.
Step S132: and determining the core vehicle corresponding to the common vehicle according to the association degree of the common vehicle.
Step S133: and adaptively adjusting the clusters corresponding to the vehicles according to the common vehicles and the corresponding core vehicles thereof.
In the embodiment, firstly, an initial state similarity prediction model is constructed, and the initial state similarity prediction model is adaptively trained, so that a target state similarity prediction model capable of determining the relevance of a common vehicle is obtained; then, according to the obtained association degree of the common vehicle, determining a core vehicle corresponding to the common vehicle; and finally, adaptively adjusting the clusters corresponding to the vehicles according to the common vehicles and the corresponding core vehicles.
In the embodiment, association degree measurement is performed on all vehicles through the target state similarity prediction model, so that the clusters corresponding to the vehicles are adaptively adjusted, and information communication disorder is avoided.
In one embodiment, since the trajectory characteristics of a vehicle are time dependent, we describe the prediction process of the vehicle trajectory characteristics as a time series prediction task. Due to the ability to have automatic feature extraction, recurrent Neural Networks (RNNs) are very efficient in processing data with time series features. Meanwhile, we train RNNs using a smooth approximation based on Dynamic Time Warping (DTW). DTW can better capture the difference between two tracks than conventional loss functions. Thus, we integrate the sequence-to-sequence (Seq 2 Seq) RNNs model with the DTW algorithm in the SRP model.
In a specific embodiment, when more than half of the average vehicles are disconnected from the core vehicle, this means that the current core vehicle deviates from its management area, it will begin the replacement process.
In order to clarify the basis of the core vehicle replacement, the present embodiment proposes a competition threshold of the core vehicle, wherein the competition threshold includes two dimensions: the historical and current driving state data may be calculated by an equation, where the calculation formula is:
wherein Δd ij Indicating the distance between the candidate core vehicle and another common vehicle, N o Representing the number of common vehicles in the same area.
Further, there are two different cases of the core vehicle:
(1) If there are multiple candidate core vehicles, the core vehicles will calculate the race threshold separately. The vehicle with the lowest race threshold replaces the front core vehicle, with its location in the area relatively centered on the front core vehicle.
(2) When the core vehicle is unable to communicate with the area due to a significant state change, the core vehicle has no exchangeable core vehicle.
Thus, the vehicle will select a suitable region or repeating region configuration in the vicinity.
As a preferred embodiment, in step S104, for information communication, as shown in fig. 4, fig. 4 is a flow chart of an embodiment of information communication provided in the present invention, including:
step S141: it is determined whether the first vehicle and the second vehicle belong to the same cluster.
Step S142: if yes, the first vehicle and the second vehicle directly communicate information.
Step S143: if not, the first vehicle sends the information to the corresponding first core vehicle, and the first core vehicle forwards the information to other core vehicles to determine the second core vehicle; the second vehicle obtains information from the second core vehicle.
In this embodiment, first, it is determined whether the first vehicle and the second vehicle belong to the same cluster, that is, whether intra-cluster information communication is first determined; then, when the first vehicle and the second vehicle are judged to belong to the same cluster, the first vehicle and the second vehicle directly carry out information communication, namely, the vehicles in the cluster can realize information communication, the information communication speed is high, the stability is high, and the accuracy of the information communication can be effectively ensured; finally, when the first vehicle and the second vehicle are judged not to belong to the same cluster, namely, the information communication between the clusters is judged, the first vehicle sends the information to the corresponding first core vehicle, and the first core vehicle forwards the information to other core vehicles to determine the second core vehicle; the second vehicle obtains information from the second core vehicle.
In this embodiment, the core vehicle is used as a transmission medium for information communication, so that information communication between two clusters is realized, stability and reliability of information communication in the clusters are guaranteed, and as information communication between all vehicles is not required to be established, communication overlapping is greatly reduced, and network connectivity is improved.
As a preferred embodiment, in step S143, the first core vehicle needs to forward the route request packet, the route reply packet, and the data packet to the other core vehicles.
Further, in order to determine the second core vehicle, as shown in fig. 5, fig. 5 is a schematic flow chart of an embodiment of determining the second core vehicle according to the present invention, including:
step S151: a first transmission direction vector of a first core vehicle is acquired.
Step S152: and judging whether the adjacent core vehicles exist or not according to the first transmission direction vector.
Step S153: if yes, determining a second core vehicle according to the first transmission direction vector and the running direction of the adjacent core vehicle.
Step S154: if not, the information is temporarily stored in the first core vehicle.
In this embodiment, first, a first transmission direction vector of a first core vehicle is acquired; then, judging whether adjacent core vehicles exist or not according to the first transmission direction vector; when judging that the adjacent core vehicles exist, determining a second core vehicle according to the first transmission direction vector and the running direction of the adjacent core vehicle; when it is determined that there is no adjacent core vehicle, the information is temporarily stored to the first core vehicle, i.e., the information communication is temporarily put aside.
In one embodiment, a transmission direction vector is defined as D T The first core vehicle is based on the data transmission direction D T The table T is queried whether there are some neighboring core vehicles in front of it.
When it is judged that there is an adjacent core vehicle, a control unit is executed in a state where the selected adjacent core vehicle (V 1 ，...，V n ) In, calculate D T .V i (wherein D T Is the transmission direction vector of the data packet); if D T .V i > 0, then the velocity vector is selected to be V i As a second core vehicle; if not meet D T .V i And (2) selecting the adjacent core vehicle which is farthest from the first core vehicle as the second core vehicle.
When it is determined that there is no neighboring core vehicle, the header stores the message for a period of time t s Then iterating continuously, and continuing to seek the second core vehicle; if t s After consumption, the first core vehicle sends a ROGER message to the first vehicle.
Further, for testing and effect evaluation of the area routing algorithm-based internet of vehicles optimization method, the selected performance indexes comprise the created cluster service life, the number of cluster reconstruction at different moments, the overlapping rate, the data packet delivery rate and the interaction delay.
According to the method, on one hand, the vehicles are divided into a plurality of clusters according to the state parameters of the vehicles, and the core vehicles and the common vehicles are arranged in each cluster, so that communication of all the vehicles can be realized only by communicating all the core vehicles in the vehicle communication process; on the other hand, the clusters corresponding to the vehicles are adjusted in real time according to the target state similarity prediction model, so that the accuracy of information communication between the vehicles can be effectively ensured, and disorder is avoided. In conclusion, the clustering processing is carried out on the vehicles, so that information connection between all vehicles is avoided, and the network connectivity of the vehicles is improved; furthermore, only the core vehicles in each cluster can communicate information to the outside, so that the phenomenon of communication overlapping is effectively reduced.
In order to solve the above-mentioned problems, the present invention further provides a regional routing algorithm-based internet of vehicles optimization system, as shown in fig. 6, fig. 6 is a schematic structural diagram of an embodiment of the regional routing algorithm-based internet of vehicles optimization system provided by the present invention, and the regional routing algorithm-based internet of vehicles optimization system 600 includes:
a vehicle information acquisition module 601, configured to acquire state parameters and track information of a vehicle;
the vehicle cluster dividing module 602 is configured to divide the vehicles into a plurality of clusters according to the status parameters, where each cluster at least includes a core vehicle and a common vehicle, and part of the clusters further include gateway vehicles;
the vehicle cluster adjustment module 603 is configured to set a target state similarity prediction model, and adaptively adjust a cluster corresponding to the vehicle according to the track information;
the information communication module 604 is configured to communicate information through a routing algorithm based on the core vehicle when the first vehicle transmits information to the second vehicle.
The invention also provides a vehicle, comprising the Internet of vehicles optimization system based on the regional routing scheme;
or;
comprising a memory and a processor, wherein,
a memory for storing a program;
and a processor coupled to the memory for executing the program stored in the memory to implement the steps in the area routing algorithm based internet of vehicles optimization method as described above.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention.
Claims (10)
1. The Internet of vehicles optimization method based on the regional routing algorithm is characterized by comprising the following steps of:
acquiring state parameters and track information of a vehicle;
dividing the vehicles into a plurality of clusters according to the state parameters, wherein each cluster at least comprises a core vehicle and a common vehicle;
setting a target state similarity prediction model, and adaptively adjusting the clusters corresponding to the vehicles according to the track information;
when a first vehicle sends information to a second vehicle, information communication is performed through a routing scheme based on the core vehicle.
2. The area routing algorithm-based internet of vehicles optimization method according to claim 1, wherein the state parameters at least include vehicle nodes, vehicle type, vehicle speed, vehicle acceleration, driving direction, travel time index.
3. The internet of vehicles optimization method based on the area routing algorithm according to claim 2, wherein the expression of the state parameter is:
wherein for a vehicle node
i represents a unique identifier, k represents a vehicle type, and γ i A section indicating a section where the vehicle stays at time t; />
Representing a vehicle speed; />
Representing acceleration of the vehicle; θ represents a relative value of the traveling direction of the vehicle and the traveling direction of the core vehicle, if the vehicle travels in the same direction as the core vehicle, θ=1, otherwise, θ= -1; for travel time index delta TTI The travel time index has the following calculation formula:
4. the area routing algorithm-based internet of vehicles optimization method according to claim 2, wherein the vehicles are divided into a plurality of clusters according to the state parameter, each cluster at least including a core vehicle and a general vehicle, comprising:
acquiring the driving direction of the vehicle and the vehicle node;
dividing the vehicles which are in the same driving direction and are in a preset node range into a cluster, and determining a plurality of clusters;
and selecting one core vehicle from each cluster, wherein the rest vehicles are the common vehicles.
5. The internet of vehicles optimization method based on the area routing algorithm according to claim 1, wherein setting a target state similarity prediction model, adaptively adjusting the cluster corresponding to the vehicle according to the track information, comprises:
constructing an initial state similarity prediction model, respectively taking core vehicle track information and common vehicle track information as inputs, taking the association degree of the common vehicle as output, and training the initial state similarity prediction model so as to determine a complete training target state similarity prediction model;
according to the association degree of the common vehicle, determining the core vehicle corresponding to the common vehicle;
and adaptively adjusting the cluster corresponding to the vehicle according to the common vehicle and the core vehicle corresponding to the common vehicle.
6. The area routing algorithm-based internet of vehicles optimization method according to claim 1, wherein when a first vehicle transmits information to a second vehicle, information communication is performed through a routing scheme based on the core vehicle, comprising:
judging whether the first vehicle and the second vehicle belong to the same cluster;
if yes, the first vehicle and the second vehicle directly communicate information;
if not, the first vehicle sends the information to the corresponding first core vehicle, and the first core vehicle forwards the information to other core vehicles to determine a second core vehicle; the second vehicle obtains the information from the second core vehicle.
7. The area routing algorithm-based internet of vehicles optimization method of claim 6, wherein the first core vehicle forwards the information to other core vehicles, comprising:
the first core vehicle forwards the route request packet, the route response packet and the data packet to other core vehicles.
8. The area routing algorithm-based internet of vehicles optimization method of claim 6, wherein the first core vehicle forwards the information to other core vehicles, and determining a second core vehicle comprises:
acquiring a first transmission direction vector of the first core vehicle;
judging whether adjacent core vehicles exist or not according to the first transmission direction vector;
if yes, determining the second core vehicle according to the first transmission direction vector and the running direction of the adjacent core vehicle;
and if not, temporarily storing the information to the first core vehicle.
9. An area routing algorithm-based internet of vehicles optimization system, which is characterized by comprising:
the vehicle information acquisition module is used for acquiring state parameters and track information of the vehicle;
the vehicle cluster dividing module is used for dividing the vehicles into a plurality of clusters according to the state parameters, wherein each cluster at least comprises a core vehicle and a common vehicle, and part of the clusters also comprise gateway vehicles;
the vehicle cluster adjustment module is used for setting a target state similarity prediction model and adaptively adjusting the clusters corresponding to the vehicles according to the track information;
and the information transmission module is used for carrying out information communication through a routing scheme based on the core vehicle when the first vehicle sends information to the second vehicle.
10. A vehicle comprising the regional routing algorithm-based internet of vehicles optimization system of claim 9;
or;
comprising a memory and a processor, wherein,
the memory is used for storing programs;
the processor, coupled to the memory, is configured to execute the program stored in the memory to implement the steps in the area routing algorithm-based internet of vehicles optimization method as set forth in any one of the preceding claims 1-8.
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| CN117290741A (en) * | 2023-11-14 | 2023-12-26 | 北京阿帕科蓝科技有限公司 | Vehicle clustering method, device, computer equipment and storage medium |
| CN117290741B (en) * | 2023-11-14 | 2024-03-19 | 北京阿帕科蓝科技有限公司 | Vehicle clustering method, device, computer equipment and storage medium |
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