Vehicle networking cluster channel allocation method and device based on fuzzy optimization

Abstract

The invention discloses an Internet of Vehicles cluster channel allocation method and device based on fuzzy optimization. The method comprises the following steps: acquiring service time, values of different performance indexes and vehicle service levels of each vehicle in a vehicle cluster using an allocated channel last time in Internet of Vehicles access points; wherein the vehicle service level is associated with the maximum relative membership degree and the minimum relative membership degree of the vehicle with respect to different performance indexes; determining weight coefficients of different performance indexes during cluster channel resource allocation; constructing a main trunk road vehicle cluster corresponding to each main trunk road according to a vehicle set which enters a service area of an Internet of Vehicles access point in a current cluster division interval period and sends out a service demand; determining a relative membership degree which uses the corresponding available channel as a preference; and according to the relative membership degree that each available channel corresponding to each trunk road vehicle cluster is optimized, and according to the number of vehicles in the trunk road vehicle clusters, allocating channels to the trunk road vehicle clusters of all trunk roads.

Description

Technical Field

[0001] The present invention relates to a method and apparatus for vehicle - to - everything (V2X) cluster channel allocation based on fuzzy optimization. Background Art

[0002] In V2X, each vehicle needs to be allocated a channel, and the channel will be recycled when the vehicle leaves the V2X network. Due to the continuous increase in the number of vehicles and the popularization of in - vehicle applications, the spectrum available for V2X is insufficient to support the high - bandwidth transmission of future automotive applications. It can be seen that reasonable channel resource allocation is one of the main challenges in V2X communication. At the same time, the dynamic topology, complex application scenarios, limited frequency resources and other characteristics of V2X bring great challenges to reasonable allocation. To promote channel resource allocation, the V2X access point can divide several adjacent and co - moving vehicles that enter the service area of the V2X access point at a similar time into a cluster, and provide a channel that can be competitively used through random access for this cluster. Thus, several vehicles in a certain area are regarded as a whole for operation. Then, the cluster is used to achieve the best match between requirements and scenarios. Therefore, necessary settings need to be made when forming the cluster. As a result, many operations in the cluster can be performed quickly in a batch processing manner. Instead of each vehicle using one channel originally, each cluster uses one channel. Thus, the channel utilization rate can be improved because different vehicles in the same cluster in V2X need to use the same channel. At the same time, when all vehicles in the cluster leave the service area of the V2X access point, the channel can be recycled, so that resources can be more fully utilized and the processing time can be greatly saved. Summary of the Invention

[0003] In order to allocate appropriate channel resources to the corresponding vehicle clusters, an embodiment of the present invention provides a method and apparatus for vehicle - to - everything (V2X) cluster channel allocation based on fuzzy optimization.

[0004] In a first aspect, an embodiment of the present invention provides a method for vehicle - to - everything (V2X) cluster channel allocation based on fuzzy optimization, which may include:

[0005] Collect the service time, the values of different performance indicators, and the vehicle service level of each vehicle in the vehicle cluster that used the previously allocated channel in the V2X access point last time; wherein, the vehicle service level is associated with the maximum relative membership degree and the minimum relative membership degree of the vehicle with respect to different performance indicators; the previously allocated channel is the channel that the V2X access point has allocated to the vehicle cluster for use;

[0006] Based on the service time, the values of different performance indicators, and the vehicle service level of each vehicle in the vehicle cluster that used the previously allocated channel in the V2X access point last time, determine the weight coefficients of different performance indicators for cluster channel resource allocation;

[0007] A set of vehicles that enter the service area of the vehicle networking access point and issue service requests during the current cluster division interval period is used to construct main road vehicle clusters corresponding to each main road according to the number of main road sets in the service area of the vehicle networking access point.

[0008] For the main road vehicle clusters of each main road, based on the weight coefficients of different performance indicators, the values of different performance indicators of different vehicles in the main road vehicle cluster of the main road, and the service levels of the vehicles, the relative membership degrees preferred to use the corresponding available channels are determined.

[0009] According to the relative membership degrees preferred to use each available channel for the main road vehicle clusters of each main road, channels are allocated to the main road vehicle clusters of all main roads according to the number of vehicles in the main road vehicle clusters.

[0010] In one or some alternative embodiments of the embodiments of the present application, determining the weight coefficients of different performance indicators for cluster channel resource allocation based on the service time, the values of different performance indicators, and the vehicle service levels of each vehicle in the vehicle cluster that used the previously allocated channel in the vehicle networking access point includes:

[0011] Based on the service time, the values of different performance indicators, and the vehicle service levels of each vehicle in the vehicle cluster that used the previously allocated channel in the vehicle networking access point, with the goal of minimizing the sum of the weighted distance-to-optimal distance squares of the channel resources corresponding to different vehicles during the service time within the preset time period of the vehicle networking access point, based on the following formula, the weight coefficients of different performance indicators for cluster channel resource allocation are determined:

[0012] The calculation formula for the sum of the weighted distance-to-optimal distance squares of the channel resources corresponding to different vehicles during the service time within the preset time period of the vehicle networking access point is as follows:

[0013]

[0014] Among them, G(ω i ,λ) is the sum of the weighted distance-to-optimal distance squares of the channel resources corresponding to different vehicles during the service time within the preset time period of the vehicle networking access point, I is the set of performance indicators for cluster channel resource allocation, i is the identifier of the performance indicator for cluster channel resource allocation, and i ∈ I, j and l are respectively the identifiers of the channel and the vehicle, Θ is the set of channels that have been allocated to the cluster for use in the vehicle networking access point, and the service level ρ of vehicle l l is used to determine its maximum relative optimal membership degree M l,i and minimum relative optimal membership degree N l,i , t i,j,lis the service time when vehicle l in the vehicle networking access point last used channel j, R i,j,l is the performance metric value when vehicle l in the vehicle networking access point last used channel j, ψ j is the set of vehicle identifiers included in the cluster that last used channel j in this vehicle networking, ω i is the weight coefficient of performance metric i for cluster channel resource allocation, λ is the Lagrange coefficient, worst(z,i) is the function to select the worst value on performance metric i from z, let x, y, z be intermediate variables, and the performance metric comparison function f i (x,y) can be expressed as follows:

[0015]

[0016] The calculation formula for the weight coefficient of the performance metric for cluster channel resource allocation is as follows:

[0017]

[0018] In one or some alternative embodiments of the embodiments of the present application, for the main road vehicle clusters of each main road, according to the weight coefficients of the different performance metrics, the values of the different performance metrics of different vehicles in the main road vehicle clusters of the main road, and the service levels of the vehicles, determining the relative membership degree of using the corresponding available channels as preferred includes:

[0019] For the main road vehicle clusters of each main road, with the goal of minimizing the sum of the weighted distance squared to the optimal value and the weighted distance squared to the worst value of the available channels for the vehicles in the main road vehicle clusters within the current cluster division interval period in the vehicle networking access point, based on the following formula, determining the relative membership degree of using the corresponding available channels as preferred:

[0020] The calculation formula for the sum of the weighted distance squared to the optimal value and the weighted distance squared to the worst value of the available channels for the vehicles in the main road vehicle clusters within the current cluster division interval period in the vehicle networking access point is as follows:

[0021]

[0022] where, E T,k is the main road vehicle cluster numbered k within the current cluster division interval period, μ j is the relative membership degree of channel j being preferred.

[0023] In one or some alternative embodiments of the embodiments of the present application, the method further includes: determining the relative membership degree of the channel being preferred based on the following calculation formula:

[0024]

[0025] Among them, num is a function for counting the number of sets or permutations and combinations.

[0026] In one or some alternative embodiments of the embodiments of the present application, allocating channels to the vehicle clusters on all main roads according to the preferred relative membership degrees of the vehicle clusters on each main road corresponding to each available channel, according to the number of vehicles in the vehicle clusters on the main roads, includes:

[0027] According to the preferred relative membership degrees of the vehicle clusters on each main road corresponding to each available channel, in the order from the largest to the smallest number of vehicles in the vehicle clusters on the main roads, sequentially select the channel corresponding to the maximum value of the relative membership degree from the available channels within the current cluster division interval period in the vehicle network access point as the channel allocated to the vehicle cluster on the corresponding main road.

[0028] In one or some alternative embodiments of the embodiments of the present application, constructing vehicle clusters on main roads corresponding to each main road for the set of vehicles that enter the service area of the vehicle network access point and send service requests to the vehicle network access point within the current cluster division interval period, includes:

[0029] Match the location information of the service area of the vehicle network access point with the electronic map to determine the set of main roads in the service area;

[0030] Obtain the set of vehicles that enter the service area of the vehicle network access point and send service requests to the vehicle network access point within the current cluster division interval period; among them, the service request sent by each vehicle to the vehicle network access point includes the location information of the vehicle;

[0031] Judge whether the corresponding vehicle is on one of the main roads in the set of main roads in the service area according to the location information of each vehicle in the vehicle set;

[0032] If so, add the vehicle to the vehicle cluster on the corresponding main road;

[0033] For the vehicles that have not been added to any vehicle cluster on the main road, determine the average vehicle positions of all vehicles in each non-empty vehicle cluster on the main road, calculate the difference between the location information of the vehicle and the average vehicle position corresponding to each non-empty vehicle cluster on the main road, and add the vehicle to the vehicle cluster corresponding to the smallest difference.

[0034] Second aspect, embodiments of the present invention provide a vehicle networking cluster channel allocation device based on fuzzy optimization, which may include:

[0035] A data acquisition module, configured to collect the service time, values of different performance indicators, and vehicle service levels of each vehicle within a vehicle cluster that has recently used the allocated channel among the vehicle networking access points; wherein, the vehicle service level is associated with the maximum relative membership degree and the minimum relative membership degree of the vehicle with respect to different performance indicators; the allocated channel is a channel that the vehicle networking access point has previously allocated for use by the vehicle cluster;

[0036] A first calculation module, configured to determine the weight coefficients of different performance indicators for cluster channel resource allocation based on the service time, values of different performance indicators, and vehicle service levels of each vehicle within a vehicle cluster that has recently used the allocated channel among the vehicle networking access points;

[0037] A cluster construction module, configured to construct main road vehicle clusters corresponding to each main road according to the number of main road sets in the service area of the vehicle networking access point for a set of vehicles that enter the service area of the vehicle networking access point and send service requests to the vehicle networking access point during the current cluster division interval period;

[0038] A second calculation module, configured to determine the relative membership degree of using the corresponding available channels as the preferred ones for the main road vehicle clusters of each main road according to the weight coefficients of different performance indicators, the values of different performance indicators of different vehicles in the main road vehicle clusters of the main road, and the vehicle service levels;

[0039] A channel allocation module, configured to allocate channels for the main road vehicle clusters of all main roads according to the relative membership degree of each main road vehicle cluster corresponding to each available channel as the preferred one, in accordance with the number of vehicles in the main road vehicle clusters.

[0040] Third aspect, embodiments of the present invention provide a computer-readable storage medium, on which a computer program / instructions is stored, and when the computer program / instructions is executed by a processor, it implements the above-mentioned vehicle networking cluster channel allocation method based on fuzzy optimization.

[0041] Fourth aspect, embodiments of the present invention provide a computer program product, including a computer program / instructions, and when the computer program / instructions is executed by a processor, it implements the above-mentioned vehicle networking cluster channel allocation method based on fuzzy optimization.

[0042] Fifth aspect, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored on the memory, and when the processor executes the computer program, it implements the above-mentioned vehicle networking cluster channel allocation method based on fuzzy optimization.

[0043] The beneficial effects of the above technical solutions provided by the embodiments of the present invention at least include:

[0044] The embodiments of the present invention provide a method for allocating channels in a vehicle networking cluster based on fuzzy optimization. By associating the vehicle service levels in the vehicle cluster with the maximum relative membership degree and the minimum relative membership degree regarding different performance indicators, through multi-objective fuzzy optimization, the weights of different performance indicators are determined. The vehicle cluster is divided according to the number of main roads in the service area of the vehicle networking access point, and the relative membership degree of each cluster using the corresponding available channels as the preference is determined. Then, according to the number of vehicles in the vehicle cluster, the channels corresponding to the maximum relative membership degree of the available channels as the preference are sequentially allocated to the corresponding clusters for use, so as to meet the maximum relative membership degree and the minimum relative membership degree of the vehicle service levels in the cluster regarding different performance indicators. This method is based on the membership function of fuzzy set theory, quantifies fuzzy information, and can flexibly and effectively determine the performance indicators and their weights of multi-attribute decision-making problems according to the actual situation, so as to transform multiple objectives into a comprehensive single objective for optimal decision-making. Using the fuzzy optimization method to reasonably allocate channels avoids the one-sidedness and blindness that may occur in traditional channel allocation methods, makes the complex allocation of cluster channel resources of the vehicle networking access point more in line with the actual needs of the vehicle cluster, improves the utilization efficiency of channel resources, enhances the flexibility and adaptability of channel allocation, and is convenient for managing and optimizing channel resources.

[0045] Other features and advantages of the present invention will be described in the following description, and, in part, will be obvious from the description, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structures specifically pointed out in the written description and the drawings.

[0046] The technical solutions of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0047] The drawings are used to provide a further understanding of the present invention, and constitute a part of the description. They are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0048] Figure 1 is a schematic flowchart of the method for allocating channels in a vehicle networking cluster based on fuzzy optimization provided by the embodiments of the present invention;

[0049] Figure 2 is a schematic diagram of the service area of the vehicle networking access point and the vehicle conditions of the vehicles entering the service area of the vehicle networking access point and sending service requests to the vehicle networking access point;

[0050] Figure 3For the time period in the embodiments of the present invention [16:00,16:03] The channels available for the clusters corresponding to the main roads 1, 2, and 3 within are the preferred relative membership degrees;

[0051] Figure 4 FIG. is a schematic structural diagram of a vehicle-to-everything (V2X) cluster channel allocation device based on fuzzy optimization provided by an embodiment of the present application. Detailed implementation manners

[0052] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0053] The inventors found that in the prior art, a vehicle-to-everything (V2X) access point cannot allocate appropriate channel resources to different clusters, and thus cannot achieve optimal allocation of channel resources. The inventors unexpectedly found through experiments that by selecting different performance indicators during cluster channel resource allocation, using the multi-objective fuzzy optimization method to obtain the weight values of the performance indicators, and based on the membership function of fuzzy set theory, the fuzzy information is quantified, so that multiple objectives are transformed into a comprehensive single objective for optimal decision-making. Therefore, the V2X access point needs to make a scientific and comprehensive judgment on the complex cluster channel resource allocation work. By continuously optimizing the method, the maximum value can be created for the channel utilization efficiency of the V2X network.

[0054] Based on this, the inventors further developed the present invention and provided a vehicle-to-everything (V2X) cluster channel allocation method and device based on fuzzy optimization. By constructing main road vehicle clusters according to the main road conditions of vehicles within the service area of the V2X access point, the channels corresponding to the maximum relative membership degrees of the available channels can be sequentially allocated to the corresponding clusters for use according to the number of vehicles in the main road vehicle clusters, so as to solve the technical problems of the maximum relative membership degree and the minimum relative membership degree of the vehicle service level in the cluster with respect to different performance indicators.

[0055] Fuzzy optimization: Fuzzy optimization is an optimization method based on fuzzy mathematics theory, mainly used to handle decision-making problems with fuzzy properties. The pair of concepts of superiority and inferiority have both differences and their respective values. They are at the two poles of evaluation, have intermediary transitional properties, and are objectively existing fuzzy concepts. This is the fuzzy nature of optimization and an objective attribute presented during the process of identifying superiority and inferiority of things.

[0056] Multi-objective fuzzy optimization method: The multi-objective fuzzy optimization method is a multi-objective decision-making method based on fuzzy mathematics theory. Its core is to determine the membership degree of the solution set with respect to the objective set belonging to the fuzzy concept of "excellent", that is, the relative membership degree, and then calculate the relative membership degree of each solution according to the fuzzy optimization formula, and then rank the pros and cons of the solutions.

[0057] Embodiment 1

[0058] In Embodiment 1 of the present invention, a vehicle networking cluster channel allocation method based on fuzzy optimization is provided. Referring to Figure 1 as shown, the method may include the following steps S101-S105:

[0059] S101: Collect the service time, the values of different performance indicators, and the vehicle service level of each vehicle in the vehicle cluster that has used the allocated channel most recently in the vehicle networking access point; wherein, the vehicle service level is associated with the maximum relative membership degree and the minimum relative membership degree of the vehicle with respect to different performance indicators; the allocated channel is the channel that the vehicle networking access point has ever allocated for the vehicle cluster to use;

[0060] S102: Based on the service time, the values of different performance indicators, and the vehicle service level of each vehicle in the vehicle cluster that has used the allocated channel most recently in the vehicle networking access point, determine the weight coefficients of different performance indicators for cluster channel resource allocation;

[0061] S103: Divide the set of vehicles that enter the service area of the vehicle networking access point and send service requests to the vehicle networking access point within the current cluster division interval period, and construct main road vehicle clusters corresponding to each main road according to the number of main road sets in the service area of the vehicle networking access point;

[0062] S104: For the main road vehicle clusters of each main road, determine the relative membership degree of using the corresponding available channels as the preferred according to the weight coefficients of different performance indicators, the values of different performance indicators of different vehicles in the main road vehicle clusters of the main road, and the vehicle service level;

[0063] S105: According to the relative membership degree of each main road vehicle cluster corresponding to each available channel as the preferred, allocate channels to the main road vehicle clusters of all main roads according to the number of vehicles in the main road vehicle clusters.

[0064] The method for allocating cluster channels in the Internet of Vehicles based on fuzzy optimization provided by the embodiments of the present invention associates the vehicle service levels in the vehicle cluster with the maximum relative membership degrees and minimum relative membership degrees regarding different performance indicators. Through multi-objective fuzzy optimization, the weight values of different performance indicators are determined. The vehicle cluster is divided according to the number of main roads in the service area of the Internet of Vehicles access point, and the relative membership degree of each cluster using the corresponding available channels as the preference is determined. Then, according to the number of vehicles in the vehicle cluster, the channels corresponding to the maximum relative membership degree of the available channels as the preference are sequentially allocated to the corresponding clusters for use, so as to meet the maximum relative membership degree and minimum relative membership degree of the vehicle service levels in the cluster regarding different performance indicators. This method is based on the membership function of fuzzy set theory, quantifies fuzzy information, and can flexibly and effectively determine the performance indicators and their weight values of multi-attribute decision-making problems according to the actual situation, thereby transforming multiple objectives into a comprehensive single objective for optimal decision-making. Using the fuzzy optimization method to reasonably allocate channels avoids the one-sidedness and blindness that may occur in traditional channel allocation methods, makes the complex cluster channel resource allocation of the Internet of Vehicles access point more in line with the actual needs of the vehicle cluster, improves the utilization efficiency of channel resources, enhances the flexibility and adaptability of channel allocation, and facilitates the management and optimization of channel resources.

[0065] In the above step S101, the maximum relative membership degree and minimum relative membership degree of each vehicle's vehicle service level regarding different performance indicators can be preset. Exemplarily, the service level of a vehicle can be determined according to the package fee of its network service. Each service level has a corresponding maximum relative membership degree and minimum relative membership degree regarding different performance indicators. Among them, in the embodiments of the present invention, the above performance indicators can be performance indicators of network transmission, for example, bandwidth, transmission delay. Of course, in some other examples, they can also be other performance indicators such as throughput, bit error rate, and packet loss rate. For the channels that have been allocated to the cluster for use in the Internet of Vehicles access point, collect the service time, performance indicator values, and their service levels of different vehicles in the vehicle cluster of this Internet of Vehicles access point when they last used the above channels. After collecting the vehicle service levels of the vehicles, the corresponding maximum relative membership degrees and minimum relative membership degrees of different performance indicators of the corresponding service levels can be obtained.

[0066] Based on the above steps, obtain the service time, values of different performance indicators, and vehicle service levels of each vehicle in the vehicle cluster that last used the allocated channels in the Internet of Vehicles access point, and obtain the maximum relative membership degree and minimum relative membership degree of each service level regarding different performance indicators, which can be used to determine the weight coefficients of different performance indicators when allocating cluster channel resources.

[0067] In a specific embodiment, in the above step S102, based on the service time of each vehicle in the vehicle cluster that used the allocated channel most recently in the vehicle Internet access point, the values of different performance indicators, and the vehicle service level, the weight coefficients of different performance indicators for cluster channel resource allocation are determined, which specifically include the following steps:

[0068] Based on the service time of each vehicle in the vehicle cluster that used the allocated channel most recently in the vehicle Internet access point, the values of different performance indicators, and the vehicle service level, with the goal of minimizing the sum of the weighted squared distances from the optimal values during the service time of the channel resources corresponding to different vehicles within the preset time period of the vehicle Internet access point, based on the following formula, the weight coefficients of different performance indicators for cluster channel resource allocation are determined:

[0069] The calculation formula for the sum of the weighted squared distances from the optimal values during the service time of the channel resources corresponding to different vehicles within the preset time period of the vehicle Internet access point is as follows:

[0070]

[0071] In formula (1), G(ω i ,λ) is the sum of the weighted squared distances from the optimal values during the service time of the channel resources corresponding to different vehicles within the preset time period of the vehicle Internet access point, I is the set of performance indicators for cluster channel resource allocation, i is the identifier of the performance indicator for cluster channel resource allocation, and i ∈ I. j and l are respectively the identifiers of the channel and the vehicle. Θ is the set of channels that have been allocated to the cluster for use in this vehicle Internet access point. The service level ρ of vehicle l l is used to determine its maximum relative membership degree M l,i and minimum relative membership degree N l,i with respect to performance indicator i. t i,j,l is the service time when vehicle l in the vehicle Internet access point used channel j most recently. R i,j,l is the value of the performance indicator when vehicle l in the vehicle Internet access point used channel j most recently. ψ j is the set constructed by the identifiers of the vehicles included in the cluster that used channel j most recently in this vehicle Internet. ω i is the weight coefficient of performance indicator i for cluster channel resource allocation. λ is the Lagrange coefficient. worst(z,i) is a function that selects the worst value of performance indicator i from z. Let x, y, and z be intermediate variables. The performance indicator comparison function f i (x,y) can be expressed as follows:

[0072]

[0073] The calculation formula for the weight coefficients of the performance indicators for cluster channel resource allocation is as follows:

[0074]

[0075] Specifically, in the above step S102, let I be a set of performance metrics for cluster channel resource allocation, i be the identifier of the performance metric for cluster channel resource allocation, and i ∈ I. Let j and l be the identifiers of the channel and the vehicle respectively, Θ be the set of channels that have been allocated to the cluster for use in this vehicle networking access point. Let vehicle l determine its maximum relative membership degree M l and minimum relative membership degree N l,i for the performance metric i through the service level ρ l,i corresponding to its own package tariff. Let t i,j,l be the service time when vehicle l last used channel j in this vehicle networking access point, R i,j,l be the performance metric value when vehicle l last used channel j in this vehicle networking access point, ψ j be the set constructed by the vehicle identifiers included in the cluster that last used channel j in this vehicle networking, and ω i be the weight coefficient of the performance metric i for cluster channel resource allocation. Considering that ω i should satisfy Set the Lagrange coefficient λ, and the objective function min{G(ω i ,λ)} can be established to minimize the sum of the weighted distance squares from the statistical channel resources corresponding to different vehicles within the preset time period of this vehicle networking access point during the service time;

[0076] where the calculation formula of G(ω i ,λ) is as follows:

[0077]

[0078] where worst(z,i) is a function to select the worst value on the performance metric i from z. Let x, y, z be intermediate variables, and the performance metric comparison function f i (x,y) can be expressed as follows:

[0079]

[0080] Considering that when min{G(ω i ,λ)} holds, it should satisfy The change relationship between ω i and λ can be obtained as shown in the following formula:

[0081]

[0082] From The general expression of λ can be determined as:

[0083]

[0084] The ω can be determined from the above formulas (3) and (4). i The general expression of... can be represented as:

[0085]

[0086] In the embodiment of the present invention, the above preset time period may be a recent period of time before the current moment. For example, it may be a time period of 5 minutes before the current moment.

[0087] In the above step S103, a vehicle set that enters the service area of the vehicle network access point and issues a service demand to the vehicle network access point within the current cluster division interval period is used to construct a main road vehicle cluster corresponding to each main road according to the number of the main road sets in the service area of the vehicle network access point. The specific steps are as follows:

[0088] Match the location information of the service area of the vehicle network access point with the electronic map to determine the main road set of the service area;

[0089] Obtain a vehicle set that enters the service area of the vehicle network access point and issues a service demand to the vehicle network access point within the current cluster division interval period; wherein, the service demand issued by each vehicle to the vehicle network access point includes the location information of the vehicle.

[0090] Judge whether the corresponding vehicle is on a main road in the main road set of the service area according to the location information of each vehicle in the vehicle set;

[0091] If so, add the vehicle to the main road vehicle cluster corresponding to the main road;

[0092] For the vehicles that have not been added to any main road vehicle cluster, determine the average vehicle position of all vehicles in each non-empty main road vehicle cluster, calculate the difference between the vehicle's location information and the average vehicle position corresponding to each non-empty main road vehicle cluster, and add the vehicle to the main road vehicle cluster with the smallest corresponding difference.

[0093] Specifically, in the above step S103, it can be implemented through the following steps during specific execution:

[0094] S1031: Set that the vehicle network access point can match and calculate the location information of the service area with the electronic map to give the main road set of the service area and can judge whether a vehicle is on a main road in the main road set of the service area through the location information given by a certain vehicle; set the main road set of the service area as Γ = {γ k|k∈K}, where k is the number of the main road, K is the set of numbers of the main roads, and γ k is the main road numbered k. Let the set of vehicles that enter the service area of this vehicle networking access point and send service requests to this vehicle networking access point within the time period [T, T+Δ] be Ξ T , where T is the moment when the first vehicle enters the service area of this vehicle networking access point and sends a service request to this vehicle networking access point within the above-mentioned preset time period, and Δ is the cluster division interval period. When vehicle l in the vehicle set Ξ T sends a service request containing the location information w l of vehicle l and the service level ρ l to this vehicle networking access point after entering the service area of this vehicle networking access point.

[0095] S1032: For any vehicle l in the Ξ T , it can be judged whether it is on a certain main road in the Γ through its location information w l . Thus, for any γ in the Γ k , the vehicle set Ξ corresponding to the main road numbered k in the service area of this vehicle networking access point within the time period [T, T+Δ] can be constructed. T,k , if it is judged through the location information w l of vehicle l that it is on the main road numbered k, then add it to the corresponding vehicle set Ξ T,k until all the vehicles in the vehicle set Ξ T are judged, and set the set K' of the numbers of the main roads where vehicles exist as If K' = K, then go to step S1035; if then go to step S1033, where is the symbol of proper inclusion of sets.

[0096] S1033: If there is a vehicle l that has not been added to Ξ corresponding to any k T,k , then construct the above vehicle as a vehicle set not on the main road and determine the average position of the vehicles in Ξ corresponding to any k in the K' T,k , where || || is the modulus operation symbol.

[0097] S1034: For vehicle l that has not been added to the vehicle set Ξ corresponding to any k T,k , give then add this vehicle l to the vehicle set Ξ corresponding to the above-mentioned numerical value k T,k .

[0098] S1035: Combine the vehicle sets Ξ corresponding to different k in the K' in step S1032 T,k ​Construct a vehicle set with the included vehicles and use it as the cluster E numbered k within the time period [T, T+Δ]. T,k .

[0099] In an embodiment of the present invention, the vehicle set that enters the service area of the vehicle network access point and issues a service demand to the vehicle network access point is clustered according to the cluster division interval period. The time length of the cluster division interval period can be set according to actual needs. For example, it can be a time length of 2 minutes.

[0100] In the above step S104, for the main road vehicle clusters of each main road, based on the weight coefficients of the different performance indicators, the values of the different performance indicators of different vehicles in the main road vehicle clusters of the main road, and the service level of the vehicles, determine the preferred relative membership degree for using the corresponding available channels, which specifically includes the following steps:

[0101] For the main road vehicle clusters of each main road, based on the weight coefficients of the different performance indicators, the values of the different performance indicators of different vehicles in the main road vehicle clusters of the main road, and the service level of the vehicles, with the goal of minimizing the sum of the weighted distance squared to the optimal and the weighted distance squared to the inferior for the vehicles in the main road vehicle clusters by the available channels within the current cluster division interval period in the vehicle network access point, based on the following formula, determine the preferred relative membership degree for using the corresponding available channels:

[0102] The calculation formula for the sum of the weighted distance squared to the optimal and the weighted distance squared to the inferior for the vehicles in the main road vehicle clusters by the available channels within the current cluster division interval period in the vehicle network access point is as follows:

[0103]

[0104] Among them, E T,k is the main road vehicle cluster numbered k within the current cluster division interval period, and μ j is the preferred relative membership degree for channel j.

[0105] In a specific embodiment, based on the following calculation formula, determine the preferred relative membership degree for the channel:

[0106]

[0107] Among them, num is a function for counting the number of statistical sets or permutations and combinations.

[0108] Specifically, in the above step S104, it can be specifically implemented through the following steps:

[0109] For the cluster E numbered k within the time period [T, T+Δ] T,k , let the preferred relative membership degree for channel j be μj , the objective function min{H(μ j )} that minimizes the sum of the weighted squared distance of the channel resource j to the vehicles in the cluster and the weighted squared distance to the inferior distance can be established, so as to meet the requirement of minimizing the overall deviation between the service levels of different vehicles and the actual situation;

[0110] Among them, the calculation formula of the said H(μ j ) is as follows:

[0111]

[0112] Considering that when min{H(μ j )} exists, it satisfies The said μ j value can be calculated according to the following formula:

[0113]

[0114] Among them, num is a function of the number of statistical sets or permutations and combinations.

[0115] In the above step S105, according to the relative membership degrees of the vehicle clusters on each main road corresponding to each available channel, and in the order of the number of vehicles in the vehicle clusters on the main road, channels are allocated to the vehicle clusters on all main roads, which specifically includes the following steps:

[0116] According to the relative membership degrees of the vehicle clusters on each main road corresponding to each available channel, in the order of the number of vehicles in the vehicle clusters on the main road from large to small, the channel corresponding to the maximum relative membership degree is selected in turn from the available channels within the current cluster division interval period in the vehicle network access points as the channel allocated to the vehicle cluster on the main road.

[0117] Specifically, in the above step S105, it can be specifically implemented through the following steps:

[0118] S1051: Considering that originally each vehicle in the vehicle network needs to be allocated a channel and the channel will be recycled when the vehicle leaves the vehicle network, and on the premise that the number of channels should meet the allocation requirements when each cluster is allocated a channel and the channels will be recycled after all vehicles in the cluster leave the vehicle network, let J T be the set of available channel resources within the time period [T, T+Δ]; for the set of vehicle numbers K' where there are vehicles on the main road, let the number of newly established clusters C T within the time period [T, T+Δ] be num(K'); sort the k in the said K' in ascending order according to the number D T,k value of the vehicles in the corresponding vehicle cluster. If there exists any k corresponding to D T,kIf the numerical values are the same, the above k are randomly sorted; after all k in the K′ are sorted, it is set as the set K″ of cluster numbers that have completed the vehicle number sorting; let the set U of used channel resources in the corresponding time period [T, T+Δ] T be an empty set and the number of iterations B in the corresponding time period [T, T+Δ] T be 0;

[0119] S1052: If B T < C T , then according to the cluster E corresponding to the k at the forefront position in the sequence in the K″ T,k and the I described in step S102, calculate If B T = C T , then end the iteration;

[0120] S1053: Determine the corresponding channel j when it holds, add this channel j to the U T and allocate this channel j to the cluster E corresponding to k at this time T,k ; add 1 to the B T and transfer to step S1052.

[0121] To make a clearer and more complete description of the vehicle - to - everything (V2X) cluster channel allocation method based on fuzzy optimization provided by the embodiments of the present invention and to illustrate the technical effects of the present invention. In the following, in a specific embodiment, take a certain V2X that adopts the V2X access point given in Figure 2 and set the service area covered by this V2X access point, that is Figure 2 the map area shown in

[0122] as an example to make a detailed description of the process of V2X cluster channel allocation.

[0123] Step 1: Corresponding to the above step S101, for a certain V2X, assume that each vehicle can determine its maximum relative membership degree and minimum relative membership degree regarding different performance indicators through the service level corresponding to its own package tariff. For the channels that have been allocated to clusters in this V2X access point, collect the service time, performance indicator values, and their service levels of different vehicles in the cluster in this V2X access point when they last used the above channels. Obtain the maximum relative membership degree and minimum relative membership degree of associated vehicles regarding different performance indicators according to the vehicle service level.

[0124] Table 1

[0125] Service level Performance index Maximum relative membership degree Minimum relative membership degree 1 Bandwidth 0.85 0.8 1 Transmission delay 0.85 0.8 2 Bandwidth 0.8 0.7 2 Transmission delay 0.8 0.7 3 Bandwidth 0.75 0.6 3 Transmission delay 0.75 0.6

[0126] As shown in Table 2, the service time and performance index values of different vehicles in the vehicle cluster of the vehicle networking access point for the last time using the above channels are provided. Among them, the unit of bandwidth is Mb / s, the unit of delay is ms, and the unit of service time is min;

[0127] Table 2

[0128]

[0129] Step 2: Corresponding to the above step S102, based on the service time, the values of different performance indexes, and the vehicle service levels of each vehicle in the vehicle cluster that used the allocated channels for the last time in the vehicle networking access point, with the goal of minimizing the sum of the weighted distance squared from the optimal distance of the channel resources corresponding to different vehicles within the preset time period of the vehicle networking access point during the service time, determine the weight coefficients of different performance indexes for cluster channel resource allocation.

[0130] Based on the data obtained from Table 1 and Table 2, the weight coefficients of the bandwidth and transmission delay performance indexes for cluster channel resource allocation are determined, as shown in Table 3.

[0131] Table 3

[0132] Performance index Weight coefficient Bandwidth 0.62 Transmission delay 0.38

[0133] Step 3: Corresponding to the above step S103, for a set of vehicles that enter the service area of the vehicle networking access point and send service requests to the vehicle networking access point within a certain time period (i.e., a cluster division interval period), by adding the above-mentioned vehicles to different service area main road sets, construct the main road vehicle clusters corresponding to different main road numbers during this time period;

[0134] Figure 2 It is set that the vehicle networking access point can match the location information of the service area with the electronic map to give the service area main road set as {Huashan 1st Road (1), Huashan 2nd Road (2), Huashan 3rd Road (3)}, where the numbers in the brackets are the main road numbers, and in Figure 2 it is given that the set of vehicles that enter the service area of the vehicle networking access point and send service requests to the vehicle networking access point within the time period [16:00,16:03] is {51, 29, 78, 34, 42, 90, 55, 67}, and the corresponding set of service levels of each vehicle is {2, 1, 1, 3, 1, 2, 1, 3};

[0135] Table 4 gives the time period [16:00,16:03]Clusters corresponding to different main road numbers within and the vehicle conditions they contain;

[0136] Table 4

[0137] Cluster Vehicle numbers included Vehicle cluster corresponding to arterial road 1 51，78，29 Vehicle cluster corresponding to arterial road 2 34，42 Vehicle cluster corresponding to arterial road 3 55，67，90

[0138] Step 4: Corresponding to the above steps S104 and S105, for the different main road vehicle clusters constructed in step 3, calculate the preferred relative membership degrees of the available channels in sequence according to the number of vehicles in the clusters, and select the channel corresponding to the maximum value as the channel resource used by the cluster.

[0139] Figure 3 In which are respectively the weight coefficients of the two performance indicators given in Table 3 above, the values of different performance indicators of different vehicles in the main road vehicle cluster of the main road, and the service levels of the vehicles. With the goal of minimizing the sum of the weighted distance squared to the optimal and the weighted distance to the inferior of the available channels for the vehicles in the main road vehicle cluster within the current cluster division interval period of the vehicle network access point, the time period of this vehicle network is determined. [16:00,16:03] The preferred relative membership degrees of the available channels for the clusters corresponding to Main Road 1, Main Road 2, and Main Road 3 within.

[0140] When selecting the channel corresponding to the maximum value of the relative membership degree as the channel allocated to the main road vehicle cluster in the order of the number of vehicles in the main road vehicle cluster from largest to smallest, since the number of vehicles in Main Road 1 and Main Road 3 is the same, they can be randomly sorted. In this embodiment, the cluster corresponding to Main Road 1 is used as the first cluster. From Figure 3 it can be seen that for the cluster corresponding to Main Road 1 in this time period, the channel corresponding to the maximum preferred relative membership degree of the available channels is Channel 13, so the channel can be allocated to this cluster at this time; then, for the cluster corresponding to Main Road 3 in this time period, the channel corresponding to the maximum preferred relative membership degree of the available channels is Channel 16, so the channel can be allocated to this cluster at this time; finally, for the cluster corresponding to Main Road 2 in this time period, the channel corresponding to the maximum preferred relative membership degree of the available channels is Channel 12, so the channel can be allocated to this cluster at this time. Thus far, the time period of this vehicle network [16:00,16:03] The available channels for the clusters corresponding to Main Road 1, Main Road 2, and Main Road 3 within are Channel 13, Channel 12, and Channel 16 respectively.

[0141] Embodiment 2

[0142] Based on the same inventive concept, an embodiment of the present invention also provides a vehicle network cluster channel allocation device based on fuzzy optimization. Referring to Figure 4 as shown, this device includes:

[0143] A data acquisition module 101, configured to collect the service time, values of different performance metrics, and vehicle service levels of each vehicle within a vehicle cluster that has recently used the allocated channel in the vehicle network access point; wherein, the vehicle service level is associated with the maximum relative membership degree and the minimum relative membership degree of the vehicle with respect to different performance metrics; the allocated channel is a channel that the vehicle network access point has previously allocated for use by the vehicle cluster.

[0144] A first calculation module 102, configured to determine the weight coefficients of different performance metrics for cluster channel resource allocation based on the service time, values of different performance metrics, and vehicle service levels of each vehicle within a vehicle cluster that has recently used the allocated channel in the vehicle network access point.

[0145] A cluster construction module 103, configured to construct main road vehicle clusters corresponding to each main road according to the number of main road sets in the service area of the vehicle network access point for a set of vehicles that enter the service area of the vehicle network access point and send service requests to the vehicle network access point during the current cluster division interval period.

[0146] A second calculation module 104, configured to determine the relative membership degree of using the corresponding available channels as preferred for each main road vehicle cluster of each main road according to the weight coefficients of different performance metrics, the values of different performance metrics of different vehicles in the main road vehicle cluster of the main road, and the vehicle service level.

[0147] A channel allocation module 105, configured to allocate channels for all main road vehicle clusters according to the relative membership degree of each main road vehicle cluster corresponding to each available channel as preferred, in accordance with the number of vehicles in the main road vehicle cluster.

[0148] Embodiment III

[0149] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program / instructions are stored, and when the computer program / instructions are executed by a processor, the method for vehicle network cluster channel allocation based on fuzzy optimization as described in Embodiment I above is implemented.

[0150] Embodiment IV

[0151] Based on the same inventive concept, an embodiment of the present invention further provides a computer program product, including a computer program / instructions, and when the computer program / instructions are executed by a processor, the method for vehicle network cluster channel allocation based on fuzzy optimization as described in Embodiment I above is implemented.

[0152] Embodiment V

[0153] Based on the same inventive concept, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory. When the processor executes the computer program, it implements the method for vehicle networking cluster channel allocation based on fuzzy optimization as described in the first embodiment above.

[0154] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0155] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0156] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means realizes the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0157] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0158] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. A vehicle networking cluster channel allocation method based on fuzzy optimization, characterized in that Including: collecting the service time, values of different performance metrics, and vehicle service levels of each vehicle within the vehicle cluster that used the allocated channel most recently among the vehicle network access points; wherein the vehicle service level is associated with the maximum relative membership degree and the minimum relative membership degree of the vehicle with respect to different performance metrics; the allocated channel is a channel that the vehicle network access point has allocated for use by the vehicle cluster; determining the weight coefficients of different performance metrics for cluster channel resource allocation based on the service time, values of different performance metrics, and vehicle service levels of each vehicle within the vehicle cluster that used the allocated channel most recently among the vehicle network access points; constructing main road vehicle clusters corresponding to each main road according to the number of main road sets in the service area of the vehicle network access point for the set of vehicles that enter the service area of the vehicle network access point and issue service requests to the vehicle network access point during the current cluster division interval period; for the main road vehicle clusters of each main road, determining the relative membership degree of preference for using the corresponding available channels based on the weight coefficients of different performance metrics, the values of different performance metrics of different vehicles in the main road vehicle clusters of the main road, and the vehicle service levels; allocating channels for the main road vehicle clusters of all main roads according to the relative membership degree of preference for each available channel corresponding to the main road vehicle clusters of each main road and the number of vehicles in the main road vehicle clusters; 2. The method according to claim 1, wherein The determining the weight coefficients of different performance metrics for cluster channel resource allocation based on the service time, values of different performance metrics, and vehicle service levels of each vehicle within the vehicle cluster that used the allocated channel most recently among the vehicle network access points includes: based on the service time, values of different performance metrics, and vehicle service levels of each vehicle within the vehicle cluster that used the allocated channel most recently among the vehicle network access points, with the goal of minimizing the sum of the weighted squared distances from the optimal distances of channel resources corresponding to different vehicles within the preset time period of the vehicle network access point during the service time, determining the weight coefficients of different performance metrics for cluster channel resource allocation based on the following formula: The formula for calculating the sum of the weighted squared distances from the optimal distances of channel resources corresponding to different vehicles within the preset time period of the vehicle network access point during the service time is as follows: Among them, G(ω i , λ) is the sum of the weighted squared distance from the optimal distance of the channel resources corresponding to different vehicles within the preset time period of the vehicle networking access point during the service time. I is the set of performance indicators for cluster channel resource allocation. i is the identifier of the performance indicator for cluster channel resource allocation, and i ∈ I. j and l are the identifiers of the channel and the vehicle respectively. Θ is the set of channels that have been allocated to the cluster for use in this vehicle networking access point. The service level ρ l of vehicle l is used to determine its maximum relative membership degree M l,i and minimum relative membership degree N l,i with respect to the performance indicator i. t i,j,l is the service time when vehicle l in the vehicle networking access point last used channel j. R i,j,l is the value of the performance indicator when vehicle l in the vehicle networking access point last used channel j. ψ j is the set constructed by the identifiers of the vehicles included in the cluster that last used channel j in this vehicle networking. ω i is the weight coefficient of the performance indicator i during cluster channel resource allocation. λ is the Lagrange coefficient. worst(z, i) is a function that selects the worst value on the performance indicator i from z. Let x, y, and z be intermediate variables. The performance indicator comparison function f i (x, y) can be expressed as follows: The formula for calculating the weight coefficients of performance metrics for cluster channel resource allocation is as follows:

3. The method according to claim 1, characterized in that The for the main road vehicle clusters of each main road, determining the relative membership degree of preference for using the corresponding available channels based on the weight coefficients of different performance metrics, the values of different performance metrics of different vehicles in the main road vehicle clusters of the main road, and the vehicle service levels includes: For the main road vehicle clusters of each main road, based on the weight coefficients of the different performance indicators, the values of the different performance indicators of different vehicles in the main road vehicle clusters of the main road, and the service levels of the vehicles, with the goal of minimizing the sum of the weighted distance squared of the optimal distances and the weighted distance squared of the inferior distances of the vehicles in the main road vehicle clusters with respect to the available channels within the current cluster division interval period of the vehicle network access point, based on the following formula, determine the relative membership degree for which using the corresponding available channel is preferred: The calculation formula for the sum of the weighted distance squared of the optimal distances and the weighted distance squared of the inferior distances of the vehicles in the main road vehicle clusters with respect to the available channels within the current cluster division interval period of the vehicle network access point is as follows: Among them, E T,k is the vehicle cluster on the main road numbered k within the current cluster division interval period, and μ j is the preferred relative membership degree of channel j.

4. The method according to claim 3, wherein It also includes: Based on the following calculation formula, determine the relative membership degree for which the channel is preferred: Among them, num is a function for counting the number of statistical sets or permutations and combinations.

5. The method according to claim 1, wherein Based on the relative membership degrees for which each available channel is preferred for the main road vehicle clusters of each main road, according to the number of vehicles in the main road vehicle clusters, allocate channels for the main road vehicle clusters of all main roads, including: Based on the relative membership degrees for which each available channel is preferred for the main road vehicle clusters of each main road, in the order from the largest to the smallest number of vehicles in the main road vehicle clusters of the main road, successively select the channel corresponding to the maximum relative membership degree from the available channels within the current cluster division interval period of the vehicle network access point as the channel allocated to the main road vehicle cluster of the main road.

6. The method according to any one of claims 1 to 5, characterized in that Construct the main road vehicle clusters corresponding to each main road for the set of vehicles that enter the service area of the vehicle network access point and send service requests to the vehicle network access point within the current cluster division interval period, including: Match the location information of the service area of the vehicle network access point with the electronic map to determine the set of main roads in the service area; Obtain the set of vehicles that enter the service area of the vehicle network access point and send service requests to the vehicle network access point within the current cluster division interval period; among them, the service request sent by each vehicle to the vehicle network access point includes the location information of the vehicle; Based on the location information of each vehicle in the vehicle set, determine whether the corresponding vehicle is on one of the main roads in the set of main roads in the service area; If so, add the vehicle to the main road vehicle cluster corresponding to the main road; For the vehicles that have not been added to any main road vehicle cluster, determine the average vehicle positions of all vehicles in each non-empty main road vehicle cluster, calculate the differences between the location information of the vehicles and the average vehicle positions corresponding to each non-empty main road vehicle cluster, and add the vehicles to the main road vehicle cluster corresponding to the smallest difference.

7. An in-vehicle network cluster channel allocation device based on fuzzy optimization, characterized in that, It includes: A data acquisition module, configured to collect the service time, values of different performance metrics, and vehicle service levels of each vehicle within a vehicle cluster that has most recently used the allocated channel in the vehicle network access point; wherein, the vehicle service level is associated with the maximum relative membership degree and the minimum relative membership degree of the vehicle with respect to different performance metrics; the allocated channel is a channel that the vehicle network access point has ever allocated for use by the vehicle cluster. A first calculation module, configured to determine the weight coefficients of different performance metrics for cluster channel resource allocation based on the service time, values of different performance metrics, and vehicle service levels of each vehicle within the vehicle cluster that has most recently used the allocated channel in the vehicle network access point. A cluster construction module, configured to construct main road vehicle clusters corresponding to each main road according to the number of main road sets in the service area of the vehicle network access point for a set of vehicles that enter the service area of the vehicle network access point and send service requests to the vehicle network access point during the current cluster division interval period. A second calculation module, configured to determine the relative membership degree of preference for using the corresponding available channels for each main road vehicle cluster of each main road according to the weight coefficients of different performance metrics, the values of different performance metrics of different vehicles in the main road vehicle cluster of the main road, and the vehicle service levels. A channel allocation module, configured to allocate channels for the main road vehicle clusters of all main roads according to the relative membership degree of preference for each available channel corresponding to the main road vehicle cluster of each main road, in accordance with the number of vehicles in the main road vehicle cluster.

8. A computer-readable storage medium having computer programs / instructions stored thereon, characterized in that, When the computer program / instructions are executed by a processor, the method for allocating cluster channels in a vehicle network based on fuzzy preference as described in any one of claims 1-6 is implemented.

9. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by a processor, the method for allocating cluster channels in a vehicle network based on fuzzy preference as described in any one of claims 1-6 is implemented.

10. A computer device, comprising a memory, a processor, and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the method for allocating cluster channels in a vehicle network based on fuzzy preference as described in any one of claims 1-6.

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