A method and apparatus for fuzzy optimization-based vehicular network trunking channel allocation

Abstract

This invention discloses a method and apparatus for channel allocation in a vehicle-to-everything (V2X) cluster based on fuzzy optimization. The method includes: collecting the service time, values ​​of different performance indicators, and vehicle service levels of each vehicle in the V2X cluster that most recently used an allocated channel at the V2X access point; wherein, the vehicle service level is associated with the maximum and minimum relative membership of the vehicle with respect to different performance indicators; determining the weighting coefficients of different performance indicators for cluster channel resource allocation; constructing a main road vehicle cluster corresponding to each main road by collecting the set of vehicles that enter the service area of ​​the V2X access point and issue service requests within the current cluster division interval; determining the relative membership of the corresponding available channel as preferred; and allocating channels to all main road vehicle clusters according to the number of vehicles in each main road vehicle cluster based on the relative membership of each available channel as preferred.

Description

Technical Field

[0001] This invention relates to a method and apparatus for allocating vehicle network trunking channels based on fuzzy optimization. Background Technology

[0002] In the Internet of Vehicles (IoV), each vehicle needs to be allocated a channel, and the channel is reclaimed when the vehicle leaves the IoV. Due to the continuous increase in the number of vehicles and the promotion of in-vehicle applications, the spectrum available for IoV is insufficient to support the high-bandwidth transmission of future automotive applications. It is evident that reasonable channel resource allocation is one of the main challenges of IoV communication. At the same time, the dynamic topology, complex application scenarios, and limited frequency resources of IoV pose significant challenges to reasonable allocation. To facilitate the allocation of channel resources, vehicle-to-everything (V2X) access points can group several adjacent vehicles traveling in the same direction that enter the service area of ​​the V2X access point at similar times into a cluster. This cluster is then provided with a channel that can be contested through random access. This allows several vehicles within a certain area to be treated as a single entity for operation, and the cluster is used to achieve optimal matching of demand and scenario. Therefore, necessary configurations are required when forming a cluster. Many operations within the cluster can be performed quickly in a batch processing manner, changing from each vehicle using a separate channel to each cluster using a separate channel. By requiring different vehicles within the same cluster to use the same channel, channel utilization is improved. Furthermore, when all vehicles in the cluster leave the V2X access point service area, the channel can be reclaimed, allowing for more efficient resource utilization and significantly reducing processing time. Summary of the Invention

[0003] To achieve the allocation of appropriate channel resources to the corresponding vehicle clusters, this invention provides a fuzzy optimization-based method and apparatus for channel allocation in vehicle networking clusters.

[0004] In a first aspect, embodiments of the present invention provide a fuzzy optimization-based method for allocating vehicle network trunking channels, which may include:

[0005] The system collects the service time, values ​​of different performance indicators, and vehicle service level of each vehicle in the vehicle cluster that most recently used an allocated channel at the vehicle network access point; wherein, the vehicle service level is associated with the vehicle's maximum and minimum relative superiority with respect to different performance indicators; and the allocated channel is the channel that the vehicle network access point has previously allocated to the vehicle cluster for use.

[0006] Based on the service time, values ​​of different performance indicators, and vehicle service level of each vehicle in the vehicle cluster that most recently used the allocated channel in the vehicle network access point, the weighting coefficients of different performance indicators are determined when allocating cluster channel resources.

[0007] The set of vehicles that enter the service area of ​​the vehicle network access point within the current cluster division interval and send service requests to the vehicle network access point will be constructed according to the number of main road sets in the service area of ​​the vehicle network access point, and the main road vehicle clusters corresponding to each main road will be constructed.

[0008] For each main road vehicle cluster, the relative membership degree of using the corresponding available channel is determined based on the weight coefficients of the different performance indicators, the values ​​of different performance indicators of different vehicles in the main road vehicle cluster, and the service level of the vehicles.

[0009] Based on the relative membership degree of each available channel corresponding to the main road vehicle cluster, channels are allocated to all main road vehicle clusters according to the number of vehicles in the main road vehicle cluster.

[0010] In one or more optional embodiments of this application, determining the weighting coefficients for different performance indicators when allocating cluster channel resources based on the service time of each vehicle in the vehicle cluster that most recently used the allocated channel in the vehicle network access point, the values ​​of different performance indicators, and the vehicle service level includes:

[0011] Based on the service time, performance index values, and vehicle service level of each vehicle in the vehicle cluster that most recently used the allocated channel at the vehicle network access point, and with the objective of minimizing the sum of the squared weighted distances of different vehicles corresponding to channel resources within a preset time period at the vehicle network access point, the weighting coefficients for different performance indices used for cluster channel resource allocation are determined based on the following formula:

[0012] The formula for calculating the sum of the weighted squares of the distances between different vehicles within the service time period corresponding to the channel resources of the vehicle network access point within a preset time period is as follows:

[0013]

[0014] Wherein G(ω) i λ) represents the sum of the weighted squared distances of different vehicles within the service time period corresponding to the channel resources of the vehicle network access point within a preset time period, I represents the set of performance indicators used for cluster channel resource allocation, i represents the identifier of the performance indicator used for cluster channel resource allocation, and i∈I, j and l are the identifiers of the channel and the vehicle, respectively, Θ represents the set of channels that have been allocated to the cluster in this vehicle network access point, and the service level ρ of vehicle l is given by the system. l Determine its maximum relative superiority M with respect to performance index i l,i and minimum relative superiority N l,i , t i,j,lR is the service time of the last time vehicle l used channel j in the vehicle-to-everything (V2X) access point. i,j,l ψ represents the performance index value of vehicle l when it last used channel j in the vehicle-to-everything (V2X) access point. j ω is the set of vehicle identifiers constructed from the cluster that most recently used channel j in this vehicle network. i Let λ be the weighting coefficient of performance index i used for trunking channel resource allocation, λ be the Lagrange multiplier, worst(z,i) be the function that selects the worst value of performance index i from z, and let x, y, z be intermediate variables. The performance index comparison function is f. i (x,y) can be represented as follows:

[0015]

[0016] The formula for calculating the weighting coefficients of performance metrics used in trunking channel resource allocation is as follows:

[0017]

[0018] In one or more optional embodiments of this application, the step of determining the relative membership degree for which the corresponding usable channel is preferred, based on the weighting coefficients of the different performance indicators, the values ​​of the different performance indicators of different vehicles in the main road vehicle cluster, and the service level of the vehicles, for each main road vehicle cluster, includes:

[0019] For each main road vehicle cluster, based on the weighting coefficients of the different performance indicators, the values ​​of different performance indicators of different vehicles in the main road vehicle cluster, and the service level of the vehicles, with the goal of minimizing the sum of the weighted squared superior distance and the weighted squared inferior distance of the available channels for vehicles in the main road vehicle cluster within the current cluster division interval in the vehicle network access point, the relative membership degree of using the corresponding available channel as the preferred method is determined based on the following formula:

[0020] The formula for calculating the sum of the weighted average of the superior distance and the weighted average of the inferior distance for vehicles in the main road vehicle cluster within the current cluster partitioning interval in the vehicle network access point is as follows:

[0021]

[0022] Among them, E T,k To divide the current cluster into a main road vehicle cluster numbered k within the interval period, μ j Let j be the preferred relative membership degree of the channel.

[0023] The method described in one or more optional embodiments of this application further includes: determining the preferred relative membership degree of the channel based on the following calculation formula:

[0024]

[0025] Where num is a function that counts the number of sets or permutations and combinations.

[0026] In one or more optional embodiments of this application, the step of allocating channels to all main road vehicle clusters according to the preferred relative membership degree of each available channel for each main road vehicle cluster, based on the number of vehicles in the main road vehicle cluster, includes:

[0027] Based on the preferred relative membership degree of each available channel for the main road vehicle cluster, and in descending order of the number of vehicles in the main road vehicle cluster, the channel corresponding to the maximum relative membership degree is selected from the available channels within the current cluster division interval in the vehicle network access point as the channel allocated to the main road vehicle cluster.

[0028] In one or more optional embodiments of this application, the step of constructing a main road vehicle cluster corresponding to each main road according to the number of main road sets in the service area of ​​the vehicle network access point within the current cluster partitioning interval includes:

[0029] The location information of the service area of ​​the vehicle network access point is matched 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 partitioning interval; wherein, the service request sent by each vehicle to the vehicle network access point contains the vehicle's location information.

[0031] Based on the location information of each vehicle in the vehicle set, determine whether the corresponding vehicle is on a main road in the main road set of the service area;

[0032] If so, the vehicle will be added to the main road vehicle cluster of the corresponding main road.

[0033] For vehicles that have not yet joined any main road vehicle cluster, determine the average position of all vehicles in each non-empty main road vehicle cluster, calculate the difference between the vehicle's position information and the average position of vehicles in each non-empty main road vehicle cluster, and add the vehicle to the main road vehicle cluster with the smallest difference.

[0034] Secondly, embodiments of the present invention provide a vehicle network trunking channel allocation device based on fuzzy optimization, which may include:

[0035] The data acquisition module is used to collect the service time, values ​​of different performance indicators, and vehicle service level of each vehicle in the vehicle cluster that most recently used the allocated channel in the vehicle network access point; wherein, the vehicle service level is associated with the maximum and minimum relative superiority of the vehicle with respect to different performance indicators; the allocated channel is the channel that the vehicle network access point has previously allocated to the vehicle cluster for use.

[0036] The first calculation module is used to determine the weighting coefficients of different performance indicators for allocating cluster channel resources based on the service time of each vehicle in the vehicle cluster that most recently used the allocated channel in the vehicle network access point, the values ​​of different performance indicators, and the vehicle service level.

[0037] The cluster construction module is used to construct a main road vehicle cluster corresponding to each main road according to the number of main road sets in the service area of ​​the vehicle network access point within the current cluster division interval period.

[0038] The second calculation module is used to determine the relative membership degree of using the corresponding available channel as the preferred method for each main road vehicle cluster, based on the weight coefficients of the different performance indicators, the values ​​of different performance indicators of different vehicles in the main road vehicle cluster, and the service level of the vehicles.

[0039] The channel allocation module is used to allocate channels to all main road vehicle clusters according to the relative membership degree of each available channel corresponding to the main road vehicle cluster, based on the number of vehicles in the main road vehicle cluster.

[0040] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing a computer program / instruction thereon, which, when executed by a processor, implements the fuzzy optimization-based vehicle network cluster channel allocation method as described above.

[0041] Fourthly, embodiments of the present invention provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements the fuzzy-optimized vehicle network cluster channel allocation method described above.

[0042] Fifthly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the fuzzy optimization-based vehicle network cluster channel allocation method as described above.

[0043] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0044] This invention provides a fuzzy optimization-based channel allocation method for vehicle-to-everything (V2X) clusters. By associating the service levels of vehicles within a cluster with the maximum and minimum relative eugenicity of different performance indicators, and through multi-objective fuzzy optimization, the weights of different performance indicators are determined. Vehicle clusters are divided according to the number of main roads within the service area of ​​the V2X access point, and the relative membership degree of each cluster is determined as the preferred available channel. Then, based on the number of vehicles in the cluster, the channel with the maximum preferred relative membership degree is allocated to the corresponding cluster for use, thus satisfying the maximum and minimum relative eugenicity of the service levels of vehicles within the cluster with respect to different performance indicators. This method is based on the membership function of fuzzy set theory, quantifying fuzzy information, and can flexibly and effectively determine the performance indicators and their weights for multi-attribute decision problems according to actual conditions, thereby transforming multi-objectives into a comprehensive single-objective optimization decision. By using the fuzzy optimization method to rationally allocate channels, the one-sidedness and blindness that may occur in traditional channel allocation methods are avoided. This makes the allocation of complex cluster channel resources at vehicle network access points more in line with the actual needs of vehicle clusters, improves the efficiency of channel resource utilization, enhances the flexibility and adaptability of channel allocation, and facilitates the management and optimization of channel resources.

[0045] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0046] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0047] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0048] Figure 1 This is a flowchart illustrating the fuzzy optimization-based vehicle network trunking channel allocation method provided in an embodiment of the present invention.

[0049] Figure 2 This is a schematic diagram illustrating the service area of ​​a vehicle network access point provided in an embodiment of the present invention, as well as the status of vehicles entering the service area of ​​the vehicle network access point and sending service requests to the vehicle network access point.

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

[0051] Figure 4 This is a schematic diagram of the structure of a vehicle network cluster channel allocation device based on fuzzy optimization provided in an embodiment of this application. Detailed Implementation

[0052] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0053] The inventors discovered that in existing technologies, vehicular network access points cannot allocate appropriate channel resources to different clusters, thus failing to achieve optimal channel resource allocation. Through experiments, the inventors unexpectedly discovered that by selecting different performance indicators for cluster channel resource allocation and employing a multi-objective fuzzy optimization method to obtain performance indicator weights, and based on the membership function of fuzzy set theory, fuzzy information is quantified, thereby transforming multiple objectives into a comprehensive single objective for optimal decision-making. Therefore, vehicular network access points need to scientifically and comprehensively assess the complex task of cluster channel resource allocation. By continuously optimizing the method, maximum value can be created for the channel utilization efficiency of vehicular networks.

[0054] Based on this, the inventors, through further research and development, have created this invention, providing a method and apparatus for channel allocation in a vehicle-to-everything (V2X) cluster based on fuzzy optimization. By constructing a main road vehicle cluster within the service area of ​​the V2X access point according to the conditions of the main road where the vehicles are located, the available channels are allocated to the corresponding clusters based on the number of vehicles in the main road vehicle cluster, according to the maximum relative membership value. This addresses the technical problem of solving the problem of the maximum and minimum relative membership values ​​of vehicle service levels with respect to different performance indicators within the cluster.

[0055] Fuzzy optimization: Fuzzy optimization is an optimization method based on fuzzy mathematics theory, mainly used to handle decision problems with fuzzy properties. The concepts of "good" and "bad" are distinct yet each has its own value. They occupy opposite ends of the evaluation spectrum, possessing a mediating transitional nature; they are objectively existing fuzzy concepts. This fuzziness in optimization is an objective attribute presented by things in the process of identifying good and bad.

[0056] Multi-objective fuzzy optimization method: This method is a multi-objective decision-making approach based on fuzzy mathematics theory. Its core is to determine the membership degree of the set of alternatives to the fuzzy concept of "excellent" with respect to the objective set, i.e., the relative membership degree. Then, based on the fuzzy optimization formula, the relative membership degree of each alternative is calculated, thereby ranking the alternatives in order of their superiority.

[0057] Example 1

[0058] Embodiment 1 of the present invention provides a fuzzy optimization-based method for allocating vehicle network trunking channels, referring to... Figure 1 As shown, the method may include the following steps S101-S105:

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

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

[0061] S103: 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, and construct the main road vehicle cluster corresponding to each main road according to the number of main road sets in the service area of ​​the vehicle network access point.

[0062] S104: For each main road vehicle cluster, 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 cluster, and the service level of the vehicles, determine the relative membership degree of using the corresponding available channel as the preferred option.

[0063] S105: Based on the relative membership degree of each available channel corresponding to the main road vehicle cluster 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.

[0064] The fuzzy optimization-based channel allocation method for vehicle-to-everything (V2X) clusters provided in this invention assigns weights to different performance indicators by associating the service levels of vehicles within a cluster with the maximum and minimum relative membership degrees of each vehicle with respect to various performance indicators. The method uses multi-objective fuzzy optimization to divide the vehicle clusters based on the number of main roads within the service area of ​​the V2X access point. It then determines the relative membership degree of each cluster, prioritizing the use of available channels. Finally, based on the number of vehicles in each cluster, the method allocates channels with the maximum relative membership degree to the corresponding clusters, thus satisfying the maximum and minimum relative membership degrees of the service levels of vehicles within the cluster with respect to different performance indicators. This method, based on the membership function of fuzzy set theory, quantifies fuzzy information and can flexibly and effectively determine the performance indicators and their weights for multi-attribute decision problems according to actual conditions, thereby transforming multiple objectives into a comprehensive single objective for optimal decision-making. By using the fuzzy optimization method to rationally allocate channels, the one-sidedness and blindness that may occur in traditional channel allocation methods are avoided. This makes the allocation of complex cluster channel resources at vehicle network access points more in line with the actual needs of vehicle clusters, improves the efficiency of channel resource utilization, enhances the flexibility and adaptability of channel allocation, and facilitates the management and optimization of channel resources.

[0065] In step S101 above, the maximum and minimum relative superiority of each vehicle's service level with respect to different performance indicators can be preset. For example, the service level can be determined based on the network service package price of the vehicle. Each service level has a corresponding maximum and minimum relative superiority of different performance indicators. In this embodiment of the invention, the aforementioned performance indicators can be network transmission performance indicators, such as bandwidth and transmission latency. Of course, in some other examples, they can also be other performance indicators such as throughput, bit error rate, and packet loss rate. For channels previously allocated to the cluster in the vehicle network access point, the service time and performance indicator values ​​of the most recent use of the aforementioned channel by different vehicles in the vehicle cluster within the vehicle network access point, as well as their service levels, are collected. By collecting the vehicle service levels, the maximum and minimum relative superiority of the corresponding service level for different performance indicators can be obtained.

[0066] Based on the above steps, we obtain the service time, values ​​of different performance indicators, and vehicle service levels of each vehicle in the vehicle cluster that most recently used the allocated channel in the vehicle network access point. By obtaining the maximum and minimum relative superiority of each service level for different performance indicators, we can determine the weight coefficients of different performance indicators that can be used for cluster channel resource allocation.

[0067] In one specific embodiment, step S102 above, based on the service time of each vehicle in the vehicle cluster that most recently used the allocated channel in the vehicle network access point, the values ​​of different performance indicators, and the vehicle service level, determines the weighting coefficients of different performance indicators for cluster channel resource allocation, specifically including the following steps:

[0068] Based on the service time, performance index values, and vehicle service level of each vehicle in the vehicle cluster that most recently used the allocated channel at the vehicle network access point, and with the objective of minimizing the sum of the squared weighted distances of different vehicles corresponding to channel resources within a preset time period at the vehicle network access point, the weighting coefficients for different performance indices used for cluster channel resource allocation are determined based on the following formula:

[0069] The formula for calculating the sum of the weighted squares of the distances between different vehicles within the service time period corresponding to the channel resources of the vehicle network access point within a preset time period is as follows:

[0070]

[0071] In formula (1), G(ω) i λ) represents the sum of the weighted squared distances of different vehicles within the service time period corresponding to the channel resources of the vehicle network access point within a preset time period, I represents the set of performance indicators used for cluster channel resource allocation, i represents the identifier of the performance indicator used for cluster channel resource allocation, and i∈I, j and l are the identifiers of the channel and the vehicle, respectively, Θ represents the set of channels that have been allocated to the cluster in this vehicle network access point, and the service level ρ of vehicle l is given by the system. l Determine its maximum relative superiority M with respect to performance index i l,i and minimum relative superiority N l,i , t i,j,l R is the service time of the last time vehicle l used channel j in the vehicle-to-everything (V2X) access point. i,j,l ψ represents the performance index value of vehicle l when it last used channel j in the vehicle-to-everything (V2X) access point. j ω is the set of vehicle identifiers constructed from the cluster that most recently used channel j in this vehicle network. i Let λ be the weighting coefficient of performance index i used for trunking channel resource allocation, λ be the Lagrange multiplier, worst(z,i) be the function that selects the worst value of performance index i from z, and let x, y, z be intermediate variables. The performance index comparison function is f. i (x,y) can be represented as follows:

[0072]

[0073] The formula for calculating the weighting coefficients of performance metrics used in trunking channel resource allocation is as follows:

[0074]

[0075] Specifically, in step S102 above, let I be the set of performance indicators used for cluster channel resource allocation, i be the identifier of the performance indicators used for cluster channel resource allocation, and i∈I, j and l are the identifiers of the channel and the vehicle, respectively, Θ be the set of channels in the vehicle network access point that have been allocated to the cluster, and let vehicle l be able to use the service level ρ corresponding to its own package tariff. l Determine its maximum relative superiority M with respect to performance index i l,i and minimum relative superiority N l,i , t i,j,l R is the service time of the last time vehicle l used channel j in this vehicle network access point. i,j,l ψ represents the performance index value of vehicle l when it last used channel j in the vehicle-to-everything (V2X) access point. j ω is the set of vehicle identifiers constructed from the cluster that most recently used channel j in this vehicle network. i Let ω be the weighting coefficient for performance metric i when allocating trunking channel resources. i Should meet By setting the Lagrange multiplier λ, an objective function can be established to minimize the sum of the squared weighted distances of different vehicles within the service time period corresponding to the statistical channel resources of the vehicle network access point during a preset time period: min{G(ω i ,λ)};

[0076] Wherein G(ω) i The formulas for calculating λ are as follows:

[0077]

[0078] Here, worst(z,i) is the function that selects the worst value for performance index i from z, and x, y, and z are intermediate variables, and the performance index comparison function is f. i (x,y) can be represented as follows:

[0079]

[0080] Considering the min{G(ω) i When ,λ)} is true, it should satisfy The ω can be derived i The relationship between the change of λ and the given value is shown in the following equation:

[0081]

[0082] Depend on The general expression for λ can be determined as follows:

[0083]

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

[0085]

[0086] In this embodiment of the invention, the preset time period can be a recent period of time before the current time, such as a period of 5 minutes before the current time.

[0087] In step S103 above, the set of vehicles that enter the service area of ​​the vehicle network access point within the current cluster division interval and send service requests to the vehicle network access point are selected. Based on the number of main road sets in the service area of ​​the vehicle network access point, a main road vehicle cluster corresponding to each main road is constructed. This specifically includes the following steps:

[0088] The location information of the service area of ​​the vehicle network access point is matched with the electronic map to determine the set of main roads in the service area.

[0089] 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 partitioning interval; wherein, the service request sent by each vehicle to the vehicle network access point contains the vehicle's location information.

[0090] Based on the location information of each vehicle in the vehicle set, determine whether the corresponding vehicle is on a main road in the main road set of the service area;

[0091] If so, the vehicle will be added to the main road vehicle cluster of the corresponding main road.

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

[0093] Specifically, step S103 above can be implemented through the following steps:

[0094] S1031: This vehicle network access point is configured to calculate and provide a set of main roads in the service area by matching the location information of the service area with the electronic map, and to determine whether a vehicle is on a main road in the set of main roads in the service area based on the location information provided by the vehicle; let the set of main roads in the service area be 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 Let Ξ be the set of vehicles that enter the service area of ​​the vehicle-to-everything (V2X) access point and send service requests to the V2X access point within the time period [T, T+Δ]. T Where T is the moment when the first vehicle enters the service area of ​​the vehicle network access point and sends a service request to the vehicle network access point within the aforementioned preset time period, and Δ is the cluster division interval period, when the vehicle set Ξ T After vehicle l enters the service area of ​​the vehicle-to-everything (V2X) access point, the service request it sends to the V2X access point includes vehicle l's location information w. l Service level ρ l .

[0095] S1032: For the aforementioned Ξ T Any vehicle l in the middle, through its location information w l It can be determined whether it is a main road in Γ, and thus for any γ in Γ k Then, a set Ξ of vehicles on the main road numbered k can be constructed within the service area of ​​the vehicle network access point during the time period [T, T+Δ]. T,k If the location information of vehicle l is used... l If a vehicle is located on the main road numbered k, it is added to the corresponding vehicle set Ξ. T,k Until the vehicles are assembled T We determine the status of all vehicles on the main road and set the set of vehicle IDs K′ to represent the vehicles present on the main road. If K′=K, then proceed to step S1035. Then proceed to step S1033, where, It is the symbol for the proper inclusion of a set.

[0096] S1033: If there exists a vehicle l that is not added to the Ξ corresponding to any k. T,k The aforementioned vehicles will then be constructed as a set of vehicles not located on main roads. And determine that any k in K′ corresponds to Ξ T,k Average position of vehicles in Where || is the modulo operator.

[0097] S1034: For the set of vehicles Ξ that are not included in any k, T,k Vehicle l in the middle, given Then add vehicle l to the vehicle set Ξ corresponding to the value k mentioned above. T,k .

[0098] S1035: The set of vehicles corresponding to different k in K′ described in step S1032, Ξ T,kThe vehicles included are used to construct a vehicle set, which is then used as cluster E numbered k within the time period [T, T+Δ]. T,k .

[0099] In this embodiment of the invention, 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 are divided into clusters 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 step S104 above, for each main road vehicle cluster, based on the weighting coefficients of the different performance indicators, the values ​​of different performance indicators of different vehicles in the main road vehicle cluster, and the service level of the vehicles, the relative membership degree of using the corresponding available channel is determined, specifically including the following steps:

[0101] For each main road vehicle cluster, based on the weighting coefficients of the different performance indicators, the values ​​of different performance indicators of different vehicles in the main road vehicle cluster, and the service level of the vehicles, with the goal of minimizing the sum of the weighted squared superior distance and the weighted squared inferior distance of the available channels for vehicles in the main road vehicle cluster within the current cluster division interval in the vehicle network access point, the relative membership degree of using the corresponding available channel as the preferred method is determined based on the following formula:

[0102] The formula for calculating the sum of the weighted average of the superior distance and the weighted average of the inferior distance for vehicles in the main road vehicle cluster within the current cluster partitioning interval in the vehicle network access point is as follows:

[0103]

[0104] Among them, E T,k To divide the current cluster into a main road vehicle cluster numbered k within the interval period, μ j Let j be the preferred relative membership degree of the channel.

[0105] In one specific embodiment, the preferred relative membership degree of the channel is determined based on the following calculation formula:

[0106]

[0107] Where num is a function that counts the number of sets or permutations and combinations.

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

[0109] For cluster E with ID k within the time period [T, T+Δ] T,k Let channel j be the preferred channel with a relative membership degree of μ.j The objective function min{H(μ)} can be established to minimize the sum of the weighted squared superior distance and the weighted squared inferior distance for vehicles within the cluster for channel resource j. j This ensures that the overall deviation between the service level of different vehicles and the actual situation is minimized.

[0110] The H(μ) j The calculation formula for ) is as follows:

[0111]

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

[0113]

[0114] Where num is a function that counts the number of sets or permutations and combinations.

[0115] In step S105 above, based on the preferred relative membership degree of each available channel for the main road vehicle cluster, channels are allocated to all main road vehicle clusters according to the number of vehicles in the main road vehicle cluster. This specifically includes the following steps:

[0116] Based on the preferred relative membership degree of each available channel for the main road vehicle cluster, and in descending order of the number of vehicles in the main road vehicle cluster, the channel corresponding to the maximum relative membership degree is selected from the available channels within the current cluster division interval in the vehicle network access point as the channel allocated to the main road vehicle cluster.

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

[0118] S1051: Considering that in the original vehicle-to-everything (V2X) network, each vehicle needed to be allocated a channel and the channel would be reclaimed when the vehicle left the V2X network, and that each cluster needs to be allocated a channel and the channel would be reclaimed after all vehicles in the cluster left the V2X network, the number of channels should meet the allocation requirements. Let J be... T Let K be the set of channel resources available within the time period [T, T+Δ]. For the set K′ of vehicle IDs on the main road, we can set C as the number of new clusters to be created within the time period [T, T+Δ]. T Let num(K′); divide k in K′ according to the number of vehicles D in its corresponding vehicle cluster. T,k The values ​​are sorted in order of size. If there exists a D corresponding to any k... T,kIf the values ​​are the same, the above k are randomly sorted; after all k in K′ are sorted, they are set as the cluster number set K″ of the completed vehicle number sorting; let U be the set of used channel resources for the corresponding time period [T, T+Δ]. T B is the number of iterations for the empty set and the corresponding time interval [T, T+Δ]. T =0;

[0119] S1052: If B T <C T Then, based on the cluster E corresponding to the first position k in the sequence of K″, T,k And the calculation of I described in step S102 If B T =C T If the iteration ends, then the iteration ends.

[0120] S1053: Confirm When the condition is established, the corresponding channel j is added to the U. T The channel j will be assigned to the cluster E corresponding to k at this time. T,k ; to the B T Add 1 and proceed to step S1052.

[0121] To provide a clearer and more complete explanation of the fuzzy optimization-based vehicle network trunking channel allocation method provided in the embodiments of the present invention, and to illustrate the technical effects of the present invention, the following is a specific embodiment, using a certain vehicle network employing... Figure 2 The document provides the vehicle-to-everything (V2X) access point and sets the service area covered by that access point. Figure 2 Taking the map area shown as an example, the process of allocating vehicle network cluster channels is explained in detail.

[0122] Step 1: Corresponding to step S101 above, for a certain vehicle network, each vehicle can determine its maximum and minimum relative superiority with respect to different performance indicators based on its service level corresponding to its own package tariff. For channels previously allocated to the cluster in the vehicle network access point, collect the service time, performance indicator values, and service level of the most recent use of the aforementioned channels by different vehicles in the cluster within the vehicle network access point. Based on the vehicle service level, obtain the maximum and minimum relative superiority with respect to different performance indicators for the associated vehicles.

[0123] As shown in Table 1, the service levels corresponding to the vehicle's own package tariff are divided into Level 1 and Level 2. In this embodiment, the performance indicators used for trunking channel resource allocation are bandwidth (1) and transmission delay (2), where the numbers in parentheses are the performance indicator identifiers used for trunking channel resource allocation. Different service levels of the vehicle correspond to the maximum and minimum relative superiority of different performance indicators.

[0124] Table 1

[0125] Service Level Performance indicators Maximum relative superiority Minimum relative superiority 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 within the vehicle network access point for the most recent use of the above channel are shown. The unit of bandwidth is Mb / s, the unit of latency is ms, and the unit of service time is min.

[0127] Table 2

[0128]

[0129] Step 2: Corresponding to step S102 above, based on the service time of each vehicle in the vehicle cluster that most recently used the allocated channel in the vehicle network access point, the values ​​of different performance indicators, and the vehicle service level, with the goal of minimizing the sum of the squared weighted distances of different vehicles corresponding to the channel resources within the preset time period of the vehicle network access point, the weight coefficients of different performance indicators that can be used for cluster channel resource allocation are determined.

[0130] Based on the data obtained from Tables 1 and 2, the weighting coefficients for bandwidth and transmission delay performance indicators that can be used for trunk channel resource allocation are shown in Table 3.

[0131] Table 3

[0132] Performance indicators Weighting coefficient bandwidth 0.62 Transmission delay 0.38

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

[0134] Figure 2 By setting the vehicle network access point, the location information of the service area can be matched with the electronic map to give the set of main roads in the service area as {Huashan Road 1 (1), Huashan Road 2 (2), Huashan Road 3 (3)}, where the numbers in parentheses are the numbers of the main roads, and in Figure 2 The time period is given in [16:00,16:03] 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 is {51, 29, 78, 34, 42, 90, 55, 67}, and the service level set corresponding to each vehicle is {2, 1, 1, 3, 1, 2, 1, 3}.

[0135] Table 4 provides the time periods. [16:00,16:03]The clusters corresponding to different main road numbers and the vehicle status they contain;

[0136] Table 4

[0137] Cluster Included vehicle numbers Vehicle cluster corresponding to main road 1 51，78，29 Vehicle clusters corresponding to main road 2 34，42 Vehicle clusters corresponding to main road 3 55，67，90

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

[0139] Figure 3 The values ​​of different performance indicators for different vehicles in the main road vehicle cluster, and the service level of the vehicles, are determined based on the weighting coefficients of the two performance indicators given in Table 3 above, the values ​​of different performance indicators for different vehicles in the main road vehicle cluster, and the service level of the vehicles. The objective is to minimize the sum of the squared weighted superior distance and the squared weighted inferior distance of the available channels for vehicles in the main road vehicle cluster within the current cluster division interval period in the vehicle network access point. [16:00,16:03] The channels that can be used by the clusters corresponding to main roads 1, 2 and 3 are the preferred relative membership degrees.

[0140] When selecting the channel corresponding to the highest relative membership degree among the vehicles in the main road vehicle cluster, arranged from largest to smallest, as the channel assigned to the main road vehicle cluster, since the number of vehicles in main road 1 and main road 3 is the same, they can be randomly ordered. In this embodiment, the cluster corresponding to main road 1 is taken as the first cluster. Figure 3 It can be seen that, during this time period, for the cluster corresponding to main road 1, the preferred available channel is channel 13, which corresponds to the highest relative membership degree. Therefore, this channel can be allocated to this cluster. Then, during this time period, for the cluster corresponding to main road 3, the preferred available channel is channel 16, which corresponds to the highest relative membership degree. Therefore, this channel can be allocated to this cluster. Finally, during this time period, for the cluster corresponding to main road 2, the preferred available channel is channel 12, which corresponds to the highest relative membership degree. Therefore, this channel can be allocated to this cluster. Thus, the time periods in this vehicle-to-everything (V2X) network... [16:00,16:03] The channels available for the clusters corresponding to Inner Main Road 1, Main Road 2 and Main Road 3 are Channel 13, Channel 12 and Channel 16, respectively.

[0141] Example 2

[0142] Based on the same inventive concept, embodiments of the present invention also provide a vehicle network trunking channel allocation device based on fuzzy optimization, referring to... Figure 4 As shown, the device includes:

[0143] The data acquisition module 101 is used to collect the service time, values ​​of different performance indicators, and vehicle service level of each vehicle in the vehicle cluster that most recently used the allocated channel in the vehicle network access point; wherein, the vehicle service level is associated with the maximum and minimum relative superiority of the vehicle with respect to different performance indicators; the allocated channel is the channel that the vehicle network access point has previously allocated to the vehicle cluster for use.

[0144] The first calculation module 102 is used to determine the weighting coefficients of different performance indicators when allocating cluster channel resources based on the service time of each vehicle in the vehicle cluster that most recently used the allocated channel in the vehicle network access point, the values ​​of different performance indicators, and the vehicle service level.

[0145] The cluster construction module 103 is used to construct a main road vehicle cluster corresponding to each main road according to the number of main road sets in the service area of ​​the vehicle network access point within the current cluster division interval period.

[0146] The second calculation module 104 is used to determine the relative membership degree of using the corresponding available channel as the preferred method for each main road vehicle cluster, based on the weight coefficients of the different performance indicators, the values ​​of different performance indicators of different vehicles in the main road vehicle cluster, and the service level of the vehicles.

[0147] The channel allocation module 105 is used to allocate channels to all main road vehicle clusters according to the relative membership degree of each available channel corresponding to the main road vehicle cluster of each main road, based on the number of vehicles in the main road vehicle cluster.

[0148] Example 3

[0149] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium storing a computer program / instruction thereon, which, when executed by a processor, implements the fuzzy optimization-based vehicle network cluster channel allocation method described in Embodiment 1 above.

[0150] Example 4

[0151] Based on the same inventive concept, embodiments of the present invention also provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements the fuzzy optimization-based vehicle network cluster channel allocation method described in Embodiment 1 above.

[0152] Example 5

[0153] Based on the same inventive concept, this embodiment of the invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory. When the processor executes the computer program, it implements the fuzzy optimization-based vehicle network cluster channel allocation method described in Embodiment 1 above.

[0154] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0155] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0156] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0157] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

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

Claims

1. A method for cluster channel allocation in vehicle-to-everything based on fuzzy preference, characterized in that, The method comprises the following steps: Collecting the service time, the values of different performance indicators, and the service level of each vehicle in the vehicle cluster that last used the assigned channel in the vehicle Internet access point; the service level of the vehicle is associated with the maximum relative optimal degree and the minimum relative optimal degree of the vehicle with respect to different performance indicators; each vehicle determines its maximum relative optimal degree and minimum relative optimal degree with respect to different performance indicators through the service level corresponding to its own package cost; the assigned channel is a channel that has been assigned to the vehicle cluster by the vehicle Internet access point; Based on the service time, the values of different performance indicators, and the service level of each vehicle in the vehicle cluster that last used the assigned channel in the vehicle Internet access point, the weight coefficients of different performance indicators for cluster channel resource allocation are determined; A set of vehicles that enter the service area of the vehicle Internet access point and send service demands to the vehicle Internet access point within the current cluster division interval period is constructed according to the number of trunk road sets in the service area of the vehicle Internet access point to form trunk road vehicle clusters corresponding to each trunk road; For each trunk road vehicle cluster, the relative membership degree of the corresponding available channel is determined as the optimal one according to the weight coefficients of different performance indicators, the values of different performance indicators of different vehicles in the trunk road vehicle cluster, and the service level of the vehicle, with the minimum sum of the weighted optimal distance square and the weighted poor distance square of the available channel to the vehicles in the trunk road vehicle cluster as the target in the current cluster division interval period in the vehicle Internet access point; According to the relative membership degree of each available channel for the trunk road vehicle cluster of each trunk road, the channel is allocated to the trunk road vehicle clusters of all trunk roads according to the number of vehicles in the trunk road vehicle cluster.

2. The method of claim 1, wherein, The method comprises the following steps: Based on the service time, the values of different performance indicators, and the service level of each vehicle in the vehicle cluster that last used the assigned channel in the vehicle Internet access point, the weight coefficients of different performance indicators for cluster channel resource allocation are determined according to the following formula: The calculation formula of the sum of the weighted optimal distance square of the channel resource corresponding to different vehicles within the service time in the preset time period of the vehicle Internet access point is as follows: ； wherein, is the sum of the weighted distance squared of different vehicles in the service time of the channel resource in the preset time period of the vehicle-to-everything access point, is a set of performance indicators for cluster channel resource allocation, is an identifier of a performance indicator for cluster channel resource allocation, and , and are identifiers of a channel and a vehicle, respectively, is a set of channels in the vehicle-to-everything access point that have been allocated to clusters for use, and a vehicle is a service level to determine the maximum relative priority and the minimum relative priority of the vehicle-to-everything access point with respect to the performance indicator , is the service time of the channel last used by the vehicle in the vehicle-to-everything access point, is the performance indicator value when the channel was last used by the vehicle in the vehicle-to-everything access point, is a set constructed from the vehicle identifiers included in the cluster that last used the channel in the vehicle-to-everything, is a weight coefficient of the performance indicator for cluster channel resource allocation, is a Lagrange coefficient, is a function that selects the worst value of the performance indicator from , provided that , , are intermediate variables, and the performance indicator comparison function can be expressed as follows: ； The calculation formula of the weight coefficient of the performance indicator for cluster channel resource allocation is as follows: 。 3. The method of claim 2, wherein, Based on the following formula, the relative membership degree of the corresponding available channel is determined as the optimal one: The calculation formula of the sum of the weighted optimal distance square and the weighted poor distance square of the available channel to the vehicles in the trunk road vehicle cluster in the current cluster division interval period in the vehicle Internet access point is as follows: ； wherein, is the current cluster division interval period numbered for the arterial vehicle cluster, is the channel is the preferred relative membership.

4. The method of claim 3, wherein, The method further comprises the following steps: Based on the following calculation formula, the relative membership degree of the channel is determined as the optimal one: ； wherein is a function to count the number of statistical sets or permutations.

5. The method of claim 1, wherein, The relative membership degrees of the trunk road vehicle clusters corresponding to each available channel are assigned to the trunk road vehicle clusters of all trunk roads in descending order of the number of vehicles in the trunk road vehicle clusters. The relative membership degrees of the trunk road vehicle clusters corresponding to each available channel are assigned to the trunk road vehicle clusters of all trunk roads in descending order of the number of vehicles in the trunk road vehicle clusters.

6. The method according to any one of claims 1 to 5, characterized in that, The set of vehicles entering the service area of the vehicle networking access point and issuing service demands to the vehicle networking access point within the current cluster division interval is divided into trunk road vehicle clusters corresponding to each trunk road in the set of trunk roads in the service area of the vehicle networking access point. The set of trunk roads in the service area of the vehicle networking access point is determined by matching the location information of the service area of the vehicle networking access point with an electronic map. The set of vehicles entering the service area of the vehicle networking access point and issuing service demands to the vehicle networking access point within the current cluster division interval is obtained; wherein the location information of each vehicle is included in the service demand issued to the vehicle networking access point. It is determined whether each vehicle in the set of vehicles is on one of the set of trunk roads in the service area of the vehicle networking access point according to the location information of the vehicle. If yes, the vehicle is added to the trunk road vehicle cluster corresponding to the trunk road. For vehicles that have not been added to any trunk road vehicle cluster, the average position of all vehicles in each non-empty trunk road vehicle cluster is determined, the difference between the location information of the vehicle and the average position of the vehicle corresponding to each non-empty trunk road vehicle cluster is calculated, and the vehicle is added to the trunk road vehicle cluster corresponding to the smallest difference. 7.A cluster channel allocation apparatus based on fuzzy preference for Internet of Vehicles, characterized in that, The data acquisition module is configured to 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 last time 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; each vehicle determines its maximum relative membership degree and minimum relative membership degree with respect to different performance indicators through the service level corresponding to its own package cost; and the allocated channel is a channel that has been allocated by the vehicle networking access point to a vehicle cluster for use. The first calculation module is configured to determine 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 level of each vehicle in the vehicle cluster that has used the allocated channel last time in the vehicle networking access point. The cluster construction module is configured to divide the set of vehicles entering the service area of the vehicle networking access point and issuing service demands to the vehicle networking access point within the current cluster division interval into trunk road vehicle clusters corresponding to each trunk road in the set of trunk roads in the service area of the vehicle networking access point. ​ a second calculating module, configured to, for each trunk road vehicle cluster of the trunk roads, determine a relative membership degree corresponding to each available channel preferred by the trunk road vehicle cluster according to the weight coefficients of the different performance indexes, the values of the different performance indexes of different vehicles in the trunk road vehicle cluster, and the service levels of the vehicles, and taking the minimum of the sum of the weighted superior distance square and the weighted inferior distance square of the available channel in the current cluster division interval period of the vehicle internet access point as the target; a channel allocation module, configured to allocate channels to all trunk road vehicle clusters of the trunk roads according to the relative membership degrees corresponding to each available channel preferred by each trunk road vehicle cluster of the trunk roads, and according to the number of vehicles in the trunk road vehicle cluster.

8. A computer readable storage medium having stored thereon computer programs / instructions, characterized in that, The computer program / instruction is executed by the processor to implement the fuzzy preference-based vehicle internet cluster channel allocation method in any one of claims 1-6.

9. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instruction is executed by the processor to implement the fuzzy preference-based vehicle internet cluster channel allocation method in any one of claims 1-6.

10. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program comprises instructions that, when executed by the processor, cause the processor to perform the method of any one of claims 1-9. The processor executes the computer program to implement the fuzzy preference-based vehicle internet cluster channel allocation method in any one of claims 1-6.

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