On-demand service matching method, device and equipment in 6G air-space-ground vehicle network

Abstract

The present invention provides a method, apparatus, and device for matching on-demand services in a 6G air-space-ground vehicle network. The method includes: virtualizing physical network resources to obtain virtual network resources using SDN and NFV technologies; obtaining user requirements and performing fine-grained demand analysis based on scenario recognition to obtain fine-grained user requirements; performing initial grouping of all users based on the virtual network resources and fine-grained user requirements to obtain multiple initial user groups; modeling the problem using the virtual network resources, fine-grained user requirements, and multiple initial user groups to obtain an optimization problem model; reconstructing the optimization problem model to obtain a Transmission-Metric Service (TMS) framework; performing R-TMSC simplification on the TMSC framework to obtain a preference list; and performing on-demand resource matching among users, initial user groups, and path mapping using a three-way matching algorithm and the preference list to obtain resource allocation results for the actual user groups.

Description

Technical Field

[0001] The present invention relates to the field of vehicle networking technology, and specifically to a method, device and equipment for on-demand service matching in a 6G air-space-ground vehicle networking. Background Art

[0002] In 6G technology, the integration of space-based networks, air-based networks, and ground-based networks (space-ground-integrated vehicular networks) will greatly enhance the communication coverage and system robustness of the Internet of Vehicles. At the same time, by integrating Network Function Virtualization (NFV) and Software Defined Networking (SDN) technologies to achieve the sequencing of virtual networks and build service function chains, SDN / NFV-enabled 6G space-air-ground integrated vehicular networks (SAGIVNs) can further provide customized services for vehicle users with growing demands for low latency and high reliability in a way that reduces functional configuration costs and improves heterogeneous physical resource utilization.

[0003] In the field of Internet of Vehicles (IoV), existing research on space-air-ground network convergence focuses on heterogeneous resource characteristic analysis, virtual network function (VNF) placement, VNF scheduling, and network routing planning. For example, prior art 1, "Service-oriented network resource orchestration in space-air-ground integrated network," considers limited SAGIN resources and heterogeneous requirements, designing an iterative optimization algorithm to maximize network revenue and minimize deployment costs by jointly optimizing rate adaptation, SFC orchestration, and radio resource allocation. Prior art 2, "Cost-aware dynamic SFC mapping and scheduling in SDN / NFV-enabled space–air–ground integrated networks for Internet of Vehicles," studies the online dynamic VNF mapping and scheduling problem in space–air–ground integrated IoVs. By optimizing VNF processing time, VNF mapping, and VNF re-instantiation, it maximizes the service provider's total profit. Prior art 3, "Space-air-ground integrated network resource allocation based on service function chain," focuses on a service-oriented SFC SAGIN architecture and designs a latency-prediction-based SFC mapping strategy to minimize system latency.

[0004] Therefore, the existing technology matches user needs in a macro-holistic matching manner, resulting in low resource matching efficiency and inability to achieve accurate matching of user needs. Summary of the Invention

[0005] In order to solve the above-mentioned problems existing in the prior art, the present invention provides a method, device and equipment for on-demand service matching in a 6G air-space-ground vehicle network.

[0006] The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0007] In a first aspect, the present invention provides a method for matching on-demand services in a 6G air-ground-vehicle network, comprising:

[0008] Through SDN and NFV technologies, physical network resources are virtualized to create virtual network resources. Physical network resources are composed of base stations, drones, and satellites. Virtual network resources include segment information, attribute information, and attribute categories. Virtual network resources respond to user needs in the form of service chains.

[0009] Obtain user needs and conduct fine-grained demand analysis based on scenario identification to obtain fine-grained user needs. The fine-grained demand analysis includes analysis of user experience, user entities, and scenarios.

[0010] Perform initial grouping of all users based on virtual network resources and fine-grained user requirements to obtain multiple initial user groups;

[0011] Utilize virtual network resources, fine-grained user requirements, and multiple initial user groups to model the problem and obtain an optimized problem model;

[0012] Reconstruct the optimization problem model to obtain the TMSC framework;

[0013] The TMSC framework is simplified by R-TMSC to obtain a preference list; the preference list is the preference relationship between users, actual user groups and path mappings;

[0014] The three-party matching algorithm and preference list are used to match users, initial user groups, and path mappings on demand to obtain the resource allocation results of the actual user group; the actual user group is the result of the three-party matching optimization of the initial user group.

[0015] Optionally, the user experience includes: user entertainment experience and user safety experience;

[0016] User entities include: user speed, user location, channel condition, user driving delay, and user driving stability;

[0017] Scenes include: aerial, rural, urban, and desert.

[0018] Optionally, the optimization problem model is restructured to obtain a TMSC framework, including:

[0019] Processing the node and link mapping in the segment information into a path mapping, obtaining a simplified problem; the simplified problem is a simplified result on the path mapping and the initial user group;

[0020] The simplified problem is reconstructed based on the TMSC model to obtain the TMSC framework.

[0021] Alternatively, the optimization problem model is expressed as:

[0022] P1:maxx,y,η o1o2;

[0023] Where x represents whether the virtual network resource VNF provides services for the sth service flow, y represents the binary indication of the link mapping process, η represents the number of service flows included in each initial user group, o1 represents user satisfaction, and o2 represents energy efficiency;

[0024]

[0025] Among them, N sat Indicates the total number of business flows that meet the requirements, N ser represents the total number of business flows, γ total represents the consumption of virtual network resources of all initial user groups, N gr represents the number of initial user groups, η w Indicates the number of service flows included in the wth initial user group, N s Indicates the number of all users, Indicates the maximum communication capacity of the virtual network resources. The communication resource consumption required for the path mapping of the qth service flow in the wth initial user group, represents the computing resource consumption required for the node virtual mapping of the qth service flow in the wth initial user group, Indicates the maximum computing capacity of the virtual network resources. Represents an integer.

[0026] Optionally, the TMSC framework is represented as:

[0027]

[0028] Indicates the stable matching result of triples, p i represents the i-th path mapping, Represents a collection of path mappings, Indicates that it contains p i The number of triplets, u k represents the kth actual user group, Indicates the actual user group set, Indicates that it contains u k The number of triplets, ∈ k Indicates the maximum number of users that the kth actual user group can contain, s j represents the jth user, Represents a user collection, Indicates that it contains s j The number of triples, BT(p i ,s j ,u k ) indicates pi , s j ,u k The obstacle triplet matching results.

[0029] Optionally, the preference list includes: a preference relationship between path mapping and user, a preference relationship between user and actual user group, and a preference relationship between actual user group and path.

[0030] Optionally, the preference relationship between the path mapping and the user is expressed as:

[0031]

[0032] represents the preference relationship between the i-th path mapping and the j-th user, PR represents the business flow model, Attributes representing path mappings, Represents a collection of path mappings, Represents the overall properties of PR and path mapping The degree of match between them.

[0033] Optionally, the preference relationship between a user and an actual user group is expressed as:

[0034]

[0035] represents the preference relationship between the jth user and the kth actual user group, Represents the PR and actual user group set The degree of match between them.

[0036] Optionally, the preference relationship of the actual user group to the path is expressed as:

[0037]

[0038] represents the preference relationship of the kth actual user group to the i-th path.

[0039] In a second aspect, the present invention provides an on-demand service matching device in a 6G air-space-ground vehicle network, comprising: a virtual processing unit, an analysis unit, a grouping unit, a modeling unit, a problem reconstruction unit, a simplified processing unit, and a matching unit;

[0040] The virtual processing unit is used to virtualize physical network resources using SDN and NFV technologies to generate virtual network resources. Physical network resources are network resources formed by base stations, drones, and satellites. Virtual network resources include segment information, attribute information, and attribute categories. Virtual network resources respond to user needs in the form of service chains.

[0041] The analysis unit is used to obtain user needs and perform fine-grained needs analysis on the user needs based on scenario identification to obtain fine-grained user needs; wherein the fine-grained needs analysis includes analysis of user experience, user entities and scenarios;

[0042] The grouping unit is used to: perform initial grouping processing on all users based on virtual network resources and fine-grained user requirements to obtain multiple initial user groups;

[0043] The modeling unit is used to: use virtual network resources, fine-grained user requirements and multiple initial user groups to model the problem and obtain an optimized problem model;

[0044] The problem reconstruction unit is used to: reconstruct the optimization problem model to obtain the TMSC framework;

[0045] The simplification processing unit is used to: perform R-TMSC simplification processing on the TMSC framework to obtain a preference list; the preference list is a preference relationship between users, actual user groups and path mappings;

[0046] The matching unit is used to use the three-party matching algorithm and preference list to match users, initial user groups and path mappings on demand to obtain resource allocation results for the actual user group; the actual user group is the result of the three-party matching optimization of the initial user group.

[0047] In a third aspect, the present invention provides an on-demand service matching device in a 6G air-space-ground-vehicle network, comprising: a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor. When the on-demand service matching device in the 6G air-space-ground-vehicle network is running, the processor and the storage medium communicate through the bus, and the processor executes the machine-readable instructions to perform the steps of the on-demand service matching method in the 6G air-space-ground-vehicle network as described in the first aspect above.

[0048] The present invention provides a method, device and equipment for matching on-demand services in a 6G air-space-ground vehicle network. Among them, a method for matching on-demand services in a 6G air-space-ground vehicle network includes: virtualizing physical network resources to obtain virtual network resources through SDN and NFV technologies; wherein the physical network resources are network resources formed by base stations, drones and satellites; the virtual network resources include: segment information, attribute information and attribute categories; the virtual network resources respond to user needs in the form of a service chain; obtain user needs and perform fine-grained demand analysis on the user needs based on scenario recognition to obtain fine-grained user needs; wherein the fine-grained demand analysis includes: analysis of user experience, user entities and scenarios; based on virtual network resources and fine-grained user needs All users are initially grouped to obtain multiple initial user groups; problem modeling is performed using virtual network resources, fine-grained user requirements, and multiple initial user groups to obtain an optimization problem model; the optimization problem model is reconstructed to obtain a TMSC framework; the TMSC framework is simplified by R-TMSC to obtain a preference list; the preference list is the preference relationship between users, actual user groups, and path mappings; resources are matched on demand among users, initial user groups, and path mappings using a three-party matching algorithm and the preference list to obtain resource allocation results for actual user groups; the actual user groups are the results of the three-party matching optimization of the initial user groups. In an embodiment of the present invention, first, physical network resources are virtualized using SDN and NFV technologies to obtain virtual network resources, establishing a foundation for optimizing actual resources; second, fine-grained demand analysis based on scenario recognition is performed on user requirements, and on this basis, an optimization problem model is established to promote on-demand and accurate resource matching; finally, a three-party matching algorithm is used to calculate and solve the optimization problem model, ultimately obtaining a stable three-party resource allocation result, thereby improving overall resource matching efficiency and matching degree with user requirements.

[0049] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A flowchart of a method for matching on-demand services in a 6G air-space-ground vehicle network provided by an embodiment of the present invention;

[0051] Figure 2 A schematic diagram of scene-based recognition requirements provided by an embodiment of the present invention;

[0052] Figure 3 is a user satisfaction result diagram provided by an embodiment of the present invention;

[0053] Figure 4 A diagram showing the user demand-service on-demand matching results provided by an embodiment of the present invention;

[0054] Figure 5 A diagram showing the network energy efficiency results provided by an embodiment of the present invention;

[0055] Figure 6 A graph showing overall network performance results provided by an embodiment of the present invention;

[0056] Figure 7 A schematic diagram of the structure of an on-demand service matching device in a 6G air-space-ground vehicle network provided by an embodiment of the present invention;

[0057] Figure 8 A schematic structural diagram of an on-demand service matching device in a 6G air-space-ground vehicle network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The present invention will be further described in detail below with reference to specific examples, but the embodiments of the present invention are not limited thereto.

[0059] In order to improve the overall resource matching efficiency and the matching degree with user needs, an embodiment of the present invention provides an on-demand service matching method in a 6G air-space-ground vehicle network. Figure 1 The present invention provides a flowchart of a method for matching on-demand services in a 6G air-ground vehicle network. Figure 1 As shown, the method includes:

[0060] S101. Virtualize physical network resources to obtain virtual network resources using SDN and NFV technologies.

[0061] SDN (Software-Defined Networking) is a new network architecture approach whose core concept is to separate the control plane from the data plane of network devices. NFV (Network Function Virtualization) involves migrating network functions (such as firewalls and load balancers) that traditionally run on dedicated hardware to virtualized implementation on standard server hardware. By running physical network resources on a general-purpose computing platform, NFV provides greater flexibility and scalability.

[0062] Among them, physical network resources are network resources formed by base stations, drones and satellites; virtual network resources include: segment information, attribute information and attribute categories; virtual network resources respond to user needs in the form of service chains.

[0063] In the embodiment of the present invention, the virtual network resources are modeled as a weighted directed graph to obtain segment information. in and They represent the set of all network infrastructure (nodes) and physical links (links), respectively. Furthermore, according to the different subjects, the network infrastructure is divided into three categories, namely base stations, drones, and satellites.

[0064] In addition, different virtual network resources have different attribute information. To simplify the subsequent model, the attribute information is divided into three levels: "low, medium, and high". For example, the delay "M" indicates that the delay provided by certain virtual network resources is at a medium level. Furthermore, the overall attribute information of the virtual network resources can be obtained by taking the Cartesian product of the attribute sets of the current node and all nodes. For the specific formula, please refer to formula (4) in the reference "A dynamic and scalable user-centric route planning algorithm based on polychromatic sets theory" by P. Li, X. Wang, H. Gao, etc.

[0065] Based on different classification criteria, attribute information can be divided into multiple categories. In this embodiment of the present invention, eight attributes of network resources are considered, each of which is divided into three categories, including layer (ground, air, space), latency (high, medium, low), bandwidth (high, medium, low), energy (high, medium, low), cost (high, medium, low), security (high, medium, low), virtual node idleness (high, medium, low), and virtual link idleness (high, medium, low).

[0066] S102: Obtain user needs and perform fine-grained needs analysis on the user needs based on scenario recognition to obtain fine-grained user needs.

[0067] Among them, fine-grained demand analysis includes: analysis of user experience, user entities and scenarios.

[0068] Optionally, the user experience includes: user entertainment experience and user safety experience;

[0069] User entities include: user speed, user location, channel condition, user driving delay, and user driving stability;

[0070] Scenes include: aerial, rural, urban, and desert.

[0071] The fine-grained demand analysis based on scene recognition is as follows:

[0072] For example, the set of user VU is Where n1 represents the first VU, N n is the total number of VUs. Assume that a VU needs at most one service at a time.s Expressed as:

[0073]

[0074] The service chain (SFC) representing the network function corresponding to the s-th service flow;

[0075] in Indicates the first virtual network resource (VNF), Indicates the number of VNFs in the sth service flow. s It is the set of user performance requirements for the sth service flow, including delay, cost, reliability, bandwidth, etc.

[0076] This embodiment of the present invention proposes a fine-grained requirements analysis for scene recognition, consisting of three aspects: user experience, user entities, and scenarios. User experience refers to the experience of the virtual user (VU), such as entertainment (in-car video, in-car gaming, etc.) or safety requirements (autonomous driving). User entities refer to the VU's speed, location, channel conditions, and the user's subjective experience. Assume that the VU's location and speed can be obtained using technologies such as radar and GPS, and channel conditions can be obtained using ray tracing. Regarding scenarios, different VUs have different requirements in different scenarios. For example, when it comes to latency or stability requirements for autonomous driving, users of flying cars have the highest requirements, followed by densely populated urban areas, then sparsely populated mountainous rural areas, and finally sparsely populated desert areas. To simplify the model, user experience, user entities, and scenarios can be categorized into three levels: low, medium, and high, to define requirements based on scene recognition. For example, by combining high-speed user entities with urban scenarios, the universal threshold requirement for medium latency (a uniformly defined requirement) can be converted to a low-latency requirement. It is worth noting that this embodiment of the present invention considers the safety requirements of virtual users based on scene recognition to be independent of the user's individual subjective experience. It can be understood that fine-grained demand analysis can identify user needs more accurately than existing technologies, thereby facilitating on-demand resource allocation.

[0077] In addition, in order to ensure user satisfaction and maximize the use of virtual network resources, resources with different attributes are combined according to the needs of VU. Figure 2 This is a schematic diagram of scene recognition requirements provided by an embodiment of the present invention. Figure 2 As shown, in fine-grained demand analysis, this invention divides user experience and user entities into k different levels and the environment into different regions. This classification helps accurately identify the needs of specific scenarios, and through the integration of the three, fine-grained requirements are obtained. Furthermore, the polychromatic set PSet reveals multi-dimensional resources and multi-standard services. Based on accurate scenario identification, the ultimate on-demand mapping of requirements and resources is achieved.

[0078] Will Represented as a combination of different virtual network resources with different attributes, where Indicates the virtual network resources involved in the sth service flow, express The properties of the virtual network resources contained in the . s ∈{0,1} indicates whether the user demand R of the sth service flow is met s , z s is a binary variable and can be expressed as:

[0079]

[0080] in For user needs s A function of the allocated virtual network resources, with a value ranging from 0 to 1. s The maximum tolerance for violation of user requirements. sat ,exist:

[0081]

[0082] Furthermore, the embodiment of the present invention adopts the VU grouping method to allocate network resources by group based on similar scene recognition requirements and network resource constraints, thereby improving the deployment efficiency of virtual network resources. To simplify the model, it is assumed that the number of initial user groups is equal to the number of business flows, and the characteristics of the initial user groups are consistent with the characteristics of the business flows. In the grouping process, the similarity δ between the sth business flow and the characteristics of the allocated wth initial user group is considered. s,w and resource constraints of the initial user group, specifically allocating virtual network resources to the initial user group in sequence and eliminating the initial user group that has been merged into other groups.

[0083] In the implementation process of the embodiment of the present invention, it is assumed that for each VNF, there is only one node providing services for it. At the same time, the service flow inlet and outlet link constraints need to be satisfied.

[0084] S103 : Performing initial grouping processing on all users based on virtual network resources and fine-grained user requirements to obtain multiple initial user groups.

[0085] S104: Utilize virtual network resources, fine-grained user requirements, and multiple initial user groups to perform problem modeling to obtain an optimized problem model.

[0086] Alternatively, the optimization problem model is expressed as:

[0087] P1:max x,y,η o1o2;

[0088] Where x represents whether the virtual network resource VNF provides services for the sth service flow, y represents the binary indication of the link mapping process, η represents the number of service flows included in each initial user group, o1 represents user satisfaction, and o2 represents energy efficiency;

[0089]

[0090] Among them, N sat Indicates the total number of business flows that meet the requirements, N ser represents the total number of business flows, γ total represents the consumption of virtual network resources of all initial user groups, N gr represents the number of initial user groups, η w Indicates the number of service flows included in the wth initial user group, N s Indicates the number of all users, Indicates the maximum communication capacity of the virtual network resources. The communication resource consumption required for the path mapping of the qth service flow in the wth initial user group, represents the computing resource consumption required for the node virtual mapping of the qth service flow in the wth initial user group, Indicates the maximum computing capacity of the virtual network resources. Represents an integer.

[0091] In addition, in the embodiment of the present invention, the value of y is generally 0 or 1.

[0092] S105. Reconstruct the optimization problem model to obtain a TMSC framework.

[0093] Optionally, S105 may specifically include:

[0094] Processing the node and link mapping in the segment information into a path mapping, obtaining a simplified problem; the simplified problem is a simplified result on the path mapping and the initial user group;

[0095] The simplified problem is reconstructed based on the TMSC model to obtain the TMSC framework.

[0096] Optionally, the TMSC framework is represented as:

[0097]

[0098] Indicates the stable matching result of triples, p i represents the i-th path mapping, Represents a collection of path mappings, Indicates that it contains p iThe number of triplets, u k represents the kth actual user group, Indicates the actual user group set, Indicates that it contains u k The number of triplets, ∈ k Indicates the maximum number of users that the kth actual user group can contain, s j represents the jth user, Represents a user collection, Indicates that it contains s j The number of triples, BT(p i ,s j ,u k ) indicates p i , s j ,u k The obstacle triplet matching results.

[0099] S106: Perform R-TMSC simplification processing on the TMSC framework to obtain a preference list.

[0100] In the embodiment of the present invention, the R-TMSC is also called a restricted three-sided matching with size and cyclic preference model (R-TMSC).

[0101] The preference list is about the preference relationship between users, actual user groups and path mappings.

[0102] Optionally, the preference list includes: a preference relationship between path mapping and user, a preference relationship between user and actual user group, and a preference relationship between actual user group and path.

[0103] Optionally, the preference relationship between the path mapping and the user is expressed as:

[0104]

[0105] represents the preference relationship between the i-th path mapping and the j-th user, PR represents the business flow model, Attributes representing path mappings, Represents a collection of path mappings, Represents the overall properties of PR and path mapping The degree of match between them.

[0106] Optionally, the preference relationship between a user and an actual user group is expressed as:

[0107]

[0108] represents the preference relationship between the jth user and the kth actual user group, Represents the PR and actual user group set The degree of match between them.

[0109] Optionally, the preference relationship of the actual user group to the path is expressed as:

[0110]

[0111] represents the preference relationship of the kth actual user group to the i-th path.

[0112] S107: Use the three-party matching algorithm and the preference list to perform resource on-demand matching on the user, the initial user group, and the path mapping to obtain a resource allocation result for the actual user group.

[0113] The actual user group is the result of three-way matching optimization of the initial user group.

[0114] An embodiment of the present invention provides an on-demand service matching method in a 6G air-space-ground vehicle network. First, physical network resources are virtualized through SDN and NFV technologies to obtain virtual network resources, establishing a foundation for optimizing actual resources. Second, a fine-grained demand analysis of user needs based on scenario recognition is performed, and an optimization problem model is established on this basis, thereby promoting on-demand and precise matching of resources. Finally, a three-party matching algorithm is used to computationally solve the optimization problem model, ultimately obtaining a stable resource allocation result for the three parties, thereby improving the overall resource matching efficiency and the matching degree with user needs.

[0115] In order to verify the effectiveness of the on-demand service matching method in the 6G air-space-ground vehicle network provided by the embodiment of the present invention, a simulation experiment was also conducted in the embodiment of the present invention. Figure 3 This is a user satisfaction result diagram provided by an embodiment of the present invention. Specifically, refer to Figure 3 In Figure (a), the number of users is increased from 50 to 100 and the user satisfaction ratios corresponding to the method of the present invention and the existing service matching methods (clusterA and costAware) are analyzed. Figure 3Figure (a) shows that as the number of users gradually increases, user satisfaction gradually decreases. This is because, with limited resources, the increase in users leads to resource scarcity, which in turn affects the satisfaction of some users. At the same time, it can be seen that compared with other existing service matching methods, the user satisfaction obtained by the algorithm of the present invention is always the highest. This is because the algorithm of the present invention comprehensively analyzes the multi-dimensional characteristics of user needs and resources and allocates resources in groups, thereby achieving on-demand matching of network resources and significantly improving network efficiency. As a result, with the same resources, more users receive service responses, resulting in higher user satisfaction. Figure 3 Figure (b) shows that user satisfaction increases significantly with the increase in available links. This is because, given a fixed user demand, increasing available resources can increase the probability of service response, thereby improving user satisfaction. Furthermore, the proposed method outperforms existing algorithms, clusterA and costAware.

[0116] Figure 4 This is a diagram showing the user demand-service on-demand matching result provided by an embodiment of the present invention. Figure 4 Figure (a) shows that as the number of vehicle users gradually increases, the system's demand-resource (virtual network resource) matching gradually decreases. This is because as the service demand increases, there is a gap in resources, and in order to serve as many users as possible, the system may schedule idle resources with less matching dimensional attributes, thereby reducing the corresponding matching degree. At the same time, it can be seen that the algorithm of the present invention achieves the best demand-resource matching degree compared to the comparison algorithm. This is because in resource scheduling, the method of the present invention comprehensively considers the multi-dimensional attributes of demand and resources and uses a matching algorithm for on-demand mapping, thereby ensuring that the algorithm of the present invention can provide a higher demand-resource matching degree. Figure 4 Figure (b) shows that as the number of available links increases, the demand-resource matching degree gradually increases. This is because the increase in resources expands the space for demand selection, thereby increasing the probability of matching with resources with similar attributes. At the same time, because the comparison algorithms costAware (a comparison algorithm that considers VNF mapping and VU scheduling costs, ignores the inefficiency of deploying VNFs individually, and fails to deeply analyze the impact of scenarios and multi-dimensional resources on demand) and clusterA (a comparison algorithm that considers the inefficiency of deploying VNFs individually and ignores in-depth analysis of demand affected by scenarios and multi-dimensional resources) do not consider the multi-dimensional attributes of demand and resources, the performance of the two comparison algorithms in demand-resource matching is relatively poor.

[0117] Figure 5 This is a diagram of network energy efficiency results provided by an embodiment of the present invention. Figure 5Figure (a) shows that as the number of users gradually increases, the energy efficiency of the algorithm of the present invention and the comparison algorithms (clusterA and costAware) gradually increases. This is because the above three algorithms all take into account the inefficiency of deploying virtual resources for individual users, and improve energy efficiency by grouping users with similar needs and deploying network resources in groups. At the same time, the energy efficiency of the algorithm of the present invention is higher than that of clusterA. This is because the algorithm of the present invention further considers the impact of the scenario on user needs and groups users according to the multi-dimensional attributes of demand-resources, so as to obtain more accurate grouping and higher energy efficiency. The energy efficiency achieved by costAware is the lowest and does not change significantly with the number of vehicle users. This is because the algorithm takes individual users as service targets. When facing new users, the network has to redeploy resources for them, resulting in a waste of resources. Figure 5 Figure (b) shows that as the number of available links increases, all algorithms achieve better network performance at the cost of reduced energy efficiency. During this period, the algorithm of the present invention achieves the best performance.

[0118] Figure 6 This is a graph showing the overall network performance results provided by an embodiment of the present invention. Figure 6 Figure (a) shows that as the number of users increases, the overall network performance of the proposed algorithm and the comparison algorithm continues to improve. This phenomenon occurs because the overall network performance defined by the proposed algorithm is composed of user satisfaction and energy efficiency. As the number of users increases, user satisfaction decreases, while energy efficiency increases. Furthermore, because the increase in energy efficiency is greater than the decrease in user satisfaction, the overall trend is upward. However, the comparison algorithm shows a slight downward trend overall, as energy efficiency does not change significantly while user satisfaction decreases. Figure 6 Figure (b) shows that as network resources increase, the overall network performance obtained by all algorithms continues to improve. During this period, the algorithm of the present invention achieves the best performance.

[0119] The method provided in the embodiment of the present invention can be applied to electronic devices. Specifically, the electronic devices can be desktop computers, portable computers, smart mobile terminals, servers, etc., which are not limited in the embodiment of the present invention.

[0120] Based on the same inventive concept, an embodiment of the present invention also provides an on-demand service matching device in a 6G air-space-ground vehicle network. Figure 7 This is a schematic diagram of a structure of an on-demand service matching device in a 6G air-ground-vehicle network provided by an embodiment of the present invention. Figure 7 As shown, it includes: a virtual processing unit 701, an analysis unit 702, a grouping unit 703, a modeling unit 704, a problem reconstruction unit 705, a simplified processing unit 706 and a matching unit 707;

[0121] The virtual processing unit 701 is configured to virtualize physical network resources using SDN and NFV technologies to generate virtual network resources. Physical network resources are network resources formed by base stations, drones, and satellites. Virtual network resources include segment information, attribute information, and attribute categories. Virtual network resources respond to user needs in the form of service chains.

[0122] The analysis unit 702 is used to obtain user needs and perform fine-grained needs analysis on the user needs based on scenario recognition to obtain fine-grained user needs; wherein the fine-grained needs analysis includes analysis of user experience, user entities and scenarios;

[0123] The grouping unit 703 is used to perform initial grouping processing on all users based on virtual network resources and fine-grained user requirements to obtain multiple initial user groups;

[0124] The modeling unit 704 is used to: use virtual network resources, fine-grained user requirements and multiple initial user groups to perform problem modeling to obtain an optimized problem model;

[0125] The problem reconstruction unit 705 is used to: reconstruct the optimization problem model to obtain a TMSC framework;

[0126] The simplification processing unit 706 is used to perform R-TMSC simplification processing on the TMSC framework to obtain a preference list; the preference list is a preference relationship between users, actual user groups and path mappings;

[0127] The matching unit 707 is used to use the three-way matching algorithm and the preference list to perform resource on-demand matching on users, initial user groups, and path mappings to obtain resource allocation results for the actual user group; the actual user group is the result of the three-way matching optimization of the initial user group.

[0128] Figure 8 A schematic diagram of the structure of an on-demand service matching device in a 6G air-space-ground vehicle network provided by an embodiment of the present invention includes: a processor 810, a storage medium 820, and a bus 830. The storage medium 820 stores machine-readable instructions executable by the processor 810. When the on-demand service matching device in the 6G air-space-ground vehicle network operates, the processor 810 and the storage medium 820 communicate via the bus 830, and the processor 810 executes the machine-readable instructions to perform the steps of the above-mentioned method embodiment. The specific implementation methods and technical effects are similar and will not be repeated here.

[0129] The storage medium may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage. Alternatively, the storage medium may be at least one storage device located away from the processor.

[0130] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0131] The present invention also provides a computer-readable storage medium having a computer program stored therein, which, when executed by a processor, implements the steps of any of the above-mentioned on-demand service matching methods in the 6G air-space-ground vehicle network.

[0132] It should be noted that the terms "first," "second," and the like are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention.

[0133] In the description of this specification, the reference terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" mean that the specific features or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described can be combined in any suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification.

[0134] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art can understand and implement other variations of the disclosed embodiments by viewing the drawings and the disclosure. In the description of the present invention, the word "comprising" does not exclude other components or steps, "one" or "an" does not exclude multiple situations, and "multiple" means two or more, unless otherwise clearly and specifically defined. In addition, certain measures are recorded in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0135] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention cannot be considered to be limited to these descriptions. For those skilled in the art of the present invention, several simple deductions or substitutions can be made without departing from the concept of the present invention, and all of these should be considered to fall within the scope of protection of the present invention.

Claims

1. A method for matching on-demand services in a 6G air-ground-vehicle network, characterized in that: include: Through SDN and NFV technologies, physical network resources are virtualized to obtain virtual network resources; The physical network resources are network resources formed by base stations, drones and satellites; The virtual network resources include: segment information, attribute information and attribute categories; the virtual network resources respond to user needs in the form of a service chain; Obtaining user needs and performing fine-grained needs analysis on the user needs based on scenario recognition to obtain fine-grained user needs; wherein the fine-grained needs analysis includes: analyzing user experience, user entities, and scenarios; Performing initial grouping processing on all users based on the virtual network resources and the fine-grained user requirements to obtain multiple initial user groups; Performing problem modeling using the virtual network resources, the fine-grained user requirements, and the multiple initial user groups to obtain an optimized problem model; Reconstructing the optimization problem model to obtain a TMSC framework; Performing R-TMSC simplification processing on the TMSC framework to obtain a preference list; the preference list is a preference relationship between users, actual user groups and path mappings; Performing on-demand resource matching on the user, the initial user group, and the path mapping using a three-party matching algorithm and the preference list to obtain a resource allocation result for the actual user group; the actual user group is a result of optimizing the three-party matching of the initial user group; The step of reconstructing the optimization problem model to obtain a TMSC framework includes: Processing the node and link mapping in the segment information into a path mapping to obtain a simplified problem; the simplified problem is a simplified result of the path mapping and the initial user group; The simplified problem is reconstructed based on the TMSC model to obtain the TMSC framework.

2. The on-demand service matching method in the 6G air-ground-vehicle network according to claim 1, characterized in that: The user experience includes: user entertainment experience and user safety experience; The user entity includes: user speed, user location, channel condition of the user, user driving delay and user driving stability; The scenarios include: aerial, rural, urban, and desert.

3. The on-demand service matching method in the 6G air-ground-vehicle network according to claim 1, characterized in that: The optimization problem model is expressed as: ； in, Indicates whether the virtual network resource VNF is Each business flow provides services. A binary indication of the link mapping process. Indicates the number of service flows included in each initial user group. Indicates user satisfaction, represents energy efficiency; ； ； ； ； ； ； ； ； in, Indicates the total number of business flows that meet the requirements, Indicates the total number of business flows. Indicates the consumption of virtual network resources of all initial user groups, Indicates the number of initial user groups, Indicates the The number of business flows included in the initial user group, Indicates the number of all users, Indicates the maximum communication capacity of the virtual network resources. No. of the initial user group The communication resource consumption required for the path mapping of each business flow, Indicates the of the initial user group The computing resource consumption required for the virtual mapping of nodes in each business flow, Indicates the maximum computing capacity of the virtual network resources. Represents an integer.

4. The on-demand service matching method in the 6G air-ground-vehicle network according to claim 1, characterized in that: The TMSC framework is represented as: ； ； ； ； ； Indicates the stable matching result of triples, Indicates the A path mapping, Represents a collection of path mappings, Indicates inclusion The number of triplets, Indicates the actual user groups, Indicates the actual user group set, Indicates inclusion The number of triplets, Indicates the The maximum number of users that an actual user group can contain is Indicates the users, Represents a user collection, Indicates inclusion The number of triplets, express , , The obstacle triplet matching results.

5. The on-demand service matching method in the 6G air-ground-vehicle network according to claim 1, characterized in that: The preference list includes: preference relationships between path mapping and users, preference relationships between users and actual user groups, and preference relationships between actual user groups and paths.

6. The on-demand service matching method in the 6G air-ground-vehicle network according to claim 5, characterized in that: The path mapping to user preference relationship is expressed as: ； Indicates the The path mapping The preference relationship of each user, Represents the business flow model, Attributes representing path mappings, Represents a collection of path mappings, express and the overall properties of the path mapping The degree of match between them.

7. The on-demand service matching method in the 6G air-ground-vehicle network according to claim 5, characterized in that: The preference relationship between the user and the actual user group is expressed as: ； Indicates the User to The preference relationship between actual user groups, express and the actual user group set The degree of match between The preference relationship of the actual user group to the path is expressed as: ； Indicates the The actual user group The preference relationship of the paths.

8. A device for matching on-demand services in a 6G air-ground-vehicle network, characterized in that: include: Virtual processing unit, analysis unit, grouping unit, modeling unit, problem reconstruction unit, simplified processing unit, and matching unit; The virtual processing unit is used to: virtualize physical network resources to obtain virtual network resources through SDN and NFV technologies; The physical network resources are network resources formed by base stations, drones and satellites; The virtual network resources include: segment information, attribute information and attribute categories; the virtual network resources respond to user needs in the form of a service chain; The analysis unit is used to obtain user needs and perform fine-grained needs analysis on the user needs based on scenario recognition to obtain fine-grained user needs; wherein the fine-grained needs analysis includes analysis of user experience, user entities and scenarios; The grouping unit is used to: perform initial grouping processing on all users based on the virtual network resources and the fine-grained user requirements to obtain multiple initial user groups; The modeling unit is used to: perform problem modeling using the virtual network resources, the fine-grained user requirements, and the multiple initial user groups to obtain an optimized problem model; The problem reconstruction unit is used to: reconstruct the optimization problem model to obtain a TMSC framework; The simplification processing unit is used to: perform R-TMSC simplification processing on the TMSC framework to obtain a preference list; the preference list is a preference relationship between users, actual user groups and path mappings; The matching unit is configured to perform resource on-demand matching on the user, the initial user group, and the path mapping using a three-party matching algorithm and the preference list to obtain a resource allocation result for the actual user group; the actual user group is a result of three-party matching optimization on the initial user group; The problem reconstruction unit is specifically used to: process the node and link mapping in the segment information into a path mapping to obtain a simplified problem; the simplified problem is a simplified result about the path mapping and the initial user group; and reconstruct the simplified problem based on the TMSC model to obtain the TMSC framework.

9. A 6G air-ground-vehicle network on-demand service matching device, characterized in that: include: A processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor. When the on-demand service matching device in the 6G air-space-ground-vehicle network is running, the processor and the storage medium communicate through the bus, and the processor executes the machine-readable instructions to perform the steps of the on-demand service matching method in the 6G air-space-ground-vehicle network as described in any one of claims 1 to 7.

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