Multi-constraint service quality routing method and device for software-defined Internet of Vehicles

Abstract

The invention discloses a multi-constraint quality-of-service routing method and device for software-defined Internet of Vehicles, and the method comprises the steps: measuring the value of each quality-of-service parameter in real time for every two adjacent nodes in a user node set within a preset time period, and obtaining a measurement parameter set; for each service quality parameter, calculating to obtain membership degrees of all adjacent nodes in the user node set about the service quality parameters, and calculating to obtain decision values among all adjacent nodes according to a preset decision function; deleting connecting edges between adjacent nodes of which the judgment values do not meet a preset screening requirement in the topological graph to obtain a basic graph; calculating a comprehensive membership degree of all adjacent nodes in the basic graph to obtain a fuzzy graph; searching a path connecting the source node and the destination node in the fuzzy graph to obtain a plurality of routing links; and selecting an optimal routing link from the plurality of routing links. The method can effectively optimize the path calculation efficiency and improve the data transmission performance.

Description

Technical Field

[0001] The present invention relates to a multi-constraint service quality routing method and device for software-defined vehicle networking. Background Art

[0002] Software Defined Networking (SDN) is a new type of centralized network architecture with separated forwarding and control planes. There is a unified open interface between the control plane and the data plane. The most mainstream one is the OpenFlow protocol, which is used to realize the control layer's control over the data forwarding layer. The core difference between its architecture and the traditional network architecture is that the forwarding function is separated from the control function. The controller takes over the network forwarding function and calls the controller to obtain information by calling the application programming interface to develop upper-level applications.

[0003] The Internet of Vehicles is composed of a large number of connected cars and roadside facilities. It can provide vehicles with various types of wireless communications such as vehicle-to-vehicle, vehicle-to-network, vehicle-to-infrastructure and vehicle-to-person, and realize the data and resource interaction needs of various applications in the intelligent transportation system. The Internet of Vehicles built on traditional networks adopts a tightly coupled wireless network device management method. Faced with the increasing demand for scalability, flexibility and reliability of the Internet of Vehicles, the traditional Internet of Vehicles can no longer meet future needs. The Internet of Vehicles based on software-defined networks can use software-defined networks to achieve flexible device management and efficient use of wireless resources, thereby effectively solving problems such as network management difficulties and low network efficiency.

[0004] In the software-defined Internet of Vehicles, the data transmission path is calculated by the control plane. By uniformly calculating the data forwarding path, it is possible to guide the data traffic flow in different areas, thereby making the network load in different areas of the network more balanced. Summary of the invention

[0005] In order to obtain the optimal routing link and improve data transmission performance, an embodiment of the present invention provides a multi-constraint service quality routing method and device for software-defined Internet of Vehicles.

[0006] In a first aspect, an embodiment of the present invention provides a multi-constraint service quality routing method for a software-defined Internet of Vehicles, which may include:

[0007] Obtaining a set of service quality parameters, a set of user nodes, and a topological map between all nodes; the set of user nodes includes a source node and a destination node;

[0008] Within a preset time period, for every two adjacent nodes in the user node set, measuring in real time the value of each service quality parameter in the service quality parameter set to obtain a measurement parameter set;

[0009] For each quality of service parameter, based on the preset performance requirement of the quality of service parameter and the set of measurement parameters, calculating the membership degree of all adjacent nodes in the user node set with respect to the quality of service parameter;

[0010] Based on the membership degree of each service quality parameter between all adjacent nodes in the user node set, according to a preset judgment function, a judgment value between all adjacent nodes is calculated;

[0011] In the topological graph, edges between adjacent nodes whose judgment values ​​do not meet the preset screening requirements are deleted to obtain a basic graph;

[0012] Based on the membership degree of each service quality parameter between all adjacent nodes in the basic graph, the comprehensive membership degree between all adjacent nodes in the basic graph is calculated to obtain a fuzzy graph;

[0013] Using a flooding method to search for a path connecting the source node and the destination node in the fuzzy graph, to obtain multiple routing links;

[0014] An optimal routing link is selected from the plurality of routing links.

[0015] In one or some optional implementations of the embodiment of the present application, for each quality of service parameter, based on the preset performance requirement of the quality of service parameter and the measurement parameter set, calculating the membership degree of all adjacent nodes in the user node set with respect to the quality of service parameter includes:

[0016] For each quality of service parameter, based on the preset performance requirement of the quality of service parameter and the set of measurement parameters, the membership degree of all adjacent nodes in the user node set with respect to the quality of service parameter is calculated according to the following formula:

[0017]

[0018] In the formula, represents the membership degree between adjacent nodes i and j regarding the service quality parameter l, α l,m,n is the preset performance requirement for the quality of service parameter l set for the source node m and the destination node n, φ i,j,l (t) represents the value of the service quality parameter l measured by node i and node j at time t, Φ i,j,lIt represents all the values ​​of the service quality parameter l measured by the node i and the node j in the measurement parameter set within the preset time length, true() is a conditional judgment function, returns 1 when the judgment in the brackets is true, and returns 0 when the judgment in the brackets is false, the symbol < indicates better than, the symbol || indicates an OR operation, i and j are two adjacent nodes, l is the service quality parameter, t is a timestamp, m indicates the source node, and n indicates the destination node.

[0019] In one or some optional implementations of the embodiment of the present application, the determination value between all adjacent nodes is calculated based on the membership degree of each service quality parameter between all adjacent nodes in the user node set according to a preset determination function, including:

[0020] Based on the membership degree of each service quality parameter between all adjacent nodes in the user node set, the determination value between all adjacent nodes is calculated according to the preset determination function shown in the following formula:

[0021]

[0022] In the formula, F m,n (e i,j ) represents the judgment value between node i and node j, L m,n Represents a set of quality of service parameters, represents the membership of nodes i and j with respect to the service quality parameter l, β l,m,n is the preset probability threshold of the service quality parameter l set for the source node m and the destination node n, true() is a conditional judgment function, which returns 1 when the judgment in the brackets is true, and returns 0 when the judgment in the brackets is false, i and j are two adjacent nodes, l is the service quality parameter, t is the timestamp, m represents the source node, and n represents the destination node.

[0023] In one or some optional implementations of the embodiments of the present application, the comprehensive membership between all adjacent nodes in the basic graph is calculated based on the membership between all adjacent nodes in the basic graph with respect to each service quality parameter to obtain a fuzzy graph, including:

[0024] Based on the membership of all adjacent nodes in the basic graph with respect to each service quality parameter, the comprehensive membership between all adjacent nodes in the basic graph is calculated according to the following formula to obtain a fuzzy graph:

[0025]

[0026] In the formula, represents the comprehensive membership between node i and node j, L m,n represents the set of service quality parameters, ω l(m,n) is the preset preference parameter for the quality of service parameter l when data is transmitted between the source node m and the destination node n. represents the membership of neighboring nodes i and j with respect to the service quality parameter l, α l,m,n is the preset performance requirement about the service quality parameter l set for the source node m and the destination node n, i and j are two adjacent nodes, l is the service quality parameter, t is the timestamp, m represents the source node, and n represents the destination node.

[0027] In one or some optional implementations of the embodiment of the present application, the selecting the optimal routing link from the multiple routing links includes:

[0028] Calculating the length of each of the routing links, and determining whether the number of the shortest routing links is greater than or equal to 2;

[0029] If yes, then the minimum value of the comprehensive membership between all adjacent nodes on each of the shortest routing links is used as the representative membership of the routing link, and the routing link with the largest representative membership is used as the optimal routing link;

[0030] If not, the routing link with the shortest length is taken as the optimal routing link.

[0031] In one or some optional implementations of the embodiment of the present application, the deleting the edges between adjacent nodes whose determination values ​​do not meet the preset screening requirements in the topological graph to obtain the basic graph includes:

[0032] In the topological graph, the edges between adjacent nodes whose determination values ​​are not equal to 1 are deleted to obtain a basic graph.

[0033] In a second aspect, an embodiment of the present invention provides a multi-constraint service quality routing device for software-defined Internet of Vehicles, which may include:

[0034] An acquisition module, used to acquire a set of service quality parameters, a set of user nodes, and a topological map between all nodes; the set of user nodes includes a source node and a destination node;

[0035] A measurement module, configured to measure the value of each quality of service parameter in the quality of service parameter set in real time for every two adjacent nodes in the user node set within a preset time period to obtain a measurement parameter set;

[0036] A first calculation module, configured to calculate, for each quality of service parameter, a degree of membership of all adjacent nodes in the user node set with respect to the quality of service parameter based on a preset performance requirement of the quality of service parameter and the measurement parameter set;

[0037] A second calculation module is used to calculate the determination value between all adjacent nodes in the user node set based on the membership degree of each service quality parameter between all adjacent nodes according to a preset determination function;

[0038] A screening module, used to delete the edges between adjacent nodes whose judgment values ​​do not meet the preset screening requirements in the topological graph, to obtain a basic graph;

[0039] A third calculation module, configured to calculate the comprehensive membership between all adjacent nodes in the basic graph based on the membership between all adjacent nodes in the basic graph with respect to each service quality parameter, to obtain a fuzzy graph;

[0040] A search module, configured to search for a path connecting the source node and the destination node in the fuzzy graph using a flooding method to obtain a plurality of routing links;

[0041] The selection module is used to select an optimal routing link from the multiple routing links.

[0042] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the multi-constraint service quality routing method for software-defined Internet of Vehicles as described above.

[0043] In a fourth aspect, an embodiment of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the multi-constraint service quality routing method for software-defined vehicle networks as described above.

[0044] In a fifth aspect, an embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory, and when the processor executes the computer program, it implements the multi-constraint service quality routing method for software-defined vehicle networks as described above.

[0045] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0046] The embodiment of the present invention provides a multi-constraint service quality routing method for software-defined vehicle networks. The method obtains a service quality parameter set, a user node set, and a topological map between nodes. Then, within a preset time, the service quality parameters of every two adjacent nodes in the user node set are measured in real time, and the membership of the adjacent nodes with respect to each service quality parameter is calculated according to the preset performance requirements, so as to calculate the judgment function to select the adjacent nodes that meet the requirements and generate a basic map. Then, the comprehensive membership between the adjacent nodes is calculated to obtain a fuzzy map, and the path connecting the source node and the destination node is searched using the flooding method to obtain multiple routing links. Finally, the optimal routing link is selected from the multiple routing links, so as to realize efficient multi-constraint service quality routing. The method can effectively optimize the path calculation efficiency, improve load balancing, and meet dynamic QoS requirements, overcome the limitations of traditional routing algorithms in complex and large-scale software-defined vehicle network environments, significantly improve data transmission performance, and have important application value. At the same time, the fuzzy map is used to enhance the adaptability of the method to uncertainty and dynamic changes, improve reliability and flexibility, and provide more stable and efficient support for real-time data transmission in software-defined vehicle networks.

[0047] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0048] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0050] Figure 1 A schematic diagram of a flow chart of a multi-constraint service quality routing method for software-defined Internet of Vehicles provided by an embodiment of the present invention;

[0051] Figure 2 A schematic diagram of a basic diagram provided for an embodiment of the present invention;

[0052] Figure 3 A schematic diagram of a fuzzy graph provided by an embodiment of the present invention;

[0053] Figure 4 A schematic diagram of the structure of a multi-constraint service quality routing device for software-defined Internet of Vehicles provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0055] The inventors found that in the prior art, although the software-defined Internet of Vehicles can provide flexible network control and path calculation methods based on the software-defined network architecture, in the multi-controller environment of the wireless communication network, how to efficiently schedule controller resources for path calculation is still a less studied topic, especially in large-scale distributed Internet of Vehicles, the coordination of controller resources is crucial to the efficiency of path calculation and data transmission performance. At the same time, Internet of Vehicles users have put forward new requirements for network quality of service (QoS), so efficient QoS support becomes particularly important. At this time, the core of the problem lies in converting quality of service parameters into multiple constraints to achieve multi-constraint quality of service routing. Finding a routing path that meets constraints such as bandwidth, delay, and packet loss rate in a given network is an NP-complete problem. Traditional routing algorithms are difficult to meet these requirements. Therefore, it is crucial to design an effective multi-constraint quality of service routing algorithm. Based on this, the inventors have made the present invention after further research and development, providing a multi-constraint quality of service routing method and device for software-defined Internet of Vehicles.

[0056] Fuzzy graphs are an important research direction in graph theory. Since they can handle the uncertainty and ambiguity of many real-world phenomena, their structures are more useful than graph structures. Since fuzzy graphs can express uncertainty well, they can be used as an important research direction for modeling uncertain optimization problems, and some generalizations have been introduced to deal with the uncertainty of complex real-world problems. Compared with classical models, fuzzy graph models can provide higher accuracy, flexibility and compatibility for the system.

[0057] Fuzzy relations are a concept in fuzzy set theory that is used to represent uncertainty or ambiguity between nodes. In traditional set theory, the relationship between nodes either exists or does not exist, while in fuzzy relations, the strength of this relationship is a value between 0 and 1. For example, the relationship between two nodes may no longer be a simple "yes" or "no", but have a membership degree that represents the strength of the relationship, such as "70%".

[0058] The anti-reflexive property in fuzzy relations means that for any node i in the set, the membership function is represented by the function μ, and the relationship μ(i,i) = 0. In other words, the membership degree between the node and itself is 0, indicating that there is no relationship between the node and itself.

[0059] Symmetry in fuzzy relations means that if there is a certain relationship between node x and node y, then the relationship between node y and node x will also be of the same strength, that is, μ(x, y) = μ(y, x), which means that the relationship is bidirectional.

[0060] Embodiment 1

[0061] In the first embodiment of the present invention, a multi-constraint service quality routing method for software-defined vehicle networking is provided. Figure 1 As shown, the method may include the following steps S101-S108:

[0062] S101: Obtain a service quality parameter set, a user node set, and a topological map between all nodes. The user node set includes a source node and a destination node.

[0063] S102: Within a preset time period, for every two adjacent nodes in the user node set, measure in real time the value of each service quality parameter in the service quality parameter set to obtain a measurement parameter set.

[0064] S103: For each service quality parameter, based on the preset performance requirement of the service quality parameter and the measurement parameter set, the membership degree of all adjacent nodes in the user node set with respect to the service quality parameter is calculated.

[0065] S104: Based on the membership of all adjacent nodes in the user node set with respect to each service quality parameter, according to a preset judgment function, a judgment value between all adjacent nodes is calculated.

[0066] S105: Screening the judgment values ​​between all adjacent nodes, if the preset screening requirements are not met, deleting the edges between the corresponding adjacent nodes in the topological graph to obtain a basic graph.

[0067] S106: Based on the membership of all adjacent nodes in the user node set with respect to each service quality parameter, the membership of all adjacent nodes in the basic graph with respect to all service quality parameters is calculated to obtain a fuzzy graph.

[0068] S107: Use the flooding method to search for a path connecting the source node and the destination node in the fuzzy graph to obtain multiple routing links.

[0069] S108: Select a routing link with the shortest length and the largest comprehensive membership from multiple routing links as the optimal routing link.

[0070] The embodiment of the present invention provides a multi-constraint service quality routing method for software-defined vehicle networks. The method obtains a service quality parameter set, a user node set, and a topological map between nodes. Then, within a preset time, the service quality parameters of every two adjacent nodes in the user node set are measured in real time, and the membership of the adjacent nodes with respect to each service quality parameter is calculated according to the preset performance requirements, so as to calculate the judgment function to select the adjacent nodes that meet the requirements and generate a basic map. Then, the comprehensive membership between the adjacent nodes is calculated to obtain a fuzzy map, and the path connecting the source node and the destination node is searched using the flooding method to obtain multiple routing links. Finally, the optimal routing link is selected from the multiple routing links, so as to realize efficient multi-constraint service quality routing. The method can effectively optimize the path calculation efficiency, improve load balancing, and meet dynamic QoS requirements, overcome the limitations of traditional routing algorithms in complex and large-scale software-defined vehicle network environments, significantly improve data transmission performance, and have important application value. At the same time, the fuzzy map is used to enhance the adaptability of the method to uncertainty and dynamic changes, improve reliability and flexibility, and provide more stable and efficient support for real-time data transmission in software-defined vehicle networks. In the above step S101, a service quality parameter set, a user node set and a topological map between all nodes are obtained, wherein the user node set includes a source node and a destination node.

[0071] Specifically, all user nodes in the software-defined Internet of Vehicles and the topological relationship between the user nodes may be obtained, where the topological relationship represents the wireless communication links between different nodes, forming a user node set and a corresponding topological graph. At the same time, an initial service quality parameter set defined in the software-defined Internet of Vehicles may be obtained, where the set includes multiple service quality parameters.

[0072] The source node initiates data transmission, determines the destination node of the data transmission, and selects multiple service quality parameters from the initial service quality adoption number set as the service quality parameters that need to be met for this data transmission to form a service quality parameter set.

[0073] For example, there are 50 user nodes in the software-defined Internet of Vehicles, and each user node has its corresponding number. If the user node set is represented by a finite point set I, the user node number i∈I, and i=1,2,3...50. Among them, the source node number m=20, and the destination node number n=30.

[0074] The initial service quality parameter set can be expressed as L, including {bandwidth, delay, loss rate, jitter rate}, and the service quality parameter set can be expressed as L m,n , including {bandwidth, delay, loss rate}, and

[0075] In the above step S102, within a preset time period, for every two adjacent nodes in the user node set, the value of each service quality parameter in the service quality parameter set is measured in real time to obtain a measurement parameter set.

[0076] Specifically, it can be to monitor the service quality parameters between every two adjacent nodes in the user node set in real time, and obtain the actual values ​​of all service quality parameters within a preset time length based on the measured data, wherein the timestamp t in the preset time length belongs to [sT,s], s is the current time, and T is the preset time limit range.

[0077] For example, at a certain time t in the preset time length, the bandwidth between the source node m and the adjacent node i is 10 Mbps, the delay is 0.02 ms, and the packet loss rate is 0.005.

[0078] In the above step S103, for each service quality parameter, based on the preset performance requirement of the service quality parameter and the measurement parameter set, the membership degree of all adjacent nodes in the user node set with respect to the service quality parameter is calculated.

[0079] Specifically, for each quality of service parameter, a preset performance requirement needs to be set as a constraint condition for the corresponding quality of service parameter, indicating an expected threshold value for the quality of service, which is used for subsequent calculation of the membership degree of each pair of adjacent nodes with respect to the quality of service parameter. For example, the preset performance requirements for bandwidth are 2Mbps, latency is 0.1ms, and packet loss rate is 0.02.

[0080] For each quality of service parameter, based on the preset performance requirements of the quality of service parameter and the actual measurement values ​​of each pair of adjacent nodes in the measurement parameter set on the quality of service parameter, the membership degree of all adjacent nodes in the user node set with respect to the quality of service parameter is calculated according to the following formula 1:

[0081]

[0082] In the formula, represents the membership degree between adjacent nodes i and j regarding the service quality parameter l, α l,m,n is the preset performance requirement for the quality of service parameter l set for the source node m and the destination node n, φ i,j,l (t) represents the value of the service quality parameter l measured by node i and node j at time t, Φ i,j,lIt represents all the values ​​of the service quality parameter l measured by nodes i and j in the measurement parameter set within the preset time length. true() is a conditional judgment function. It returns 1 when the judgment in the brackets is true, and returns 0 when the judgment in the brackets is false. The symbol < indicates better than, and the symbol || indicates an OR operation. i and j are two adjacent nodes, l is the service quality parameter, t is a timestamp, m indicates the source node, and n indicates the destination node.

[0083] For example, for adjacent nodes i and j, the service quality parameter l is the bandwidth, and the corresponding preset performance requirement is 2Mbps, that is, α l,m,n =2Mbps, there are 100 actual measurement values ​​of each pair of adjacent nodes on each service quality parameter in the measurement parameter set, that is, Φ i,j,l =100.

[0084] According to the above formula 1, it can be seen that the membership degree actually reflects the degree of closeness between the actual performance of each pair of adjacent nodes on each service quality parameter and the preset performance requirement.

[0085] In the above step S104, based on the membership degree of each service quality parameter between all adjacent nodes in the user node set, the determination value between all adjacent nodes is calculated according to the preset determination function.

[0086] Specifically, based on the membership degree of each service quality parameter between all adjacent nodes in the user node set, the determination value between all adjacent nodes is calculated according to the preset determination function shown in the following formula 2:

[0087]

[0088] In the formula, F m,n (e i,j ) represents the judgment value between node i and node j, L m,n Represents a set of quality of service parameters, is the membership degree between nodes i and j regarding the service quality parameter l, β l,m,n is the preset probability threshold for the service quality parameter l set for the source node m and the destination node n, true() is a conditional judgment function, returns 1 when the judgment in the brackets is true, and returns 0 when the judgment in the brackets is false, i and j are two adjacent nodes, l is the service quality parameter, t is a timestamp, m represents the source node, and n represents the destination node. The preset probability threshold can be set to 0.7 by way of example.

[0089] According to the above formula 2, it can be seen that the preset judgment function evaluates the comprehensive performance of data transmission between each pair of adjacent nodes by combining the membership of each pair of adjacent nodes on multiple service quality parameters, and determines whether they meet the data transmission standard. For example, if the membership of adjacent nodes i and j on all service quality parameters is higher than the preset probability threshold, the judgment value is 1, indicating that the data transmission performance between adjacent nodes i and j is qualified. On the contrary, if the membership of adjacent nodes i and j on any service quality parameter is lower than the preset probability threshold, the judgment value is 0, indicating that the data transmission performance between adjacent nodes i and j is unqualified.

[0090] In the above step S105, the edges between adjacent nodes whose determination values ​​do not meet the preset screening requirements are deleted in the topological graph to obtain a basic graph.

[0091] Specifically, it may be that the adjacent nodes whose values ​​of the quality determination function are not equal to 1 are screened out from all adjacent nodes, and the edges between the adjacent nodes are deleted in the topological graph.

[0092] For example, there are 50 user nodes in the software-defined Internet of Vehicles, and each user node has its corresponding number. If the user node set is represented by a finite point set I, the user node number i∈I, and i=1,2,3...50. Among them, the source node number m=20, and the destination node number n=30.

[0093] Based on the software-defined Internet of Vehicles, through the above steps S102-S105, the basic diagram obtained is as follows: Figure 2 As shown, the graph includes 9 nodes, numbered 20, 25, 12, 3, 7, 22, 18, 38 and 30 respectively.

[0094] In the embodiment of the present application, through the above steps S103-S105, the network topology structure is optimized, and the stability and transmission efficiency of the network connection are effectively improved. The connection path that meets the performance requirements is screened out to avoid data loss or transmission delay caused by low-quality connections, thereby providing a more reliable and efficient network path for data transmission. This process is crucial to ensure an efficient and stable communication environment in the software-defined Internet of Vehicles, especially in complex Internet of Vehicles scenarios, which can adapt to different service quality requirements and meet real-time communication needs.

[0095] In the above step S106, based on the membership of all adjacent nodes in the basic graph with respect to each service quality parameter, the comprehensive membership of all adjacent nodes in the basic graph is calculated to obtain a fuzzy graph.

[0096] Specifically, based on the membership of all adjacent nodes in the basic graph with respect to each service quality parameter, the comprehensive membership of all adjacent nodes in the basic graph is calculated according to the following formula 3 to obtain the fuzzy graph:

[0097]

[0098] In the formula, represents the comprehensive membership between node i and node j, L m,n represents the set of service quality parameters, ω l (m,n) is the preset preference parameter for the quality of service parameter l when data is transmitted between the source node m and the destination node n. represents the membership of neighboring nodes i and j with respect to the service quality parameter l, α l,m,n is the preset performance requirement about the service quality parameter l set for the source node m and the destination node n, i and j are two adjacent nodes, l is the service quality parameter, t is the timestamp, m represents the source node, and n represents the destination node.

[0099] Among them, ω l (m,n) is the preset preference parameter for the service quality parameter l when data is transmitted between the source node m and the destination node n, which is used to indicate the degree of preference and needs to satisfy

[0100] In the above step S105 Figure 2 As an example, the fuzzy image obtained by the above step S106 is as follows: Figure 3 As shown in the figure, the comprehensive membership of each pair of adjacent nodes is marked next to the edge between the adjacent nodes. For example, the edge between node 20 and node 25 is marked as “e 20,25 0.91”, where e 20,25 represents the wireless communication link between node i and node j, and 0.91 is the comprehensive membership degree of the adjacent node.

[0101] In order to facilitate those skilled in the art to understand the present solution, the composition of the fuzzy graph described in the above steps S103-S105 is explained more clearly and completely below in combination with mathematical expressions:

[0102] Let E be the set of unordered pairs of different node combinations in the user node set I, that is, E is {e i,j |i,j∈I,i≠j}, and then corresponding to any adjacent node i,j∈I, if F m,n (e i,j ) is 0, then e i,j Delete from E and update to all elements in I, set the updated I to I m,n , I m,n It can be expressed as

[0103]

[0104] Set the updated E to E m,n , then Em,n is the set of links that meet all the performance requirements of the quality of service parameters between the source node m and the destination node n, E m,n It can be expressed as {e i,j |F m,n (e i,j )=1,i,j∈I,i≠j}, then E m,n For I m,n A collection of unordered pairs of different nodes in .

[0105] Then I m,n E m,n Binary ordered groups That is the basic graph, let R m,n isI m,n The fuzzy relationship with anti-reflexivity and symmetry that satisfies all the performance requirements of the service quality parameters between the source node m and the destination node n can be converted into the ordered pair G according to the fuzzy graph correlation theory in fuzzy mathematics. m,n =<I m,n ,R m,n >It is called a fuzzy graph that satisfies all service quality parameter performance requirements between source node m and destination node n.

[0106] In the above step S107, a flooding method is used to search for a path connecting a source node and a destination node in a fuzzy graph to obtain multiple routing links.

[0107] Specifically, it can be that the path connecting the source node and the destination node is searched in the fuzzy graph by flooding method, and P k To represent the kth routing link, according to the theoretical information of the fuzzy graph mathematical representation described in the above step S106, the routing link P k It can be expressed as:

[0108] P k =me m,a a...e i,j ...be b,n n

[0109] Among them, m is the source node, n is the destination node, a and b are other user nodes in the fuzzy graph, and e m,a represents the wireless communication link between node m and node a. Similarly, e i,j represents the wireless communication link between node i and node j, e b,n Represents the wireless communication link between node b and node n.

[0110] Among them, the flooding method is an existing technology and will not be described in detail here.

[0111] In the above step S106 Figure 3As an example of the fuzzy graph shown in FIG. 1 , there are 5 routing links that can be obtained through the above step S107, which are P from left to right. 1 =20e 20,25 25e 25,22 22e 22,30 30. P 2 =20e 20,12 12e 12,18 18e 18, 30 30. P 3 =20e 20,12 12e 12,7 7e 7,38 38e 38,30 30. P 4 =20e 20,3 3e 3,7 7e 7,12 12e 12,18 18e 18,30 30 and P 5 =20e 20,3 3e 3,7 7e 7,38 38e 38,30 30.

[0112] In the above step S108, the optimal routing link is selected from multiple routing links. Specifically, the following steps S1081-S1083 are included:

[0113] S1081: Calculate the length of each routing link, and determine whether the number of the shortest routing links is greater than or equal to 2: If so, execute step S1082; if so, execute step S1083.

[0114] Specifically, for each routing link, the length of the routing link may be calculated based on the following formula 4:

[0115]

[0116] In the formula, C(P k ) represents the routing link P k The length of the condition, true() is a conditional judgment function, which returns 1 when the condition in the brackets is true, and returns 0 when the condition in the brackets is false.

[0117] Determine whether the number of the shortest routing links is greater than or equal to 2: if so, execute step S1082; if so, execute step S1083.

[0118] S1082: Taking the minimum value of the comprehensive membership between all adjacent nodes on each routing link with the shortest length as the representative membership of the routing link, and taking the routing link with the maximum representative membership as the optimal routing link.

[0119] Specifically, the minimum value of the comprehensive membership between all adjacent nodes on each routing link with the shortest length may be used as the representative membership of the routing link, as shown in the following formula 5:

[0120]

[0121] In the formula, S(P k ) represents the routing link P k The representative membership degree of Represents the comprehensive membership between node i and node j.

[0122] The routing link with the largest representative membership degree is taken as the optimal routing link.

[0123] S1083: The routing link with the shortest length is used as the optimal routing link.

[0124] Taking the routing link described in step S107 as an example, let Ψ be the set of all routing links obtained in step S107, and the length of each routing link is calculated by step S1081. 1 To P 5 The lengths of are 3, 3, 4, 5 and 4 respectively, where P 1 and P 2 are the two shortest routing links. Through the above step S1082, routing link P can be obtained. 1 and P 2 The representative memberships of are 0.80 and 0.76 respectively, so the routing link P corresponding to the maximum value 0.80 is 1 This is the optimal routing link obtained in step S108.

[0125] Embodiment 2

[0126] Based on the same inventive concept, the embodiment of the present invention also provides a multi-constraint service quality routing device for software-defined vehicle networking, referring to Figure 4 As shown, the device comprises:

[0127] The first acquisition module 101 is used to acquire a set of service quality parameters, a set of user nodes, and a topological map between all nodes; the set of user nodes includes a source node and a destination node;

[0128] The data measurement module 102 is used to measure the value of each service quality parameter in the service quality parameter set in real time for every two adjacent nodes in the user node set within a preset time period to obtain a measurement parameter set;

[0129] A first calculation module 103 is used to calculate, for each quality of service parameter, the membership degree of all adjacent nodes in the user node set with respect to the quality of service parameter based on the preset performance requirement of the quality of service parameter and the measurement parameter set;

[0130] A second calculation module 104 is used to calculate the determination value between all adjacent nodes in the user node set based on the membership degree of each service quality parameter between all adjacent nodes according to a preset determination function;

[0131] A first screening module 105 is used to delete the edges between adjacent nodes whose judgment values ​​do not meet the preset screening requirements in the topological graph to obtain a basic graph;

[0132] A third calculation module 106, configured to calculate the comprehensive membership between all adjacent nodes in the basic graph based on the membership between all adjacent nodes in the basic graph with respect to each service quality parameter, to obtain a fuzzy graph;

[0133] A first search module 107, configured to use a flooding method to search for a path connecting the source node and the destination node in the fuzzy graph to obtain a plurality of routing links;

[0134] The first selection module 108 is configured to select an optimal routing link from the multiple routing links.

[0135] Embodiment 3

[0136] Based on the same inventive concept, an embodiment of the present invention also provides a computer-readable storage medium on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the multi-constraint service quality routing method for software-defined vehicle networks as described in the above-mentioned embodiment 1 is implemented.

[0137] Embodiment 4

[0138] Based on the same inventive concept, an embodiment of the present invention also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the multi-constraint service quality routing method for software-defined vehicle networks as described in the above embodiment 1.

[0139] Embodiment 5

[0140] Based on the same inventive concept, an embodiment of the present 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 multi-constraint service quality routing method for software-defined vehicle networks as described in the above embodiment 1.

[0141] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0142] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0143] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0144] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0145] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A multi-constraint service quality routing method for software-defined vehicle networks, characterized in that: include: Obtain a set of service quality parameters, a set of user nodes, and a topological map between all nodes; The user node set includes a source node and a destination node; Within a preset time period, for every two adjacent nodes in the user node set, measuring in real time the value of each service quality parameter in the service quality parameter set to obtain a measurement parameter set; For each quality of service parameter, based on the preset performance requirement of the quality of service parameter and the set of measurement parameters, calculating the membership degree of all adjacent nodes in the user node set with respect to the quality of service parameter; Based on the membership degree of each service quality parameter between all adjacent nodes in the user node set, according to a preset judgment function, a judgment value between all adjacent nodes is calculated; In the topological graph, edges between adjacent nodes whose judgment values ​​do not meet the preset screening requirements are deleted to obtain a basic graph; Based on the membership degree of each service quality parameter between all adjacent nodes in the basic graph, the comprehensive membership degree between all adjacent nodes in the basic graph is calculated to obtain a fuzzy graph; Using a flooding method to search for a path connecting the source node and the destination node in the fuzzy graph, to obtain multiple routing links; An optimal routing link is selected from the plurality of routing links.

2. The method according to claim 1, characterized in that The calculating, for each quality of service parameter, the membership degree of all adjacent nodes in the user node set with respect to the quality of service parameter based on the preset performance requirement of the quality of service parameter and the measurement parameter set, includes: For each quality of service parameter, based on the preset performance requirement of the quality of service parameter and the set of measurement parameters, the membership degree of all adjacent nodes in the user node set with respect to the quality of service parameter is calculated according to the following formula: In the formula, μ lR (i,j,α l,m,n ) represents the membership degree between adjacent nodes i and j with respect to the service quality parameter l, α l,m,n is the preset performance requirement for the quality of service parameter l set for the source node m and the destination node n, φ i,j,l (t) represents the value of the service quality parameter l measured by node i and node j at time t, Φ i,j,l It represents all the values ​​of the service quality parameter l measured by the node i and the node j in the measurement parameter set within the preset time length, true() is a conditional judgment function, returns 1 when the judgment in the brackets is true, and returns 0 when the judgment in the brackets is false, the symbol < indicates better than, the symbol || indicates an OR operation, i and j are two adjacent nodes, l is the service quality parameter, t is a timestamp, m indicates the source node, and n indicates the destination node.

3. The method according to claim 1, characterized in that: The step of calculating the determination value between all adjacent nodes based on the membership degree of each service quality parameter between all adjacent nodes in the user node set and according to a preset determination function comprises: Based on the membership degree of each service quality parameter between all adjacent nodes in the user node set, the determination value between all adjacent nodes is calculated according to the preset determination function shown in the following formula: In the formula, F m,n (e i,j ) represents the judgment value between node i and node j, L m,n represents the set of service quality parameters, μ lR (i,j,α l,m,n ) represents the membership of nodes i and j with respect to the service quality parameter l, β l,m,n is the preset probability threshold of the service quality parameter l set for the source node m and the destination node n, true() is a conditional judgment function, which returns 1 when the judgment in the brackets is true, and returns 0 when the judgment in the brackets is false, i and j are two adjacent nodes, l is the service quality parameter, t is the timestamp, m represents the source node, and n represents the destination node.

4. The method according to claim 1, characterized in that: The method of calculating the comprehensive membership between all adjacent nodes in the basic graph based on the membership between all adjacent nodes in the basic graph with respect to each service quality parameter to obtain a fuzzy graph includes: Based on the membership of all adjacent nodes in the basic graph with respect to each service quality parameter, the comprehensive membership between all adjacent nodes in the basic graph is calculated according to the following formula to obtain a fuzzy graph: In the formula, represents the comprehensive membership between node i and node j, L m,n represents the set of service quality parameters, ω l (m,n) is the preset preference parameter for the quality of service parameter l when data is transmitted between the source node m and the destination node n, μ lR (i,j,α l,m,n ) represents the membership of neighboring nodes i and j with respect to the service quality parameter l, α l,m,n is the preset performance requirement about the service quality parameter l set for the source node m and the destination node n, i and j are two adjacent nodes, l is the service quality parameter, t is the timestamp, m represents the source node, and n represents the destination node.

5. The method according to claim 1, characterized in that The selecting an optimal routing link from the multiple routing links comprises: Calculating the length of each of the routing links, and determining whether the number of the shortest routing links is greater than or equal to 2; If yes, then the minimum value of the comprehensive membership between all adjacent nodes on each of the shortest routing links is used as the representative membership of the routing link, and the routing link with the largest representative membership is used as the optimal routing link; If not, the routing link with the shortest length is taken as the optimal routing link.

6. The method according to claim 1, characterized in that Deleting the edges between adjacent nodes whose determination values ​​do not meet the preset screening requirements in the topological graph to obtain a basic graph includes: In the topological graph, the edges between adjacent nodes whose determination values ​​are not equal to 1 are deleted to obtain a basic graph.

7. A multi-constraint service quality routing device for software-defined vehicle networking, characterized in that: include: An acquisition module is used to acquire a set of service quality parameters, a set of user nodes, and a topological map between all nodes; The user node set includes a source node and a destination node; A measurement module, configured to measure the value of each quality of service parameter in the quality of service parameter set in real time for every two adjacent nodes in the user node set within a preset time period to obtain a measurement parameter set; A first calculation module, configured to calculate, for each quality of service parameter, a degree of membership of all adjacent nodes in the user node set with respect to the quality of service parameter based on a preset performance requirement of the quality of service parameter and the measurement parameter set; A second calculation module is used to calculate the determination value between all adjacent nodes in the user node set based on the membership degree of each service quality parameter between all adjacent nodes according to a preset determination function; A screening module, used to delete the edges between adjacent nodes whose judgment values ​​do not meet the preset screening requirements in the topological graph, to obtain a basic graph; A third calculation module, configured to calculate the comprehensive membership between all adjacent nodes in the basic graph based on the membership between all adjacent nodes in the basic graph with respect to each service quality parameter, to obtain a fuzzy graph; A search module, configured to search for a path connecting the source node and the destination node in the fuzzy graph using a flooding method to obtain a plurality of routing links; The selection module is used to select an optimal routing link from the multiple routing links.

8. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the multi-constraint service quality routing method for software-defined vehicle networks described in any one of claims 1-6 is implemented.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the multi-constraint service quality routing method for software-defined vehicle networks described in any one of claims 1-6 is implemented.

10. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the multi-constraint service quality routing method for software-defined vehicle networks as described in any one of claims 1-6.

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