Multi-constraint quality of service routing method and device for software-defined internet of vehicles

Abstract

The application discloses a kind of multi-constraint quality of service oriented to software-defined internet of vehicles routing method and device, the method includes: in preset length, for every two adjacent nodes in user node set, the value of each quality of service parameter is measured in real time, and measurement parameter set is obtained;For each quality of service parameter, the membership degree of all adjacent nodes in user node set about quality of service parameter is calculated, and the determination value between all adjacent nodes is calculated according to preset determination function;In topological graph, the edge between adjacent nodes that determination value does not satisfy preset screening requirement is deleted, and basic graph is obtained;The comprehensive membership degree of all adjacent nodes in basic graph is calculated, and fuzzy graph is obtained;Path connecting source node and destination node is searched in fuzzy graph, and multiple routing links are obtained;Optimal routing link is selected from multiple routing links.The method can effectively optimize path calculation efficiency and improve data transmission performance.

Description

TECHNICAL FIELD

[0001] The application relates to a multi-constraint quality of service routing method and device for software-defined vehicle networking. BACKGROUND

[0002] Software Defined Networking (SDN) is a new network architecture with centralized and separated control plane and forwarding plane, and the control plane and the data plane have a unified open interface, and the most mainstream is the OpenFlow protocol, so as to realize the control of the control layer on the data forwarding layer, and the core difference between the architecture and the traditional network architecture is that the forwarding function and the control function are separated, the controller takes over the network forwarding function, and the upper layer application is developed by calling the application programming interface to call the controller to obtain information.

[0003] Vehicle networking is composed of a large number of networkable vehicles and roadside facilities, and can provide vehicles with various types of wireless communication such as vehicle-to-vehicle, vehicle-to-network, vehicle-to-infrastructure and vehicle-to-person, and realize the data and resource interaction requirements of various applications in the intelligent transportation system. The vehicle networking based on the traditional network adopts a tightly coupled wireless network device management mode, and in the face of the increasing demand for scalability, flexibility and reliability of vehicle networking, the traditional vehicle networking has been unable to meet the future demand. Based on software-defined networking, the vehicle networking can realize flexible device management and efficient use of wireless resources by using software-defined networking, thereby effectively solving the problems of network management difficulty and low network efficiency.

[0004] In the software-defined vehicle networking, the data transmission path is calculated by the control plane, and by uniformly calculating the data forwarding path, the data flow direction of different regions can be guided, so that the network load of different regions in the network is more balanced. SUMMARY

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

[0006] In the first aspect, the application embodiment provides a multi-constraint quality of service routing method for software-defined vehicle networking, which can include:

[0007] Obtain a quality of service parameter set, a user node set and a topology graph between all nodes; the user node set includes a source node and a destination node;

[0008] In a preset time length, for each two adjacent nodes in the user node set, the value of each quality of service parameter in the quality of service parameter set is measured in real time to obtain a measurement parameter set;

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

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

[0011] In the topology graph, an edge between adjacent nodes whose decision value does not meet preset screening requirement is deleted to obtain a base graph;

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

[0013] A path connecting the source node and the destination node is searched in the fuzzy graph using a flooding method to obtain a plurality of routing links;

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

[0015] In one or some optional embodiments of the present application, the calculation of the membership between all adjacent nodes in the set of user nodes with respect to each quality of service parameter based on the preset performance requirement of the quality of service parameter and the set of measurement parameters includes:

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

[0017]

[0018] In the formula, μ represents the membership between adjacent nodes i and j with respect to quality of service parameter l, α l,m,n is preset performance requirement of quality of service parameter l set for source node m and destination node n, φ i,j,l (t) represents a value measured by node i and node j at time t for quality of service parameter l, Φ i,j,ldenotes all values of the service quality parameter l measured by node i and node j in the preset time length, true() is a conditional judgment function, 1 is returned when the condition in the bracket is true, 0 is returned when the condition in the bracket is false, the symbol < denotes better than, the symbol || denotes or operation, i and j are two adjacent nodes, l is a service quality parameter, t is a time stamp, m denotes a source node, and n denotes a destination node.

[0019] In one or some optional embodiments of the embodiments of the present application, the membership degrees of all adjacent nodes in the user node set with respect to each service quality parameter are used to calculate the judgment values between all adjacent nodes according to a preset judgment function, including:

[0020] The membership degrees of all adjacent nodes in the user node set with respect to each service quality parameter are used to calculate the judgment values between all adjacent nodes according to a preset judgment function shown in the following formula:

[0021]

[0022] In the formula, F m,n (e i,j ) denotes the judgment value between node i and node j, L m,n denotes a set of service quality parameters, denotes the membership degree of node i and j with respect to the service quality parameter l, β l,m,n is a 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, 1 is returned when the condition in the bracket is true, 0 is returned when the condition in the bracket is false, i and j are two adjacent nodes, l is a service quality parameter, t is a time stamp, m denotes a source node, and n denotes a destination node.

[0023] In one or some optional embodiments of the embodiments of the present application, the membership degrees of all adjacent nodes in the base graph with respect to each service quality parameter are used to calculate the comprehensive membership degrees between all adjacent nodes in the base graph to obtain a fuzzy graph, including:

[0024] The membership degrees of all adjacent nodes in the base graph with respect to each service quality parameter are used to calculate the comprehensive membership degrees between all adjacent nodes in the base graph according to the following formula to obtain a fuzzy graph:

[0025]

[0026] In the formula, denotes the comprehensive membership degree between node i and node j, L m,n denotes a set of service quality parameters, ω l(m, n) is a preset preference parameter of a quality of service parameter l when data transmission is performed between a source node m and a destination node n, denotes membership of adjacent nodes i and j with respect to a quality of service parameter l, a l,m,n is a preset performance requirement of a quality of service parameter l set for a source node m and a destination node n, i and j are two adjacent nodes, l is a quality of service parameter, t is a time stamp, m represents a source node, and n represents a destination node.

[0027] In one or some optional embodiments of the application, the selecting the optimal routing link from the plurality of routing links comprises:

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

[0029] if yes, taking a minimum value in comprehensive membership degrees between all adjacent nodes on each of the routing links with the shortest length as a representative membership degree of the routing link, and taking a routing link with the maximum representative membership degree as the optimal routing link;

[0030] if no, taking the routing link with the shortest length as the optimal routing link.

[0031] In one or some optional embodiments of the application, the deleting, in the topology graph, an edge between adjacent nodes whose determination value does not satisfy a preset screening requirement to obtain a base graph comprises:

[0032] deleting, in the topology graph, an edge between adjacent nodes whose determination value is not equal to 1 to obtain a base graph.

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

[0034] an acquisition module configured to acquire a quality of service parameter set, a user node set, and a topology graph between all nodes; the user node set includes a source node and a destination node;

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

[0036] a first calculation module configured to calculate, for each quality of service parameter, a membership degree between 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, configured to calculate a decision value between all adjacent nodes in the user node set according to a preset decision function based on the membership degrees between all adjacent nodes in the user node set with respect to each quality of service parameter;

[0038] a screening module, configured to delete an edge between adjacent nodes in the topology graph whose decision value does not meet a preset screening requirement, to obtain a base graph;

[0039] a third calculation module, configured to calculate a comprehensive membership degree between all adjacent nodes in the base graph based on the membership degrees between all adjacent nodes in the base graph with respect to each quality of service 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] a selection module, configured to select an optimal routing link from the plurality of routing links.

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

[0043] In a fourth aspect, an embodiment of the present application provides a computer program product comprising a computer program / instruction, and the computer program / instruction, when executed by a processor, implements the multi-constrained quality of service routing method for software-defined Internet of Vehicles as described above.

[0044] In a fifth aspect, an embodiment of the present application provides a computer device comprising a memory, a processor, and a computer program stored on the memory, and the processor, when executing the computer program, implements the multi-constrained quality of service routing method for software-defined Internet of Vehicles as described above.

[0045] The above technical solution provided by the embodiments of the present application has at least the following beneficial effects:

[0046] The embodiment of the present application provides a multi-constraint quality of service routing method for software-defined vehicle networking, which comprises the following steps: acquiring a quality of service parameter set, a user node set and a topology graph between nodes; then, in a preset time length, measuring the quality of service parameters of each two adjacent nodes in the user node set in real time, and calculating the membership degree of the adjacent nodes with respect to each quality of service parameter according to a preset performance requirement, so as to calculate a decision function to screen out the adjacent nodes meeting the requirement, and generate a basic graph; then, calculating the comprehensive membership degree between the adjacent nodes to obtain a fuzzy graph, and searching for a path connecting a source node and a destination node by using a flooding method to obtain a plurality of routing links; finally, selecting an optimal routing link from the plurality of routing links, so that the multi-constraint quality of service routing is realized. The method can effectively optimize the path calculation efficiency, improve the load balancing, meet the dynamic QoS requirement, overcome the limitations of a traditional routing algorithm in a complex and large-scale software-defined vehicle networking environment, significantly improve the data transmission performance, has important application value, and simultaneously, the fuzzy graph is used to enhance the adaptability of the method to uncertainty and dynamic change, improve the reliability and flexibility, and can provide more stable and efficient support for real-time data transmission in the software-defined vehicle networking.

[0047] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be achieved and obtained by means of the structure particularly pointed out in the written description and the accompanying drawings.

[0048] The technical solutions of the present application will be further described in detail below with the help of the accompanying drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

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

[0050] Figure 1 A flowchart of the multi-constraint quality of service routing method for software-defined vehicle networking provided by the embodiment of the present application is shown in the figure.

[0051] Figure 2 A basic graph diagram provided by the embodiment of the present application is shown in the figure.

[0052] Figure 3 A fuzzy graph diagram provided by the embodiment of the present application is shown in the figure.

[0053] Figure 4 A structure diagram of the multi-constraint quality of service routing device for software-defined vehicle networking provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0054] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be thoroughly understood, and the scope of the present disclosure can be completely conveyed to those skilled in the art.

[0055] The inventors found that in the prior art, although the software-defined vehicle-to-everything can provide flexible network control and path calculation methods based on the software-defined network architecture, how to efficiently schedule controller resources for path calculation in the multi-controller environment of the wireless communication network is still a less researched topic, especially in large-scale distributed vehicle-to-everything, the coordination of controller resources is crucial to the efficiency of path calculation and data transmission performance. At the same time, vehicle-to-everything users have new requirements for network quality of service (QoS), so efficient QoS support becomes particularly important, at this time, the core of the problem is to convert the quality of service parameters into multiple constraint conditions to realize multi-constraint quality of service routing. Finding a routing path that meets the constraints of bandwidth, delay, packet loss rate, etc. in a given network is an NP-complete problem, and 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 further research and development, and made the present application to provide a multi-constraint quality of service routing method and device for software-defined vehicle-to-everything.

[0056] Fuzzy graph, as an important research direction of graph theory, is more useful than graph structure because it can handle the uncertainty and fuzziness of many real-world phenomena. Because fuzzy graph can better express uncertainty, it can be an important research direction for modeling uncertain optimization problems, and some extensions have been introduced to handle the uncertainty of complex real-world problems. Compared with the classical model, the fuzzy graph model can provide higher precision, flexibility and compatibility for the system.

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

[0058] Antireflexivity in fuzzy relation refers to that for any node i in the set, the membership function is represented by the function μ, and the relationship μ(i,i) = 0. That is, the membership degree between a 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 some relation between node x and node y, then the relation between node y and node x will also be the same strength, that is, μ(x, y) = μ(y, x), which means that the relation is bidirectional.

[0060] Embodiment one

[0061] In the embodiment one of the application, a multi-constraint quality of service routing method for software-defined vehicle networking is provided, referring to Figure 1 The method can include the following steps S101-S108:

[0062] S101: Obtain a set of quality of service parameters, a set of user nodes, and a topology graph between all nodes. The set of user nodes includes a source node and a destination node.

[0063] S102: In a preset time period, for each two adjacent nodes in the set of user nodes, the value of each quality of service parameter in the set of quality of service parameters is measured in real time to obtain a set of measurement parameters.

[0064] S103: 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 set of user nodes with respect to the quality of service parameter is calculated.

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

[0066] S105: Filter the decision values between all adjacent nodes. If the preset filtering requirement is not met, delete the edge between the corresponding adjacent nodes in the topology graph to obtain a base graph.

[0067] S106: Based on the membership degree of all adjacent nodes in the set of user nodes with respect to each quality of service parameter, the membership degree of all adjacent nodes in the base graph with respect to all quality of service 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 a plurality of routing links.

[0069] S108: Select the routing link with the shortest length and the maximum comprehensive membership degree from the plurality of routing links as the optimal routing link.

[0070] The embodiment of the application provides a multi-constraint quality of service routing method for software-defined vehicle networking, which comprises the following steps: obtaining a quality of service parameter set, a user node set and a topology graph between nodes; then, in a preset time length, the quality of service parameters of each two adjacent nodes in the user node set are measured in real time, the membership degrees of the adjacent nodes with respect to each quality of service parameter are calculated according to a preset performance requirement, a judgment function is used to screen out the adjacent nodes meeting the requirement, a basic graph is generated, then, the comprehensive membership degrees between the adjacent nodes are calculated to obtain a fuzzy graph, a flooding method is used to search for a path connecting a source node and a destination node, a plurality of routing links are obtained, finally, the optimal routing link is selected from the plurality of routing links, so that the multi-constraint quality of service routing is realized. The method can effectively optimize the path calculation efficiency, improve the load balancing and meet the dynamic QoS requirement, overcomes the limitations of the traditional routing algorithm in the complex and large-scale software-defined vehicle networking environment, significantly improves the data transmission performance, has important application value, meanwhile, the fuzzy graph is used to enhance the adaptability of the method to uncertainty and dynamic change, improves the reliability and flexibility, and can provide more stable and efficient support for real-time data transmission in the software-defined vehicle networking. In the step S101, the quality of service parameter set, the user node set and the topology graph between all nodes are obtained. The user node set comprises a source node and a destination node.

[0071] Specifically, all user nodes in the software-defined vehicle networking and the topology relationship between the user nodes can be obtained, the topology relationship represents wireless communication links between different nodes, and the user node set and the corresponding topology graph are formed. Meanwhile, an initial quality of service parameter set defined in the software-defined vehicle networking is obtained, and the set comprises a plurality of quality of service parameters.

[0072] The source node initiates data transmission, determines a destination node of the data transmission, and selects a plurality of quality of service parameters from the initial quality of service parameter set as the quality of service parameters required to be met in this data transmission, to form the quality of service parameter set.

[0073] For example, there are 50 user nodes in the software-defined vehicle networking, each user node has a corresponding number, if a finite point set I is used to represent the user node set, then the user node number i is in I, and i = 1, 2, 3,..., 50. The source node number m = 20, and the destination node number n = 30.

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

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

[0076] Specifically, the service quality parameters between each pair of adjacent nodes in the user node set can be monitored in real time, and the actual values of all service quality parameters within a preset time period can be obtained according to the measured data, wherein the timestamp t in the preset time period belongs to [s-T, s], s is the current time, and T is a preset time limit range.

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

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

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

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

[0081]

[0082] In the formula, μ represents the membership degree between the adjacent nodes i and j with respect to the service quality parameter l, α l,m,n is the preset performance requirement of the service quality parameter l set for the source node m and the destination node n, φ i,j,l (t) represents the value measured by the node i and the node j at time t, Φ i,j,lΦ (i, j, l, t) = true (Φ (i, j, l, t) < α (l) ), i, j∈N, l∈L, t∈T, where Φ (i, j, l, t) represents all values of the service quality parameter l measured by the nodes i and j in the preset time length, true() is a conditional judgment function, 1 is returned when the condition in the parentheses is true, 0 is returned when the condition in the parentheses is false, the symbol < represents better than, the symbol || represents or operation, i and j are two adjacent nodes, l is a service quality parameter, t is a timestamp, m represents a source node, and n represents a destination node.

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

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

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

[0087]

[0088] In the formula, F m,n (e i,j ) represents the decision value between the nodes i and j, L m,n represents a set of service quality parameters, is the membership degree between the nodes i and j on the service quality parameter l, β l,m,n is a preset probability threshold set for the source node m and the destination node n on the service quality parameter l, true() is a conditional judgment function, 1 is returned when the condition in the parentheses is true, 0 is returned when the condition in the parentheses is false, i and j are two adjacent nodes, l is a service quality parameter, t is a timestamp, m represents a source node, and n represents a destination node. The preset probability threshold can be set to 0.7, for 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 degrees of each pair of adjacent nodes on multiple quality of service parameters, and determines whether they meet the standard of data transmission. For example, if the membership degrees of adjacent nodes i and j on all quality of service parameters are higher than the preset probability threshold, the determination value is 1, indicating that the data transmission performance between adjacent nodes i and j is qualified, otherwise, if the membership degrees of adjacent nodes i and j on any quality of service parameter are lower than the preset probability threshold, the determination 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 requirement are deleted in the topology graph, and a base graph is obtained.

[0091] Specifically, the adjacent nodes whose quality judgment function values are not equal to 1 are screened out from all adjacent nodes, and the edges between adjacent nodes are deleted in the topology graph.

[0092] For example, there are 50 user nodes in the software-defined Internet of Vehicles, each user node has its corresponding number, if the user node set is represented by a finite point set I, then 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 base graph obtained is as shown in Figure 2 The graph includes 9 nodes, numbered 20, 25, 12, 3, 7, 22, 18, 38 and 30.

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

[0095] In the above step S106, based on the membership degrees of all adjacent nodes in the base graph on each quality of service parameter, the comprehensive membership degrees between all adjacent nodes in the base graph are calculated, and a fuzzy graph is obtained.

[0096] Specifically, based on the membership degrees of all adjacent nodes in the base graph on each quality of service parameter, the comprehensive membership degrees of all adjacent nodes in the base graph are calculated according to the following formula 3, and a fuzzy graph is obtained:

[0097]

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

[0099] Where, ω l (m,n) represents the preset preference parameters for the service quality parameter l when transmitting data between the source node m and the destination node n. These parameters indicate the degree of preference and must satisfy certain conditions.

[0100] In step S105 above Figure 2 Taking the basic image shown as an example, the corresponding blurred image obtained through step S106 above is as follows: Figure 3 As shown in the figure, the overall membership degree of each pair of adjacent nodes is marked next to the edge connecting them. Taking the edge between nodes 20 and 25 as an example, the label next to this edge is "e". 20,25 0.91”, where e 20,25 This represents the wireless communication link between node i and node j, and 0.91 is the combined membership degree of this adjacent node.

[0101] To facilitate understanding of this solution by those skilled in the art, the composition of the fuzzy graph described in steps S103-S105 above will be explained more clearly and completely below in conjunction 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 for any adjacent node i,j∈I, if F m,n (e i,j If ) is 0, then e i,j Remove from E and update all elements in I, then set the updated I as I. m,n I m,n It can be represented as

[0103]

[0104] Set the updated E to E m,n Then Em,n To meet the link set of all quality of service parameter performance requirements between source node m and destination node n, E m,n may be represented as {e i,j |F m,n (e i,j )=1,i,j∈I,i≠j}, then E m,n is a set of unordered pairs composed of different nodes in I m,n .

[0105] I m,n is E m,n composed of a binary ordered group , that is, the base graph, let R m,n be a fuzzy relation in I m,n that satisfies all quality of service parameter performance requirements between source node m and destination node n, and has anti-reflexivity and symmetry, then according to the fuzzy graph related theory in fuzzy mathematics, the ordered pair G m,n =<I m,n ,R m,n > can be called a fuzzy graph that meets all quality of service parameter performance requirements between source node m and destination node n.

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

[0107] Specifically, the flooding method can be used to search for a path connecting the source node and the destination node in the fuzzy graph, and the kth routing link can be represented by P k , according to the theoretical information of the fuzzy graph mathematical representation explained in the above step S106, the routing link P k may be represented as:

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

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

[0110] Wherein, the flooding method is a prior art and will not be described here.

[0111] In the above step S106 Figure 3The shown fuzzy graph is an example, and the routing links obtained through the above step S107 are 5, from left to right, P1=20e 20,25 25e 25,22 22e 22,30 30, P2=20e 20,12 12e 12,18 18e 18, 30 30, P3=20e 20,12 12e 12,7 7e 7,38 38e 38,30 30, P4=20e 20,3 3e 3,7 7e 7,12 12e 12,18 18e 18,30 30 and P5=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 the plurality of 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 routing links with the shortest length is greater than or equal to 2: if yes, execute step S1082; if no, execute step S1083.

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

[0115]

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

[0117] Determine whether the number of routing links with the shortest length is greater than or equal to 2: if yes, execute step S1082; if no, execute step S1083.

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

[0119] Specifically, the minimum value in the comprehensive membership between all adjacent nodes on each shortest length routing link can be taken 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 representative membership of the routing link P k , represents the comprehensive membership between node i and node j.

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

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

[0124] Taking the routing link explained in the above step S107 as an example, let Ψ be the set of all routing links obtained in the above step S107, the length of each routing link is calculated through the above step S1081, the lengths of the routing links P1 to P5 are 3, 3, 4, 5 and 4 respectively, wherein P1 and P2 are the two routing links with the shortest length, the representative memberships of the routing links P1 and P2 are 0.80 and 0.76 respectively through the above step S1082, and then the routing link P1 corresponding to the maximum value 0.80 is the optimal routing link obtained in the step S108.

[0125] Embodiment Two

[0126] Based on the same inventive concept, the embodiments of the present application also provide a multi-constraint quality of service routing device for software-defined Internet of Vehicles, referring to the device shown in Figure 4 , the device comprises:

[0127] A first acquisition module 101 is configured to acquire a quality of service parameter set, a user node set and a topology graph between all nodes, wherein the user node set comprises a source node and a destination node.

[0128] A data measurement module 102 is configured to measure the value of each quality of service parameter in the quality of service parameter set in real time for each two adjacent nodes in the user node set within a preset time length to obtain a measurement parameter set.

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

[0130] The second calculation module 104 is configured to calculate a decision value between all adjacent nodes according to a preset decision function based on the membership degrees between all adjacent nodes in the user node set with respect to each service quality parameter.

[0131] The first screening module 105 is configured to delete an edge between adjacent nodes in the topology graph whose decision value does not meet a preset screening requirement, to obtain a base graph.

[0132] The third calculation module 106 is configured to calculate a comprehensive membership degree between all adjacent nodes in the base graph based on the membership degrees between all adjacent nodes in the base graph with respect to each service quality parameter, to obtain a fuzzy graph.

[0133] The first search module 107 is 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.

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

[0135] Embodiment three

[0136] Based on the same inventive concept, the embodiments of the present application further provide a computer readable storage medium having a computer program / instruction stored thereon, and the computer program / instruction is executed by a processor to implement the multi-constrained quality of service routing method for software-defined vehicle networking as described in the above embodiment one.

[0137] Embodiment four

[0138] Based on the same inventive concept, the embodiments of the present application further provide a computer program product comprising a computer program / instruction, and the computer program / instruction is executed by a processor to implement the multi-constrained quality of service routing method for software-defined vehicle networking as described in the above embodiment one.

[0139] Embodiment five

[0140] Based on the same inventive concept, the embodiments of the present application further provide a computer device comprising a memory, a processor and a computer program stored in the memory, and the processor executes the computer program to implement the multi-constrained quality of service routing method for software-defined vehicle networking as described in the above embodiment one.

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

[0142] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0143] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0144] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0145] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for multi-constrained quality of service routing for software-defined vehicular networking, the method comprising: The method comprises the following steps: acquiring a set of quality of service parameters, a set of user nodes, and a topology graph among all nodes; the set of user nodes comprises a source node and a destination node; within a preset time length, for each two adjacent nodes in the set of user nodes, the value of each quality of service parameter in the set of quality of service parameters is measured in real time to obtain a set of measurement parameters; 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 between all adjacent nodes in the set of user nodes with respect to the quality of service parameter is calculated; based on the membership degree between all adjacent nodes in the set of user nodes with respect to each quality of service parameter, a decision value between all adjacent nodes is calculated according to a preset decision function; in the topology graph, the connection between adjacent nodes whose decision value does not meet a preset screening requirement is deleted to obtain a base graph; based on the membership degree between all adjacent nodes in the base graph with respect to each quality of service parameter, a comprehensive membership degree between all adjacent nodes in the base graph is calculated to obtain a fuzzy graph; a path connecting the source node and the destination node is searched in the fuzzy graph using a flooding method to obtain a plurality of routing links; an optimal routing link is selected from the plurality of routing links.

2. The method of claim 1, wherein, The calculation of the membership degree between all adjacent nodes in the set of user nodes with respect to each quality of service parameter based on the preset performance requirement of the quality of service parameter and the set of measurement parameters comprises: for each quality of service parameter, the membership degree between all adjacent nodes in the set of user nodes with respect to the quality of service parameter is calculated according to the following formula based on the preset performance requirement of the quality of service parameter and the set of measurement parameters: wherein μ lR (i,j,α l,m,n ) represents the membership between adjacent nodes i and j with respect to the quality of service parameter l, α l,m,n is the preset performance requirement with respect to 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 measured by the node i and the node j at the time t with respect to the quality of service parameter l, Φ i,j,l represents all the values measured by the node i and the node j within a preset time length with respect to the quality of service parameter l in the set of measurement parameters, true() is a conditional judgment function, 1 is returned when the condition in the bracket is true, 0 is returned when the condition in the bracket is false, the symbol < represents better than, the symbol || represents or operation, i and j are two adjacent nodes, l is a quality of service parameter, t is a time stamp, m represents a source node, and n represents a destination node.

3. The method of claim 1, wherein, The calculation of the decision value between all adjacent nodes based on the membership degree between all adjacent nodes in the set of user nodes with respect to each quality of service parameter according to a preset decision function comprises: the decision value between all adjacent nodes is calculated according to a preset decision function shown in the following formula based on the membership degree between all adjacent nodes in the set of user nodes with respect to each quality of service parameter: where F m,n (e i,j ) represents the decision value between node i and node j, L m,n represents the set of quality of service parameters, μ lR (i,j,α l,m,n ) represents the membership degree of node i and j on quality of service parameter l, β l,m,n is the preset probability threshold on quality of service parameter l set for source node m and destination node n, true() is a conditional judgment function, 1 is returned when the judgment in the bracket is true, 0 is returned when the judgment in the bracket is false, i and j are two adjacent nodes, l is a quality of service parameter, t is a time stamp, m represents a source node, and n represents a destination node.

4. The method of claim 1, wherein, The calculation of the comprehensive membership degree between all adjacent nodes in the base graph based on the membership degree between all adjacent nodes in the base graph with respect to each quality of service parameter comprises: the comprehensive membership degree between all adjacent nodes in the base graph is calculated according to the following formula based on the membership degree between all adjacent nodes in the base graph with respect to each quality of service parameter to obtain a fuzzy graph: wherein, denotes the overall membership between node i and node j, L m,n denotes the set of quality of service parameters, ω l (m, n) is the pre-set preference parameter of the quality of service parameter l when data transmission is performed between the source node m and the destination node n, μ lR (i, j, α l,m,n ) denotes the membership of the adjacent nodes i and j with respect to the quality of service parameter l, α l,m,n is the pre-set performance requirement with respect to the quality of service parameter l for the source node m and the destination node n, i and j are two adjacent nodes, l is the quality of service parameter, t is the time stamp, m denotes the source node, and n denotes the destination node.

5. The method of claim 1, wherein, The selection of the optimal routing link from the plurality of routing links comprises: the length of each routing link is calculated, and it is determined whether the number of routing links with the shortest length is greater than or equal to 2; if yes, the minimum value of the comprehensive membership degree between all adjacent nodes on each routing link with the shortest length is taken as the representative membership degree of the routing link, and the routing link with the maximum representative membership degree is taken as the optimal routing link; if no, the routing link with the shortest length is taken as the optimal routing link.

6. The method of claim 1, wherein, The method comprises the following steps: The method comprises the following steps:

7. A multi-constrained quality of service routing device for software-defined vehicular networking, comprising: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The user node set comprises a source node and a destination node. The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

8. A computer readable storage medium having stored thereon computer programs / instructions, characterized in that, The method comprises the following steps:

9. A computer program product comprising computer programs / instructions, characterized in that, The method comprises the following steps:

10. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program comprises instructions that, when executed by the processor, cause the processor to perform the method of any one of claims 1-9. 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