Routing method based on named data networking, electronic device, and storage medium

Abstract

The embodiment of the application provides a routing method based on a named data network, an electronic device and a storage medium, and belongs to the technical field of Internet of Vehicles. The method determines the weight values of a plurality of decision attributes through an adaptive maximum deviation algorithm, the decision attributes include a node distance attribute, a content connection quality attribute, a link quality attribute and a link availability attribute, then attribute values of the plurality of decision attributes of the candidate link are respectively acquired, the link reliability of the candidate link is accurately calculated according to the attribute values and the weight values of the plurality of decision attributes, and the candidate link with the maximum link reliability is selected from a plurality of candidate links in the candidate link set of the current node as the next hop transmission link of the current node, so that the optimal transmission link can be selected by considering the factors of transmission distance, content connection quality, link signal-to-noise ratio and link availability in the node routing process, the broadcast storm is avoided, the communication delay is reduced, the signal and content receiving and transmitting quality is improved, and the communication efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle networking technology, and in particular to a routing method, electronic device and storage medium based on named data networks. Background Technology

[0002] The data generated during vehicle-to-everything (V2X) communication consists of two parts: data generated by the vehicle's own wireless equipment and data sent from other vehicles to the vehicle node. Each vehicle node requires an Onboard Unit (OBU). The OBU provides a network interface, enabling the vehicle node to communicate with other nodes in the V2X network. Vehicles within each other's communication range can communicate directly; otherwise, relay nodes are needed to forward data via multiple hops to complete the communication. Due to the high-speed movement of vehicle nodes in the V2X network and the frequent changes in node positions leading to changes in network topology, it is difficult to establish end-to-end connections in the V2X network, making the TCP / IP architecture unsuitable for V2X.

[0003] In related technologies, Named Data Networking (NDN) is used in vehicle-to-everything (V2X) applications to achieve communication between nodes. NDN emphasizes the importance of content and supports communication from multiple content sources, thus reducing the necessity of location-based host-to-host communication. NDN uses Interest Packets and Data Packets to complete the communication process: consumers send out Interest Packets to obtain content, and other nodes, upon receiving the Interest Packet, record the interface information in their routers. If the node has cached the corresponding data for the received Interest Packet, it directly responds to the consumer's request and sends the data back along the reverse path; otherwise, the Interest Packet is forwarded hop-by-hop to the content provider, which then responds to the consumer's request.

[0004] Although named data networks can adapt to changes in network topology in vehicle-to-everything (V2X) networks to complete content transmission, in complex communication environments, factors such as shadow fading, node hierarchies, and rapid changes in node positions can lead to unstable communication links and high routing latency for interest packets, thus affecting communication efficiency. Summary of the Invention

[0005] The main objective of this application is to propose a routing method, electronic device, and storage medium based on named data networks, aiming to improve the communication efficiency of named data networks.

[0006] To achieve the above objectives, a first aspect of this application proposes a routing method based on a named data network, comprising the following steps:

[0007] Get the set of alternative links for the current node;

[0008] Calculate the link reliability of each candidate link in the candidate link set;

[0009] Select the candidate link with the highest link reliability as the next hop transmission link for the current node;

[0010] The reliability of the alternative links is obtained through the following steps:

[0011] The weight values ​​of multiple decision attributes are determined by an adaptive deviation maximization algorithm. These multiple decision attributes include node distance attribute, content connection quality attribute, link quality attribute, and link availability attribute.

[0012] Obtain the attribute values ​​of multiple decision attributes for the candidate links of the current node;

[0013] The link reliability of the candidate link is determined based on the attribute values ​​and weight values ​​of multiple decision attributes.

[0014] In some embodiments, the attribute value of the node distance attribute is obtained through the following steps:

[0015] Obtain the first position information of the current node and the second position information of the candidate nodes;

[0016] The Euclidean distance between the current node and the candidate node is determined based on the first location information and the second location information.

[0017] The Euclidean distance is normalized based on the maximum wireless communication distance across layers of the node to obtain the attribute value of the node distance attribute.

[0018] In some embodiments, the attribute value of the content connectivity quality attribute is obtained through the following steps:

[0019] Obtain the number of data packets and interest packets forwarded by the candidate node with the target name as the prefix;

[0020] The initial content connection quality of the candidate nodes at the current moment is determined based on the number of data packets and the number of interest packets.

[0021] The initial content connection quality and the weighted content connection quality of the previous time step are weighted and calculated to obtain the weighted content connection quality of the current time step, wherein the weighted content connection quality of the current time step is the attribute value of the content connection quality attribute.

[0022] In some embodiments, the attribute value of the link quality attribute is obtained through the following steps:

[0023] Obtain the distribution status of the current node and candidate nodes;

[0024] When the distribution state is a same-layer distribution, the first remaining distance between the current node and the candidate node is determined based on line-of-sight propagation, and the signal propagation loss is determined based on the first remaining distance.

[0025] When the distribution state is a cross-layer distribution, the second remaining distance between the current node and the candidate node is determined based on non-line-of-sight propagation, and the signal propagation loss is determined based on the second remaining distance and the shadow fading factor.

[0026] The attribute value of the link quality attribute is determined based on the signal propagation loss.

[0027] In some embodiments, obtaining the set of alternative links for the current node includes the following steps:

[0028] Get the location information of multiple neighboring nodes of the current node;

[0029] Based on the location information, candidate nodes are selected from multiple neighboring nodes, and candidate links are determined based on the current node and the candidate nodes;

[0030] The set of alternative links is determined based on multiple alternative links.

[0031] In some embodiments, determining the weight values ​​of multiple decision attributes using an adaptive deviation maximization algorithm includes the following steps:

[0032] An initial decision matrix is ​​determined based on all attribute values ​​of the decision attributes of the candidate link set, wherein the decision matrix includes a decision attribute dimension and a candidate link dimension;

[0033] Construct a standard decision matrix based on the attribute weight vector and the initial decision matrix;

[0034] Determine the total deviation function of all decision attributes with respect to the attribute weight vector based on the standard decision matrix;

[0035] The objective function for maximizing deviation is determined based on the attribute weight constraints of the total deviation function;

[0036] The weight values ​​of the decision attributes are determined based on the deviation maximization objective function.

[0037] In some embodiments, determining the weight values ​​of the decision attributes based on the deviation maximization objective function includes the following steps:

[0038] The objective function for maximizing deviation based on conditional constraints is transformed into an unconditional Lagrangian function.

[0039] The Lagrange function is subjected to extremum and normalization operations to obtain the weight values ​​of the decision attributes.

[0040] In some embodiments, determining the link reliability of the candidate links based on the attribute values ​​and weight values ​​of the plurality of decision attributes includes the following steps:

[0041] Determine if any candidate links have an attribute value of zero;

[0042] If there is an attribute value that is zero, then the link reliability of the candidate link is determined to be zero.

[0043] If there is no attribute value of zero, the attribute values ​​and weight values ​​of multiple decision attributes are substituted into the multi-attribute utility function to obtain the link reliability of the candidate link.

[0044] To achieve the above objectives, a second aspect of this application provides an electronic device, which includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for enabling communication between the processor and the memory. When the program is executed by the processor, it implements the routing method based on a named data network described in the first aspect.

[0045] To achieve the above objectives, a third aspect of the present application provides a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs that can be executed by one or more processors to implement the routing method based on named data networks described in the first aspect.

[0046] The routing method, electronic device, and storage medium based on named data networks proposed in this application involve determining the weight values ​​of multiple decision attributes for evaluating link reliability during the routing process of nodes in the named data network through an adaptive deviation maximization algorithm. Each decision attribute includes node distance, content connection quality, link quality, and link availability. Then, the attribute values ​​of multiple decision attributes for candidate links are obtained. Based on the attribute values ​​and weight values ​​of multiple decision attributes, the link reliability of the candidate links is accurately calculated. Finally, the candidate link with the highest link reliability is selected from multiple candidate links in the current node's candidate link set as the next-hop transmission link for the current node. This method can select the optimal transmission link by considering factors such as transmission distance, content connection quality, link signal-to-noise ratio, and link availability during node routing, avoiding broadcast storms, reducing communication latency, improving signal and content transmission and reception quality, and thus improving communication efficiency. Attached Figure Description

[0047] Figure 1 This is a flowchart of a routing method based on a named data network provided in an embodiment of this application;

[0048] Figure 2 yes Figure 1 The flowchart of the link reliability calculation method for the alternative links in step S102 is shown below.

[0049] Figure 3 yes Figure 2 The flowchart of the node distance attribute value calculation method in step S201;

[0050] Figure 4 yes Figure 2 The content in step S201 is connected to the flowchart of the method for calculating the attribute value of the quality attribute;

[0051] Figure 5 yes Figure 2 The flowchart of the link quality attribute value calculation method in step S201;

[0052] Figure 6 yes Figure 1 The flowchart of step S101 in the text;

[0053] Figure 7 yes Figure 2 The flowchart of step S201 in the text;

[0054] Figure 8 yes Figure 7 The flowchart of step S705 in the process;

[0055] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application;

[0056] Figure 10 This is a schematic diagram of the hourglass architecture model provided in the embodiments of this application;

[0057] Figure 11 This is a schematic diagram of the vehicle network architecture provided in an embodiment of this application;

[0058] Figure 12 This is a schematic diagram of the named data network forwarding model provided in the embodiments of this application;

[0059] Figure 13 This is a schematic diagram of the remaining spacing between vehicle nodes provided in an embodiment of this application;

[0060] Figure 14 This is a schematic diagram of the multi-attribute routing decision process provided in the embodiments of this application. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0062] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0064] First, let's analyze some of the terms used in this application:

[0065] Named Data Networking (NDN) is a new internet architecture designed to organize data transmission within a network using data names as the core element. By associating data names with data content, NDN makes data transmission more efficient, reliable, and secure. Figure 10 As shown, both NDN and traditional TCP / IP networks use an hourglass architecture model, but NDN is content-centric, while traditional network architecture is IP-centric. The hourglass architecture model uses a unified interface for data exchange, making layers transparent to each other. In TCP / IP networks, the most important part is the network layer at the "waist" of the hourglass, where IP packet-based network communication basically covers all functional requirements. However, this "waist" structure cannot well adapt to the trend of networks shifting towards information-centric networks. Therefore, the NDN architecture retains the hourglass model and layered concept of traditional TCP / IP networks, but makes a revolutionary improvement to the middle layer at the "waist." The core improvement is to completely abandon IP address-based communication and adopt content-based communication. All content data in the network is named according to the content itself, and information is retrieved by matching the names of the content data in the forwarding and routing mechanism, thereby establishing a distributed network. This communication method no longer cares about the storage location of content data, but directly provides content-oriented services.

[0066] The Internet of Vehicles (IoV) is a vast self-organizing communication network composed of vehicles equipped with onboard units. It serves as a link to achieve collaboration between "people, vehicles, roads, and the cloud," and promotes the arrival of the era of intelligent interconnection of everything.

[0067] The vehicle-to-everything (V2X) network topology is an intelligent and three-dimensional structure, divided into an application layer, a coordination and control layer, a network access and transmission layer, and a terminal data layer. The V2X network architecture is as follows: Figure 11 As shown. The terminal data layer primarily enables data transmission and interaction between vehicles and nodes with communication capabilities. The network access and transmission layer primarily handles data processing and transmission. The coordination computing and control layer primarily implements data computing and control, including data processing, resource allocation, and swarm intelligence computing. The application layer is used to aggregate various types of data to provide services centered on roads, vehicles, and users.

[0068] Named Data Networking (NDN) is a novel future network architecture where users engage in content-centric communication. As an emerging network architecture, NDN has gained significant attention from researchers. NDN uses Interest Packets (IPCs) and Data Packets (DPCs) to facilitate communication: consumers send IPCs to retrieve content; other nodes, upon receiving IPCs, record the interface information in their routers. If the node has cached the corresponding data from the received IPC, it directly responds to the consumer's request and sends the data back along the reverse path; otherwise, the IPC is forwarded hop-by-hop to the content provider, who then responds to the consumer's request. NDN emphasizes the importance of content and supports multi-content-source communication, reducing the necessity of location-based host-to-host communication. Therefore, the NDN architecture possesses a series of features not found in traditional networks: content naming, intra-network caching, and content forwarding.

[0069] Named data networks transmit information within the network by forwarding interest packets and data packets. Routers at each node within the network contain three data structures: Content Store (CS), Pending Interest Table (PIT), and Forwarding Information Base (FIB). The CS acts as an intra-network cache, caching content according to caching policies. The PIT records the port and content name used to receive content, enabling the aggregation of interest packets with the same name and limiting the transmission frequency of interest packets. The FIB records the requested next-hop interface, and interest packets are sent according to the interface record in the FIB.

[0070] Specifically, in combination Figure 12The NDN forwarding model involves a node querying the CS, FIB, and PIT tables based on the interest packet name when it receives an interest packet. First, the node checks its own router's CS table for a match. If a match is found, it directly responds to the content request and sends the packet to the user. Otherwise, it checks the PIT table to see if the node has previously forwarded an interest packet with the same name. If a forwarding record exists, the node stops forwarding the packet, records the interface that received it, and discards it. This aggregation of interest packets with the same name prevents duplicate transmissions. Otherwise, a new entry for the interest packet is created in the PIT table, and a matching record is found in the FIB table to forward the packet. The data packet is then transmitted back along the reverse path of the interest packet forwarding, based on the PIT table established during the packet transmission process. When a node receives a data packet, it checks the PIT table for a record corresponding to the packet's name. If a record exists, it responds to the content request based on the PIT table record and decides whether to cache the packet. Otherwise, the packet is discarded.

[0071] In the Internet of Vehicles (IoV), effective routing and forwarding strategies are crucial for ensuring reliable data transmission and efficient system operation. The IoV primarily faces the following problems:

[0072] High node mobility: Due to the rapid changes in vehicle nodes in the Internet of Vehicles, traditional address-based routing and forwarding will result in inefficient acquisition of user information.

[0073] Network topology dynamism: The movement of nodes causes their positions to change constantly, resulting in a highly dynamic network topology and unstable communication links.

[0074] The complexity of the communication environment: The distribution of nodes in the 3D scene is complex, and the shadow fading is severe. Many routing and forwarding strategies are designed based on idealized communication environments, and they do not take into account signal propagation and attenuation as well as noise interference during communication, so they cannot adapt well to real-world scenarios.

[0075] Currently, the challenges facing the Internet of Vehicles (IoV) stem from the interaction between vehicle nodes and the real-world traffic environment, which directly impacts the user experience within the network. Content-centric NDN (Network Node Networking) can effectively circumvent the problems inherent in traditional TCP / IP-based IoV systems: Firstly, NDN's content-centric communication approach better aligns with users' content acquisition needs; secondly, NDN can cache content on network nodes, allowing users to access information from multiple content sources, thus optimizing the user experience; and thirdly, NDN supports node mobility, which is crucial for establishing end-to-end connections during rapid changes in vehicle movement, while NDN's focus on content-centric communication effectively addresses this issue.

[0076] Based on different content discovery methods, NDN forwarding strategies for vehicular networks can be divided into three aspects: Proactive Forwarding Strategy (PFS), Reactive Forwarding Strategy (RFS), and Opportunistic Forwarding Strategy (OFS). The basic idea of ​​Proactive Forwarding is to build a routing table for all nodes in the network in real time to construct transmission paths. It obtains routing information from other nodes in the network by periodically sending broadcast information and updates the global routing table in real time based on this information. Reactive Forwarding does not build a global route in real time. The basic idea is that nodes in the network only start the routing process when they have a need for information forwarding. This type of forwarding strategy does not broadcast information to all nodes, but only exchanges information with neighboring nodes. Opportunistic Forwarding adopts a "store-and-carry-forward" model, no longer actively discovering content, but using a passive discovery method to avoid the huge broadcast overhead caused by flooding and routing failures caused by topology changes.

[0077] Reactive and opportunistic strategies employ interest-based and packet-based routing methods, which can avoid broadcast storms. However, during routing, the propagation of wireless signals is significantly affected by the inherent road layering, road obstruction, and shadow fading in a three-dimensional environment. Links between nodes across layers are more unstable than links between nodes within the same layer, and link quality has a greater impact on cross-layer communication. Current forwarding strategies do not fully consider the problems existing in the links, resulting in high communication latency and low efficiency.

[0078] Based on this, embodiments of this application provide a routing method, electronic device, and storage medium based on a named data network, aiming to improve the communication efficiency of the named data network.

[0079] The routing method, electronic device, and storage medium based on named data networks provided in this application are specifically described through the following embodiments. First, the routing method based on named data networks in this application embodiment is described.

[0080] The routing method based on named data networks provided in this application relates to the field of vehicle networking technology. This routing method can be applied to terminals, servers, or software running on either a terminal or server. The terminal or server applying the routing method based on named data networks in this application, based on the spatial distribution of the vehicle networking system, can be a mobile node (such as a vehicle terminal or personal mobile phone terminal) or a fixed node (such as a roadside base station or infrastructure) within the vehicle networking system. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the routing method based on named data networks, but is not limited to the above forms.

[0081] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0082] Figure 1 This is an optional flowchart of a routing method based on named data networks provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S106.

[0083] Step S101: Obtain the set of alternative links for the current node;

[0084] Step S102: Calculate the link reliability of each candidate link in the candidate link set;

[0085] Step S103: Select the candidate link with the highest link reliability as the next-hop transmission link for the current node.

[0086] Among them, reference Figure 2 The reliability of the alternative links is obtained through the following steps:

[0087] Step S201: Determine the weight values ​​of multiple decision attributes using an adaptive deviation maximization algorithm. The multiple decision attributes include node distance attribute, content connection quality attribute, link quality attribute, and link availability attribute.

[0088] Step S202: Obtain the attribute values ​​of multiple decision attributes of the candidate links of the current node;

[0089] Step S203: Determine the link reliability of the candidate links based on the attribute values ​​and weight values ​​of multiple decision attributes.

[0090] In steps S101 to S103 and S201 to S203 as illustrated in this embodiment, the current node of the named data network determines the weight values ​​of multiple decision attributes for evaluating link reliability during the routing process using an adaptive deviation maximization algorithm. This strengthens the expression of decision attributes that have a significant impact on the decision-making process. Each decision attribute includes node distance attribute, content connection quality attribute, link quality attribute, and link availability attribute. Then, the attribute values ​​of multiple decision attributes of candidate links are obtained respectively. Based on the attribute values ​​and weight values ​​of multiple decision attributes, the link reliability of the candidate links is accurately calculated. Finally, the candidate link with the highest link reliability is selected from multiple candidate links in the current node's candidate link set as the next-hop transmission link of the current node. This embodiment can take into account the factors and their importance, such as transmission distance, content connection quality, link signal-to-noise ratio, and link availability, during the routing process of the current node to select the optimal transmission link, avoid broadcast storms, reduce communication latency, improve signal and content transmission and reception quality, and thus improve communication efficiency.

[0091] In step S101 of some embodiments, the current node is a mobile or fixed node in the vehicle network that needs to forward interest packets or data, such as a vehicle node. The alternative link set includes multiple alternative links of the current node. The alternative links are formed by the current node and alternative nodes. The alternative nodes can be neighboring nodes of the current node within its communication range (e.g., nearby vehicle nodes or fixed nodes). Specifically, all neighboring nodes within the communication range of the current node are searched, and neighboring nodes that meet the conditions are selected as alternative nodes based on the location information of the neighboring nodes. The current node and the alternative nodes are respectively used as the data sender and data receiver to form the alternative links. For example, all alternative links formed by the vehicle that needs to send interest packets and its neighboring nodes, i.e., the alternative link set, are represented as l. i (i = 1, 2, ..., n), where n is the number of neighboring nodes of vehicle m that sent the interest packet.

[0092] In steps S102 and S103 of some embodiments, the optimal candidate link in the candidate link set can be determined as the next-hop transmission link for the current node through multi-attribute decision-making. The multi-attribute decision-making process is divided into preparation and decision-making phases. The preparation phase requires obtaining the candidate link set, decision attributes, and attribute weights. The decision-making phase uses the data obtained in the preparation phase to construct a utility function to evaluate the decision attributes, calculates the link reliability of each candidate link based on the utility function, sorts all candidate links, and selects the optimal candidate link based on the sorting result. Among them, the decision attributes are common characteristics of all candidate links, used to evaluate the quality of candidate links. The selection and calculation of decision attributes directly affect the effectiveness of the decision result, and are usually represented by matrices. Attribute weights are technical indicators based on decision attributes, reflecting the degree of influence of different decision attributes on the decision result.

[0093] In steps S201 to S203 of some embodiments, the basic idea of ​​the deviation maximization algorithm is that, for a certain decision attribute, if the attribute values ​​of all candidate links have small differences, it indicates that the decision attribute has a smaller impact on the link reliability of the candidate links; conversely, if the attribute values ​​of candidate links have large differences, it indicates that the decision attribute will play an important role in the link reliability of the candidate links. Therefore, the larger the deviation of the decision attribute values ​​in all samples, the greater the weight should be assigned, and the smaller the deviation, the smaller the weight should be assigned. Specifically, a decision matrix is ​​constructed by all attribute values ​​in the candidate link set. The deviation maximization algorithm is applied to the decision matrix to adaptively analyze the weight of each decision attribute. Each candidate link, based on its own decision attribute value and corresponding weight value, uses the constructed utility function to finally obtain the link reliability of each candidate link.

[0094] Please see Figure 3 In some embodiments, the value of the node distance attribute is obtained through the following steps:

[0095] Step S301: Obtain the first location information of the current node and the second location information of the candidate nodes;

[0096] Step S302: Determine the Euclidean distance between the current node and the candidate nodes based on the first location information and the second location information;

[0097] Step S303: Normalize the Euclidean distance based on the maximum wireless communication distance across layers of the node to obtain the attribute value of the node distance attribute.

[0098] In this embodiment, the basic information that a vehicle node can generate is its motion state information. In a broad sense, motion state information is the velocity vector and displacement vector at each moment. In a narrow sense, motion state information includes the energy consumption, steering information, and operating status of the vehicle node. Since the performance and configuration are different, this embodiment uses the motion information between nodes, that is, the broad motion state information. Therefore, velocity, displacement, etc. can be used as the standard for calculating node performance.

[0099] The calculation of the node distance attribute value for the candidate links is as follows:

[0100] The first position information of the current node m in the dummy device selection link is (x m y m z m The displacement coordinates of the second position information of the candidate node n at time τ are (x) n y n z n Then the Euclidean distance d between nodes m and n is... mn As shown in formula (1):

[0101]

[0102] For ease of subsequent calculations, the Euclidean distance is normalized to H(d), as shown in formula (2):

[0103]

[0104] Among them, R' max R' is the maximum wireless communication range across layers for vehicle nodes. max =δR max This indicates that cross-layer communication has a reduced communication range compared to intra-layer communication due to shadow fading.

[0105] The value of the node distance attribute H(d) is positively correlated with the node's position priority. That is, the smaller the value of the node distance attribute, the higher the position priority of the corresponding candidate node. Therefore, the node distance attribute can also represent position priority.

[0106] Please see Figure 4 In some embodiments, the attribute value of the content connectivity quality attribute is obtained through the following steps:

[0107] Step S401: Obtain the number of data packets and interest packets forwarded by the candidate node with the target name as the prefix;

[0108] Step S402: Determine the initial content connection quality of the candidate nodes at the current moment based on the number of data packets and the number of interest packets;

[0109] Step S403: Perform a weighted calculation on the initial content connection quality and the weighted content connection quality of the previous time step to obtain the weighted content connection quality of the current time step, wherein the weighted content connection quality of the current time step is the attribute value of the content connection quality attribute.

[0110] In this embodiment, since NDN is based on the characteristics of the content itself, the impact of the content name on the node must be considered when calculating the node performance. If the node frequently forwards packets with commonly used names, the node will have a higher forwarding priority when forwarding new packets.

[0111] Content Connective Quality (CCQ) is a parameter related to content naming and is based on the naming prefix. Different content naming prefixes have different CCQ values. Therefore, when a current node sends an interest packet, it queries the CCQ values ​​of candidate nodes for interest packets with that name, and nodes with higher values ​​have higher forwarding priority.

[0112] Specifically, the CCQ prefix is ​​calculated as the ratio of the number of packets a node receives in response to the number of packets of interest it sends. The number of packets forwarded by the node with the prefix "j" is D. j The number of interest packets forwarded with the prefix "j" is I. j ,des (j) It is a set containing the descendants of prefix j. The CCQ of this prefix includes the interest and data count of the name prefix descendants. In other words, the CCQ of a name prefix represents the average connection quality of all known name prefixes in that namespace. During CCQ calculation, the producer also needs to add the prefix to the packet's field. When a node receives a packet, it updates the packet count value for the corresponding name prefix.

[0113] CCQ is calculated as shown in formula (3):

[0114]

[0115] When making forwarding decisions for interest packets, in order to calculate the CCQ value more accurately, the CCQ value of the current time and the previous time is weighted and calculated. This can better evaluate the node performance and facilitate the comprehensive calculation of node performance and link status. H(q) represents the weighted result of CCQ, that is, the attribute value of the content connection quality attribute is shown in formula (4):

[0116]

[0117] Where i is the prefix of the data name to be forwarded, and P is the time when the candidate node receives the beacon with the status information. Assuming 6 seconds is the unit time for beacon updates, CCQ... i,PThe initial content connectivity quality at time P is calculated using formula (3). The weighted content connection quality for the 6 seconds preceding time P. ξ represents the weighted content connectivity quality at time P, i.e., the attribute value of the content connectivity quality attribute. ξ is the weight value, which can be set to 0.125, a setting that aligns with the node movement state.

[0118] Please see Figure 5 In some embodiments, the attribute values ​​of the link quality attributes are obtained through the following steps:

[0119] Step S501: Obtain the distribution status of the current node and the candidate nodes;

[0120] Step S502: When the distribution state is a same-layer distribution, the first remaining distance between the current node and the candidate node is determined based on line-of-sight propagation, and the signal propagation loss is determined based on the first remaining distance.

[0121] Step S503: When the distribution state is a cross-layer distribution, the second remaining distance between the current node and the candidate node is determined based on non-line-of-sight propagation, and the signal propagation loss is determined based on the second remaining distance and the shadow fading factor.

[0122] Step S504: Determine the attribute value of the link quality attribute based on the signal propagation loss.

[0123] In this embodiment, when the current node m sends data to the candidate node n at time τ1, the interference calculation method for node n at time τ1 based on the long-distance loss model remains unchanged, still being the sum of Gaussian white noise and propagation loss, but the signal propagation loss... The signal propagation loss at time τ1 changes. As shown in formula (5):

[0124]

[0125] Where, β k ∈{0,1} represents the distribution state of the current node and the candidate nodes, β k =0 indicates that the candidate nodes and the current node are distributed in the same layer, β k =1 represents the cross-layer distribution of candidate nodes and the current node. ρ mn ∈{ρ L ,ρ NL} can characterize the shadow fading of alternative links; ρ L =1 indicates that the signal between nodes propagates at line of sight (LOS), and communication between nodes on the same layer in a 2D scene is also at line of sight; ρ NL=ρ indicates that the signal between nodes is non-line-of-sight (NLOS). Cross-layer node communication in a 3D scene is non-line-of-sight propagation. ρ is the shadow fading factor, ρ = exp(-σ 2 / 2+σψ) and σ=σ (dB) log(10) / 10, σ (dB) ψ is the standard deviation of the log-normal shaded distribution, and ψ is the standard normal quantity.

[0126] In the calculation of vehicle interference nodes, the parameter affected by shadow fading is the remaining communication distance d between vehicles. r nk Assume that the communication range of a vehicle within the same layer is R, and the communication range across layers is R', where R' < R. Combined with... Figure 13 The remaining distance d between vehicles in the 3D scene r nk The calculation is as follows:

[0127] Case 1: When the two vehicle nodes move in the same direction, and v k >v n (i.e., k is in front of n), or the two nodes move in opposite directions, d r nk The calculation is shown in formula (6):

[0128]

[0129] Case 2: When the two vehicle nodes move in the same direction, and v k <v n (i.e., k is after n) or the two nodes move in opposite directions, d r nk The calculation is shown in formula (7):

[0130]

[0131] Finally, after a time interval Δτ, the signal propagation loss when the packet sent by the current node m at τ1 reaches the candidate node n is shown in equation (8):

[0132]

[0133] As can be seen from formula (8), when two nodes are distributed at the same level, then When two nodes are distributed across layers, then Where, d 1 nk,τ1+Δτ This represents the distance between a neighboring node and the node that sent the message when two nodes are on the same layer. Similarly, d 2 nk,τ1+ΔτThis represents the distance between a neighboring node and the node sending the information when two nodes are not on the same layer. θ represents the antenna gain (the antenna of the signal transmitting device). 2 < α < 4 is the path loss exponent (the loss exponent varies when wireless signals propagate in different environments).

[0134] After obtaining the signal propagation loss of the candidate links, the signal-to-interference plus-noise ratio (SINR) of the link can be determined based on the signal propagation loss. The SINR characterizes the link quality, that is, the attribute value of the link quality attribute is the probability that the link SINR is greater than a threshold. In some embodiments, when the distance between vehicle nodes is less than the wireless communication range, the vehicle nodes can communicate. Link availability is defined as the probability that there is a stable communication link between nodes within a certain period of time. The attribute value of the link availability attribute can be calculated by the probability density function f(T), as shown in formula (9):

[0135] Mean and variance of velocity, β k R represents the node's hierarchical status and the signal communication range threshold. By constructing a probability density function in the form of a Gaussian distribution using the above parameters, and then integrating it, we can obtain the probability of the link being available within time T.

[0136] Please see Figure 6 In some embodiments, step S101, which involves obtaining the set of candidate links for the current node, includes, but is not limited to, the following steps:

[0137] Step S601: Obtain the location information of multiple neighboring nodes of the current node;

[0138] Step S602: Select candidate nodes from multiple neighboring nodes based on location information, and determine candidate links based on the current node and candidate nodes;

[0139] Step S603: Determine the set of alternative links based on multiple alternative links.

[0140] In this embodiment, all neighboring nodes within the communication range of the current node are searched. Based on the location information of these neighboring nodes, qualified neighboring nodes are selected as candidate nodes. The current node and the candidate nodes are then designated as the data sender and receiver, respectively, forming a candidate link. All candidate links of the current node constitute the candidate link set for the current node. The neighboring node selection criteria based on location information can be nodes within the communication range of the current node.

[0141] Please see Figure 7In some embodiments, step S201, which involves determining the weight values ​​of multiple decision attributes using an adaptive deviation maximization algorithm, includes, but is not limited to, the following steps:

[0142] Step S701: Determine the initial decision matrix based on all attribute values ​​of the decision attributes of the candidate link set, wherein the decision matrix includes the decision attribute dimension and the candidate link dimension;

[0143] Step S702: Construct a standard decision matrix based on the attribute weight vector and the initial decision matrix;

[0144] Step S703: Determine the total deviation function of all decision attributes with respect to the attribute weight vector based on the standard decision matrix;

[0145] Step S704: Determine the objective function for maximizing deviation based on the weight constraints of the total deviation function attributes;

[0146] Step S705: Determine the weight values ​​of the decision attributes based on the deviation maximization objective function.

[0147] In this embodiment, combined with Figure 14 Node distance, content connectivity quality, link quality, and link availability are defined as decision attributes, denoted as A. j (j = 1, 2, 3, 4). H(d), H(q), H(s), and H(a) represent the utility functions used to calculate the attribute values ​​of each decision attribute of the candidate links, namely, location priority, content connectivity quality, probability that the SINR of the node link is greater than the threshold, and probability that the link is available. H(d), H(q), H(s), and H(a) are all between [0, 1] and satisfy the normalization criteria of attribute weights. Define candidate link l i Corresponding decision attribute A j The attribute value is h ij The decision matrix formed by the attribute values ​​of all candidate links in the candidate link set is shown in formula (10):

[0148]

[0149] In this decision matrix, the rows represent the candidate link dimension, with a total of n candidate links, and the columns represent the decision attribute dimension, with a total of 4 decision attributes.

[0150] After obtaining the initial decision matrix Λ (0) =[h ij ]∈R n×4 Next, let the attribute weight vector of the decision attribute be... Constructing a normalized standard decision matrix The total deviation function of the decision attributes is constructed based on the standard decision matrix, as follows:

[0151] Calculate alternative links based on the standard decision matrix. i The total attribute evaluation value is

[0152] For decision attribute A j Determine alternative links i Other alternatives k deviation Then determine the deviation of all candidate links.

[0153] Based on the deviations of all the above alternative links, the total deviation function of all decision attributes is:

[0154] After obtaining the total deviation function of all decision attributes, according to the constraints and Construct a deviation maximization objective function, and then solve the deviation maximization objective function by optimizing the weight attribute vector to obtain the weight values ​​of the decision attributes.

[0155] Please see Figure 8 In some embodiments, step S705, which involves determining the weight values ​​of multiple decision attributes based on the deviation maximization objective function, includes, but is not limited to, the following steps:

[0156] Step S801: Convert the objective function of maximizing deviation based on conditional constraints into an unconditional Lagrangian function;

[0157] Step S802: Perform extremum and normalization operations on the Lagrange function to obtain the weight values ​​of the decision attributes.

[0158] In this embodiment, when solving for the optimal value of the deviation maximization objective function, the above-mentioned deviation maximization objective function based on conditional constraints can be transformed into an unconditional Lagrangian function, as shown in formula (11):

[0159]

[0160] Find the extrema of the Lagrange function, i.e. Then, normalization is performed to obtain the optimal weight values ​​for the decision attributes. And ω i d +ω i q +ω i s +ω i a =1.

[0161] In some embodiments, step S203, which involves determining the link reliability of candidate links based on the attribute values ​​and weight values ​​of multiple decision attributes, includes, but is not limited to, the following steps:

[0162] Step S901: Determine whether there is an attribute value of zero in the candidate link;

[0163] Step S902: If there is an attribute value that is zero, then the link reliability of the candidate link is determined to be zero.

[0164] Step S903: When there is no attribute value with a value of zero, the attribute values ​​and weight values ​​of multiple decision attributes are substituted into the multi-attribute utility function to obtain the link reliability of the candidate link.

[0165] In this embodiment, based on the characteristics of NDN vehicle-to-everything (V2X) networks, node distance, content connection quality, SINR, and link availability are selected as decision attributes for information forwarding, and link reliability is used to characterize the priority of forwarding nodes and links. For a given candidate link, when any decision attribute is 0, the reliability is 0, and information cannot be forwarded through that candidate link. Conversely, when each decision attribute value of the candidate link is not 0, any change in the decision attribute can affect the final comprehensive evaluation of link reliability.

[0166] Specifically, both the link reliability and decision attribute values ​​are between [0,1], and the link reliability should satisfy the following constraint formula:

[0167]

[0168]

[0169] In equations (12) and (13), h(x) j Let H(x) be the attribute value of each decision attribute of the candidate link, H(x) be the link reliability value, and q be the number of decision attributes. The above constraint formula shows that when any decision attribute value is 0, the reliability value of the link is 0, and the forwarding probability is also 0; conversely, when all decision attribute values ​​approach 1, the reliability of the link approaches 1; the constraints jointly characterize the reliability of the forwarding link.

[0170] By constraining link reliability and based on multi-attribute decision theory, a link reliability evaluation function consisting of two nodes is constructed in the NDN (Network Vehicle Networking) scenario as follows:

[0171]

[0172] Where q is the total number of decision attributes, ω j Different decision attributes x for the same link j The corresponding decision weights, and satisfying h(xj ) represents the attribute value of the decision attribute j of this link.

[0173] Combination Figure 14 The current node has multiple backup links in its backup link set. For the backup link set used to calculate backup link l... i The multi-attribute utility function of reliability is expressed as:

[0174]

[0175] Among them, H i Indicates alternative link l i Link reliability, H i (d), H i (q), H i (s) and H i (a) respectively represent l i The attribute values ​​of the transmitting and receiving node distance, CCQ, SINR, and link availability, ω i , and These represent the weight values ​​of the four decision attributes. When any evaluation attribute has a value of 0, the link reliability is 0.

[0176] During the forwarding process, the multi-attribute decision model of the relay node is represented as follows:

[0177]

[0178] The constraints for link reliability calculation are as follows:

[0179]

[0180]

[0181]

[0182] Constraints (17)-(19) indicate that the sum of the weight values ​​of the four decision attributes is 1; the individual weight value is between (0, 1), and the attribute value of the decision attribute is between [0, 1].

[0183] According to some embodiments of the present invention, the complexity of the vehicular network communication environment and node distribution in a 3D scene has a significant impact on the effectiveness of routing and forwarding strategies. Based on this, embodiments of the present invention provide an adaptive routing and forwarding method by analyzing the node characteristics and wireless link performance in a 3D scene to improve the routing efficiency of vehicular networks in 3D scenes. Embodiments of the present invention consider the changes in links and nodes caused by shadow fading and node layering in a 3D scene, and then make decisions based on multiple attributes of nodes and links to derive the optimal next-hop transmission link. The routing method of the present invention utilizes dynamic link attribute values ​​and adaptive weight calculation techniques to analyze the next-hop routing node, enabling it to adaptively and quickly respond to changes in network topology and dynamically adjust routing and forwarding decisions according to network conditions.

[0184] This application also provides an electronic device, which includes: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for communication between the processor and the memory. When the program is executed by the processor, it implements the aforementioned routing method based on a named data network. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0185] Please see Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0186] The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0187] The memory 902 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 to execute the routing method based on named data networks according to the embodiments of this application.

[0188] The input / output interface 903 is used to implement information input and output;

[0189] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0190] Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904);

[0191] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0192] This application embodiment also provides a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, which can be executed by one or more processors to implement the above-described routing method based on named data networks.

[0193] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0194] The recommendation method, recommendation device, electronic device, and storage medium provided in this application embodiment determine the weight values ​​of multiple decision attributes used to evaluate link reliability through an adaptive deviation maximization algorithm, thereby strengthening the expression of decision attributes that have a significant impact on the decision-making process. Each decision attribute includes node distance attribute, content connection quality attribute, link quality attribute, and link availability attribute. Then, the attribute values ​​of multiple decision attributes of candidate links are obtained respectively. Based on the attribute values ​​and weight values ​​of multiple decision attributes, the link reliability of candidate links is accurately calculated. Then, the candidate link with the highest link reliability is selected from multiple candidate links in the current node's candidate link set as the next-hop transmission link of the current node. This embodiment can take into account the factors and their importance, such as transmission distance, content connection quality, link signal-to-noise ratio, and link availability, during the routing process of the current node to select the optimal transmission link, avoid broadcast storms, reduce communication latency, improve signal and content transmission and reception quality, and thus improve communication efficiency.

[0195] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0196] It will be understood by those skilled in the art that Figure 1-8 The technical solutions shown do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0197] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0198] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0199] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0200] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0201] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0202] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0203] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0204] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0205] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A routing method based on named data networks, characterized in that, Includes the following steps: Get the set of alternative links for the current node; Calculate the link reliability of each candidate link in the candidate link set; Select the candidate link with the highest link reliability as the next hop transmission link for the current node; The reliability of the alternative links is obtained through the following steps: The weight values ​​of multiple decision attributes are determined by an adaptive deviation maximization algorithm. These multiple decision attributes include node distance attribute, content connection quality attribute, link quality attribute, and link availability attribute. Obtain the attribute values ​​of multiple decision attributes for the candidate links of the current node; The link reliability of the candidate links is determined based on the attribute values ​​and weight values ​​of multiple decision attributes; The attribute value of the link quality attribute is obtained through the following steps: Obtain the distribution status of the current node and candidate nodes; When the distribution state is a same-layer distribution, the first remaining distance between the current node and the candidate node is determined based on line-of-sight propagation, and the signal propagation loss is determined based on the first remaining distance. When the distribution state is a cross-layer distribution, the second remaining distance between the current node and the candidate node is determined based on non-line-of-sight propagation, and the signal propagation loss is determined based on the second remaining distance and the shadow fading factor. The attribute values ​​of the link quality attributes are determined based on the signal propagation loss. The process of determining the weight values ​​of multiple decision attributes using the adaptive deviation maximization algorithm includes the following steps: An initial decision matrix is ​​determined based on all attribute values ​​of the decision attributes of the candidate link set, wherein the decision matrix includes a decision attribute dimension and a candidate link dimension; Construct a standard decision matrix based on the attribute weight vector and the initial decision matrix; Determine the total deviation function of all decision attributes with respect to the attribute weight vector based on the standard decision matrix; The objective function for maximizing deviation is determined based on the attribute weight constraints of the total deviation function; The weight values ​​of the decision attributes are determined based on the objective function of maximizing the deviation. Determining the link reliability of the candidate links based on the attribute values ​​and weight values ​​of multiple decision attributes includes the following steps: Determine if any candidate links have an attribute value of zero; If there is an attribute value that is zero, then the link reliability of the candidate link is determined to be zero. If there is no attribute value of zero, the attribute values ​​and weight values ​​of multiple decision attributes are substituted into the multi-attribute utility function to obtain the link reliability of the candidate link.

2. The routing method based on named data networks according to claim 1, characterized in that, The value of the node distance attribute is obtained through the following steps: Obtain the first position information of the current node and the second position information of the candidate nodes; The Euclidean distance between the current node and the candidate node is determined based on the first location information and the second location information. The Euclidean distance is normalized based on the maximum wireless communication distance across layers of the node to obtain the attribute value of the node distance attribute.

3. The routing method based on named data networks according to claim 1, characterized in that, The attribute value of the content connection quality attribute is obtained through the following steps: Obtain the number of data packets and interest packets forwarded by the candidate node with the target name as the prefix; The initial content connection quality of the candidate nodes at the current moment is determined based on the number of data packets and the number of interest packets. The initial content connection quality and the weighted content connection quality of the previous time step are weighted and calculated to obtain the weighted content connection quality of the current time step, wherein the weighted content connection quality of the current time step is the attribute value of the content connection quality attribute.

4. The routing method based on named data networks according to claim 1, characterized in that, Obtaining the set of alternative links for the current node includes the following steps: Get the location information of multiple neighboring nodes of the current node; Based on the location information, candidate nodes are selected from multiple neighboring nodes, and candidate links are determined based on the current node and the candidate nodes; The set of alternative links is determined based on multiple alternative links.

5. The routing method based on named data networks according to claim 1, characterized in that, Determining the weight values ​​of the decision attributes based on the deviation maximization objective function includes the following steps: The objective function for maximizing deviation based on conditional constraints is transformed into an unconditional Lagrangian function. The Lagrange function is subjected to extremum and normalization operations to obtain the weight values ​​of the decision attributes.

6. An electronic device, characterized in that, The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for enabling communication between the processor and the memory. When the program is executed by the processor, it implements the steps of the routing method based on a named data network as described in any one of claims 1 to 5.

7. A storage medium, said storage medium being a computer-readable storage medium for computer-readable storage, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the steps of the routing method based on a named data network as described in any one of claims 1 to 5.

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