Network-free relay communication method, equipment and vehicle

Abstract

The invention provides a network-free relay communication method and device and a vehicle, and the method comprises the steps: obtaining a communication node set of the vehicle under the condition that the vehicle is located in a network-free scene; the communication node set comprises a plurality of vehicle nodes, the plurality of vehicle nodes comprise at least one vehicle node in network communication with the network side, no-network short-distance communication can be performed between adjacent vehicle nodes in the plurality of vehicle nodes, and the vehicle can perform no-network short-distance communication with at least one vehicle node in the plurality of vehicle nodes; determining an optimal communication path of the vehicle according to the communication node set based on the return report; the return report comprises a mapping relation between different path selections and reward values under different communication node sets; the optimal communication path is a path selection strategy corresponding to the maximum reward value in the return report; and transmitting the to-be-transmitted data of the vehicle to a network side through the optimal communication path. According to the embodiment of the invention, the reliability of the vehicle in network-free relay communication can be improved.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a network-free relay communication method, device and vehicle. Background Technology

[0002] With the rapid development of intelligent transportation, emergency communications, and other fields, the demand for reliable communication in scenarios without network infrastructure is becoming increasingly urgent. In extreme environments such as tunnels, remote mountainous areas, and natural disasters, network-free relay communication methods have become a key technology for ensuring information transmission.

[0003] Traditional wireless relay communication methods rely on communication protocols and networking mechanisms centered around fixed nodes. In scenarios lacking infrastructure support, such as tunnels, remote mountainous areas, or natural disasters, the lack of reliable fixed nodes makes it difficult for existing wireless relay communication methods to quickly establish effective relay links, leading to communication interruptions and consequently low communication reliability. Summary of the Invention

[0004] This application provides a network-free relay communication method, device, and vehicle that can improve the reliability of vehicles performing network-free relay communication.

[0005] In a first aspect, embodiments of this application provide a network-free relay communication method, the method comprising:

[0006] When the vehicle is in a network-free scenario, the communication node set of the vehicle is obtained; the communication node set includes multiple vehicle nodes, including at least one vehicle node that communicates with the network side, adjacent vehicle nodes among the multiple vehicle nodes can perform short-range communication without network, and the vehicle can perform short-range communication without network with at least one of the multiple vehicle nodes.

[0007] Based on the reward table and the set of communication nodes, the optimal communication path for the vehicle is determined. The reward table includes the mapping relationship between different path selections and reward values ​​under different sets of communication nodes. The optimal communication path is the path selection strategy corresponding to the maximum reward value in the reward table. The reward value is determined based on the received signal strength and packet loss rate of the path.

[0008] The vehicle's data to be transmitted is transmitted to the network side via the optimal communication path.

[0009] Secondly, this application provides a network-free relay communication device, the device comprising:

[0010] The acquisition module is used to acquire the communication node set of the vehicle when the vehicle is in a network-free scenario; the communication node set includes multiple vehicle nodes, the multiple vehicle nodes include at least one vehicle node that communicates with the network side, adjacent vehicle nodes among the multiple vehicle nodes can perform short-range communication without network, and the vehicle can perform short-range communication without network with at least one of the multiple vehicle nodes.

[0011] The determination module is used to determine the optimal communication path for the vehicle based on the reward table and the communication node set; the reward table includes the mapping relationship between different path selections and reward values ​​under different communication node sets; the optimal communication path is the path selection strategy corresponding to the maximum reward value in the reward table; the reward value is determined based on the received signal strength and packet loss rate of the path;

[0012] The transmission module is used to transmit the vehicle's data to be transmitted to the network side through the optimal communication path.

[0013] Thirdly, embodiments of this application provide an electronic device, which includes: a processor and a memory storing computer program instructions;

[0014] When the processor executes computer program instructions, it implements the wireless relay communication method as described in any of the embodiments of the first aspect.

[0015] Fourthly, embodiments of this application provide a computer storage medium storing computer program instructions, which, when executed by a processor, implement the wireless relay communication method as described in any of the embodiments of the first aspect.

[0016] Fifthly, embodiments of this application provide a computer program product in which instructions, when executed by a processor of an electronic device, cause the electronic device to perform a network-free relay communication method as described in any of the embodiments of the first aspect above.

[0017] Sixthly, embodiments of this application also provide a vehicle, which includes at least one of the following:

[0018] Such as the wireless relay communication device in the second aspect;

[0019] Such as electronic devices in the third aspect;

[0020] Such as the computer-readable storage medium in the fourth aspect.

[0021] Such as computer program products in the fifth aspect.

[0022] In the wireless relay communication method, device, and vehicle provided in this application embodiment, a communication node set containing multiple vehicle nodes is obtained, including at least one vehicle node capable of communicating with the network side. Adjacent vehicle nodes can perform short-range wireless communication, thereby avoiding dependence on fixed infrastructure and fully utilizing mobile vehicle nodes to construct a communication network, greatly enhancing adaptability and flexibility in wireless scenarios. Furthermore, based on a reward table, the optimal communication path for the vehicle is determined according to the communication node set. The reward value in the reward table is determined based on the received signal strength and packet loss rate of the path, ensuring higher stability and efficiency of the selected optimal communication path. This quantitative evaluation mechanism ensures high stability and transmission quality of the selected path in actual communication, avoiding communication interruptions or data loss due to improper path selection, thus improving the reliability of vehicle wireless relay communication. Finally, the vehicle's data to be transmitted is transmitted to the network side through the optimal communication path, ensuring that the data can be efficiently delivered using the reliable link constructed by the vehicle nodes in a wireless scenario, thereby improving the reliability of vehicle wireless relay communication. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is one of the flowcharts illustrating the wireless relay communication method provided in the embodiments of this application;

[0025] Figure 2-1 and Figure 2-2 This is the second flowchart illustrating the wireless relay communication method provided in the embodiments of this application;

[0026] Figure 3 This is the third flowchart illustrating the wireless relay communication method provided in the embodiments of this application;

[0027] Figure 4 This is a schematic diagram of the architecture of the infrastructure-free relay communication system provided in the embodiments of this application;

[0028] Figure 5 This is one of the schematic diagrams illustrating the principle of the network-free relay communication method provided in the embodiments of this application;

[0029] Figure 6 This is the second schematic diagram illustrating the principle of the network-free relay communication method provided in the embodiments of this application;

[0030] Figure 7 This is the fourth flowchart illustrating the wireless relay communication method provided in the embodiments of this application;

[0031] Figure 8 This is the fifth flowchart illustrating the wireless relay communication method provided in the embodiments of this application;

[0032] Figure 9 This is a schematic diagram of the structure of a network-free relay communication device provided in an embodiment of this application;

[0033] Figure 10 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0034] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0035] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0036] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0037] To address the problems existing in related technologies, embodiments of this application provide a network-free relay communication method, device, and vehicle.

[0038] The following section first describes the wireless relay communication method provided in the embodiments of this application. For example... Figure 1 As shown, the method specifically includes the following steps:

[0039] S100, when the vehicle is in a network-free scenario, obtain the communication node set of the vehicle; the communication node set includes multiple vehicle nodes, the multiple vehicle nodes include at least one vehicle node that communicates with the network side, adjacent vehicle nodes among the multiple vehicle nodes can perform short-range communication without network, and the vehicle can perform short-range communication without network with at least one of the multiple vehicle nodes.

[0040] Optionally, in this embodiment, a "no network scenario" refers to an environment lacking network infrastructure support. In such scenarios, network devices such as mobile communication base stations, Wi-Fi hotspots, and routers cannot provide network services. Examples include remote mountainous areas, deserts, deep seas, or areas where communication base stations are damaged or networks are paralyzed due to natural disasters (such as earthquakes or floods), as well as locations with severe signal shielding such as tunnels and underground parking lots.

[0041] A vehicle node refers to a vehicle equipped with specific communication equipment or modules. Vehicle nodes can be various types of intelligent connected vehicles, special-purpose vehicles equipped with communication devices, or vehicle-mounted mobile platforms used for emergency communication. Each vehicle node has independent communication capabilities and possesses its own location information, battery status, signal strength, and other parameters, which are used to calculate communication paths. By establishing communication links with other nodes, vehicle nodes can form a distributed communication network in a network-free environment, thereby enabling multi-hop relay transmission of data.

[0042] The network side represents the entity or facility capable of providing conventional network services and resources, and is the target end for vehicle data transmission in network-free scenarios. The network side can be an operator's mobile communication core network, an internet server cluster, or an edge computing node deployed in a specific area. In this application, the vehicle node ultimately sends the data to be transmitted to the network side through an optimal communication path, thereby achieving connection with the external network and obtaining network services or uploading data. The network side provides a bridge for vehicle nodes to interact with the outside world and is the key interface for the network-free relay communication system to achieve complete communication functions.

[0043] Networkless short-range communication refers to a communication method in which vehicle nodes exchange data through short-range wireless communication technology without relying on traditional network infrastructure. This communication method can be based on wireless technologies such as Bluetooth and dedicated short-range communication, and the communication distance is generally limited to a short range (such as tens to hundreds of meters). Through networkless short-range communication, adjacent vehicle nodes can quickly exchange their own status information (such as location, speed, and battery level) and data to be transmitted, laying the foundation for building communication paths and realizing data relay forwarding.

[0044] Adjacent vehicle nodes refer to vehicle nodes that are spatially close to each other and meet the coverage requirements of wireless short-range communication technology. These nodes can directly establish wireless communication links to achieve direct data transmission. Determining whether vehicle nodes are adjacent depends not only on physical distance but also on the propagation characteristics of wireless signals and the coverage capabilities of communication equipment. Adjacent vehicle nodes constitute the basic connection units of a wireless relay communication network, and the stability of their communication links and data transmission efficiency directly affect the performance of the entire communication system. Information about adjacent vehicle nodes is crucial for network topology construction and path selection. By rationally utilizing the communication links between adjacent nodes, efficient multi-hop data transmission can be achieved, expanding communication coverage.

[0045] Optionally, in one feasible implementation of this application, direct neighbor discovery is first performed. The target vehicle broadcasts a probe frame carrying its own GPS coordinates (including vehicle ID and communication capability identifier) ​​at a preset period, receives response frames, and filters nodes that meet the constraints. A list of direct neighbors is established through bidirectional handshake verification.

[0046] Next, a cascading public network node search is performed. Based on the list of directly connected neighbors, an on-demand routing request is initiated. The request message carries a target characteristic identifier: it must have cellular network access capability (4G / 5G). Intermediate nodes spread the request through multi-hop forwarding. When a public network node receives the request, it replies with a routing response along the reverse path. The target vehicle collects all routing responses to construct a communication node set.

[0047] S200, based on the reward table, determine the optimal communication path for the vehicle according to the communication node set; the reward table includes the mapping relationship between different path selections and reward values ​​under different communication node sets; the optimal communication path is the path selection strategy corresponding to the maximum reward value in the reward table; the reward value is determined according to the received signal strength and packet loss rate of the path.

[0048] Optionally, in this embodiment, the reward table records the correspondence between different choices made by the vehicle for communication paths and their corresponding reward values ​​in a network environment composed of different communication node sets. During network-free relay communication, the vehicle faces multiple path selection possibilities. The reward table assigns a quantified reward value to each possible path selection strategy. This reward value comprehensively reflects the path's performance in communication quality and other aspects, thus providing data support for the vehicle to determine the optimal communication path. The reward value depends on factors such as the received signal strength and packet loss rate during actual or simulated transmission. Higher received signal strength and lower packet loss rate result in a higher reward value, and vice versa.

[0049] Path selection refers to the different choices made by a vehicle in a network composed of communication node sets to select a data transmission path in order to transmit data to the network side.

[0050] In a network-free relay communication scenario, based on the reward values ​​corresponding to different path selections in the reward table, the optimal communication path is the one selected by the vehicle from all possible communication paths that maximizes the reward value. This path, under the current set of communication nodes, can best meet the vehicle's communication quality requirements, ensuring relatively high received signal strength and relatively low packet loss rate during data transmission, making it the best path selection for the vehicle to achieve efficient and reliable communication.

[0051] Path selection strategy refers to the specific decision-making methods and rules adopted by vehicles when determining communication paths. Based on the analysis of the state information of each vehicle node within the communication node set, combined with the reward values ​​corresponding to different path selections in the reward table, a suitable transmission path is selected from numerous possible paths through a specific algorithm or logic. Different path selection strategies lead to different path selection results, and the optimal communication path is determined by comparing reward values ​​under a specific path selection strategy.

[0052] Received signal strength refers to the strength of the wireless signal received by a vehicle from other vehicle nodes during communication. It is one of the important indicators for measuring the quality of the communication link. The higher the signal strength, the less attenuation occurs during signal transmission, the higher the reliability of data transmission, and the higher the corresponding reward value in the reward table. Conversely, too low a signal strength may lead to data transmission errors or interruptions, affecting the communication effect.

[0053] Packet loss rate refers to the proportion of data packets lost during data transmission out of the total number of transmitted data packets. In wireless relay communication, due to the complexity and uncertainty of the network environment, data packet loss may occur. A lower packet loss rate indicates better data transmission integrity and higher communication quality, resulting in a higher reward value in the reward table. A higher packet loss rate leads to data transmission failures or the need for retransmission, reducing communication efficiency and affecting the evaluation of reward values.

[0054] Optionally, in one feasible implementation of this application, firstly, real-time topology awareness is performed on the acquired set of communication nodes. By analyzing information such as the received signal strength and movement vector of the nodes, a topology map reflecting the current state of the network is dynamically generated. This topology map intuitively presents the connection relationships and link quality between each vehicle node. Next, based on the generated topology map, the reward table is updated using the Q-learning algorithm. The reward table stores the value of each path selection strategy under different network states. Through continuous learning and optimization, the reward table can more accurately reflect the advantages and disadvantages of each path. After the reward table is updated, based on the updated reward table, the path selection strategy with the highest value is selected from all possible paths. The path corresponding to this strategy is the optimal communication path that can meet the requirements of the current network state.

[0055] S300, the vehicle's data to be transmitted is communicated with the network side via the optimal communication path.

[0056] Optionally, in one feasible implementation of this application, during the data transmission phase, the vehicle encapsulates the data to be transmitted into data packets with path identifiers and sends them to the first vehicle node on the optimal path via short-range wireless communication. The vehicle node forwards the data hop-by-hop according to the path sequence until it reaches a node connected to the network side, where the node accesses the network to complete the data upload. If the vehicle detects a deterioration in path quality during travel (such as a sudden drop in signal strength or link interruption), it immediately triggers S200 to recalculate the path and switches to the new optimal path to continue transmission, ensuring the continuity of data transmission.

[0057] During the data reception phase, the network first sends the response data to the edge vehicle node connected to the network. At this point, the edge node, acting as the starting point of the reception process, establishes a downlink through the following mechanism:

[0058] 1. Edge nodes broadcast a path request message containing the target vehicle ID to neighboring vehicle nodes. The message also carries the quality of service requirements (such as latency thresholds) provided by the network side.

[0059] 2. The request message is forwarded hop-by-hop between vehicle nodes, and each relay node records the forwarding path. When the target vehicle receives the request message, it returns an acknowledgment message along the recorded forwarding path, thereby establishing a temporary path from the edge node to the target vehicle.

[0060] 3. Based on the current set of communication nodes, the target vehicle triggers S200 to recalculate the optimal receiving path and replaces the temporary path with the new path. Network-side data is then transmitted hop-by-hop to the target vehicle via the optimized path.

[0061] Throughout the process, the path is adjusted in real time according to vehicle movement and node status. In particular, the establishment of the receiving path relies on the active broadcasting of edge nodes to ensure that even if the vehicle is in a network-free environment, it can still obtain network-side data through the dynamic relay network. This bidirectional path calculation mechanism effectively solves the symmetry problem of data transmission in network-free scenarios.

[0062] In the wireless relay communication method, device, and vehicle provided in this application embodiment, a communication node set containing multiple vehicle nodes is obtained, including at least one vehicle node capable of communicating with the network side. Adjacent vehicle nodes can perform short-range wireless communication, thereby avoiding dependence on fixed infrastructure and fully utilizing mobile vehicle nodes to construct a communication network, greatly enhancing adaptability and flexibility in wireless scenarios. Furthermore, based on a reward table, the optimal communication path for the vehicle is determined according to the communication node set. The reward value in the reward table is determined based on the received signal strength and packet loss rate of the path, ensuring higher stability and efficiency of the selected optimal communication path. This quantitative evaluation mechanism ensures high stability and transmission quality of the selected path in actual communication, avoiding communication interruptions or data loss due to improper path selection, thus improving the reliability of vehicle wireless relay communication. Finally, the vehicle's data to be transmitted is transmitted to the network side through the optimal communication path, ensuring that the data can be efficiently delivered using the reliable link constructed by the vehicle nodes in a wireless scenario, thereby improving the reliability of vehicle wireless relay communication.

[0063] In one embodiment, determining the optimal communication path for the vehicle based on the communication node set and the report table includes:

[0064] Obtain the node information of each vehicle node in the communication node set;

[0065] Based on the node information, determine the network topology information between the vehicle and each vehicle node;

[0066] Based on the node information and the network topology information, at least one relay node is determined from the plurality of vehicle nodes; the relay node can communicate with the network side.

[0067] Based on the report table, the optimal communication path is determined according to the node information corresponding to the at least one relay node and the network topology information.

[0068] Optionally, in this embodiment, node information refers to the key status parameters and attribute data of each vehicle node in the communication node set. This information includes, but is not limited to, the vehicle node's geographical coordinates (such as latitude and longitude), speed and direction of movement (forming a movement vector), remaining battery power, signal transmission power, received signal strength (RSSI), communication coverage radius, and network connection status (whether it can access the network). Node information is dynamically changing; vehicles collect this data through periodic broadcasting or active detection for subsequent network topology construction and relay node selection.

[0069] Network topology information is a virtual network structure built based on node information, describing the communication connections and link quality between vehicle nodes. By analyzing the location, signal strength, and movement trends of each node, the system generates a weighted directed graph, where nodes represent vehicles, edges represent communication links between nodes, and edge weights comprehensively consider factors such as signal strength, distance attenuation, and relative movement speed. For example, the higher the RSSI value between two adjacent nodes, the greater the link weight, indicating better communication quality. The network topology information is dynamically updated; when vehicles move or node states change, the topology graph is adjusted in real time, providing an accurate network view for path planning.

[0070] Relay nodes are special vehicle nodes selected from a set of communication nodes. They possess two core characteristics: the ability to establish a stable connection with the network via their own communication modules (such as 4G / 5G); and the ability to forward data in environments without network infrastructure. Relay nodes act as a bridge in network-less relay communication, transmitting data from the source vehicle node (such as the first node) to the network via multi-hop relays. When selecting relay nodes, priority is given to vehicle nodes with good signal quality, high remaining battery power, and stable movement trajectories. A dynamic load balancing algorithm is used to prevent any single node from excessively consuming resources.

[0071] In these alternative embodiments, by acquiring node information of each vehicle node in the communication node set, a comprehensive understanding of the status and capabilities of each node can be obtained, providing fundamental data support for subsequent path selection. Determining the network topology information between vehicles and nodes based on the node information clearly depicts the current network connectivity and communication environment. Furthermore, identifying at least one relay node capable of communicating with the network side from multiple vehicle nodes ensures the connectivity and reliability of the communication link. Finally, based on the report table and combined with the relay node information and network topology information, the optimal communication path is determined, achieving intelligent and dynamic optimization of path selection. Overall, this improves the efficiency, stability, and reliability of network-free relay communication.

[0072] In one embodiment, the node information includes the received signal strength of the vehicle node, the location of the vehicle node, and the movement vector of the vehicle node;

[0073] The step of determining the network topology information between the vehicle and each vehicle node based on the node information includes:

[0074] Based on the location of each vehicle node, an initial topology graph is constructed between the vehicles and each vehicle node; one vehicle node corresponds to one vertex in the initial topology graph; the edge between two vertices represents that the corresponding vehicle nodes can perform short-distance communication without network.

[0075] The initial topology map is optimized using a triangulation algorithm based on the received signal strength and movement vector of each vehicle node to obtain the network topology information.

[0076] Optionally, in this embodiment, the movement vector is a parameter vector describing the motion state of a vehicle node, which can be composed of three elements: speed, direction, and acceleration. The movement vector is used to predict the positional changes of vehicle nodes in the near future, helping to predict the stability of the communication link. For example, when two vehicle nodes move in the same direction and at similar speeds, their communication link is more likely to maintain a long-term connection; conversely, if their directions of movement are opposite or their speeds differ significantly, the link may break down quickly. By introducing the movement vector, the system can avoid unstable links in advance when constructing the network topology, improving the foresight of path planning.

[0077] Optionally, in one specific implementation of this application, the process first enters the node discovery phase: vehicle nodes broadcast beacon frames containing GPS coordinates at 100ms intervals, and an initial topology graph is constructed based on the received beacon information. Each vehicle node is mapped to a vertex in the graph. If the Euclidean distance between two nodes is less than the maximum coverage radius of short-range communication technology, an undirected edge is added between the corresponding vertices. This step forms a static connectivity model based on geometric location.

[0078] Subsequently, the received signal strength of each node is collected, and the actual communication quality is calculated using a logarithmic distance path loss model. For each edge in the initial topology graph, a link quality score is calculated by combining the RSSI value and the theoretical signal propagation formula. If the measured RSSI value corresponding to an edge is lower than the theoretical threshold, it is marked as a weak connection and considered for removal, forming a preliminary optimized topology.

[0079] Finally, dynamic optimization is introduced using movement vectors. A Kalman filter is used to predict position changes within the next t seconds. The Delaunay triangulation algorithm is employed to geometrically model the predicted positions, ensuring that the circumcircle of each triangle contains no other vertices, thus constructing a geometrically optimal structure. For edges shared by adjacent triangles, if the movement trend of two nodes causes the distance to exceed the communication radius within the next t seconds, the edge is disconnected in advance; conversely, if a new connectivity relationship is predicted, a temporary edge is added. The dynamic topology graph generated in this step can be updated once at a preset frequency, ensuring real-time reflection of the vehicle node's movement status.

[0080] In these alternative embodiments, by combining the location of vehicle nodes, received signal strength, and movement vectors to construct and optimize network topology information, the communication connection status and stability between vehicle nodes can be more accurately reflected. Utilizing triangulation algorithms combined with signal strength and movement vectors to optimize the topology allows for early prediction of link changes, enhancing the dynamic adaptability of the network topology and providing a more reliable basis for subsequently determining the optimal communication path, thereby improving the efficiency and stability of network-free relay communication.

[0081] In one embodiment, determining at least one relay node from the plurality of vehicle nodes based on the node information and the network topology information includes:

[0082] For any one of the plurality of vehicle nodes, a weight calculation operation is performed to obtain the weight value of each vehicle node. The weight calculation operation includes: weighted summation of the node information corresponding to the vehicle node and the topology information corresponding to the vehicle node in the network topology information to obtain the weight value corresponding to the vehicle node; the weight value is used to characterize the adaptability of the vehicle node as a relay node.

[0083] Based on the weight values, at least one relay node is determined from the plurality of vehicle nodes.

[0084] Optionally, in this embodiment, the weight value is a quantitative indicator used to evaluate the suitability of a vehicle node as a relay node. The weight value can be calculated by weighted summation of the multi-dimensional node information and network topology information of the vehicle node. For example, a node with high received signal strength, slow movement speed, and sufficient remaining battery power will receive a higher weight value, indicating that it is more suitable as a relay node.

[0085] Topology information is a set of local features related to a specific vehicle node in the network topology. It describes the node's position and connectivity characteristics within the entire network. Topology information can include node degree, the number of directly connected neighboring nodes, reflecting the node's communication coverage; link quality, such as signal strength and packet loss rate with neighboring nodes, reflecting communication reliability. For example, if a node's topology information shows that it is connected to 5 neighboring nodes, has an average RSSI of -80dBm, and a betweenness centrality of 0.3, it indicates that the node is in a moderately critical position in the network. Combining topology information with node information provides a comprehensive network perspective for relay node selection, ensuring that the selected node can efficiently forward data while maintaining overall network connectivity.

[0086] In these alternative embodiments, on the one hand, by combining multi-dimensional evaluation of node information and topology information, the bias in decision-making caused by a single indicator is avoided, and the adaptability and reliability of relay nodes are improved. On the other hand, the weight values ​​quantify the merits of nodes as relays, enabling the system to prioritize highly adaptable nodes and optimize network resource allocation, thereby significantly improving communication efficiency and stability in network-free environments.

[0087] In one embodiment, the node information includes the received signal strength of the vehicle node, the location of the vehicle node, the movement vector of the vehicle node, and the communication power of the vehicle node.

[0088] The step of weighting and summing the node information corresponding to the vehicle node and the topology information corresponding to the vehicle node in the network topology information to obtain the weight value corresponding to the vehicle node includes:

[0089] The received signal strength of the vehicle node is logarithmically compressed to obtain a first value;

[0090] Based on the exponential decay model, the movement vector of the vehicle node is calculated to obtain the second value;

[0091] Based on the nonlinear attenuation coefficient, the communication power of the vehicle node is calculated to obtain a third value;

[0092] Based on the moving average algorithm, the topological information of the vehicle node is calculated to obtain the fourth value;

[0093] The weighted sum of the first value, the second value, the third value, and the fourth value is used to obtain the weight value corresponding to the vehicle node.

[0094] Optionally, in one specific implementation of this application, such as Figure 2-1 and Figure 2-2As shown, the original data of the vehicle nodes, such as the received signal strength, movement vector, communication power, and topology information, are first validated (e.g., checking whether they are valid values ​​and whether there is any missing data). If the data is invalid, the "fault tolerance mechanism" is triggered (e.g., replacing it with the historical average or default value); if it is valid, the process proceeds to "replacing with the historical average" (an optional process used for data smoothing), completing the initial data preprocessing and preparing for subsequent calculations.

[0095] Subsequently, the verified data is normalized to map parameters of different dimensions (such as signal strength, power, etc.) to a unified interval (such as [0,1]) to ensure that the weights of each parameter are comparable when weighted summing.

[0096] Subsequently, multi-dimensional factor calculations were performed; specifically, the signal strength P received by the vehicle node was calculated. rssi For logarithmic compression, the formula is: ( Figure 2-2 Formula 1), where P min This is the minimum identifiable signal power. Logarithmic compression can non-linearly map a wide range of signal strength values, highlighting differences in signal strength and making strong signal nodes more distinguishable in relay contention (the stronger the signal, the higher the power of F_sig (i.e., F)). sig (The larger)

[0097] The formula for processing moving vectors based on the exponential decay model is: ( Figure 2-2 Formula 2), where v is the real-time speed of the vehicle, v ref This represents the ideal relay speed (indicating relatively stable movement), and τ is the attenuation constant (adjusting the steepness of the curve). The closer the speed is to v... ref F_stab (i.e., F stab The closer the value is to 1, the more stable the node movement is, making it suitable for relaying (because nodes that move violently are prone to disconnecting the link).

[0098] The communication signal E is calculated using a nonlinear attenuation coefficient, and the formula is as follows: ( Figure 2-2 Formula 3), where E max This represents the vehicle's full charge level, where k is a non-linear coefficient (emphasizing the influence of charge level differences). The higher the charge level, the higher F_eng (i.e., F...). eng The larger the value, the more likely it is to be a node with sufficient power to act as a relay, thus avoiding communication interruption due to power depletion.

[0099] The moving average algorithm is used to process topological information, and the formula is: ( Figure 2-2 Formula 4), where N is the sliding window length (e.g., 5 represents the topology data from the last 5 iterations), and S iIt is the i-th topological feature value within the window. The moving average can smooth out topological fluctuations and highlight the long-term network value of nodes (such as nodes located in communication hubs, F_struc (i.e., F...)). struc )higher).

[0100] Subsequently, the weight value W is obtained by summing the weights of the above four dimensions using the following formula:

[0101] W=ω_sig·F_sig+ω_stab·F_stab+ω_eng·F_eng+ω_struc·F_struc;

[0102] Where ω_sig, ω_stab, ω_eng, and ω_struc are the weights of each factor (e.g., depending on the scenario, ω_sig = 0.3, ω_stab = 0.25, ω_eng = 0.2, and ω_struc = 0.25, with a total of 1).

[0103] After calculating the weights of all nodes, they are sorted from high to low weights. Nodes with weight values ​​greater than a preset threshold are selected as relay nodes. This implements a relay selection logic based on adaptability, making nodes with strong signals, stable mobility, sufficient power, and critical network locations more likely to become relays, thus ensuring the reliability and efficiency of the communication link.

[0104] In these alternative embodiments, node information is processed from multiple dimensions: logarithmic compression of signal strength accurately characterizes communication quality; exponential decay models adapt to movement vectors, ensuring relay stability; nonlinear decay processes power consumption to balance energy usage; and moving averages optimize topology information, highlighting long-term value. Weighted summation integrates multiple factors to quantify node suitability, enabling the selection of relays with superior signal strength, stable movement, sufficient power, and critical topology, thereby improving the reliability and efficiency of wireless communication links and adapting to the dynamic needs of vehicular scenarios.

[0105] In one embodiment, determining the optimal communication path based on the report table, according to the node information corresponding to the at least one relay node, and the network topology information, includes:

[0106] Using a reinforcement learning algorithm, the reward table is updated based on the node information and the network topology information to obtain the updated reward table;

[0107] Based on the updated reward table, the optimal communication path is determined, wherein the optimal communication path is the path corresponding to the maximum reward value in the updated reward table.

[0108] Optionally, in one specific implementation of this application, the core elements of the reinforcement learning algorithm are first defined, including the state (composed of node information and network topology information, which together form the environmental state S perceived by the agent), the action (selecting different relay node combinations to form a communication path, i.e., action A), and the reward (the reward value R stored in the reward table, used to evaluate the quality of the path). Initially, the reward table pre-sets the initial reward value corresponding to each state-action, which can be set as an empirical value or a random value.

[0109] Next, reinforcement learning algorithms, such as the Q-learning algorithm, are used. Its core formula is:

[0110] Q(S,A)←Q(S,A)+α[R+γmax A′ Q(S′,A′)-Q(S,A)]

[0111] Here, α is the learning rate, controlling the update step size and determining the importance attached to new experiences; γ is the discount factor, reflecting the importance attached to future rewards; and S' is the new state transitioned to after performing action A. Based on the current node information and network topology information, the agent selects an action A (i.e., trying a communication path that includes relay nodes). Then, based on the actual communication situation (the reward R is generated by a combination of factors such as signal strength and battery power in node information, and link stability in network topology information; for example, a strong signal, sufficient battery power, and stable links in the topology result in a higher reward), combined with the new state S' (the new state formed after the action, due to changes in node and topology information such as vehicle movement), the corresponding value of Q(S,A) in the reward table is updated according to the Q-learning formula described above. This process is continuously iterated, collecting feedback from actions performed in different states and updating the reward table multiple times, allowing the reward table to more accurately reflect the long-term value of each state-action combination.

[0112] Finally, once the reward table updates stabilize, iterate through all possible state-action reward values ​​in the table, find the maximum reward value, and identify the corresponding communication path consisting of relay nodes. This path is the optimal communication path. Because this path, under the current node information and network topology, has been repeatedly learned and evaluated by the reinforcement learning algorithm, it is determined to be the path that brings the highest reward and has the best communication performance (such as stable transmission, low energy consumption, and low latency). This provides a reliable path selection basis for efficient data transmission in vehicle-to-vehicle communication without a network.

[0113] Alternatively, in other implementations of this application, the energy balancing strategy formula can be integrated into the core logic of reinforcement learning path selection by adding energy parameters such as the remaining power of nodes and the average power of the network in the state definition, designing terms related to the balance of energy consumption in the reward function (such as making power consumption more equitable by adjusting the transmission power), allowing energy factors to influence the path value assessment when updating the Q value, and finally selecting a path that can both ensure communication quality and avoid excessive power consumption by individual nodes, thereby achieving a balance between efficient transmission and network lifetime.

[0114] The energy balance strategy formula is as follows:

[0115]

[0116] Among them, E residual For the remaining power of the node, E avg The average energy level of all nodes, P base Where P is the base transmit power, α is the path loss coefficient, and d is the transmission distance; tx This represents the node's transmit power.

[0117] In these alternative embodiments, by leveraging reinforcement learning algorithms and dynamically updating the reward table in conjunction with node information and network topology information, the reward table can continuously adapt to the real-time communication environment. Based on the updated reward table, the path with the maximum reward value is selected. This approach utilizes the adaptability of reinforcement learning to discover the optimal solution while also using a reward mechanism to balance factors such as signal strength and energy consumption. This results in a communication path that is superior in terms of transmission efficiency, stability, and energy utilization, adapting to dynamic scenarios of vehicular offline communication and improving link reliability and network endurance.

[0118] In one embodiment, the step of communicating the vehicle's data to be transmitted with the network side via the optimal communication path includes:

[0119] The data to be transmitted is compressed using a preset compression algorithm to obtain compressed data; the preset compression algorithm includes Huffman coding and differential compression algorithm.

[0120] The compressed data is encrypted using a preset encryption algorithm to obtain encrypted data. The preset encryption algorithm includes the Chinese national cryptographic algorithm and a dynamic key rotation mechanism.

[0121] The encrypted data is fragmented to obtain fragmented data;

[0122] According to the transmission priority corresponding to the data to be transmitted, the fragmented data is transmitted to the network side via the optimal communication path.

[0123] Optionally, in one specific implementation of this application, the data to be transmitted is first compressed. When using Huffman coding, the frequency of characters in the data can be counted first, a Huffman tree can be constructed, and high-frequency characters can be represented by shorter codes and low-frequency characters by longer codes, thereby reducing the amount of data. For example, if a character appears frequently in the data, its code may only be 1 bit, while the code for a rare character may be 8 bits. Then, a differential compression algorithm is applied. For continuous data (such as time series or location data), the difference between the current value and the previous value is calculated and the difference is transmitted instead of the original value, further reducing redundant information. For example, the vehicle location sequence [100,105,110] becomes [100,5,5] after differential compression.

[0124] Subsequently, the compressed data is encrypted. Using the SM4 symmetric encryption algorithm from the Chinese national cryptographic algorithm, a 128-bit session key is generated. The compressed data is divided into 16-byte blocks, and ciphertext is generated through 32 rounds of iterative transformation. Simultaneously, the SM2 asymmetric encryption algorithm from the Chinese national cryptographic algorithm is used to securely distribute the session key. The network side generates a public / private key pair, vehicle nodes encrypt the session key with the public key before transmission, and the network side decrypts it using the private key. To enhance security, the system introduces a dynamic key rotation mechanism and lightweight blockchain technology. The dynamic key pool automatically updates the key pairs every 5 minutes using the Diffie-Hellman algorithm, generating a new session key and encrypting the new key with the current key, achieving seamless key switching and reducing the risk of key cracking.

[0125] A lightweight blockchain is constructed using the Byzantine Fault Tolerance (BFT) consensus algorithm to ensure rapid synchronization and verification of key update information in the distributed network. When a vehicle node detects a topology change or key usage reaches a threshold (e.g., transmitting N=100 data packets), a consensus process is triggered. The legitimacy of the new key is verified through a multi-node voting mechanism to prevent man-in-the-middle attacks.

[0126] Next, the encrypted data is fragmented. The encrypted data is divided into multiple fragments of a fixed size (e.g., 1024 bytes), and header information is added to each fragment. The header information includes a fragment identifier (e.g., [3 / 10] indicates the 3rd fragment out of 10), a 32-bit Cyclic Redundancy Check (CRC32) checksum for the receiver to verify data integrity, and a transmission priority flag inherited from the original data (e.g., high / medium / low).

[0127] Finally, the system schedules and transmits data based on a priority-based strategy. Data fragments are stored in different queues according to their priority: emergency distress signals (QoS=0) > voice data (QoS=1) > regular data (QoS=2). High-priority data (such as emergency distress signals with QoS=0) is transmitted first through the node with the best link quality in the optimal path (such as the relay with the strongest RSSI) to ensure low latency; low-priority data (such as regular data with QoS=2) is transmitted using nodes with slightly weaker link quality but lower energy consumption to balance resource utilization.

[0128] For example, during fragmentation, data packets can be fixedly divided into 512-bit segments, and a priority marker can be added to each segment. If the receiving end detects a lost segment through the checksum, it will send a negative acknowledgment (NAK) via the reverse path. The sending end will then use a buffer window mechanism to retransmit only the lost segment.

[0129] Optionally, in other embodiments of this application, the fragment retransmission mechanism is executed according to QoS levels, and the triggering conditions include timeout without ACK, check error, and explicit NACK. QoS0 (urgent) data is immediately retransmitted and sent simultaneously with Forward Error Correction (FEC) redundant coding to achieve zero-wait error correction, with a retransmission interval of 20-50ms and supporting up to 5 retransmissions; QoS1 (voice) triggers batch retransmission when the packet loss rate exceeds a threshold (e.g., 5%), discarding expired fragments with a delay >150ms, with an interval of 100-200ms and ≤3 retransmissions; QoS2 (normal) only responds with NACK for critical fragments (e.g., file headers, video I-frames), and non-critical fragments may be selectively discarded, with an interval of 300-500ms, ≤2 retransmissions, and low-value data may be abandoned. This mechanism uses priority-differentiated configuration to enable high-priority data to have shorter retransmission intervals, stronger error correction capabilities, and preemptive resource scheduling, ensuring the reliability of emergency data (such as emergency distress signal packet loss rate <0.1%). Low-priority data, on the other hand, reduces resource consumption by extending the retransmission interval and reducing the number of retransmissions, thus balancing the overall network efficiency. In resource-constrained vehicle environments, it can improve the availability of critical services while reducing unnecessary retransmission traffic.

[0130] Alternatively, in another implementation of this application, millimeter-wave vehicle-to-vehicle communication (V2V) channel dynamic allocation technology can be adopted. The adaptive modulation module dynamically switches between quadrature phase shift keying (QPSK) and 16-quadrature amplitude modulation (16QAM) modes according to the real-time signal-to-noise ratio, thereby improving spectrum utilization while ensuring transmission reliability.

[0131] Meanwhile, the multi-band aggregation unit enables parallel transmission across 2.4GHz and 5.8GHz dual channels, doubling the actual available bandwidth. Specifically, an intelligent frequency band selection algorithm prioritizes the transmission of critical data (such as emergency alarms) in the less-interference 5.8GHz band, while the 2.4GHz band serves as a supplementary link for transmitting non-real-time data. This improves end-to-end throughput and reduces transmission latency in high-density vehicle scenarios, effectively supporting the high-speed data transmission requirements of in-vehicle environments.

[0132] In these alternative embodiments, Huffman coding and differential compression reduce data volume to improve transmission efficiency; national cryptographic algorithms and dynamic key rotation are used to encrypt data, ensuring communication security and preventing data leakage; data fragmentation enables data to adapt to transmission across different links, reducing the impact of packet loss; priority transmission ensures that urgent data is processed first, optimizing resource allocation. The collaboration of multiple technologies not only improves transmission efficiency and security but also ensures the real-time nature of critical data, adapts to the dynamically changing environment of in-vehicle offline communication, extends network lifespan, and enhances reliability.

[0133] In one embodiment, before communicating the vehicle's data to be transmitted with the network side via the optimal communication path, the method further includes:

[0134] Using the K-shortest path algorithm, at least one candidate path is generated based on the set of communication nodes, wherein the candidate path is different from the optimal communication path;

[0135] The at least one candidate path is identified as a backup path;

[0136] The step of communicating the vehicle's data to be transmitted with the network side via the optimal communication path includes:

[0137] During the data transmission with the network side through the optimal communication path, a heartbeat interaction packet is sent to the optimal communication path according to a preset heartbeat cycle;

[0138] If no heartbeat response is received from any node in the optimal communication path for a preset number of consecutive times, it is determined that there is a transmission failure in the optimal communication path, and the data to be transmitted is communicated with the network side through the backup path.

[0139] Optionally, in one specific implementation of this application, before data transmission, three disjoint paths are computed in parallel as backup paths using an improved Dijkstra algorithm based on real-time link status (such as latency, bandwidth, and failure rate). This algorithm, based on the traditional Dijkstra algorithm, introduces multi-dimensional Quality of Service (QoS) parameters to construct a weighted graph. During computation, the primary path is first derived, and then edges of the selected path are iteratively removed, forcing the algorithm to generate paths that are physically or logically isolated from the optimal communication path, thereby ensuring the independence of the backup paths from the optimal communication path. Simultaneously, combined with a QoS classification strategy, the weight parameters are dynamically adjusted for data of different priorities. For example, QoS level 0 data prioritizes links with latency below 50ms, and resources are pre-allocated and reserved for relay nodes on each backup path to ensure that resources do not need to be renegotiated during handover.

[0140] During data transmission, a dual-threshold detection mechanism is used to monitor path status. Compressed heartbeat packets are sent to the optimal path at preset heartbeat intervals. If no response is received after two consecutive heartbeats, a link quality degradation warning is triggered, and a backup path connection is established in advance. If no response is received after five consecutive heartbeats, path interruption is confirmed, and a switchover is initiated immediately. During the switchover, the source node sends a fast rerouting signaling message containing the new path ID, pre-allocated resource parameters, and an encrypted session key to the backup path relay node through the control plane. Transmission Control Protocol (TCP) state transition technology is used to synchronize the current connection's sequence number, window size, and other state information to the backup path, ensuring uninterrupted data transmission.

[0141] like Figure 3As shown, in the vehicle fault diagnosis data transmission scenario, the faulty vehicle's OBD first broadcasts a routing request carrying QoS requirements. Upon receiving this, relay vehicle A sends a relay probe packet with a Time To Live (TTL) of 5 to relay vehicle B. Relay vehicle B returns a link quality report including packet loss rate and latency. Based on this, relay vehicle A generates a candidate path list and sends it back to the faulty vehicle's OBD. The faulty vehicle's OBD generates the optimal path according to S200, establishing a multi-hop tunnel to the cloud access vehicle via relay vehicles A and B. The cloud access vehicle returns an acknowledgment (ACK) message along with time synchronization information, confirming the route establishment. Afterward, a keep-alive detection phase begins. The faulty vehicle's OBD sends a heartbeat packet to relay vehicle A every 30 seconds, and relay vehicle A returns a network status code, continuously monitoring the process. When the RSSI between the cloud access vehicle and relay vehicle B is less than a threshold, dynamic route switching is triggered, ensuring stable transmission of fault diagnosis data to the cloud for analysis even under vehicle movement and network changes.

[0142] In these alternative embodiments, a backup path different from the optimal communication path is generated using the K-shortest path algorithm, and heartbeat packets are sent at a preset heartbeat cycle during data transmission to monitor the status of the optimal communication path in real time. This mechanism can promptly detect path failures and quickly switch to the backup path for data transmission when a transmission failure occurs on the optimal communication path, ensuring the continuity and reliability of communication. Simultaneously, the existence of the backup path provides redundancy, enhancing the network's fault tolerance and stability, and is particularly suitable for complex environments without network infrastructure. It effectively reduces the risk of data transmission interruption due to path failures, improving overall communication efficiency and user experience.

[0143] It should be noted that the various optional implementation methods described in the embodiments of this application can be combined with each other or implemented individually without conflict, and the embodiments of this application do not limit this.

[0144] To facilitate understanding of the wireless relay communication method provided in the above embodiments, the following describes the wireless relay communication method using a specific scenario embodiment.

[0145] like Figure 4 As shown, this application provides an infrastructure-free relay communication system with a three-level relay protocol stack as its core architecture. The physical layer is based on dynamic allocation of millimeter-wave V2V channels, configured with an adaptive modulation module, and flexibly switches between QPSK / 16QAM modulation according to the signal-to-noise ratio to improve spectrum utilization. It also achieves dual-channel parallel transmission of 2.4GHz / 5.8GHz through a multi-band aggregation unit, thus doubling the bandwidth.

[0146] The network layer constructs an autonomous networking relay system with zero infrastructure dependence. It utilizes an improved d-hoc on-demand distance vector version 2 routing protocol (AODVv2) and introduces a weighted scoring model based on node power, signal quality, and topology location to dynamically elect relay nodes. It develops a dynamic routing algorithm with a topology change response time of <100ms, achieves intelligent power control with node power consumption differences of <15%, supports concurrent communication of multiple protocols such as long-range radio (LoRa), Wi-Fi, and Bluetooth, enhances the intelligence and robustness of path selection, and evaluates link quality using RSSI and packet loss rate as dual dimensions to repair unstable links in real time.

[0147] The application layer employs a diagnostic data packet compression algorithm. The system is equipped with a dynamic role allocation module that calculates weight values ​​based on factors such as node power to elect relay nodes; the adaptive routing engine establishes communication paths and monitors link quality using an improved AODV protocol; and the data encapsulation unit implements a hybrid processing of layered compression and lightweight encryption.

[0148] like Figure 5 As shown, vehicle A without a network connects to relay vehicle B via V2V short-range communication. Relay vehicle B then connects to vehicle C with a network via V2V / V2I hybrid communication, and finally to the Internet via a cellular network. The various protocol stacks (application layer, network layer, link layer, physical layer, etc.) work together to ensure efficient data transmission. In scenarios without infrastructure, this provides a stable, intelligent, and efficient solution for vehicle-to-vehicle communication, improving the reliability and adaptability of in-vehicle communication.

[0149] Specifically, the dynamic routing algorithm first uses a real-time topology awareness module to analyze the received signal strength indication and movement vector of each node in the network, dynamically generating a topology map of the current network to comprehensively understand its real-time status. Then, a Q-learning reinforcement learning algorithm is used to optimize routing decisions. The system continuously updates the Q-table—the core data structure storing the value of each state-action pair—based on the generated topology map, gradually finding the optimal path selection strategy through continuous learning and iteration. Finally, based on the updated Q-table, the system selects the optimal path from numerous possible paths for data transmission.

[0150] By deeply integrating real-time topology awareness with Q-learning algorithms, the system can dynamically adapt to changes in the network environment, adjust routing strategies in a timely manner, effectively avoid network congestion or faulty nodes, and significantly improve the stability and transmission efficiency of network communication. It is especially suitable for vehicular ad hoc network scenarios with frequent node movement and dynamic changes in topology.

[0151] like Figure 6 As shown, in a vehicle diagnostic scenario of this application, the vehicle to be diagnosed first sends its diagnostic data to a mobile relay vehicle (such as a cruising intelligent service vehicle) using a relay protocol; the mobile relay vehicle then forwards the data to a fixed relay station (such as a communication base station deployed along a road); the fixed relay station transmits the data to a vehicle with network access (a vehicle or facility with stable network connectivity); finally, the vehicle with network access uploads the data to the diagnostic cloud platform, where the cloud completes the vehicle fault diagnosis and analysis, forming a multi-level relay diagnostic link of "vehicle-mobile relay-fixed base station-connected vehicle-cloud", ensuring reliable data transmission and enabling remote intelligent diagnosis even without direct network coverage.

[0152] like Figure 7 As shown, in a traffic congestion disaster scenario, within the congestion area, damaged vehicles use V2V relays, stranded vehicles use multi-hop transmission, and unmanned equipment uses in-situ transmission to aggregate information to a data command vehicle. The data command vehicle then transmits the information to the emergency command center via a satellite backhaul link, while simultaneously ensuring local communication through 5G emergency coverage (radius 800m). Upon receiving the information, the emergency command center issues instructions to the rescue convoy, which then proceeds to the congested area to conduct rescue operations at collapsed buildings and other trapped locations. This establishes a traffic congestion emergency response chain of "multi-source information collection - command vehicle aggregation and forwarding - command center decision-making - rescue force response," enabling efficient and coordinated handling of congestion emergencies.

[0153] like Figure 8 As shown, in a remote vehicle diagnostics scenario, the vehicle to be diagnosed first sends a diagnostic request carrying its Vehicle Identification Number (VIN) and security certificate to relay vehicle 1. Relay vehicle 1 performs quality checks on the path to relay vehicle 2 (requiring QoS ≥ 0.7). After the condition is met, relay vehicle 2 establishes a secure tunnel with the cloud access vehicle using Transport Layer Security (TLS) version 1.3. The cloud access vehicle then uploads cached data to the cloud server in batches. The cloud server returns diagnostic command packets, which the cloud access vehicle splits into transmission units with a Maximum Transmission Unit (MTU) of 1500 and sends to relay vehicle 2. Relay vehicle 2 performs CRC verification and delivers the result to the vehicle to be diagnosed via relay vehicle 1. This achieves diagnostic data interaction and command response between the vehicle, multi-level relays, and the cloud, ensuring the safe and efficient execution of the remote diagnostic process in complex network environments.

[0154] Figure 9 A schematic diagram of a network-free relay communication device provided in another embodiment of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0155] Reference Figure 9 The wireless relay communication device may include:

[0156] The acquisition module 901 is used to acquire the communication node set of the vehicle when the vehicle is in a network-free scenario; the communication node set includes multiple vehicle nodes, the multiple vehicle nodes include at least one vehicle node that communicates with the network side, adjacent vehicle nodes among the multiple vehicle nodes can perform short-range communication without network, and the vehicle can perform short-range communication without network with at least one of the multiple vehicle nodes.

[0157] The determination module 902 is used to determine the optimal communication path of the vehicle based on the reward table and the communication node set; the reward table includes the mapping relationship between different path selections and reward values ​​under different communication node sets; the optimal communication path is the path selection strategy corresponding to the maximum reward value in the reward table; the reward value is determined based on the received signal strength and packet loss rate of the path;

[0158] The transmission module 903 is used to transmit the vehicle's data to be transmitted to the network side through the optimal communication path.

[0159] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application, and are devices corresponding to the above-mentioned methods. All implementation methods in the above-mentioned method embodiments are applicable to the embodiments of this device. For details on its specific functions and the technical effects it brings, please refer to the method embodiment section, which will not be repeated here.

[0160] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0161] Figure 10 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.

[0162] The device may include a processor 1001 and a memory 1002 storing program instructions.

[0163] When the processor 1001 executes the program, it implements the steps in any of the above method embodiments.

[0164] For example, the program can be divided into one or more modules / units, one or more of which are stored in memory 1002 and executed by processor 1001 to complete this application. The one or more modules / units can be a series of program instruction segments capable of performing a specific function, which describe the execution process of the program in the device.

[0165] Specifically, the processor 1001 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0166] Memory 1002 may include mass storage for data or instructions. For example, and not limitingly, memory 1002 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 1002 may include removable or non-removable (or fixed) media. Where appropriate, memory 1002 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 1002 is non-volatile solid-state memory.

[0167] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the methods according to one aspect of this disclosure.

[0168] The processor 1001 implements any of the methods described in the above embodiments by reading and executing program instructions stored in the memory 1002.

[0169] In one example, the electronic device may also include a communication interface 1003 and a bus 1010. The processor 1001, memory 1002, and communication interface 1003 are connected via the bus 1010 and communicate with each other.

[0170] The communication interface 1003 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0171] Bus 1010 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 1010 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0172] Furthermore, in conjunction with the methods in the above embodiments, this application embodiment can provide a storage medium for implementation. This storage medium stores program instructions; when these program instructions are executed by a processor, they implement any of the methods in the above embodiments.

[0173] This application also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above method embodiments and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0174] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0175] This application provides a computer program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the above method embodiments and achieve the same technical effects. To avoid repetition, it will not be described again here.

[0176] This application embodiment also provides a vehicle, which includes at least one of the following:

[0177] As in the aforementioned embodiments, the wireless relay communication device;

[0178] The electronic device as described in the foregoing embodiments;

[0179] The computer-readable storage medium as described in the foregoing embodiments.

[0180] The computer program product as described in the foregoing embodiments.

[0181] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0182] The functional modules shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on machine-readable media or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable media" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer grids such as the Internet, intranets, etc.

[0183] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0184] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and program products according to embodiments of this disclosure. It should be understood that each block in 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 program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to create a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0185] The above are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A network-free relay communication method, characterized in that, The method includes: When the vehicle is in a network-free scenario, the communication node set of the vehicle is obtained; the communication node set includes multiple vehicle nodes, and the multiple vehicle nodes include at least one vehicle node that communicates with the network side. Adjacent vehicle nodes among the multiple vehicle nodes can perform short-range communication without network, and the vehicle can perform short-range communication without network with at least one of the multiple vehicle nodes. Based on the reward table, the optimal communication path for the vehicle is determined according to the set of communication nodes. The reward table includes the mapping relationship between different path selections and reward values ​​under different sets of communication nodes. The optimal communication path is the path selection strategy corresponding to the maximum reward value in the reward table. The reward value is determined based on the received signal strength and packet loss rate of the path. The vehicle's data to be transmitted is communicated with the network side via the optimal communication path.

2. The method according to claim 1, characterized in that, The step of determining the optimal communication path for the vehicle based on the report table and the set of communication nodes includes: Obtain the node information of each vehicle node in the communication node set; Based on the node information, determine the network topology information between the vehicle and each vehicle node; Based on the node information and the network topology information, at least one relay node is determined from the plurality of vehicle nodes; the relay node can communicate with the network side. Based on the report table, the optimal communication path is determined according to the node information corresponding to the at least one relay node and the network topology information.

3. The method according to claim 2, characterized in that, The node information includes the received signal strength of the vehicle node, the location of the vehicle node, and the movement vector of the vehicle node. The step of determining the network topology information between the vehicle and each vehicle node based on the node information includes: Based on the location of each vehicle node, an initial topology graph is constructed between the vehicles and each vehicle node; one vehicle node corresponds to one vertex in the initial topology graph; the edge between two vertices represents that the corresponding vehicle nodes can perform short-distance communication without network. The initial topology map is optimized using a triangulation algorithm based on the received signal strength and movement vector of each vehicle node to obtain the network topology information.

4. The method according to claim 2, characterized in that, The step of determining at least one relay node from the plurality of vehicle nodes based on the node information and the network topology information includes: For any one of the plurality of vehicle nodes, a weight calculation operation is performed to obtain the weight value of each vehicle node. The weight calculation operation includes: weighted summation of the node information corresponding to the vehicle node and the topology information corresponding to the vehicle node in the network topology information to obtain the weight value corresponding to the vehicle node; the weight value is used to characterize the adaptability of the vehicle node as a relay node. Based on the weight values, at least one relay node is determined from the plurality of vehicle nodes.

5. The method according to claim 4, characterized in that, The node information includes the received signal strength of the vehicle node, the location of the vehicle node, the movement vector of the vehicle node, and the communication power of the vehicle node. The step of weighting and summing the node information corresponding to the vehicle node and the topology information corresponding to the vehicle node in the network topology information to obtain the weight value corresponding to the vehicle node includes: The received signal strength of the vehicle node is logarithmically compressed to obtain a first value; Based on the exponential decay model, the movement vector of the vehicle node is calculated to obtain the second value; Based on the nonlinear attenuation coefficient, the communication power of the vehicle node is calculated to obtain a third value; Based on the moving average algorithm, the topological information of the vehicle node is calculated to obtain the fourth value; The weighted sum of the first value, the second value, the third value, and the fourth value is used to obtain the weight value corresponding to the vehicle node.

6. The method according to claim 2, characterized in that, The step of determining the optimal communication path based on the report table, according to the node information corresponding to the at least one relay node, and the network topology information, includes: Using a reinforcement learning algorithm, the reward table is updated based on the node information and the network topology information to obtain the updated reward table; Based on the updated reward table, the optimal communication path is determined, wherein the optimal communication path is the path corresponding to the maximum reward value in the updated reward table.

7. The method according to claim 1, characterized in that, The step of communicating the vehicle's data to be transmitted with the network side via the optimal communication path includes: The data to be transmitted is compressed using a preset compression algorithm to obtain compressed data; the preset compression algorithm includes Huffman coding and differential compression algorithm. The compressed data is encrypted using a preset encryption algorithm to obtain encrypted data. The preset encryption algorithm includes the Chinese national cryptographic algorithm and a dynamic key rotation mechanism. The encrypted data is fragmented to obtain fragmented data; According to the transmission priority corresponding to the data to be transmitted, the fragmented data is transmitted to the network side via the optimal communication path.

8. The method according to claim 1, characterized in that, Before communicating the vehicle's data to be transmitted with the network side via the optimal communication path, the method further includes: Using the K-shortest path algorithm, at least one candidate path is generated based on the set of communication nodes, wherein the candidate path is different from the optimal communication path; The at least one candidate path is identified as a backup path; The step of communicating the vehicle's data to be transmitted with the network side via the optimal communication path includes: During the data transmission with the network side through the optimal communication path, a heartbeat interaction packet is sent to the optimal communication path according to a preset heartbeat cycle; If no heartbeat response is received from any node in the optimal communication path for a preset number of consecutive times, it is determined that there is a transmission failure in the optimal communication path, and the data to be transmitted is communicated with the network side through the backup path.

9. An electronic device, characterized in that, The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the wireless relay communication method as described in any one of claims 1-8.

10. A vehicle, characterized in that, include: The electronic device as described in claim 9.

Images