Vehicle-mounted data transmission method and system based on space-time domain autonomous load balancing routing

Abstract

This invention provides a method and system for vehicular data transmission based on spatiotemporal autonomous load balancing routing, belonging to the field of intelligent network routing technology. The method includes: sending vehicle driving data to a non-congested network relay device; filtering the vehicle driving data by the non-congested network relay device to obtain a reference trajectory path; sensing the location and storage information of neighboring network relay devices and outputting a set of candidate relay devices; calculating the spatiotemporal parameter set of the candidate relay device set, and obtaining the comprehensive data transmission capability of the candidate relay device set based on weights and the spatiotemporal parameter set; updating network status data and the congestion status of network relay devices; and arranging the data transmission order in ascending order according to the remaining lifetime, transmitting the vehicle driving data to the data transmission destination. This invention employs a weighted and spatiotemporal autonomous load balancing routing method to comprehensively evaluate the data transmission capability of network relay devices and select the optimal next-hop network relay device for the data.

Description

Technical Field

[0001] This invention relates to the field of intelligent network routing technology, and in particular to a vehicle-mounted data transmission method and system based on spatiotemporal autonomous load balancing routing. Background Technology

[0002] With the rapid development of vehicle-to-everything (V2X) technology, communication technology, and the automotive industry, data-driven intelligent connected vehicles have become a future trend. The integration of V2X with infrastructure networks benefits from reliable traffic and data services provided by communication and control technologies, promoting the development of audiovisual V2X applications in the big data era and improving traffic safety and user experience. Secondly, ultra-low latency V2X applications place high demands on communication services, requiring ultra-low latency, ultra-high bandwidth, and ultra-high reliability data services. The real-time nature of data affects the feasibility of new V2X applications. For example, high latency between remote driving video data transmission and control data transmission (i.e., high end-to-end latency) can cause a mismatch between the vehicle's actual operating status and control data, potentially leading to traffic accidents and personal injury or property damage.

[0003] Traditional vehicle-to-everything (V2X) communication technologies primarily rely on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. V2V communication focuses on trunking management and resolving channel collisions, while V2I emphasize utilizing roadside units (Roadside Units) for rapid data transmission. However, due to the high-speed mobility and limited payload of vehicles, V2X wireless communication links face challenges such as bandwidth constraints and frequent network topology changes. This makes it difficult for V2X communication technologies to support new V2X applications requiring ultra-low latency and ultra-high reliability. Therefore, enabling "vehicle-road cooperation" (V2L) through data transmission via the infrastructure network (core network) is a necessary pathway to achieving these applications.

[0004] Traditional core network communication technologies and architectures fail to meet the massive data transmission demands of innovative vehicular network (V2V) applications, are insufficient to support user requirements such as ultra-low latency, numerous terminals, and ultra-fast computing, and are unsuitable for intelligent new terminal devices. New network architectures address these issues by optimizing data transmission and network load control efficiency. For example, Software-Defined Networking (SDN) decouples the control plane from the data plane, centralizes control logic, abstracts data forwarding strategies, and enables custom network programming. The emergence of SDN signifies that the core network possesses the capability to evolve from static configuration to dynamic adaptation, supporting functions such as data flow control, data collaborative processing, and network load balancing, achieving data-driven intelligence, customization, and empowerment of the core network. Therefore, researching core network load balancing routing protocols based on new network architectures has significant practical implications for the development of V2V. Summary of the Invention

[0005] This invention provides a vehicle data transmission method and system based on spatiotemporal autonomous load balancing routing, which solves the defects of unbalanced core network routing protocol load, unintelligent routing, and inaccurate prediction in the prior art when transmitting data in the vehicle network.

[0006] In a first aspect, the present invention provides a method for in-vehicle data transmission based on spatiotemporal autonomous load balancing routing, comprising:

[0007] Acquire vehicle driving data, determine the data transmission destination, and send the vehicle driving data to a non-congested network relay device;

[0008] The vehicle driving data is filtered by the non-congested network relay device, and a reference trajectory path is calculated based on the network topology information stored in the software-defined network.

[0009] It senses the location and storage information of relay devices in the neighboring network and outputs a set of candidate relay devices;

[0010] Calculate the spatiotemporal domain parameter set of the candidate relay device set, and obtain the comprehensive data transmission capability of the candidate relay device set based on the weights and the spatiotemporal domain parameter set;

[0011] Based on historical network status data and current network status data, and combined with the comprehensive data transmission capability, update the network status data and the congestion status of network relay devices;

[0012] The data transmission order is sorted in ascending order according to the remaining lifespan, and the vehicle driving data is transmitted to the data transmission destination.

[0013] According to the present invention, a vehicle data transmission method based on spatiotemporal autonomous load balancing routing is provided, wherein the non-congestion network relay device filters the vehicle driving data and calculates a reference trajectory path based on the network topology information stored in the software-defined network, including:

[0014] If it is determined that the filtered data does not have a reference trajectory path or the transmission path contains loops, then based on the network topology information, the shortest path from the current network relay device to the data transmission destination is calculated using the Dijkstra algorithm to obtain the reference trajectory path;

[0015] Otherwise, the data filtering is performed by the non-congested network relay device.

[0016] According to the present invention, a vehicle-mounted data transmission method based on spatiotemporal autonomous load balancing routing is provided, which senses the location information and storage information of relay devices in neighboring networks and outputs a set of candidate relay devices, including:

[0017] Obtain the reference vector of the neighboring network relay device, and the candidate vectors of each neighboring network relay device and the current network relay device;

[0018] If it is determined that the angle between the reference vector and the candidate vector is less than 90 degrees, and the neighbor network relay device is in a non-congested state, then the candidate relay device set is output.

[0019] According to the present invention, a method for vehicular data transmission based on spatiotemporal autonomous load balancing routing is provided, which calculates the spatiotemporal parameter set of the candidate relay device set, and obtains the comprehensive data transmission capability of the candidate relay device set based on the weights and the spatiotemporal parameter set, including:

[0020] Calculate trajectory similarity based on the reference vector and candidate vector:

[0021]

[0022] Among them, v r，x and v r，y Representing candidate vectors v r The value mapped onto the x-axis and y-axis, v m，x and v m，y Representing the reference vector v m The value mapped onto the x-axis and y-axis, ξ m，r Indicates trajectory similarity;

[0023] Calculate the cache space ratio based on the remaining cache space of the neighboring network relay devices and the total cache space of the neighboring network relay devices:

[0024]

[0025] Among them, κ r Indicates the remaining buffer space size of the relay device in the neighboring network, ν r θ represents the amount of cache space already used by relay devices in the neighboring network. r Indicates the cache space ratio;

[0026] Based on the size of any observed data and the link transmission speed between any candidate network relay device and the current network relay device, calculate the estimated buffer time:

[0027]

[0028] Where, m i This represents the i-th observation data. Represents the observed data m i Size, in MB. Indicates r i The link transmission speed between r and r, where r represents the current network relay device, ri Let represent candidate network relay devices, and n represent the number of data points among neighboring network relay devices, ordered by remaining lifetime in ascending order, that precedes the observed data m. Indicates the estimated cache time;

[0029] Calculate the delivery efficiency based on the time cost of delivering any observation data to the data transmission destination via the current network relay device:

[0030]

[0031] Where k represents the number of data successfully delivered through the current network relay device r. This indicates that the i-th data is delivered to its destination through the current network relay device r. Time expenditure, Indicates delivery efficiency;

[0032] Calculate the distance cost based on the Euclidean distance between the current network relay device and the data transmission destination:

[0033]

[0034] in, This indicates the current network relay device r and the data transmission destination. Euclidean distance between them, x r This represents the value of the current network relay device r on the x-axis, and y-axis... r This represents the value of the current network relay device r on the y-axis. Indicates the data destination The value on the x-axis Indicates the destination of data transmission The value on the y-axis, Indicates distance cost;

[0035] Based on the trajectory similarity, the estimated cache time, the cache space ratio, the delivery efficiency, and the distance overhead, the comprehensive data transmission capability is obtained:

[0036]

[0037] Among them, α, β, γ, σ and ξ represents the weighting coefficients for trajectory similarity, estimated cache time, cache space value, delivery efficiency, and distance cost, respectively. m，r Indicates trajectory similarity. θ represents the estimated cache time. r Indicates the cache space ratio. Indicates delivery efficiency. Q represents the distance overhead, and Q represents the overall data transmission capability.

[0038] According to the present invention, a vehicle-mounted data transmission method based on spatiotemporal autonomous load balancing routing updates network status data and network relay device congestion status based on historical network status data and current network status data, combined with the comprehensive data transmission capability, including:

[0039] The network relay device with the highest overall data transmission capability is selected as the next hop for data transmission.

[0040] Predicted cache space ratio based on momentum method:

[0041]

[0042] Where ρ represents the inertia coefficient, θ t-1 θ represents the cache space ratio for cycle t-1. t This represents the cache space ratio over period t. This represents the predicted cache space ratio for period t;

[0043] Based on the actual cache space ratio, the predicted cache space ratio, and the mean square error, the inertia coefficient ρ is dynamically adjusted:

[0044]

[0045]

[0046]

[0047] Where n represents the upper limit of the period, and Ω represents the mean square error. θ represents the predicted cache space ratio at period t-1. t-2 This represents the cache space ratio in cycle t-2. This represents the cache space ratio in cycle t+1. This represents the adjusted inertia coefficient.

[0048] According to the present invention, a vehicle data transmission method based on spatiotemporal autonomous load balancing routing arranges the data transmission order in ascending order according to the remaining lifetime, and transmits the vehicle driving data to the data transmission destination, including:

[0049] Obtain the remaining lifetime, and arrange the data transmission order according to the remaining lifetime, sorting them in ascending order from smallest to largest;

[0050] If it is determined that there is untransmitted data, the untransmitted data is transmitted to the data transmission destination; otherwise, the transmission process ends.

[0051] Secondly, the present invention also provides an in-vehicle data transmission system based on spatiotemporal autonomous load balancing routing, comprising:

[0052] The acquisition and transmission module is used to acquire vehicle driving data, determine the data transmission destination, and send the vehicle driving data to a non-congested network relay device.

[0053] The filtering calculation module is used to filter the vehicle driving data by the non-congested network relay device and calculate the reference trajectory path based on the network topology information stored in the software-defined network.

[0054] The sensing output module is used to sense the location and storage information of relay devices in the neighboring network and output a set of candidate relay devices.

[0055] The calculation and filtering module is used to calculate the spatiotemporal domain parameter set of the candidate relay device set, and obtain the comprehensive data transmission capability of the candidate relay device set based on the weights and the spatiotemporal domain parameter set.

[0056] The integrated update module is used to update network status data and network relay device congestion status based on historical network status data and current network status data, combined with the integrated data transmission capability.

[0057] The data transmission module is used to arrange the data transmission order in ascending order according to the remaining lifespan, and transmit the vehicle driving data to the data transmission destination.

[0058] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle data transmission method based on spatiotemporal autonomous load balancing routing as described above.

[0059] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the vehicle data transmission method based on spatiotemporal autonomous load balancing routing as described above.

[0060] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the vehicle data transmission method based on spatiotemporal domain autonomous load balancing routing as described above.

[0061] The present invention provides a vehicle-mounted data transmission method and system based on spatiotemporal autonomous load balancing routing. By employing software-defined networking, it fully considers spatiotemporal parameters, predicts the congestion status of network relay devices, avoids data transmission along loops, reduces end-to-end data latency, and extends trajectory-based routing protocols. Compared with traditional core network routing protocols, it considers spatiotemporal parameters, comprehensively evaluates the data transmission capabilities of network relay devices, and finally, based on the momentum method, senses and predicts network traffic changes, estimates the congestion status of network relay devices, avoids network congestion, and achieves network load balancing. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0063] Figure 1 This is one of the flowcharts of the in-vehicle data transmission method based on spatiotemporal autonomous load balancing routing provided by the present invention;

[0064] Figure 2 This is the overall architecture diagram provided by the present invention;

[0065] Figure 3 This is the second flowchart of the in-vehicle data transmission method based on spatiotemporal autonomous load balancing routing provided by the present invention;

[0066] Figure 4 This is a schematic diagram of the data delivery process provided by the present invention;

[0067] Figure 5 This is a schematic diagram of candidate device filtering provided by the present invention;

[0068] Figure 6 This is a schematic diagram of the estimated cache time provided by the present invention;

[0069] Figure 7 This is a schematic diagram of the distance overhead provided by the present invention;

[0070] Figure 8 This is a schematic diagram of the vehicle data transmission system based on spatiotemporal autonomous load balancing routing provided by the present invention;

[0071] Figure 9 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0073] Figure 1 This is one of the flowcharts illustrating the in-vehicle data transmission method based on spatiotemporal autonomous load balancing routing provided in this embodiment of the invention, such as... Figure 1 As shown, it includes:

[0074] Step 100: Obtain vehicle driving data, determine the data transmission destination, and send the vehicle driving data to the non-congested network relay device;

[0075] Step 200: The non-congested network relay device filters the vehicle driving data and calculates the reference trajectory path based on the network topology information stored in the software-defined network.

[0076] Step 300: Sensing the location and storage information of relay devices in the neighboring network, and outputting a set of candidate relay devices;

[0077] Step 400: Calculate the spatiotemporal domain parameter set of the candidate relay device set, and obtain the comprehensive data transmission capability of the candidate relay device set based on the weights and the spatiotemporal domain parameter set;

[0078] Step 500: Based on historical network status data and current network status data, and combined with the comprehensive data transmission capability, update the network status data and the network relay device congestion status;

[0079] Step 600: Sort the data transmission order in ascending order according to the remaining lifespan, and transmit the vehicle driving data to the data transmission destination.

[0080] The in-vehicle data transmission method based on spatiotemporal autonomous load balancing routing proposed in this invention has the following architecture: Figure 2 As shown, a software-defined network (SDN) virtual manager is composed of computing clusters and data centers. It forms an infrastructure network with multiple physical devices, such as routers, switches, and base stations. Vehicles belong to the access side and are used to send and receive data. A spatiotemporal autonomous load balancing routing protocol is adopted. This routing protocol dynamically senses the network status and comprehensively calculates the data transmission capabilities of devices by fusing spatiotemporal parameters to achieve the load balancing goal, reduce end-to-end data latency and network load, and improve data delivery rate.

[0081] It should be noted that the embodiments of the present invention are based on a novel network architecture, namely software-defined networking, and construct a software-defined network virtual manager to realize the generation of reference trajectory paths supported by global topology information; based on spatiotemporal domain parameters, multi-dimensional network situational awareness and network relay device status assessment are realized, including trajectory similarity, cache space ratio, estimated cache time, delivery efficiency, and distance overhead. Trajectory similarity represents the degree of similarity between the vector formed by the candidate relay device and the current network relay device and the pre-transmission trajectory path, which is used to control the direction of data transmission; cache space ratio represents the ratio of the remaining cache space of the network relay device to its total cache space, which is positively correlated with the storage capacity of the network relay device; estimated cache time represents the estimated cache time of data in the candidate relay device, which is negatively correlated with the data transmission capacity of the candidate relay device; delivery efficiency represents the time it takes for data to be delivered to its destination through a certain network relay device, which is negatively correlated with the data transmission capacity of the candidate relay device; distance overhead represents the Euclidean distance between a certain network relay device and the data destination. Based on the momentum method, the predicted congestion state of network relay devices is calculated to reduce the size of the candidate relay device set; based on weights, methods and spatiotemporal parameters, the data transmission capacity of network relay devices is comprehensively evaluated to achieve load balancing.

[0082] The overall process is as follows Figure 3 As shown, vehicles travel along streets, generating data, setting destinations for the data, and transmitting the data to non-congested network relay devices. The current network relay device filters the stored data, removing data without a reference trajectory path or with loops in the transmission path. Based on the network topology information stored by the software-defined network virtual manager during network initialization, Dijkstra's shortest path algorithm is used to calculate the shortest path from the current network relay device to the data destination, forming a reference trajectory path. The location and stored information of neighboring network relay devices are sensed, and the angle between the candidate vector and the data's reference vector is calculated, then... Based on the angle between the congestion status of neighboring network relay devices and the vector, candidate network relay devices are filtered to form a set of candidate network relay devices. Trajectory similarity, estimated buffer time, buffer space ratio, delivery efficiency, and distance overhead are calculated. Multi-dimensional data fusion is achieved using weighted methods to obtain the comprehensive data transmission capabilities of the candidate network relay devices. The network relay device with the strongest data transmission capability is selected as the next-hop network relay device for the data. Based on historical and current network state data, the network state and the congestion status of network relay devices are estimated and updated. The data transmission order is arranged in ascending order according to the remaining lifetime. The entire data delivery process can be understood as follows: Figure 4 As shown.

[0083] This invention employs software-defined networking, fully considering spatiotemporal parameters, to predict the congestion status of network relay devices, avoid data transmission along loops, reduce end-to-end data latency, and extend trajectory-based routing protocols. Compared to traditional core network routing protocols, it considers spatiotemporal parameters, comprehensively evaluates the data transmission capabilities of network relay devices, and finally, based on the momentum method, senses and predicts network traffic changes, estimates the congestion status of network relay devices, avoids network congestion, and achieves network load balancing.

[0084] Based on the above embodiments, step 200 includes:

[0085] If it is determined that the filtered data does not have a reference trajectory path or the transmission path contains loops, then based on the network topology information, the shortest path from the current network relay device to the data transmission destination is calculated using the Dijkstra algorithm to obtain the reference trajectory path;

[0086] Otherwise, the data filtering is performed by the non-congested network relay device.

[0087] Specifically, such as Figure 3 As shown, the current network relay device filters the stored data, filtering out data that does not have a reference trajectory path set or whose transmission path contains loops; based on the network topology information stored by the software-defined network virtual manager during network initialization, the shortest path from the current network relay device to the data destination is calculated using Dijkstra's shortest path algorithm, and a reference trajectory path is formed.

[0088] Here, this embodiment of the invention considers the following two conditions; a reference trajectory path can be generated and stored in the data packet header if either condition is met:

[0089] 1) No reference trajectory path was set;

[0090] 2) A loop appears in the transmission path.

[0091] Based on the above embodiments, step 300 includes:

[0092] Obtain the reference vector of the neighboring network relay device, and the candidate vectors of each neighboring network relay device and the current network relay device;

[0093] If it is determined that the angle between the reference vector and the candidate vector is less than 90 degrees, and the neighbor network relay device is in a non-congested state, then the candidate relay device set is output.

[0094] Specifically, such as Figure 5As shown, the location and storage information of the neighboring network relay devices are considered, including geographical location, network topology, and cache space. There are four network relay devices: A, B, D, and G. Network relay device A stores observation data and has links with network relay devices B and D. Candidate vectors are formed between network relay devices A and B, and D, while a reference vector is formed between network relay device A and G. Based on this, according to the angle between the reference vector and the candidate vectors, devices corresponding to candidate vectors with an angle less than 90 degrees (green area) are filtered out as candidate network relay devices, forming a set of candidate network relay devices. Therefore, network relay device B is a candidate device.

[0095] Based on the above embodiments, step 400 includes:

[0096] Calculate trajectory similarity based on reference vectors and candidate vectors;

[0097] Calculate the cache space ratio based on the remaining cache space size of the neighboring network relay devices and the total cache space size of the neighboring network relay devices;

[0098] Calculate the estimated buffer time based on the size of any observation data and the link transmission speed between any candidate network relay device and the current network relay device;

[0099] Calculate delivery efficiency based on the time cost of delivering any observation data to the data transmission destination via the current network relay device;

[0100] Calculate the distance cost based on the Euclidean distance between the current network relay device and the data transmission destination;

[0101] The comprehensive data transmission capability is obtained based on the trajectory similarity, the estimated cache time, the cache space ratio, the delivery efficiency, and the distance overhead.

[0102] Specifically, in this embodiment of the invention, the trajectory similarity is calculated using the following formula:

[0103]

[0104] Among them, v r，x and v r，y Representing candidate vectors v r The value mapped onto the x-axis and y-axis, v m，x and v m，y Representing the reference vector v m The value mapped onto the x-axis and y-axis, ξ m，r Indicates trajectory similarity.

[0105] The formula for calculating the buffer space ratio of candidate network relay devices is as follows:

[0106]

[0107] Among them, κ r v represents the remaining buffer space size of the relay device in the neighboring network. r θ represents the amount of cache space already used by relay devices in the neighboring network. r This indicates the cache space ratio.

[0108] The estimated buffering time of data in candidate network relay devices is calculated using the following formula:

[0109]

[0110] Where, m i This represents the i-th observation data. Represents the observed data m i Size, in MB. Indicates r i The link transmission speed between r and r, where r represents the current network relay device, r i Let represent candidate network relay devices, and n represent the number of data points among neighboring network relay devices, ordered by remaining lifetime in ascending order, that precedes the observed data m. This indicates the estimated cache time.

[0111] like Figure 6 As shown, Figure 6 (a) is a schematic diagram of the data distribution for period t. Figure 6 (b) is a schematic diagram of the data distribution predicted in period t. A, B, and C represent three network relay devices, and M1, M2, M3, M4, and M5 represent data, where M1 is the observed data. The arrows indicate the data transmission direction. In period t, network relay device A evaluates the estimated buffer time of M1 in network relay device B. Figure 6 (b) It shows that when M1 is transmitted to network relay device B, both M2 and M3 are received by network relay device B. After sorting by remaining lifetime in ascending order, the estimated buffer time of M1 in network relay device B is calculated according to the above formula.

[0112] The delivery efficiency of candidate network relay devices is calculated using the following formula:

[0113]

[0114] Where k represents the number of data successfully delivered through the current network relay device r. This indicates that the i-th data is delivered to its destination through the current network relay device r. Time expenditure, This indicates delivery efficiency.

[0115] The distance cost of candidate network relay devices is calculated using the following formula:

[0116]

[0117] in, This indicates the current network relay device r and the data transmission destination. Euclidean distance between them, x r This represents the value of the current network relay device r on the x-axis, and y-axis... r This represents the value of the current network relay device r on the y-axis. Indicates data destination The value on the x-axis, Indicates the destination of data transmission The value on the y-axis, This indicates the distance cost.

[0118] like Figure 7 As shown, A, B, and D are three network relay devices. A and B are neighboring network relay devices, and D is the destination of the observation data. A stores the observation data, and the dashed line represents the distance cost of this data traveling to the data destination through network relay device B.

[0119] Based on the above spatiotemporal parameter calculation process, trajectory similarity, estimated buffer time, buffer space ratio, average delivery delay, and distance overhead are obtained. Multidimensional data fusion is achieved using weighted sum methods to obtain the comprehensive data transmission capability of candidate network relay devices. The calculation formula is shown below:

[0120]

[0121] Among them, α, β, γ, σ and ξ represents the weighting coefficients for trajectory similarity, estimated cache time, cache space value, delivery efficiency, and distance cost, respectively. m，r Indicates trajectory similarity. θ represents the estimated cache time. r Indicates the cache space ratio. Indicates delivery efficiency. Q represents the distance overhead, and Q represents the overall data transmission capability.

[0122] Based on the above embodiments, step 500 includes:

[0123] The network relay device with the highest overall data transmission capability is selected as the next hop for data transmission.

[0124] Predicted cache space ratio based on momentum method;

[0125] The inertia coefficient is dynamically adjusted based on the actual cache space ratio, the predicted cache space ratio, and the mean square error.

[0126] Specifically, selecting the network relay device with the strongest data transmission capability as the next-hop network relay device for data transmission is expressed as:

[0127] Q * =max 0≤i≤n Q

[0128] Where Q represents the overall data transmission capability, Q * This indicates the strongest overall data transmission capability.

[0129] Based on historical and current network status data, the network status and congestion status of network relay devices are estimated and updated, including congestion status, cache space score, predicted cache space ratio, and inertia index.

[0130] The inertia coefficient ρ can be dynamically adjusted according to the network state to adapt to network fluctuations and reflect network traffic fluctuations. To dynamically adjust the inertia coefficient ρ, the difference between the actual cache space ratio and the predicted cache space ratio is calculated using the mean square error. First, the adjusted cache space ratio is set equal to the cache space ratio in period t+1, i.e. Secondly, establish The relationship with ρ. Therefore, let the mean square error be Ω, its formula is as follows:

[0131]

[0132] Where n represents the upper limit of the period, Ω represents the mean square error, and θ is calculated. t-1 The predicted cache space ratio at period t-1 is calculated using the following formula:

[0133]

[0134] in, θ represents the predicted cache space ratio at period t-1. t-1 θ represents the cache space ratio in cycle t-1. t-2 This represents the cache space ratio in cycle t-2.

[0135] Based on this, the formula for adjusting the inertia coefficient is derived:

[0136]

[0137] in, This represents the cache space ratio in cycle t+1. This represents the adjusted inertia coefficient.

[0138] Based on the above embodiments, step 600 includes:

[0139] Obtain the remaining lifetime, and arrange the data transmission order according to the remaining lifetime, sorting them in ascending order from smallest to largest;

[0140] If it is determined that there is untransmitted data, the untransmitted data is transmitted to the data transmission destination; otherwise, the transmission process ends.

[0141] Specifically, such as Figure 3 As shown, data transmission is arranged in ascending order of remaining lifetime, meaning data with a shorter remaining lifetime is transmitted earlier to reduce end-to-end latency. The system checks for any untransmitted data; if so, it continues transmitting data, otherwise the transmission process ends.

[0142] The in-vehicle data transmission system based on spatiotemporal autonomous load balancing routing provided by the present invention will be described below. The in-vehicle data transmission system based on spatiotemporal autonomous load balancing routing described below can be referred to in correspondence with the in-vehicle data transmission method based on spatiotemporal autonomous load balancing routing described above.

[0143] Figure 8 This is a schematic diagram of the structure of the in-vehicle data transmission system based on spatiotemporal autonomous load balancing routing provided in an embodiment of the present invention, as shown below. Figure 8 As shown, it includes: an acquisition and transmission module 81, a filtering and calculation module 82, a sensing and output module 83, a calculation and filtering module 84, a comprehensive update module 85, and an arrangement and transmission module 86, wherein:

[0144] The acquisition and transmission module 81 is used to acquire vehicle driving data, determine the data transmission destination, and send the vehicle driving data to a non-congested network relay device; the filtering and calculation module 82 is used to filter the vehicle driving data by the non-congested network relay device and calculate a reference trajectory path based on the network topology information stored in the software-defined network; the perception and output module 83 is used to perceive the location information and storage information of neighboring network relay devices and output a set of candidate relay devices; the calculation and filtering module 84 is used to calculate the spatiotemporal domain parameter set of the candidate relay device set and obtain the comprehensive data transmission capability of the candidate relay device set based on the weight and the spatiotemporal domain parameter set; the comprehensive update module 85 is used to update the network status data and the network relay device congestion status based on historical network status data and current network status data, combined with the comprehensive data transmission capability; and the transmission ordering module 86 is used to arrange the data transmission order in ascending order according to the remaining lifetime and transmit the vehicle driving data to the data transmission destination.

[0145] Figure 9 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 9As shown, the electronic device may include: a processor 910, a communication interface 920, a memory 930, and a communication bus 940, wherein the processor 910, the communication interface 920, and the memory 930 communicate with each other through the communication bus 940. The processor 910 can call logic instructions in the memory 930 to execute a vehicle data transmission method based on spatiotemporal autonomous load balancing routing. This method includes: acquiring vehicle driving data, determining the data transmission destination, and sending the vehicle driving data to a non-congested network relay device; the non-congested network relay device filtering the vehicle driving data and calculating a reference trajectory path based on network topology information stored in a software-defined network; sensing the location and storage information of neighboring network relay devices and outputting a candidate relay device set; calculating the spatiotemporal parameter set of the candidate relay device set, and obtaining the comprehensive data transmission capability of the candidate relay device set based on weights and the spatiotemporal parameter set; updating network status data and network relay device congestion status based on historical network status data and current network status data, combined with the comprehensive data transmission capability; and arranging the data transmission order in ascending order according to the remaining lifetime, and transmitting the vehicle driving data to the data transmission destination.

[0146] Furthermore, the logical instructions in the aforementioned memory 930 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a 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 several 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 described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0147] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the vehicle data transmission method based on spatiotemporal autonomous load balancing routing provided by the above methods. The method includes: acquiring vehicle driving data, determining the data transmission destination, and sending the vehicle driving data to a non-congested network relay device; filtering the vehicle driving data by the non-congested network relay device and calculating a reference trajectory path based on the network topology information stored in the software-defined network; sensing the location information and storage information of neighboring network relay devices and outputting a candidate relay device set; calculating the spatiotemporal parameter set of the candidate relay device set and obtaining the comprehensive data transmission capability of the candidate relay device set based on weights and the spatiotemporal parameter set; updating the network status data and the network relay device congestion status based on historical network status data and current network status data, combined with the comprehensive data transmission capability; and arranging the data transmission order in ascending order according to the remaining lifetime and transmitting the vehicle driving data to the data transmission destination.

[0148] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the vehicle data transmission method based on spatiotemporal autonomous load balancing routing provided by the methods described above. This method includes: acquiring vehicle driving data, determining the data transmission destination, and sending the vehicle driving data to a non-congested network relay device; filtering the vehicle driving data by the non-congested network relay device and calculating a reference trajectory path based on network topology information stored in a software-defined network; sensing the location and storage information of neighboring network relay devices and outputting a candidate relay device set; calculating a spatiotemporal parameter set of the candidate relay device set, and obtaining the comprehensive data transmission capability of the candidate relay device set based on weights and the spatiotemporal parameter set; updating network status data and network relay device congestion status based on historical network status data and current network status data, combined with the comprehensive data transmission capability; and arranging the data transmission order in ascending order according to the remaining lifetime, and transmitting the vehicle driving data to the data transmission destination.

[0149] The device embodiments described above are merely illustrative. The units described 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0150] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for in-vehicle data transmission based on spatiotemporal autonomous load balancing routing, characterized in that, include: Acquire vehicle driving data, determine the data transmission destination, and send the vehicle driving data to a non-congested network relay device; The vehicle driving data is filtered by the non-congested network relay device, and a reference trajectory path is calculated based on the network topology information stored in the software-defined network. It senses the location and storage information of relay devices in the neighboring network and outputs a set of candidate relay devices; Calculate the spatiotemporal domain parameter set of the candidate relay device set, and obtain the comprehensive data transmission capability of the candidate relay device set based on the weights and the spatiotemporal domain parameter set; Based on historical network status data and current network status data, and combined with the comprehensive data transmission capability, update the network status data and the congestion status of network relay devices; The data transmission order is sorted in ascending order according to the remaining lifespan, and the vehicle driving data is transmitted to the data transmission destination. The non-congested network relay device filters the vehicle driving data and calculates a reference trajectory path based on the network topology information stored in the software-defined network, including: If it is determined that the filtered data does not have a reference trajectory path or the transmission path contains loops, then based on the network topology information, the shortest path from the current network relay device to the data transmission destination is calculated using the Dijkstra algorithm to obtain the reference trajectory path; Otherwise, the data filtering is continued by the non-congested network relay device; It senses the location and stored information of relay devices in neighboring networks and outputs a set of candidate relay devices, including: Obtain the reference vector of the neighboring network relay device, and the candidate vectors of each neighboring network relay device and the current network relay device; If it is determined that the angle between the reference vector and the candidate vector is less than 90 degrees, and the neighbor network relay device is in a non-congested state, then the candidate relay device set is output. Calculate the spatiotemporal domain parameter set of the candidate relay device set, and obtain the comprehensive data transmission capability of the candidate relay device set based on the weights and the spatiotemporal domain parameter set, including: Calculate trajectory similarity based on the reference vector and candidate vector: in, and Representing candidate vectors respectively Mapped in shaft and Values ​​on the axis and Representing the reference vectors respectively Mapped in shaft and Values ​​on the axis Indicates trajectory similarity; Calculate the cache space ratio based on the remaining cache space of the neighboring network relay devices and the total cache space of the neighboring network relay devices: in, This indicates the remaining buffer space size of the relay device in the neighboring network. This indicates the amount of cache space already used by relay devices in the neighboring network. Indicates the cache space ratio; Based on the size of any observed data and the link transmission speed between any candidate network relay device and the current network relay device, calculate the estimated buffer time: in, Indicates the first One observation data, Representing observation data Size, in units , express and The link transmission speed between them Indicates the current network relay device. Indicates a candidate network relay device. This indicates that in the neighbor network relay device, the data is arranged in ascending order according to the remaining lifetime, and is listed in the order of the observed data. Previous data volume Indicates the estimated cache time; Calculate the delivery efficiency based on the time cost of delivering any observation data to the data transmission destination via the current network relay device: in, Indicates through the current network relay device The number of data successfully delivered. Indicates the first Data is transmitted through the current network relay device. Delivery to its destination Time expenditure, Indicates delivery efficiency; Calculate the distance cost based on the Euclidean distance between the current network relay device and the data transmission destination: in, Indicates the current network relay device With data transmission destination The Euclidean distance between them Indicates the current network relay device exist Values ​​on the axis Indicates the current network relay device exist Values ​​on the axis Indicates the data destination exist Values ​​on the axis Indicates the destination of data transmission exist Values ​​on the axis Indicates distance cost; Based on the trajectory similarity, the estimated cache time, the cache space ratio, the delivery efficiency, and the distance overhead, the comprehensive data transmission capability is obtained: in, , , , and These represent the weighting coefficients for trajectory similarity, estimated cache time, cache space value, delivery efficiency, and distance cost, respectively. Indicates trajectory similarity. Indicates the estimated cache time. Indicates the cache space ratio. Indicates delivery efficiency. Indicates distance cost. Indicates comprehensive data transmission capability; Based on historical and current network status data, and combined with the comprehensive data transmission capability, update the network status data and network relay device congestion status, including: The network relay device with the highest overall data transmission capability is selected as the next hop for data transmission. Predicted cache space ratio based on momentum method: in, Represents the coefficient of inertia. express The ratio of cache space per cycle, express The ratio of cache space per cycle, Indicates period The predicted cache space ratio; The inertia coefficient is dynamically adjusted based on the actual cache space ratio, the predicted cache space ratio, and the mean square error. : in, Indicates the upper limit of the period. This represents the mean square error. Indicates in Predicted cache space ratio during the cycle Indicates in The ratio of cache space per cycle, express The ratio of cache space per cycle, This represents the adjusted inertia coefficient.

2. The in-vehicle data transmission method based on spatiotemporal autonomous load balancing routing according to claim 1, characterized in that, The data transmission order is sorted in ascending order according to the remaining lifetime, and the vehicle driving data is transmitted to the data transmission destination, including: Obtain the remaining lifetime, and arrange the data transmission order according to the remaining lifetime, sorting them in ascending order from smallest to largest; If it is determined that there is untransmitted data, the untransmitted data is transmitted to the data transmission destination; otherwise, the transmission process ends.

3. A vehicle-mounted data transmission system based on spatiotemporal autonomous load balancing routing, based on the vehicle-mounted data transmission method based on spatiotemporal autonomous load balancing routing as described in claim 1 or 2, characterized in that, include: The acquisition and transmission module is used to acquire vehicle driving data, determine the data transmission destination, and send the vehicle driving data to a non-congested network relay device. The filtering calculation module is used to filter the vehicle driving data by the non-congested network relay device and calculate the reference trajectory path based on the network topology information stored in the software-defined network. The sensing output module is used to sense the location and storage information of relay devices in the neighboring network and output a set of candidate relay devices. The calculation and filtering module is used to calculate the spatiotemporal domain parameter set of the candidate relay device set, and obtain the comprehensive data transmission capability of the candidate relay device set based on the weights and the spatiotemporal domain parameter set. The integrated update module is used to update network status data and network relay device congestion status based on historical network status data and current network status data, combined with the integrated data transmission capability. The data transmission module is used to arrange the data transmission order in ascending order according to the remaining lifespan, and transmit the vehicle driving data to the data transmission destination.

4. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the in-vehicle data transmission method based on spatiotemporal autonomous load balancing routing as described in claim 1 or 2.

5. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the vehicle data transmission method based on spatiotemporal autonomous load balancing routing as described in claim 1 or 2.

6. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the vehicle data transmission method based on spatiotemporal autonomous load balancing routing as described in claim 1 or 2.

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