Internet of vehicles network resource scheduling method and device

Abstract

The invention belongs to the technical field of Internet of Vehicles, and particularly relates to an Internet of Vehicles network resource scheduling method and device. The Internet of Vehicles network resource scheduling method comprises the following steps: collecting vehicle state data and user interaction data; calculating a network resource weight at least according to the vehicle state data and the user interaction data; determining a network resource configuration strategy according to the network resource weight; wherein the network resource configuration strategy comprises a network resource quota, a network slice mapping relation and a service priority, the network resource quota allocated by the vehicle is increased along with the increase of the network resource weight, and the network slice mapping relation is used for configuring a mapping relation between data of the vehicle and different network slices; the service priorities are used for configuring the priorities of different data. According to the invention, network resources can be reasonably utilized.

Description

Technical Field

[0001] This invention belongs to the field of vehicle networking technology, and specifically relates to a method and apparatus for scheduling vehicle networking network resources. Background Technology

[0002] Existing vehicle-to-everything (V2X) network resource scheduling methods mainly rely on static priority queues, allocating bandwidth according to preset priorities. However, they cannot dynamically respond to changes in vehicle status (such as vehicle speed and user interaction behavior). This inability to adapt to changes in vehicle status easily leads to unreasonable network resource allocation and poor user experience. Summary of the Invention

[0003] This invention provides a method and apparatus for scheduling network resources in the Internet of Vehicles (IoV) that can make reasonable use of network resources.

[0004] In a first aspect, embodiments of the present invention provide a method for scheduling network resources in a vehicle-to-everything (V2X) network, including: Collect vehicle status data and user interaction data; The network resource weights are calculated based at least on the vehicle status data and the user interaction data. The network resource allocation strategy is determined based on the network resource weights; wherein... The network resource configuration strategy includes network resource quotas, network slice mapping relationships, and service priorities. The network resource quotas allocated to vehicles increase as the network resource weight increases. The network slice mapping relationship is used to configure the mapping relationship between vehicle data and different network slices, and the service priority is used to configure the priority of different data.

[0005] In an optional embodiment, calculating network resource weights based at least on the vehicle status data and the user interaction data includes: The vehicle status data includes vehicle speed, and the user interaction data includes interaction frequency, which is the number of times the user interacts with the vehicle terminal within a set time interval. The network resource weights are configured to decrease with increasing vehicle speed and increase with increasing interaction frequency.

[0006] In an optional embodiment, calculating network resource weights based at least on the vehicle status data and the user interaction data includes: The network resource weight is calculated based on the vehicle status data, the number of user interactions, and the network congestion index data, whereby the network congestion index represents the degree of network congestion. The network resource weights are configured to decrease as network congestion increases.

[0007] In an optional embodiment, the formula for calculating network resource weights is as follows: W_{final} = (W_{base} + α × UOI) × (1 - β × NCI) - γ × V_{status} Wherein, W_{final} is the network resource weight, W_{base} is the base weight, α is the interaction enhancement coefficient, which is a constant that determines the degree of influence of user interaction behavior on the weight, UOI is the interaction intensity, which is the ratio of interaction frequency to interaction threshold, and its value range is [0, 1], NCI is the network congestion index, which ranges from [0, 1], and the larger the value of the network congestion index, the more severe the network congestion, β is the network congestion penalty coefficient, which is a constant that determines the degree of influence of network congestion on the weight, γ is the vehicle speed penalty coefficient, which is a constant that represents the amount of penalty caused to the weight per unit vehicle speed, and V_{status} is the vehicle speed influence factor, which is obtained by the ratio of the current vehicle speed to the vehicle speed threshold, and its value range is [0, 1].

[0008] In an optional embodiment, determining the network resource allocation strategy based on the network resource weights includes: The range of values ​​for the network resource weights is divided into multiple non-overlapping weight ranges, and each weight range corresponds to a network resource configuration strategy. The network resource allocation strategy is determined based on the mapping relationship between the weight range of the network resource rights and the network resource allocation strategy.

[0009] In an optional embodiment, the network slice mapping relationship includes mapping vehicle data to URLLC slices or eMBB slices.

[0010] In an optional embodiment, vehicle data is divided into safety data and non-safety data; The network resource allocation strategy includes: The first network resource allocation strategy corresponds to the largest weight range. The network resource quota in the first network resource allocation strategy is the maximum allocable network resource. Vehicle data is preferentially mapped to eMBB slices. The proportion of network resource quota allocated to non-safe data is greater than the proportion of safe data. The second network resource allocation strategy has a weight range that is smaller than the weight range of the first network resource allocation strategy, and its network resource quota is smaller than the network resource allocation of the first network resource allocation strategy. The secure data is mapped to a URLLC slice, the insecure data is mapped to an eMBB slice, and the priority of the secure data is higher than the priority of the insecure data. The third network resource allocation strategy corresponds to the minimum weight range. The network resource quota in the third network resource allocation strategy is the minimum required network resource. The secure data is mapped to the URLLC slice, and the insecure data is mapped to the eMBB slice. The priority of the secure data is higher than that of the insecure data.

[0011] In an optional embodiment, the third network resource configuration strategy includes: mapping the less urgent secure data to an eMBB slice; and interrupting the transmission of the insecure data.

[0012] Secondly, embodiments of the present invention provide a vehicle network resource scheduling device, comprising: The data acquisition module is used to collect vehicle status data and user interaction data. A weighting module is used to calculate network resource weights based at least on the vehicle status data and the user interaction data. The configuration module is used to determine the network resource configuration strategy based on the network resource weights; wherein, The network resource configuration strategy includes network resource quotas, network slice mapping relationships, and service priorities. The network resource quotas allocated to vehicles increase as the network resource weight increases. The network slice mapping relationship is used to configure the mapping relationship between vehicle data and different network slices, and the service priority is used to configure the priority of different data.

[0013] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor. The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the vehicle network resource scheduling method described in the embodiments of the present invention.

[0014] The beneficial effects of this invention are as follows: In the vehicle-to-everything (V2X) network resource scheduling method of this invention, vehicle status data and user interaction data are collected in real time; network resource weights are calculated based on the collected data; and a network resource configuration strategy is determined based on the network resource weights. The network resource configuration strategy includes network resource quotas, network slice mapping relationships, and service priorities. The network resource quota allocated to a vehicle increases with the network resource weight. The network slice mapping relationship is used to configure the mapping relationship between vehicle data and different network slices, and the service priority is used to configure the priority of different data. The network resource weights calculated in this invention reflect the vehicle's status, and the network resource configuration strategy corresponding to these weights can be designed with full consideration of the vehicle's status, thereby adapting the network resource quotas, network slice mapping relationships, and service priorities to the vehicle's current status, meeting user needs while ensuring vehicle safety. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the vehicle network resource scheduling method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the composition of the vehicle network resource scheduling device according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the composition structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0016] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical strategies of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. 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.

[0017] like Figure 1 As shown, this embodiment of the invention provides a method for scheduling network resources in a vehicle-to-everything (V2X) network, including: S1. Collect vehicle status data and user interaction data; S2. Calculate network resource weights based at least on vehicle status data and user interaction data; S3. Determine the network resource configuration strategy based on the network resource weight; the network resource configuration strategy includes network resource quota, network slice mapping relationship and service priority. The network resource quota allocated to the vehicle increases with the increase of the network resource weight. The network slice mapping relationship is used to configure the mapping relationship between the vehicle's data and different network slices. The service priority is used to configure the priority of different data.

[0018] In the vehicle-to-everything (V2X) network resource scheduling method of this invention, vehicle status data and user interaction data are collected in real time; network resource weights are calculated based on the collected data; and a network resource configuration strategy is determined based on the network resource weights. The network resource configuration strategy includes network resource quotas, network slice mapping relationships, and service priorities. The network resource quota allocated to a vehicle increases with the network resource weight. The network slice mapping relationship is used to configure the mapping relationship between vehicle data and different network slices, and the service priority is used to configure the priority of different data. The network resource weights calculated in this invention reflect the vehicle's status, and the network resource configuration strategy corresponding to these weights can be designed with full consideration of the vehicle's status, thereby adapting the network resource quotas, network slice mapping relationships, and service priorities to the vehicle's current status, meeting user needs while ensuring vehicle safety.

[0019] In some embodiments, network resource weights are calculated based at least on vehicle status data and user interaction data, including: vehicle status data including vehicle speed, user interaction data including interaction frequency, where interaction frequency is the number of interactions between the user and the vehicle terminal within a set time interval; network resource weights are configured to decrease as vehicle speed increases and increase as interaction frequency increases.

[0020] In this embodiment of the invention, the network resource weight is configured to decrease as the vehicle speed increases. This reduces the network resource weight when the vehicle speed is high, thereby reducing the network resource quota. When the vehicle speed is high, the user is highly focused on driving and has less demand for data traffic such as entertainment. Therefore, a smaller network resource quota is sufficient to meet the transmission of safety data related to vehicle driving.

[0021] Network resource weights are configured to increase with interaction frequency, satisfying users' personalized needs when they frequently interact with the vehicle's infotainment system. As interaction frequency increases, users typically travel at lower speeds or are stationary in places like parking lots. In these situations, increasing network resource quotas can fulfill users' needs for transmitting insecure data for entertainment purposes such as audio and video.

[0022] In some embodiments, the formula for calculating network resource weights is as follows: W_{final} = (W_{base} + α × UOI) - γ × V_{status} Wherein, W_{final} is the network resource weight, W_{base} is the base weight, α is the interaction enhancement coefficient, which is a constant and determines the degree of influence of user interaction behavior on the weight, UOI is the interaction intensity, which is the ratio of interaction frequency to interaction threshold, and its value range is [0, 1], γ is the vehicle speed penalty coefficient, which is a constant and represents the amount of penalty caused to the weight per unit vehicle speed, and V_{status} is the vehicle speed influence factor, which is obtained by the ratio of the current vehicle speed to the vehicle speed threshold, and its value range is [0, 1].

[0023] With other parameters remaining constant, the interaction enhancement coefficient α is a fixed value. The higher the interaction frequency and the greater the interaction intensity UOI, the larger the network resource weight W_{final} of the vehicle, and the more network resources are allocated to the vehicle.

[0024] With other parameters remaining constant, the speed penalty coefficient γ is a fixed value. The faster the vehicle speed, the larger the speed influence factor V_{status}, and the smaller the network resource weight W_{final} of the vehicle, resulting in less network resources allocated to that vehicle.

[0025] In some embodiments, network resource weights are calculated based at least on vehicle status data and user interaction data, including: calculating network resource weights based on vehicle status data, user interaction counts, and network congestion index data, where the network congestion index represents the degree of network congestion; the network resource weights are configured to decrease as the degree of network congestion increases. This embodiment of the invention also introduces a network congestion index into the calculation of network resource weights, thereby allowing the network resources allocated to vehicles to be adjusted according to the external network environment. An increase in the network congestion index indicates a weakening of vehicle communication capabilities due to a poor external network environment. When network congestion increases, the communication quality of all vehicles decreases. At this time, by reducing the weights, the transmission of non-critical data can be proactively reduced, alleviating network pressure and reserving space for critical data.

[0026] In some embodiments, the formula for calculating network resource weights is as follows: W_{final} = (W_{base} + α × UOI) × (1 - β × NCI) - γ × V_{status} Wherein, W_{final} is the network resource weight, W_{base} is the base weight, α is the interaction enhancement coefficient, which is a constant and determines the degree of influence of user interaction behavior on the weight, UOI is the interaction intensity, which is the ratio of interaction frequency to interaction threshold, and its value range is [0, 1], NCI is the network congestion index, which ranges from [0, 1], and the larger the value of the network congestion index, the more severe the network congestion, β is the network congestion penalty coefficient, which is a constant and determines the degree of influence of network congestion on the weight, γ is the vehicle speed penalty coefficient, which is a constant and represents the amount of penalty caused to the weight per unit vehicle speed, and V_{status} is the vehicle speed influence factor, which is obtained by the ratio of the current vehicle speed to the vehicle speed threshold, and its value range is [0, 1].

[0027] With other parameters remaining constant, the network congestion penalty coefficient β is a fixed value. The more severe the network congestion, the larger the network congestion index NCI, and the smaller the network resource weight W_{final} of the vehicle, the less network resources are allocated to the vehicle.

[0028] In this embodiment of the invention, the base weight W_{base} is a constant, and its value can be determined based on various conditions. Specifically, the value of the base weight W_{base} can be determined by statistically analyzing vehicle network traffic usage. Alternatively, the value of the base weight W_{base} can be determined through model simulation. The value of the base weight W_{base} can also be determined in other appropriate ways. For example, the value of the base weight W_{base} can be 0.5.

[0029] The speed penalty coefficient γ is a constant that represents the amount of penalty imposed on network resource weights per unit of vehicle speed (e.g., km / h). The specific value of the speed penalty coefficient γ can be set according to specific circumstances. For example, γ = 0.005. To increase the impact of vehicle speed on network resource weights, the value of the speed penalty coefficient γ can be appropriately increased. To decrease the impact of vehicle speed on network resource weights, the value of the speed penalty coefficient γ can be appropriately decreased.

[0030] V_{status} is the vehicle speed influence factor, which can be obtained by normalizing the vehicle speed. In the exemplary embodiment, V_{status} = current vehicle speed / vehicle speed threshold, and its value range is [0, 1]. The vehicle speed threshold can be determined according to specific circumstances. In the exemplary embodiment, the vehicle speed threshold can be determined by the maximum speed limit of the highway, such as the maximum speed limit of 120 km / h for expressways. The vehicle speed threshold can also be determined by the maximum design speed, such as a vehicle speed threshold of 180 km / h, 200 km / h, etc.

[0031] α×UOI can represent the user interaction gain factor, which represents the increase in vehicle communication demand due to user-initiated interaction. When the driver or passenger actively interacts with the vehicle's infotainment system through touch, voice, or other means (such as setting navigation or issuing voice commands), the system considers the current communication need to be more timely, and therefore increases its weight to request better service from the network.

[0032] The interaction intensity (UOI) is a normalized value ranging from [0, 1]. The UOI is calculated as the ratio of the interaction frequency to the interaction threshold. The interaction frequency is the number of interactions between the user and the vehicle's infotainment system within a set time interval. This time interval could be, for example, 1 minute. The interaction threshold can be set according to specific circumstances. For example, a threshold of 5 interactions per minute. UOI = Current Interaction Frequency / Interaction Threshold.

[0033] Taking an interaction threshold of 5 times / minute as an example, if a user interacts with the vehicle's infotainment system 3 times in the last minute via touch or voice, then the UOI = 3 / 5 = 0.6. If the user interacts 10 times, since the interaction intensity UOI ranges from [0, 1], UOI = min(1, 10 / 5) = 1.

[0034] The interaction enhancement coefficient α is a constant that determines the maximum weight increase that user interaction behavior can bring. The value of the interaction enhancement coefficient α can be determined according to specific circumstances. For example, α = 0.5. To increase the impact of user interaction behavior on weight, the value of the interaction enhancement coefficient α can be appropriately increased. To decrease the impact of user interaction behavior on weight, the value of the interaction enhancement coefficient α can be appropriately decreased.

[0035] Scenario A: The user is focused on driving and does not interact with the system, so UOI = 0. α × UOI = 0.5 × 0 = 0. This term does not generate any gain.

[0036] Scenario B: The user is setting navigation and interacting frequently with the vehicle's infotainment system. The interaction frequency exceeds the interaction threshold, so UOI = 1. α × UOI = 0.5 × 1 = 0.5. This contributes 0.5 to the final weight.

[0037] The more frequently a user operates, the higher their demand for real-time network performance, such as wanting navigation to respond quickly. Therefore, the network needs to allocate more resources to ensure a good experience.

[0038] β×NCI can represent the network congestion penalty factor, signifying the weakening of vehicle communication capabilities due to adverse external network conditions. When the network is congested, the communication quality of all vehicles deteriorates. In this situation, by reducing the weight of network resources, the transmission of non-critical data can be proactively reduced, alleviating network pressure and freeing up space for critical data, thus achieving a collaborative optimization strategy.

[0039] The Network Congestion Index (NCI) is a quantitative indicator of network status, with values ​​ranging from [0, 1]. NCI = 0 indicates that the network is completely idle. NCI = 1 indicates that the network is extremely congested and communication is almost impossible.

[0040] The Network Congestion Index (NCI) can be measured by the base station or SDN controller and fed back to the vehicle. Specifically, the NCI can be a composite index based on parameters such as channel utilization, packet loss rate, and average latency. In an exemplary embodiment, NCI = 0.7 × channel utilization + 0.3 × packet loss rate.

[0041] The network congestion penalty coefficient β is a constant. It determines the degree to which network congestion affects the weights. Its specific value can be determined according to specific circumstances. For example, β = 0.1. To increase the degree to which network congestion affects the weights, the value of the network congestion penalty coefficient β can be appropriately increased. To decrease the degree to which network congestion affects the weights, the value of the network congestion penalty coefficient β can be appropriately decreased.

[0042] Scenario A: The network is good, NCI = 0.1, β×NCI = 0.1×0.1 = 0.01. This only incurs a tiny 1% penalty.

[0043] Scenario B: Network congestion, NCI = 0.8, β×NCI = 0.1×0.8 = 0.08. This will result in a significant penalty of 8%.

[0044] The more congested the network, the greater the penalty, and the lower the overall network resource weight of each vehicle. This prompts vehicles to reduce data transmission, such as lowering video stream bitrates, thereby helping the entire network recover from congestion and achieving global optimization.

[0045] In some embodiments, determining a network resource allocation strategy based on network resource weights includes: dividing the range of network resource weight values ​​into multiple non-overlapping weight ranges, with each weight range corresponding to a network resource allocation strategy; and determining the network resource allocation strategy based on the mapping relationship between the weight range of the network resource weight and the network resource allocation strategy.

[0046] Network resource weights can be mapped to discrete, pre-defined network resource configuration strategies. Each network resource configuration strategy explicitly specifies the specific network resource configuration parameters for the vehicle, including network resource quotas, network slice mapping relationships, and service priorities. Changes in network resource weights directly reflect changes in the vehicle's service level within the network, thereby triggering different resource scheduling strategies. Based on the vehicle's specific status, intelligent switching from best-effort fulfillment to strict guarantee can be achieved.

[0047] In some embodiments, the network slice mapping relationship includes mapping vehicle data to URLLC slices or eMBB slices. In this embodiment of the invention, vehicle data can be mapped to URLLC slices or eMBB slices based on allocated network resources and data types. For example, vehicle data clients are divided into secure data and non-secure data, with secure data having a higher priority than non-secure data. This ensures that secure data can be successfully delivered under any circumstances, thereby ensuring driving safety.

[0048] In an exemplary embodiment, secure data may be mapped to a URLLC slice, and non-secure data may be mapped to an eMBB slice.

[0049] In some embodiments, vehicle data is divided into security data and non-security data. Network resource allocation strategies include: The first network resource allocation strategy corresponds to the largest weight range. The network resource quota in the first network resource allocation strategy is the maximum allocable network resource. Vehicle data is preferentially mapped to eMBB slices. The proportion of network resource quota allocated to non-safe data is greater than the proportion allocated to safe data.

[0050] The second network resource allocation strategy has a smaller weight range than the first network resource allocation strategy, and its network resource quota is smaller than the network resource allocation strategy of the first network resource allocation strategy. Secure data is mapped to URLLC slices, and insecure data is mapped to eMBB slices. The priority of secure data is higher than that of insecure data.

[0051] The third network resource allocation strategy corresponds to the minimum weight range. The network resource quota in the third network resource allocation strategy is the minimum required network resource. Secure data is mapped to URLLC slices, and non-secure data is mapped to eMBB slices. Secure data has a higher priority than non-secure data.

[0052] In some embodiments, the third network resource allocation strategy includes: mapping less urgent security data to eMBB slices; and interrupting the transmission of insecure data.

[0053] In practice, when the network resource weight is greater than 0.8, it is mapped to the first network resource allocation strategy, which can be considered a reward mode.

[0054] The vehicle's status at this time could be low speed, parked, with good network conditions, or actively interacting with the user. For example, if the vehicle is stationary in a parking lot, the calculated network resource weight is 0.95.

[0055] The core of the first network resource allocation strategy can be to fully satisfy and optimize the user experience. This means maximizing the vehicle's data throughput and user experience while ensuring the network remains uncongested.

[0056] Among these, network resource quotas allocate a relatively high bandwidth limit, for example, more than 20% of the total available bandwidth. Network slice mapping allows all data, including both secure and insecure data, to preferentially use high-quality eMBB slices. Service priority adopts a lenient policy for different service flows within the vehicle, allowing insecure data such as entertainment traffic to consume the majority of resources.

[0057] When 0.5 ≤ W_{final} ≤ 0.8, it is mapped to the second network resource allocation strategy, which can be considered as a balanced mode.

[0058] The vehicle's status at this time could be normal city driving, moderate speed, or light network congestion. For example, if the vehicle is cruising at 40 km / h, W_{final} = 0.7.

[0059] The core of the second network resource allocation strategy is: standard services, allocated on demand. It follows the basic principle of mapping secure data (y) to URLLC slices and insecure data to eMBB slices.

[0060] Among these, network resource quotas include: allocating standard bandwidth, such as 5%-10% of the total available bandwidth; network slice mapping: strict segmentation, with secure data mapped to URLLC slices and insecure data mapped to eMBB slices; and service priority: enforcing high-priority policies to ensure that secure data has absolute priority.

[0061] When W_{final} < 0.5, it is mapped to the third network resource configuration strategy, which can be considered as a penalty / guarantee mode.

[0062] The vehicle's status at this time could be high-speed driving, an emergency, or severe network congestion. For example, if a vehicle enters a congested section at 120 km / h, W_{final} = 0.3.

[0063] The core of the third network resource allocation strategy is: sacrifice user experience for safety. The core objective is to ensure the absolute safety of the vehicle and its surrounding vehicles.

[0064] Among these, network resource quotas strictly limit total bandwidth, especially the quota for non-security traffic. For example, the total bandwidth for this vehicle is reduced to below 1 Mbps. Network slice mapping relationship: Security data has the highest priority within the URLLC slice. Non-security data, such as video calls, can be forcibly interrupted to free up control channel resources. Service priority: Triggers service degradation. For example, some less urgent security data, such as periodic security status reports (S1 level), is downgraded from the URLLC slice to the eMBB slice to ensure the safety of more urgent security data, such as emergency braking commands (S0 level).

[0065] When network resource quotas are reduced, proactively and significantly decreasing the resource consumption of insecure traffic secures absolute priority and deterministic guarantees for secure traffic, thereby achieving maximum security in extreme situations. By reducing network resource allocation to vehicles, the overall network remains operational, concentrating superior resources to ensure the transmission of secure data. This ensures that even in the most dangerous moments, crucial communications (such as braking commands) can be transmitted without obstruction. The method of this invention demonstrates the ability to make optimal trade-off decisions when facing resource conflicts.

[0066] See Figure 2 This invention provides a vehicle-to-everything (V2X) network resource scheduling device, including a data acquisition module, a weighting module, and a configuration module. The data acquisition module is used to collect vehicle status data and user interaction data; the weighting module is used to calculate network resource weights based at least on the vehicle status data and user interaction data; and the configuration module is used to determine network resource configuration strategies based on the network resource weights.

[0067] Network resource allocation strategies include network resource quotas, network slice mapping relationships, and service priorities. The network resource quota allocated to a vehicle increases as the network resource weight increases. The network slice mapping relationship is used to configure the mapping relationship between the vehicle's data and different network slices. The service priority is used to configure the priority of different data.

[0068] The vehicle network resource scheduling device of this invention can implement the vehicle network resource scheduling method of the above embodiments. The description of the vehicle network resource scheduling device embodiment is similar to the description of the vehicle network resource scheduling method embodiment, and has similar beneficial effects, so it will not be described again. For technical details not disclosed in the description of the vehicle network resource scheduling device embodiment of this invention, please refer to the description of the vehicle network resource scheduling method embodiment of this invention. To save space, it will not be described again.

[0069] This invention provides a computer device, including a memory and a processor. The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the vehicle network resource scheduling method of this invention.

[0070] This invention provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, the processor executes the vehicle network resource scheduling method of this invention.

[0071] Figure 3 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0072] like Figure 3 As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 802 or a computer program loaded from storage unit 808 into random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0073] Multiple components in device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of monitors, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0074] The computing unit 801 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the methods of the embodiments of the present invention described above. For example, in some embodiments, the methods of the embodiments of the present invention can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the methods of the embodiments of the present invention described above can be performed. Alternatively, in other embodiments, the computing unit 801 can be configured to perform the methods of the embodiments of the present invention by any other suitable means (e.g., by means of firmware).

[0075] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0076] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0077] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0078] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0079] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0080] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0081] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0082] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0083] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for scheduling network resources in a vehicle-to-everything (V2X) network, characterized in that, include: Collect vehicle status data and user interaction data; The network resource weights are calculated based at least on the vehicle status data and the user interaction data. The network resource allocation strategy is determined based on the network resource weights; wherein... The network resource configuration strategy includes network resource quotas, network slice mapping relationships, and service priorities. The network resource quotas allocated to vehicles increase as the network resource weight increases. The network slice mapping relationship is used to configure the mapping relationship between vehicle data and different network slices, and the service priority is used to configure the priority of different data.

2. The vehicle network resource scheduling method according to claim 1, characterized in that, Calculating network resource weights based at least on the vehicle status data and the user interaction data includes: The vehicle status data includes vehicle speed, and the user interaction data includes interaction frequency, which is the number of times the user interacts with the vehicle terminal within a set time interval. The network resource weights are configured to decrease with increasing vehicle speed and increase with increasing interaction frequency.

3. The vehicle network resource scheduling method according to claim 1, characterized in that, Calculating network resource weights based at least on the vehicle status data and the user interaction data includes: The network resource weight is calculated based on the vehicle status data, the number of user interactions, and the network congestion index data, whereby the network congestion index represents the degree of network congestion. The network resource weights are configured to decrease as network congestion increases.

4. The vehicle network resource scheduling method according to claim 1, characterized in that, The formula for calculating network resource weights is as follows: W_{final} = (W_{base} + α × UOI) × (1 - β × NCI) - γ × V_{status} Wherein, W_{final} is the network resource weight, W_{base} is the base weight, α is the interaction enhancement coefficient, which is a constant that determines the degree of influence of user interaction behavior on the weight, UOI is the interaction intensity, which is the ratio of interaction frequency to interaction threshold, and its value range is [0, 1], NCI is the network congestion index, which ranges from [0, 1], and the larger the value of the network congestion index, the more severe the network congestion, β is the network congestion penalty coefficient, which is a constant that determines the degree of influence of network congestion on the weight, γ is the vehicle speed penalty coefficient, which is a constant that represents the amount of penalty caused to the weight per unit vehicle speed, and V_{status} is the vehicle speed influence factor, which is obtained by the ratio of the current vehicle speed to the vehicle speed threshold, and its value range is [0, 1].

5. The vehicle network resource scheduling method according to claim 1, characterized in that, Determining the network resource allocation strategy based on the network resource weights includes: The range of values ​​for the network resource weights is divided into multiple non-overlapping weight ranges, and each weight range corresponds to a network resource configuration strategy. The network resource allocation strategy is determined based on the mapping relationship between the weight range of the network resource rights and the network resource allocation strategy.

6. The vehicle network resource scheduling method according to claim 5, characterized in that, Network slice mapping relationships include mapping vehicle data to URLLC slices or eMBB slices.

7. The vehicle network resource scheduling method according to claim 6, characterized in that, Vehicle data is divided into safety data and non-safety data; The network resource allocation strategy includes: The first network resource allocation strategy corresponds to the largest weight range. The network resource quota in the first network resource allocation strategy is the maximum allocable network resource. Vehicle data is preferentially mapped to eMBB slices. The proportion of network resource quota allocated to non-safe data is greater than the proportion of safe data. The second network resource allocation strategy has a weight range that is smaller than the weight range of the first network resource allocation strategy, and its network resource quota is smaller than the network resource allocation of the first network resource allocation strategy. The secure data is mapped to a URLLC slice, the insecure data is mapped to an eMBB slice, and the priority of the secure data is higher than the priority of the insecure data. The third network resource allocation strategy corresponds to the minimum weight range. The network resource quota in the third network resource allocation strategy is the minimum required network resource. The secure data is mapped to the URLLC slice, and the insecure data is mapped to the eMBB slice. The priority of the secure data is higher than that of the insecure data.

8. The vehicle network resource scheduling method according to claim 7, characterized in that, The third network resource allocation strategy includes: mapping the less urgent security data to an eMBB slice; and interrupting the transmission of the insecure data.

9. A vehicle-to-everything (V2X) network resource scheduling device, characterized in that, include: The data acquisition module is used to collect vehicle status data and user interaction data. A weighting module is used to calculate network resource weights based at least on the vehicle status data and the user interaction data. The configuration module is used to determine the network resource configuration strategy based on the network resource weights; wherein, The network resource configuration strategy includes network resource quotas, network slice mapping relationships, and service priorities. The network resource quotas allocated to vehicles increase as the network resource weight increases. The network slice mapping relationship is used to configure the mapping relationship between vehicle data and different network slices, and the service priority is used to configure the priority of different data.

10. A computer device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the vehicle network resource scheduling method according to any one of claims 1 to 8.

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