Benefit-based on-demand data synchronization method in digital twin connected vehicles

Abstract

The present invention discloses an on-demand data synchronization method in a digital twin vehicle network, which mainly solves the problem of data asynchrony between vehicles and digital twins in the cloud in the prior art. Its implementation scheme is as follows: constructing a digital twin vehicle network system; vehicles and infrastructure upload data information to the digital twin in the cloud server; calculating the transmission rate between the vehicle and three types of infrastructure; the digital twin calculates the total expected benefits of the infrastructure and vehicle and their maximum utility; according to the utility of the vehicle, the optimal strategy for the vehicle to purchase bandwidth is obtained, and bandwidth is purchased according to this optimal strategy; according to the total amount of bandwidth purchased by the vehicle, the price iteration equation of the infrastructure is obtained, the optimal price strategy for selling bandwidth is calculated, and bandwidth is sold at this optimal price. While fully utilizing the bandwidth resources of the infrastructure, the present invention reduces the transmission delay of data transmitted from the vehicle to the infrastructure, improves the service quality, and can be used for data synchronization services in intelligent transportation systems.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent transportation technology, and in particular relates to an on-demand data synchronization method that can be used for digital twin heterogeneous vehicle networks. Background Art

[0002] With the widespread application and deployment of roadside units (ROSs), cellular base stations, and drones, heterogeneous connected vehicle networks (CVNs) can provide diverse in-vehicle services. In these networks, vehicles can selectively access RSUs, cellular base stations, and drones based on their service needs and preferences to perform mission computing, content distribution, data transmission, and other services. However, traditional heterogeneous CVNs lack the effective integration of differentiated infrastructure resources, making them unable to provide personalized services for the growing number of vehicles.

[0003] The combination of digital twins and heterogeneous connected vehicle networks can significantly improve network resource utilization to meet the personalized service needs of vehicles. In a digital twin heterogeneous connected vehicle network, by creating digital twins of physical entities—vehicles and infrastructure—in the cloud, these twins not only map the state parameters and operational data of the physical entities in the network in real time but also provide feedback to the physical entities on the decisions made by the twins in the digital network. As a result, resources in the digital twin heterogeneous connected vehicle network can be dynamically managed and scheduled from a global perspective, optimizing the quality of vehicle services.

[0004] In the heterogeneous vehicle-to-vehicle (V2X) network of a digital twin, vehicles need to selectively access heterogeneous networks to upload updated or cached data to the digital twin deployed in the cloud, enabling data interaction and decision-making between the two. Obviously, different network infrastructures in a heterogeneous network have different service performance and pricing. Therefore, for each network infrastructure, determining resource pricing based on vehicle needs and its own service capabilities to maximize efficiency is a key issue. Furthermore, different vehicles have different data update requirements and preferences, namely latency and cost. For example, vehicles with high latency requirements may choose high-speed networks, but incur the high transmission costs. Therefore, in the heterogeneous V2X network of a digital twin, how to comprehensively consider the capabilities of different networks and the individual needs of vehicles to obtain the optimal bandwidth resources for vehicles to synchronize data between the physical entity and the digital twin becomes a key challenge.

[0005] The patent document with application publication number CN202210327999.5A discloses "A method and system for allocating resources in the Internet of Vehicles". It first establishes a system cache model based on the structure of mobile vehicles, edge devices RSU and base stations; then uses the graph coloring method to cluster the vehicle-to-infrastructure V2V links to obtain a set of V2V link clusters, and obtains the optimal solution of V2I link transmission power and V2V link transmission power while ensuring the minimum communication service quality of the vehicle-to-vehicle V2I link and the reliability of the V2V link; then uses a three-dimensional matching algorithm to allocate channels for V2I links, V2V link clusters and resource blocks to maximize the total rate of the V2I link.

[0006] Although this method can overcome the defect of improper spectrum allocation for high-bandwidth transmission in the Internet of Vehicles, it still has the following three shortcomings:

[0007] First, this method only considers the channel model of vehicle communication, but does not take into account the high mobility of vehicles and the rapidly changing network topology. Therefore, when multiple vehicles simultaneously access the edge device RSU or base station for channel allocation, it cannot provide real-time data information, affecting the accuracy of the optimal solution for the V2I link transmission power and V2V link transmission power.

[0008] Second, since this method only considers the clustering of V2V but does not consider the personal preferences of vehicle users for different base stations or RSUs, it is easy to lead to a decrease in vehicle users' satisfaction with network resource allocation.

[0009] Third, since the edge nodes for bandwidth allocation in this method are RSUs and base stations with limited communication coverage, when the vehicle is in a high-speed moving scenario and the network access to the RSUs and base stations is intermittent, it is easy to cause a decline in the service quality of the Internet of Vehicles. Summary of the Invention

[0010] The purpose of the present invention is to address the deficiencies of the above-mentioned existing technologies and propose a benefit-based on-demand data synchronization method in a digital twin heterogeneous Internet of Vehicles, so as to provide real-time data information when multiple vehicles simultaneously access the edge device RSU or base station for channel allocation, so that different vehicles can obtain the optimal amount of bandwidth resources from different networks, thereby improving the service quality of the Internet of Vehicles and the satisfaction of vehicle users with network resource allocation.

[0011] The technical solution for achieving the objectives of the present invention includes: establishing a digital twin model and a communication model in a heterogeneous vehicle-to-vehicle network; establishing benefit functions for the vehicles and infrastructure based on their respective needs; and obtaining the optimal bandwidth strategy for the vehicles and the optimal pricing strategy for the infrastructure through an iterative algorithm to maximize their benefits. The steps are as follows:

[0012] (1) Constructing a DT system consisting of N vehicles, M infrastructures, and N vehicle digital twins n , M infrastructure digital twin DT m A heterogeneous vehicle network system, where each infrastructure consists of a roadside unit r, a cellular base station b and a drone u, N ≥ 2, M ≥ 2;

[0013] (2) Vehicles and infrastructure upload data information to the digital twin in the cloud server:

[0014] Each infrastructure I m Its own spatial position coordinates {x m ,y m ,l m Bandwidth price p m This information is uploaded to the vehicle digital twin DT m , DT m Self-update based on actual data information;

[0015] Each vehicle V n The transmission power P m , bandwidth requirement q n Upload to the infrastructure digital twin DT n , DT n Self-update based on actual data information;

[0016] (3) Calculate V for each vehicle separately n The transmission rate per unit bandwidth connected to the roadside unit r nr , the unit bandwidth transmission rate r connected to the cellular base station b nb , the unit bandwidth transmission rate r connected to the drone u nu :

[0017] (4) Total expected benefits of digital twin computing infrastructure and vehicles:

[0018] (4a) Infrastructure Digital Twin DT m By providing the vehicle's digital twin DT n Total expected revenue from selling bandwidth

[0019] (4b) Vehicle Digital Twin DT n By moving towards digital twin DT of infrastructure m Total expected benefit from transmitting data

[0020] (5) Maximizing the utility of digital twin computing infrastructure and maximum utility of the vehicle

[0021] (6) Maximum utility for infrastructure Perform a first-order derivative and set it to zero to obtain the optimal strategy for vehicle bandwidth purchase: Vehicles follow the optimal bandwidth strategy Purchase bandwidth,

[0022] where α n The digital twin DT of the vehicle n The satisfaction parameter, r n ' m Indicates that during data transmission DT n Required transmission rate per unit bandwidth, g nm represents the signal-to-noise ratio between vehicles and infrastructure;

[0023] (7) Obtain the infrastructure digital twin DT based on the total amount of bandwidth purchased by the vehicle digital twin m The price iteration equation is:

[0024] When used with infrastructure digital twins DT m The total amount of bandwidth purchased by all associated vehicle digital twins Not greater than DT m Maximum bandwidth capacity DT m The price iteration equation is:

[0025] p m (t+1)=p m (t)+v m λ;

[0026] When used with infrastructure digital twins DT m The total amount of bandwidth purchased by all associated vehicle digital twins Greater than DT m Maximum bandwidth capacity DT m The price iteration equation is:

[0027]

[0028] Among them, p m (t+1) represents the time t+1 DT m Bandwidth pricing strategy, p m (t) represents time t DT m Bandwidth pricing strategy, v m is the price strategy p m The iteration step size, λ is a value less than 10 -3 Positive value of

[0029] (8) Solve the price iteration equation to obtain the digital twin DT of the infrastructure m The utility function U DTm (p m ) The largest optimal price strategy Infrastructure based on optimal price strategy Selling bandwidth.

[0030] Compared with the prior art, the present invention has the following advantages:

[0031] 1. Improved service quality of heterogeneous vehicle networks

[0032] In the heterogeneous vehicle-to-vehicle network framework of digital twins, the present invention takes into account the high mobility of vehicles and the rapidly changing network topology and establishes two types of digital twins, namely the digital twin of the vehicle and the digital twin of the infrastructure. Through the interaction between these two digital twins to formulate resource allocation and pricing strategies, the capture of real-time data information and the unified and efficient allocation and scheduling of network resources are achieved.

[0033] At the same time, since both digital twins are built on the cloud to map the global information of the vehicle and driving environment in real time, when the vehicle has data transmission needs, the vehicle digital twin and the infrastructure digital twin map their respective data information and make decisions in the cloud. After the decision is made, the decision is sent to the corresponding vehicle and infrastructure. After the vehicle reaches the coverage area of ​​the infrastructure, the decision is executed. The infrastructure digital twin maps the infrastructure's operating data, including the infrastructure's transmission power, channel power gain, spatial position coordinates, and bandwidth price. It continuously interacts with the infrastructure and updates itself according to actual data information, thereby effectively capturing the time-varying resource supply and demand situation to achieve unified resource scheduling and allocation, and improve the service quality of heterogeneous vehicle networks.

[0034] 2. Optimize the data synchronization process

[0035] The present invention takes into account that during the data synchronization process, vehicles can selectively access roadside units, cellular base stations and drones to complete data transmission according to their service needs and preferences. Based on the diverse data synchronization needs of vehicles and their preferences for different infrastructures, a utility function for vehicles is established. At the same time, based on the different access capabilities and service costs of heterogeneous network infrastructures, a utility function for infrastructure is established to optimize the data synchronization process and improve vehicle users' satisfaction with network resource allocation.

[0036] 3. Reduce the complexity of problem solving

[0037] Since the present invention obtains the optimal network access strategy and optimal resource pricing strategy formulated by vehicles and network infrastructure in an iterative manner, it only needs to know some local information in the network and does not need to obtain all the information of vehicles and network facilities. Compared with traditional solution methods, the complexity of problem solving is reduced and the efficiency and service quality of heterogeneous wireless network systems are further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is an implementation flow chart of the present invention;

[0039] Figure 2 This is a schematic diagram of the digital twin heterogeneous vehicle networking system architecture in the present invention.

[0040] Figure 3 It is a comparison chart of simulation results of average vehicle benefit and average infrastructure benefit of the present invention and the existing solution respectively. DETAILED DESCRIPTION

[0041] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0042] Reference Figure 1 , this example is implemented as follows:

[0043] Step 1: Build a digital twin heterogeneous Internet of Vehicles system.

[0044] like Figure 2 As shown in the figure, the heterogeneous Internet of Vehicles system constructed in this step includes N cars V = {V1,...,V n ,...,V N}, M infrastructures I={I1,...,I m ,...,I M N vehicle digital twins DT n and M infrastructure digital twins DT m , where each infrastructure consists of a roadside unit r, a cellular base station b and a drone u, I m represents the mth infrastructure, V n Denotes the nth car N ≥ 2, M ≥ 2. In this network, vehicles can select different infrastructures, namely roadside units, cellular base stations, and drones, to transmit data to their digital twins for data synchronization.

[0045] Step 2: Vehicles and infrastructure upload data information to the digital twin in the cloud server.

[0046] Vehicle Digital Twin DT m and infrastructure digital twin DT n All are built in cloud servers.

[0047] Each infrastructure I m Its own spatial position coordinates {x m ,y m ,l m Bandwidth price p m This information is uploaded to the vehicle digital twin DT in the cloud server. m DT m Self-update based on actual data information;

[0048] Each vehicle V n The transmission power P m , bandwidth requirement q n Infrastructure digital twin DT uploaded to the cloud server n , DT n Self-update based on actual data information;

[0049] These two types of digital twins can reconstruct corresponding digital twins based on the current data information of the physical entity. The vehicle digital twin and the infrastructure digital twin interact and share data information. Through simulation, emulation, and calculation, the optimal bandwidth allocation strategy for the vehicle and the optimal price strategy for the infrastructure are obtained. When the vehicle reaches the coverage area of ​​the selected infrastructure, data transmission can be completed according to the decision made in the digital twin network.

[0050] Step 3: Calculate the transmission rate per unit bandwidth when the vehicle is connected to different infrastructures.

[0051] 3.1) Calculate vehicle V n The transmission rate per unit bandwidth connected to the roadside unit r nr :

[0052]

[0053] Among them, g nr Indicates vehicle V n and the signal-to-noise ratio between the roadside unit r, T r represents the time period of average throughput in RSU r, T nr Indicates T r The time interval with the average throughput of vehicle n, p n represents the transmission power of the vehicle, h r represents the channel gain between vehicle n and roadside unit r, δ 2 represents the noise power spectral density;

[0054] 3.2) Calculate vehicle V n The transmission rate per unit bandwidth connected to the cellular base station b is r nb :

[0055]

[0056] Among them, g nb Indicates vehicle V n and the signal-to-noise ratio between the cellular base station b, h b Indicates vehicle V n and the channel gain between cellular base station b, δ 2 represents the noise power spectral density;

[0057] 3.3) Calculate vehicle V n The unit bandwidth transmission rate r connected to the drone u nu :

[0058]

[0059]

[0060]

[0061] Among them, g nu Indicates vehicle V n and the signal-to-noise ratio between the drone u, p n Indicates vehicle V n The transmission power of UAV u and vehicle V n The channel gain between nu represents the distance between the vehicle and the UAV, and ρ represents the vehicle V n The path loss between the link and the UAV u, δ 2 represents the noise power spectral density, {x u ,y u ,l u} represents the coordinates of the drone u, {x0,y0,0} represents the coordinates of the vehicle V n The location coordinates of .

[0062] In step 4, the digital twin calculates the total expected benefits of the infrastructure and vehicles.

[0063] (4.1) Infrastructure Digital Twin DT m Towards the digital twin of the vehicle (DT) n Sell ​​bandwidth for profit and DT m The profit obtained is DT m To DT n The difference between the fees earned from selling bandwidth and the bandwidth cost is defined as DT m The utility function is DT m The total expected return of DT m Obtain the total expected benefit based on your unit bandwidth resource price, unit bandwidth transmission cost, and data transmission cost

[0064]

[0065] H(p m )=p m Q m

[0066] L(μ m )=μ m Q m

[0067] stp m ≥0,μ m ≥0

[0068]

[0069] Among them, p m It's DT m To DT n The price per unit of bandwidth for selling bandwidth, μ m Indicates DT m The unit bandwidth transmission cost of the sold bandwidth, H(p m ) is DT m Sell ​​bandwidth to DT n The benefits obtained, L(μ m ) is DT n and DT m The data transmission cost between m Indicates that DT m The total bandwidth of the digital twins of all associated vehicles, q nm Indicates DT m Sold to DT n The bandwidth of It's DT m Sold to DT n Maximum bandwidth capacity;

[0070] (4.2) Vehicle Digital Twin DT n Towards digital twin DT of infrastructure m Purchase bandwidth to transmit data and transfer DT n The difference between the revenue gained based on bandwidth and the cost of purchasing bandwidth is defined as DT n The utility function is DT n The total expected return of DT n According to the bandwidth you get, the required unit bandwidth transmission rate, the actual unit bandwidth transmission power, m Service satisfaction parameters and DT mThe price of selling bandwidth and the total expected revenue

[0071]

[0072]

[0073] C(q nm )=p m q nm

[0074]

[0075] Among them, O(q nm ) is DT n The satisfaction function, q nm Indicates DT m Sold to DT n The bandwidth, C(q nm ) is DT n To DT m The cost of purchasing bandwidth, α n It's DT n DT m Service satisfaction parameter, r nm Indicates that during the actual transmission process, the vehicle V n Connecting to Infrastructure I n The unit bandwidth transmission rate, r n ' m Indicates that during data transmission DT n Required transmission rate per unit bandwidth, p m It's DT m To DT n The price per unit of bandwidth resources for selling bandwidth, Indicates that DT m The total bandwidth of all vehicle digital twins associated, It's DT m Sold to DT n The maximum capacity of the bandwidth.

[0076] Step 5: Maximizing the Utility of Digital Twin Computing Infrastructure and maximum utility of the vehicle

[0077] In order to obtain the optimal bandwidth strategy for vehicles and the optimal pricing strategy for infrastructure, it is necessary to maximize the utility of both vehicles and infrastructure. The specific implementation is as follows:

[0078] (5.1) The digital twin calculates the vehicle V based on the bandwidth it obtains, the required transmission rate per unit bandwidth, and the actual transmission power per unit bandwidth.n Digital Twin DT n Maximum utility

[0079]

[0080]

[0081] Among them, q nm Indicates DT m Sold to DT n The bandwidth, r nm Indicates that during the actual transmission process, the vehicle V n Connecting to Infrastructure I n The transmission rate per unit bandwidth, r′ nm Indicates that during data transmission DT n Required transmission rate per unit bandwidth, p m Indicates DT m To DT n The price per unit of bandwidth resources for selling bandwidth, Indicates that DT m The total bandwidth of all vehicle digital twins associated, It's DT m Sold to DT n Maximum bandwidth capacity;

[0082] (5.2) The digital twin calculates the digital twin DT of the infrastructure based on its own unit bandwidth resource price, unit bandwidth transmission cost, and data transmission cost. m Maximum utility

[0083]

[0084] stp m ≥0,μ m ≥0

[0085]

[0086] Among them, p m Indicates DT m To DT n The price per unit of bandwidth resources for selling bandwidth, Indicates that DT m The total bandwidth of all associated vehicle digital twins, μ m Indicates DT m The unit bandwidth transmission cost of the sold bandwidth, It's DT m Sold to DT n The maximum capacity of the bandwidth.

[0087] Step 6: Obtain the optimal strategy for vehicle bandwidth purchase

[0088] (6.1) For vehicle V n Digital Twin DT n The utility function Taking the first-order derivative, we get The first derivative of

[0089]

[0090] Among them, q nm Indicates DT m Sold to DT n The bandwidth of a n represents the vehicle's satisfaction parameter with infrastructure services, r nm Indicates that during the actual transmission process, the vehicle V n Connecting to Infrastructure I n The unit bandwidth transmission rate, r n ' m Indicates that during data transmission DT n Required transmission rate per unit bandwidth, p m Indicates DT m To DT n The price per unit of bandwidth resources for selling bandwidth;

[0091] (6.2) Vehicle Digital Twin DT n The best strategy for purchasing bandwidth

[0092] Due to DT n The utility function of DT m Sold to DT n Bandwidth q nm The increase of DT is first increased and then decreased, so we can set DT n The utility function The first-order derivative is zero, and the optimal bandwidth strategy of the vehicle is obtained

[0093] Among them, g nm represents the signal-to-noise ratio between vehicles and infrastructure;

[0094] Digital Twin DT of Vehicles n According to the optimal strategy Purchase bandwidth.

[0095] Step 7: Obtain the optimal pricing strategy for the infrastructure based on the distributed iterative algorithm.

[0096] Considering the traditional reverse induction method, in obtaining DT m When determining the optimal price strategy, it is necessary to know all the information of the infrastructure, which is difficult to achieve in a heterogeneous wireless network system. Therefore, this step uses a distributed iterative algorithm to obtain DT m The optimal price strategy of the infrastructure is obtained by only knowing some local information of the vehicle and infrastructure, which reduces the complexity of the algorithm. The specific implementation is based on the digital twin DT of the infrastructure. m The bandwidth purchased by the digital twin of all vehicles associated with its DT m Maximum bandwidth capacity Compare to determine:

[0097] If the digital twin DT of infrastructure m The total amount of bandwidth purchased by all associated vehicle digital twins Not greater than DT m Maximum bandwidth capacity When DT m The price iteration equation is:

[0098] p m (t+1)=p m (t)+v m λ;

[0099] If the digital twin DT of infrastructure m The total amount of bandwidth purchased by all associated vehicle digital twins Greater than DT m Maximum bandwidth capacity DT m The price iteration equation is:

[0100]

[0101] Among them, p m (t+1) represents the time t+1 DT m Bandwidth pricing strategy, p m (t) represents time t DT m Bandwidth pricing strategy, v m is the price strategy p m The iteration step size is λ, and λ is a small positive value.

[0102] Step 8: Digital Twin DT of Infrastructure m Obtaining the optimal bandwidth strategy through price iteration

[0103] According to the optimal bandwidth strategy of the vehicle Solve the price iteration equation to obtain the utility function U of the infrastructure DTm (p m ) The largest optimal price strategy

[0104] Infrastructure based on optimal price strategy Selling bandwidth.

[0105] In the digital twin heterogeneous vehicle network, when vehicles selectively synchronize updated or cached data to the digital twin deployed in the cloud, considering that vehicles have diverse data synchronization requirements and heterogeneous network infrastructures have different access capabilities, during the data synchronization process, vehicles and infrastructure need to formulate optimal network access strategies and resource pricing strategies to improve the efficiency of data synchronization. m The profit obtained is defined as DT m The utility function of DT n The difference between the revenue gained based on bandwidth and the cost of purchasing bandwidth is defined as DT n The utility function of the vehicle is derived by first-order derivative of the vehicle's utility function to obtain the vehicle's optimal bandwidth policy. The vehicle then purchases bandwidth from the infrastructure based on this optimal bandwidth policy. Based on the vehicle's optimal bandwidth policy, the infrastructure uses a distributed iterative algorithm to obtain the infrastructure's optimal pricing policy. The infrastructure then sells bandwidth based on this optimal pricing policy. This optimal bandwidth policy for the vehicle and the infrastructure's optimal pricing policy simultaneously maximizes the utility of both the vehicle and the infrastructure, improving the efficiency of data exchange and decision-making while reducing latency.

[0106] The technical effects of the present invention are further illustrated below in conjunction with simulation experiments.

[0107] 1. Simulation conditions

[0108] Assume that N vehicles are simultaneously covered by a roadside unit, a cellular base station, and a drone. The value of N is varied in simulation to evaluate the performance of the scheme. For each vehicle, the bandwidth allocated to each infrastructure must be determined to synchronize data, while the optimal resource price must be determined for each network infrastructure to maximize its benefits.

[0109] The simulation experiment platform is: Windows 10 operating system and Python 3.7.

[0110] The simulation experiment parameter settings are shown in Table 1:

[0111] Table 1

[0112]

[0113] 2. Simulation content

[0114] In the above scenario, the average vehicle benefits and average infrastructure benefits of the present invention and the existing solutions FBOP, RBOP, OBFP, and OBRP are simulated respectively. The results are as follows: Figure 3 .in:

[0115] Figure 3 (a) is a comparison chart of the vehicle average benefit simulation results of the present invention and the existing solutions FBOP, RBOP, OBFP, and OBRP;

[0116] Figure 3 (b) is a comparison chart of the average infrastructure benefit simulation results of the present invention and the existing solutions FBOP, RBOP, OBFP, and OBRP.

[0117] The FBOP means that each vehicle digital twin purchases a fixed amount of bandwidth resources from each infrastructure digital twin, and each infrastructure digital twin determines its strategy based on the price iteration equation.

[0118] The RBOP refers to each vehicle digital twin randomly purchasing a certain amount of bandwidth resources from each infrastructure digital twin, and each infrastructure digital twin determines its strategy based on the price iteration equation.

[0119] The OBFP refers to the amount of bandwidth resources that each vehicle digital twin purchases according to the bandwidth iteration equation, and each infrastructure digital twin sells its resources at a fixed price;

[0120] The OBRP refers to the amount of bandwidth resources purchased by each vehicle digital twin according to the bandwidth iteration equation, and each infrastructure digital twin sells its resources at a random price.

[0121] In the solution of the present invention, the digital twin of each vehicle obtains the optimal bandwidth strategy based on the first-order derivative of the vehicle's utility function, and the digital twin of each infrastructure obtains the optimal price strategy of the infrastructure through a distributed iterative algorithm based on the optimal bandwidth strategy of the vehicle.

[0122] 3. Simulation results analysis

[0123] from Figure 3 (a) It can be seen that the average utility of vehicles in all scenarios decreases as the number of vehicles increases. This is because the price of bandwidth sold by the infrastructure increases as the number of vehicles increases. In addition, Figure 3As shown in (a), the present invention achieves the highest average vehicle benefit compared to existing solutions (FBOP, RBOP, OBFP, and OBRP). This is because existing solutions (FBOP, RBOP, OBFP, and OBRP) employ random or fixed strategies and lack comprehensive optimization and iteration of vehicle and infrastructure policies, resulting in lower average vehicle benefits.

[0124] from Figure 3 (b) It can be seen that as the number of vehicles increases, the average utility of the infrastructure in all schemes will increase. The specific reasons are as follows. First, the increase in the number of vehicles will lead to intensified competition for bandwidth resources among vehicles, so the digital twin of the infrastructure needs to increase the price of its bandwidth resources. Second, the amount of bandwidth resources allocated by the digital twin of the infrastructure also increases with the increase in the number of vehicles. Since the average utility of the infrastructure is proportional to the product of bandwidth and price, the average utility of the infrastructure will increase. In addition, from Figure 3 As can be seen in (b), the proposed method can bring higher benefits to each infrastructure than the existing solutions FBOP, RBOP, OBFP, and OBRP. This is because the proposed method comprehensively considers the requirements of vehicles and infrastructure and determines the optimal strategy to maximize benefits.

[0125] In summary, the method proposed in the present invention can bring higher benefits to vehicles and network infrastructure compared with the above solutions (FBOP, RBOP, OBFP, OBRP).

Claims

1. A benefit-based on-demand data synchronization method in a digital twin vehicle network, characterized in that: The steps include: (1) Constructing a DT system consisting of N vehicles, M infrastructures, and N vehicle digital twins n , M infrastructure digital twin DT m A heterogeneous vehicle network system, where each infrastructure consists of a roadside unit r, a cellular base station b and a drone u, N ≥ 2, M ≥ 2; (2) Vehicles and infrastructure upload data information to the digital twin in the cloud server: Each infrastructure I m Its own spatial position coordinates {x m ,y m ,l m Bandwidth price p m This information is uploaded to the vehicle digital twin DT m , DT m Self-update based on actual data information; Each vehicle V n The transmission power P n , bandwidth requirement q n Upload to the infrastructure digital twin DT n , DT n Self-update based on actual data information; (3) Calculate V for each vehicle separately n The transmission rate per unit bandwidth connected to the roadside unit r nr , the unit bandwidth transmission rate r connected to the cellular base station b nb , the unit bandwidth transmission rate r connected to the drone u nu : (4) Total expected benefits of digital twin computing infrastructure and vehicles: (4a) Infrastructure Digital Twin DT m By providing the vehicle's digital twin DT n Total expected revenue from selling bandwidth (4b) Vehicle Digital Twin DT n By moving towards digital twin DT of infrastructure m Total expected benefit from transmitting data (5) Maximizing the utility of digital twin computing infrastructure and maximum utility of the vehicle (6) Maximum utility for infrastructure Perform a first-order derivative and set it to zero to obtain the optimal strategy for vehicle bandwidth purchase: Vehicles follow the optimal bandwidth strategy Purchase bandwidth, where α n The digital twin DT of the vehicle n Satisfaction parameter, r′ nm Indicates that during data transmission DT n Required transmission rate per unit bandwidth, g nm represents the signal-to-noise ratio between vehicles and infrastructure; (7) Based on the total amount of bandwidth purchased by vehicles, the price iteration equation of the infrastructure is obtained: When used with infrastructure digital twins DT m The total amount of bandwidth purchased by all associated vehicle digital twins Not greater than DT m Maximum bandwidth capacity DT m The price iteration equation is: p m (t+1)=p m (t)+v m λ; When used with infrastructure digital twins DT m The total amount of bandwidth purchased by all associated vehicle digital twins Greater than DT m Maximum bandwidth capacity DT m The price iteration equation is: Among them, p m (t+1) represents the time t+1 DT m Bandwidth pricing strategy, p m (t) represents time t DT m Bandwidth pricing strategy, v m is the price strategy p m The iteration step size, λ is a value less than 10 -3 Positive value of (8) Solve the price iteration equation to obtain the utility function U of the infrastructure DTm (p m ) The largest optimal price strategy Infrastructure based on optimal price strategy Selling bandwidth.

2. The method according to claim 1, characterized in that In step (3), the vehicle V is calculated n The transmission rate per unit bandwidth connected to the roadside unit r nr , the formula is as follows: Among them, g nr Indicates vehicle V n and the signal-to-noise ratio between the roadside unit r, T r represents the time period of average throughput in RSU r, T nr Indicates T r The time interval with the average throughput of vehicle n, h r represents the channel gain between vehicle n and roadside unit r, δ 2 represents the noise power spectral density.

3. The method according to claim 1, characterized in that In step (3), the vehicle V is calculated n The transmission rate per unit bandwidth connected to the cellular base station b is r nb , the formula is as follows: Among them, g nb Indicates vehicle V n and the signal-to-noise ratio between the cellular base station b, h b Indicates vehicle V n and the channel gain between cellular base station b, δ 2 represents the noise power spectral density.

4. The method according to claim 1, wherein In step (3), the vehicle V is calculated n The unit bandwidth transmission rate r connected to the drone u nu , the formula is as follows: Among them, g nu Indicates vehicle V n and the signal-to-noise ratio between the UAV u and the vehicle V. n The channel gain between nu represents the distance between the vehicle and the UAV, and ρ represents the vehicle V n The path loss between the link and the UAV u, δ 2 represents the noise power spectral density, {x u ,y u ,l u } represents the coordinates of the drone u, and {x0,y0,0} represents the coordinates of the vehicle V n The location coordinates of .

5. The method according to claim 1, wherein The infrastructure digital twin DT described in step (4a) m By providing the vehicle's digital twin DT n Total expected revenue from selling bandwidth The formula is as follows: H(p m )=p m Q m L(μ m )=μ m Q m s.t.p m ≥0,μ m ≥0 Among them, p m It's DT m To DT n The price per unit of bandwidth for selling bandwidth, μ m Indicates DT m The unit bandwidth transmission cost of the sold bandwidth, H(p m ) is DT m Sell ​​bandwidth to DT n The benefits obtained, L(μ m ) is DT n and DT m The data transmission cost between m Indicates that DT m The total bandwidth of the digital twins of all associated vehicles, q nm Indicates DT m Sold to DT n The bandwidth of It's DT m Sold to DT n The maximum capacity of the bandwidth.

6. The method according to claim 1, characterized in that The vehicle digital twin DT described in step (4b) n By moving towards digital twin DT of infrastructure m Total expected benefit from transmitting data The formula is as follows: C(q nm )=p m q nm Among them, O(q nm ) is DT n The satisfaction function, q nm Indicates DT m Sold to DT n The bandwidth, C(q nm ) is DT n To DT m The cost of purchasing bandwidth, α n It's DT n DT m Service satisfaction parameter, r nm Indicates that during the actual transmission process, the vehicle V n Connecting to Infrastructure I n The transmission rate per unit bandwidth, r′ nm Indicates that during data transmission DT n Required transmission rate per unit bandwidth, p m It's DT m To DT n The price per unit of bandwidth resources for selling bandwidth, Indicates that DT m The total bandwidth of all vehicle digital twins associated, It's DT m Sold to DT n The maximum capacity of the bandwidth.

7. The method according to claim 1, characterized in that The maximum utility of the digital twin computing infrastructure in step (5) The formula is as follows: Among them, q nm Indicates DT m Sold to DT n The bandwidth, r nm Indicates that during the actual transmission process, the vehicle V n Connecting to Infrastructure I n The transmission rate per unit bandwidth, r′ nm Indicates that during data transmission DT n Required transmission rate per unit bandwidth, p m Indicates DT m To DT n The price per unit of bandwidth resources for selling bandwidth, Indicates that DT m The total bandwidth of all vehicle digital twins associated, It's DT m Sold to DT n The maximum capacity of the bandwidth.

8. The method according to claim 1, characterized in that The digital twin in step (5) calculates the maximum utility of the vehicle The formula is as follows: s.t.p m ≥0,μ m ≥0 Among them, p m Indicates DT m To DT n The price per unit of bandwidth resources for selling bandwidth, Indicates that DT m The total bandwidth of all associated vehicle digital twins, μ m Indicates DT m The unit bandwidth transmission cost of the sold bandwidth, It's DT m Sold to DT n The maximum capacity of the bandwidth.

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