CN112700639B - Intelligent traffic path planning method based on federal learning and digital twins - Google Patents

Abstract

The invention discloses an intelligent traffic path planning method based on federal learning and digital twins, which comprises the following steps: s1: vehicle registration is carried out in a vehicle system, and vehicle identity information is verified; s2: according to local historical data of the vehicle, participating in federal learning and training a local model; s3: aggregating local models of all vehicles to obtain an aggregate model; s4: judging whether the aggregation model converges to the preset precision or exceeds the time limit, if so, entering the step S5, otherwise, returning to the step S2; s5: establishing a global digital twin model of the Internet of vehicles; s6: periodically updating the global digital twin model of the Internet of vehicles; s7: and initiating a routing request to the roadside unit, and updating the optimal path and the local prediction model in real time. The planning method is applied to the field of vehicle networking and aims to solve the problems that in the current road traffic system, the flow prediction and path planning are low in accuracy, high in time delay and prone to privacy disclosure.

Description

Intelligent traffic path planning method based on federal learning and digital twins

Technical Field

The invention belongs to the technical field of vehicle networking, and particularly relates to an intelligent traffic path planning method based on federal learning and digital twins.

Background

In real life, digital twins are deeply combined with artificial intelligence technology, real-time interaction and fusion of information space and physical space are promoted, so that more real digital simulation is carried out in an information platform, and wider application is realized. The digital twin system is combined with machine learning framework learning, and the digital twin system can perform self-learning according to multiple feedback source data, so that the real condition of a physical entity is presented in the digital world almost in real time, and the impending events can be presumed and previewed.

However, the digital twinning technique also faces many challenges. First, the digital twin mirroring of the network requires the periodic collection of large data tables from the devices. Secondly, the virtual mirror of the network needs to communicate with the physical network frequently to ensure the real-time performance of the information. In addition, the wireless communication link may be subject to much interference, which may result in problems such as long data transmission time.

However, the following problems are prevalent in the existing studies: (1) due to the fact that the topology of the internet of vehicles is complex and various in changes, and the communication state is unstable and the bandwidth is short, the current scheme cannot collect the running state and road condition information of the vehicles in real time, so that an existing decision mechanism is difficult to make effective decisions in real time according to the dynamic changes of network parameters; (2) due to the storage limit and the communication range limit of the roadside unit, the data scale of decision-making is limited, and the accuracy of the prediction model needs to be improved. The existing scheme lacks research on a larger-scale collaborative decision mechanism, so that the decision accuracy rate is difficult to meet the requirement; (3) there is a high risk of privacy disclosure to the data generated by the vehicle and the user. Since data generated in the internet of vehicles may contain user sensitive information, the existing solutions have a high risk of leakage during data transmission and processing.

Therefore, how to establish a set of safe, reliable, intelligent and efficient intelligent traffic decision-making mechanism aiming at the characteristics of dynamic changes of the internet of vehicles is a research focus of the invention, and the data privacy of the user is protected while the decision-making efficiency and the accuracy are improved.

Disclosure of Invention

The invention aims to solve the problem of establishing a safe, reliable, intelligent and efficient intelligent traffic decision mechanism, and provides an intelligent traffic path planning method based on federal learning and digital twins.

The technical scheme of the invention is as follows: an intelligent traffic path planning method based on federal learning and digital twins comprises the following steps:

s1: vehicle registration is carried out in a vehicle system, and vehicle identity information is verified;

s2: passing vehicle c according to verification_rLocal history data D of_iParticipating in federal learning and training local model m_i；

S3: using roadside units R_kEdge server of (2) aggregating all vehicles c_rLocal model m of_iTo obtain a polymerization model M_k；

S4: repeating the steps S2-S3 to judge the aggregation model M_kWhether to converge to a predetermined accuracy theta_thOr exceeding the time limit T_thIf yes, go to step S5, otherwise return to step S2;

s5: updating vehicles c_rOf real-time local State S'_iTo roadside unit R_kAnd using a plurality of roadside units R_kEstablishing a global digital twin model G (t) of the Internet of vehicles;

s6: using roadside units R_kPeriodically updating the global digital twin model G (t) of the Internet of vehicles;

s7: according to the updated global digital twin model G (t) of the Internet of vehicles, utilizing the vehicle c_rTo roadside unit R_kInitiating a way finding request req_r,iAnd updating the optimal path and the local prediction model in real time to complete the optimal path planning of the intelligent traffic.

The invention has the beneficial effects that:

(1) the planning method is applied to the field of vehicle networking and aims to solve the problems that in the current road traffic system, the flow prediction and path planning are low in accuracy, high in time delay and prone to privacy disclosure. In the invention, in the process of traffic flow optimization control, by introducing a vehicle digital twin network model, the process of analyzing and calculating traffic flow data is effectively transferred to the roadside unit edge layer, thereby avoiding the risks brought by unreliable communication states and limited calculation resources of the vehicle layer and reducing the time delay of the whole system.

(2) According to the invention, by embedding federal learning into edge calculation, a reliable cooperation mechanism is established, a centralized manager is saved, roadside units exist in a parameter server mode, and the risk of data leakage caused by a third-party server is avoided. And finally, returning corresponding optimal paths to the requests of different vehicles in real time through a federal learning training model, and simultaneously ensuring the privacy of original data and improving the data privacy protection degree.

Further, in step S2, the local history data D_iSet of timestamps t comprising vehicle travel_i＝{t_i,1,t_i.2,...，t_i,jV set of historical speeds of vehicle_i＝{v_i,1,v_i,2,...,v_i,j}, historical position set loc of vehicle_i＝{loc_i,1,loc_i,2,...,loc_i,jAnd the set of driving directions of the vehicle dir_i＝{dir_i,1,dir_i.2,...,dir_i,j}; wherein, t_i,nRepresenting a sequence of time points at which data is collected by the vehicle, v_i,nIndicating the speed of the vehicle at the moment of each data acquisition, loc_i,nIndicating the position coordinates of the vehicle at the moment of each data acquisition, dir_i,nJ, j represents the jth federal training data.

Further, in step S3, the aggregation model M_kThe calculation formula of (2) is as follows:

where N represents the total number of vehicles participating in federal learning, D_iRepresenting local history of the vehicle, m_iRepresenting a local model of a participating federally learned vehicle.

The beneficial effects of the further scheme are as follows: in the invention, a road traffic flow prediction model is constructed based on federal learning, and a plurality of vehicles are combined to participate in training the prediction model through federal learning, so that the privacy of data is protected.

Further, in step S4, the time limit T_thThe calculation formula of (2) is as follows:

wherein v is_iRepresenting the speed of travel, x, of vehicle i_i,aInitial abscissa, y, indicating the beginning of connection of vehicle i into communication range of edge server_i,aInitial ordinate, x, indicating the beginning of a connection of a vehicle i into the communication range of an edge server_i,bInitial abscissa, y, representing the last connection of vehicle i out of the edge server communication range_i,bAn initial ordinate representing the last connection of vehicle i out of the edge server communication range.

Further, step S5 includes the following sub-steps:

s51: updating vehicles c_rOf real-time local State S'_iTo roadside unit R_k；

S52: according to the updated vehicle c_rOf real-time local State S'_iUsing roadside units R_kThe edge server establishes a vehicle digital twin model DT_iAnd vehicle digital twin network model G_k；

S53: by a plurality of wayside units R_kUsing a vehicle digital twin network model G_kAnd establishing a global digital twin model G (t) of the Internet of vehicles.

The beneficial effects of the further scheme are as follows: in the invention, by introducing the digital twin into the Internet of vehicles, a vehicle digital twin network model is provided, and analysis and calculation can be rapidly and effectively carried out on the edge layer formed by the roadside units. Vehicle digital twin model DT_iIn effect, the information that the ith vehicle is within the current wayside unit service range, including that the vehicle's locality is for federal learningData set size and real-time information of the vehicle. Vehicle digital twin model DT_iAre used to construct vertices in the digital twin network model.

Further, in step S51, the vehicle c is updated_rOf real-time local State S'_iIs S'_i＝{v′_i,loc′_i,t′_i,dir_i'}, wherein, v'_iIndicating updated vehicle c_rReal-time speed of, loc'_iIndicating updated vehicle c_rReal-time position of t'_iIndicating updated vehicle c_rReal time of (di)_i' indicating updated vehicle c_rReal-time driving direction of the vehicle;

in step S52, vehicle digital twin model DT_iIs given by the expression of_i＝{M_k,D_i,s′_iVehicle digital twin network model G_kIs expressed as G_k＝(V_k,E_k) Wherein, s'_iRepresenting real-time information of the ith vehicle, M_kRepresenting an aggregate model, D_iIndicating a passing vehicle c_rLocal history data of V_k＝{DT_i}，V_kA vertex representing a twin network formed by all vehicles within the service range of the kth wayside unit, E_k＝{e_ij}，E_kAn edge indicating that the kth wayside unit forms a twin network according to the relationship between the vehicles, e_ijIndicating that when communication between two vehicles is possible, an edge in a twin network is established;

in step S53, the expression of the global digital twin model G (t) of the Internet of vehicles is

Where K represents the number of wayside units.

Further, in step S6, the roadside unit R is used_kReceiving vehicle c_rThe formula for periodically updating the global digital twin model of the Internet of vehicles is as follows:

wherein G is_kRepresenting a digital twin network model of a vehicle, V_kA vertex representing a twin network formed by all vehicles within the service range of the kth wayside unit, E_kIndicating that the kth wayside unit forms an edge of the twin network according to the relationship between the vehicles,

representing a hamiltonian operation.

The beneficial effects of the further scheme are as follows: in the invention, the global digital twin model of the Internet of vehicles is updated in real time, and the whole planned path is ensured to be up to date.

Further, step S7 includes the following sub-steps:

s71: by vehicles c_rTo roadside unit R_kInitiating a way finding request req_r,i；

S72: by means of approaching vehicles c_rRoad side unit R_kReceiving a way-finding request req_r,iAnd using roadside units R_kThe edge server analyzes and obtains the optimal driving path p according to the global digital twin model G (t) of the Internet of vehicles_r,i(t_r,d_r) And a local prediction model M;

s73: communication via V2R, using roadside units R_kThe polymerization model M_kBack to vehicle c_r；

S74: passing vehicle c_rSlave road side unit R_kReceived aggregation model M_kPredicting according to the real-time information of the vehicle to obtain an optimal path;

s75: repeating steps S71-S74 to update vehicle c_rThe optimal path planning of the intelligent traffic is completed by the optimal path and the local prediction model.

The beneficial effects of the further scheme are as follows: in the invention, a vehicle initiates a route searching request to a roadside unit, after receiving the route searching request, the adjacent roadside unit analyzes and calculates the optimal running route and the prediction model according to the local vehicle digital twin network model and returns the optimal running route and the prediction model to the requesting vehicle, and the vehicle obtains the optimal running route and updates the current optimal route in real time according to the obtained prediction model, thereby realizing the optimal control of the road flow. In step S73, the returned model should be a model obtained by aggregation at a roadside unit within the range of the current vehicle. The formula for its calculation is shown in claim 3. After the vehicles receive the aggregated model from the roadside unit, the aggregated model can be fed according to the real-time information of the current vehicles, and the optimal driving path at the current position can be obtained, so that the path planning of intelligent traffic is realized.

Further, in step S71, the way-finding request req_r,iIs expressed as req_r,i＝{v_r,i,loc_r,i,t_r,i,dir_r,i,des_r,iIn which v is_r,iIndicating vehicle c_rCurrent running speed, loc_r,iIndicating vehicle c_rCurrent position, t_r,iIndicating vehicle c_rTime of initiation of the request, dir_r,iIndicating vehicle c_rCurrent direction of travel, des_r,iIndicating vehicle c_rThe destination of (2);

in step S72, the optimal travel route p_r,i(t_r,d_r) Is expressed as p_r,i(t_r,d_r)＝M(req_r,i) Wherein, t_rTime taken to represent a path, d_rIndicating the path distance, req_r,iRepresenting a way-finding request, and M representing a local prediction model.

Drawings

FIG. 1 is a flow chart of an intelligent traffic path planning method;

fig. 2 is a structural diagram of intelligent traffic path planning.

Detailed Description

The embodiments of the present invention will be further described with reference to the accompanying drawings.

As shown in fig. 1, the invention provides an intelligent traffic path planning method based on federal learning and digital twin, comprising the following steps:

s1: vehicle registration is carried out in a vehicle system, and vehicle identity information is verified;

s2: passing vehicle c according to verification_rLocal history data D of_iParticipating in federal learning and training local model m_i；

S3: using roadside units R_kEdge server of (2) aggregating all vehicles c_rLocal model m of_iTo obtain a polymerization model M_k；

S4: repeating the steps S2-S3 to judge the aggregation model M_kWhether to converge to a predetermined accuracy theta_thOr exceeding the time limit T_thIf yes, go to step S5, otherwise return to step S2;

s5: updating vehicles c_rOf real-time local State S'_iTo roadside unit R_kAnd using a plurality of roadside units R_kEstablishing a global digital twin model G (t) of the Internet of vehicles;

s6: using roadside units R_kPeriodically updating the global digital twin model G (t) of the Internet of vehicles;

s7: according to the updated global digital twin model G (t) of the Internet of vehicles, utilizing the vehicle c_rTo roadside unit R_kInitiating a way finding request req_r,iAnd updating the optimal path and the local prediction model in real time to complete the optimal path planning of the intelligent traffic.

In the embodiment of the present invention, as shown in fig. 1, in step S2, the local history data D_iSet of timestamps t comprising vehicle travel_i＝{t_i,1,t_i.2,...，t_i,jV set of historical speeds of vehicle_i＝{v_i,1,v_i,2,...,v_i,j}, historical position set loc of vehicle_i＝{loc_i,1,loc_i,2,...,loc_i,jAnd the set of driving directions of the vehicle dir_i＝{dir_i,1,dir_i.2,...,dir_i,j}; wherein, t_i,nRepresenting a sequence of time points at which data is collected by the vehicle, v_i,nIndicating the speed of the vehicle at the moment of each data acquisition, loc_i,nIndicating the position coordinates of the vehicle at the moment of each data acquisition, dir_i,nJ, j represents the jth federal training data.

In the practice of the inventionIn an example, as shown in FIG. 1, in step S3, the aggregation model M_kThe calculation formula of (2) is as follows:

where N represents the total number of vehicles participating in federal learning, D_iRepresenting local history of the vehicle, m_iRepresenting a local model of a participating federally learned vehicle.

In the invention, a road traffic flow prediction model is constructed based on federal learning, and a plurality of vehicles are combined to participate in training the prediction model through federal learning, so that the privacy of data is protected.

In the embodiment of the present invention, as shown in fig. 1, in step S4, the time limit T_thThe calculation formula of (2) is as follows:

wherein v is_iRepresenting the speed of travel, x, of vehicle i_i,aInitial abscissa, y, indicating the beginning of connection of vehicle i into communication range of edge server_i,aInitial ordinate, x, indicating the beginning of a connection of a vehicle i into the communication range of an edge server_i,bInitial abscissa, y, representing the last connection of vehicle i out of the edge server communication range_i,bAn initial ordinate representing the last connection of vehicle i out of the edge server communication range.

In the embodiment of the present invention, as shown in fig. 1, step S5 includes the following sub-steps:

s51: updating vehicles c_rOf real-time local State S'_iTo roadside unit R_k；

S52: according to the updated vehicle c_rOf real-time local State S'_iUsing roadside units R_kThe edge server establishes a vehicle digital twin model DT_iAnd vehicle digital twin network model G_k；

S53: by a plurality of wayside units R_kUsing a vehicle digital twin network model G_kAnd establishing a global digital twin model G (t) of the Internet of vehicles.

In the invention, by introducing the digital twin into the Internet of vehicles, a vehicle digital twin network model is provided, and analysis and calculation can be rapidly and effectively carried out on the edge layer formed by the roadside units. Vehicle digital twin model DT_iIn effect, is the information that the ith vehicle is within the current wayside unit service range, including the data set size for federal learning local to that vehicle and the vehicle's real-time information. Vehicle digital twin model DT_iAre used to construct vertices in the digital twin network model.

In the embodiment of the invention, as shown in fig. 1, in step S51, the vehicle c is updated_rOf real-time local State S'_iIs S'_i＝{v′_i,loc′_i,t′_i,dir_i'}, wherein, v'_iIndicating updated vehicle c_rReal-time speed of, loc'_iIndicating updated vehicle c_rReal-time position of t'_iIndicating updated vehicle c_rReal time of (di)_i' indicating updated vehicle c_rReal-time driving direction of the vehicle;

in step S52, vehicle digital twin model DT_iIs given by the expression of_i＝{M_k,D_i,s′_iVehicle digital twin network model G_kIs expressed as G_k＝(V_k,E_k) Wherein, s'_iRepresenting real-time information of the ith vehicle, M_kRepresenting an aggregate model, D_iIndicating a passing vehicle c_rLocal history data of V_k＝{DT_i}，V_kA vertex representing a twin network formed by all vehicles within the service range of the kth wayside unit, E_k＝{e_ij}，E_kAn edge indicating that the kth wayside unit forms a twin network according to the relationship between the vehicles, e_ijIndicating that when communication between two vehicles is possible, an edge in a twin network is established;

in step S53, Internet of vehicles GlobalThe expression of the digital twin model G (t) is

Where K represents the number of wayside units.

In the embodiment of the present invention, as shown in fig. 1, in step S6, a roadside unit R is used_kReceiving vehicle c_rThe formula for periodically updating the global digital twin model of the Internet of vehicles is as follows:

wherein G is_kRepresenting a digital twin network model of a vehicle, V_kA vertex representing a twin network formed by all vehicles within the service range of the kth wayside unit, E_kIndicating that the kth wayside unit forms an edge of the twin network according to the relationship between the vehicles,

representing Hamiltonian operation, wherein the Hamiltonian operation represents the change of a digital twin network formed by different wayside units, summing the changes of all the wayside units, and adding the summed changes to the global digital twin model at the current moment to form the global digital twin model at the next moment.

In the invention, the global digital twin model of the Internet of vehicles is updated in real time, and the whole planned path is ensured to be up to date.

In the embodiment of the present invention, as shown in fig. 1, step S7 includes the following sub-steps:

s71: by vehicles c_rTo roadside unit R_kInitiating a way finding request req_r,i；

S72: by means of approaching vehicles c_rRoad side unit R_kReceiving a way-finding request req_r,iAnd using roadside units R_kThe edge server analyzes and obtains the optimal driving path p according to the global digital twin model G (t) of the Internet of vehicles_r,i(t_r,d_r) And a local prediction model M;

s73: communication via V2R, using roadside units R_kThe polymerization model M_kBack to vehicle c_r；

S74: passing vehicle c_rSlave road side unit R_kReceived aggregation model M_kPredicting according to the real-time information of the vehicle to obtain an optimal path;

s75: repeating steps S71-S74 to update vehicle c_rThe optimal path planning of the intelligent traffic is completed by the optimal path and the local prediction model.

In the invention, a vehicle initiates a route searching request to a roadside unit, after receiving the route searching request, the adjacent roadside unit analyzes and calculates the optimal running route and the prediction model according to the local vehicle digital twin network model and returns the optimal running route and the prediction model to the requesting vehicle, and the vehicle obtains the optimal running route and updates the current optimal route in real time according to the obtained prediction model, thereby realizing the optimal control of the road flow. In step S73, the returned model should be a model obtained by aggregation at a roadside unit within the range of the current vehicle. The formula for its calculation is shown in claim 3. After the vehicles receive the aggregated model from the roadside unit, the aggregated model can be fed according to the real-time information of the current vehicles, and the optimal driving path at the current position can be obtained, so that the path planning of intelligent traffic is realized.

In the embodiment of the present invention, as shown in fig. 1, in step S71, the way-finding request req_r,iIs expressed as req_r,i＝{v_r,i,loc_r,i,t_r,i,dir_r,i,des_r,iIn which v is_r,iIndicating vehicle c_rCurrent running speed, loc_r,iIndicating vehicle c_rCurrent position, t_r,iIndicating vehicle c_rTime of initiation of the request, dir_r,iIndicating vehicle c_rCurrent direction of travel, des_r,iIndicating vehicle c_rThe destination of (2);

in step S72, the optimal travel route p_r,i(t_r,d_r) Is expressed as p_r,i(t_r,d_r)＝M(req_r,i) Wherein, t_rTime taken to represent a path, d_rIndicating the path distance, req_r,iTo representAnd (4) a way finding request, wherein M represents a local prediction model.

The working principle and the process of the invention are as follows: the invention considers the following factors which influence the flow prediction and the path planning in the intelligent traffic system: firstly, the communication resources between the vehicle and the road infrastructure are limited, so that the communication of the vehicle has larger uncertainty; secondly, a large amount of data is transmitted and calculated and analyzed, and a large amount of bandwidth and calculation resources are consumed; in addition, the owner of the vehicle needs to protect the privacy journey of the historical data of the owner of the vehicle.

The car networking scene applied by the invention is as follows: on any road that covers the network. As shown in fig. 2, after vehicles running on a highway are registered in an intelligent traffic path planning system and verified, federal learning is performed locally, an edge server of a roadside unit aggregates the federal learning model to construct a vehicle digital twin model and a vehicle digital twin network model, a vehicle which needs to find a path initiates a request, and after calculation and analysis by the edge server, the roadside unit returns an optimal path.

The purpose of the invention is realized as follows: the invention relates to an intelligent traffic path planning method based on federal learning and digital twin technology, which is divided into two stages; the first stage is model construction: the method comprises the steps that a vehicle is registered in a system at first, after system verification, the vehicle participates in federal learning locally, all federal learning models are aggregated by an edge server of a roadside unit, after the models converge to preset precision, a vehicle digital twin model and a vehicle digital twin network model are established, and then a plurality of roadside units cooperatively establish a vehicle networking global digital twin model; the second phase is path planning: the vehicles send a road finding request to the roadside unit, the adjacent roadside unit analyzes and calculates the optimal running path and the prediction model according to the local vehicle digital twin network model after receiving the road finding request, and returns the optimal running path and the prediction model to the requesting vehicle, and the vehicles obtain the optimal running path and update the current optimal path in real time according to the obtained prediction model, so that the optimal control of the road flow is realized.

The invention has the beneficial effects that:

(1) the planning method is applied to the field of vehicle networking and aims to solve the problems that in the current road traffic system, the flow prediction and path planning are low in accuracy, high in time delay and prone to privacy disclosure. In the invention, in the process of traffic flow optimization control, by introducing a vehicle digital twin network model, the process of analyzing and calculating traffic flow data is effectively transferred to the roadside unit edge layer, thereby avoiding the risks brought by unreliable communication states and limited calculation resources of the vehicle layer and reducing the time delay of the whole system.

(2) According to the invention, by embedding federal learning into edge calculation, a reliable cooperation mechanism is established, a centralized manager is saved, roadside units exist in a parameter server mode, and the risk of data leakage caused by a third-party server is avoided. And finally, returning corresponding optimal paths to the requests of different vehicles in real time through a federal learning training model, and simultaneously ensuring the privacy of original data and improving the data privacy protection degree.

It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.

Claims (6)

1. An intelligent traffic path planning method based on federal learning and digital twins is characterized by comprising the following steps:

s1: vehicle registration is carried out in a vehicle system, and vehicle identity information is verified;

s2: passing vehicle c according to verification_rLocal history data D of_iParticipating in federal learning and training local model m_i；

S3: using roadside units R_kEdge server of (2) aggregating all vehicles c_rLocal model m of_iTo obtain a polymerization model M_k；

In the step S3, the model M is aggregated_kThe calculation formula of (2) is as follows:

where N represents the total number of vehicles participating in federal learning, D_iRepresenting local history of the vehicle, m_iA local model representing a vehicle participating in federal learning;

s4: repeating the steps S2-S3 to judge the aggregation model M_kWhether to converge to a predetermined accuracy theta_thOr exceeding the time limit T_thIf yes, go to step S5, otherwise return to step S2;

s5: updating vehicles c_rOf real-time local State S'_iTo roadside unit R_kAnd using a plurality of roadside units R_kEstablishing a global digital twin model G (t) of the Internet of vehicles;

the step S5 includes the following sub-steps:

s51: updating vehicles c_rOf real-time local State S'_iTo roadside unit R_k；

S52: according to the updated vehicle c_rOf real-time local State S'_iUsing roadside units R_kThe edge server establishes a vehicle digital twin model DT_iAnd vehicle digital twin network model G_k；

S53: by a plurality of wayside units R_kUsing a vehicle digital twin network model G_kEstablishing a global digital twin model G (t) of the Internet of vehicles;

in step S51, the vehicle c is updated_rOf real-time local State S'_iIs S'_i＝{v′_i,loc′_i,t′_i,dir′_iV 'therein'_iIndicating updated vehicle c_rReal-time speed of, loc'_iIndicating updated vehicle c_rReal-time position of t'_iIndicating updated vehicle c_rReal time of dir'_iIndicating updated vehicle c_rReal-time driving direction of the vehicle;

in the step S52, the vehicle digital twin model DT_iIs given by the expression of_i＝{M_k,D_i,S′_SVehicle digital twin network model G_kIs expressed as G_k＝(V_k,E_k) Wherein, S'_iIndicating updated vehicle c_rReal-time local state of (M)_kRepresenting an aggregate model, D_iIndicating a passing vehicle c_rLocal history data of V_k＝{DT_i}，V_kA vertex representing a twin network formed by all vehicles within the service range of the kth wayside unit, E_k＝{e_ij}，E_kAn edge indicating that the kth wayside unit forms a twin network according to the relationship between the vehicles, e_ijIndicating that when communication between two vehicles is possible, an edge in a twin network is established;

in the step S53, the expression of the global digital twin model g (t) of the internet of vehicles is

Wherein K represents the number of wayside units;

s6: using roadside units R_kPeriodically updating the global digital twin model G (t) of the Internet of vehicles;

s7: according to the updated global digital twin model G (t) of the Internet of vehicles, utilizing the vehicle c_rTo roadside unit R_kInitiating a way finding request req_r,iAnd updating the optimal path and the local prediction model in real time to complete the optimal path planning of the intelligent traffic.

2. The intelligent traffic path planning method based on federal learning and digital twin of claim 1, wherein in step S2, the local historical data D_iSet of timestamps t comprising vehicle travel_i＝{t_i,1,t_i.2,...，t_i,jV set of historical speeds of vehicle_i＝{v_i,1,v_i,2,...,v_i,jCalendar of vehicleLocation history set loc_i＝{loc_i,1,loc_i,2,...,loc_i,jAnd the set of driving directions of the vehicle dir_i＝{dir_i,1,dir_i.2,...,dir_i,j}; wherein, t_i,nRepresenting a sequence of time points at which data is collected by the vehicle, v_i,nIndicating the speed of the vehicle at the moment of each data acquisition, loc_i,nIndicating the position coordinates of the vehicle at the moment of each data acquisition, dir_i,nJ, j represents the jth federal training data.

3. The intelligent traffic path planning method based on federal learning and digital twin of claim 1, wherein in step S4, the time limit T is_thThe calculation formula of (2) is as follows:

wherein v is_iRepresenting the speed of travel, x, of vehicle i_i,aInitial abscissa, y, indicating the beginning of connection of vehicle i into communication range of edge server_i,aInitial ordinate, x, indicating the beginning of a connection of a vehicle i into the communication range of an edge server_i,bInitial abscissa, y, representing the last connection of vehicle i out of the edge server communication range_i,bAn initial ordinate representing the last connection of vehicle i out of the edge server communication range.

4. The intelligent traffic path planning method based on federal learning and digital twin of claim 1, wherein in step S6, a roadside unit R is used_kReceiving vehicle c_rThe formula for periodically updating the global digital twin model of the Internet of vehicles is as follows:

wherein G is_kRepresenting a digital twin network model of a vehicle, V_kA vertex representing a twin network formed by all vehicles within the service range of the kth wayside unit, E_kIndicating that the kth wayside unit forms an edge of the twin network according to the relationship between the vehicles,

representing a hamiltonian operation.

5. The federal learning and digital twin-based intelligent transportation path planning method as claimed in claim 1, wherein the step S7 comprises the following sub-steps:

s71: by vehicles c_rTo roadside unit R_kInitiating a way finding request req_r,i；

S72: by means of approaching vehicles c_rRoad side unit R_kReceiving a way-finding request req_r,iAnd using roadside units R_kThe edge server analyzes and obtains the optimal driving path p according to the global digital twin model G (t) of the Internet of vehicles_r,i(t_r,d_r) And a local prediction model M;

s73: communication via V2R, using roadside units R_kThe polymerization model M_kBack to vehicle c_r；

S74: passing vehicle c_rSlave road side unit R_kReceived aggregation model M_kPredicting according to the real-time information of the vehicle to obtain an optimal path;

s75: repeating steps S71-S74 to update vehicle c_rThe optimal path planning of the intelligent traffic is completed by the optimal path and the local prediction model.

6. The intelligent traffic path planning method based on federal learning and digital twin as claimed in claim 5, wherein in step S71, the route searching request req is sent_r,iIs expressed as req_r,i＝{v_r,i,loc_r,i,t_r,i,dir_r,i,des_r,iIn which v is_r,iIndicating vehicle c_rCurrent running speed, loc_r,iIndicating vehiclesc_rCurrent position, t_r,iIndicating vehicle c_rTime of initiation of the request, dir_r,iIndicating vehicle c_rCurrent direction of travel, des_r,iIndicating vehicle c_rThe destination of (2);

in step S72, the optimal travel route p_r,i(t_r,d_r) Is expressed as p_r,i(t_r,d_r)＝M(req_r,i) Wherein, t_rTime taken to represent a path, d_rIndicating the path distance, req_r,iRepresenting a way-finding request, and M representing a local prediction model.

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