A shared car passenger and cargo co-transportation method based on vehicle-mounted cargo unmanned aerial vehicle technology

Abstract

The present application relates to a kind of based on the shared car passenger and freight co-transportation method under the technology of vehicle freight unmanned plane, the method combines unmanned plane and shared car and carries out passenger and freight co-transportation, wherein unmanned plane is used to handle delivery time sensitive lightweight package, shared car is assisted unmanned plane to complete package distribution task while completing transport passenger, including: according to unmanned plane-shared car passenger and freight co-transportation mode, construct unmanned plane-shared car passenger and freight co-transportation path optimization objective function and constraint, and select decision variable;Based on objective function and constraint, shared car path planning is carried out, and unmanned plane-shared car passenger and freight co-transportation scheme is generated according to the result of path planning.Compared with prior art, the present application provides a kind of logistics form of shared car-unmanned plane passenger and freight co-transportation, realizes freight network flow expansion cost minimization under the premise of guaranteeing passenger transport service efficiency and level.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of shared car dispatching and logistics distribution, in particular to a shared car passenger and cargo co-transportation method based on vehicle-mounted cargo unmanned aerial vehicle technology. BACKGROUND

[0002] Passenger and cargo co-transportation, as a new type of transportation mode, has attracted widespread attention in recent years. By connecting passenger and cargo flow through common transportation methods, this concept has great potential in urban transportation systems. It is based on shared car resources to complete cargo transportation demand while realizing passenger transportation demand, so as to improve transportation efficiency and reduce transportation cost. After integrating passenger and cargo flow into a single system, the number of shared cars driving in the city can be reduced, and passenger and cargo flow can be better synchronized. The passenger and cargo co-transportation mode indeed brings a solution to the increasing demand for express delivery, but on the other hand, due to the inconsistency of passenger and cargo demand points, the promotion of this mode is hindered by problems such as shared car detours, passenger and cargo space allocation conflicts, etc. Therefore, some scholars have turned their attention to unmanned aerial vehicles responsible for the last mile of logistics distribution, hoping to improve the flexibility of the distribution system by adding unmanned aerial vehicles to the passenger and cargo co-transportation system, solve the problem of order delay caused by shared car detours, and realize the construction of urban passenger and cargo co-transportation mode. From the perspective of the way unmanned aerial vehicles are integrated into the logistics transportation system, the existing several common ways, such as unmanned aerial vehicles forming a cargo transportation network alone, unmanned aerial vehicles combined with large cargo vehicles, and unmanned aerial vehicles combined with public transportation lines, focus on path planning to improve the delivery rate of goods. For example, Chinese patent application CN113139678A provides a method for joint delivery of unmanned aerial vehicles and shared cars, which allocates as many customer points as possible to unmanned aerial vehicles for delivery, and unmanned aerial vehicles can deliver multiple packages at a time under the constraints of load and flight distance, shared cars can carry unmanned aerial vehicles for delivery or deliver at the same time as unmanned aerial vehicles, both of which work together to complete the delivery task, improving the efficiency of goods delivery and reducing the total length of the delivery path. Chinese patent application CN113359821A provides a path planning method and system based on shared cars and unmanned aerial vehicles working together, which solves the problem that unmanned aerial vehicles and shared cars cannot work together to plan paths in the prior art. Although both of the above can achieve efficient delivery of unmanned aerial vehicles and shared cars, they cannot guarantee the flexibility of unmanned aerial vehicles and shared cars during collaboration and can only realize joint cargo transportation, i.e., cannot simultaneously meet the demand for the number of express delivery and flexibility.

[0003] Therefore, it is a technical problem to provide a method that can meet the demand for the number of express delivery and flexibility. SUMMARY

[0004] The present application aims to overcome the defects of the prior art and provides a shared car passenger and cargo co-transportation method based on vehicle-mounted cargo unmanned aerial vehicle technology, which combines unmanned aerial vehicles and shared cars (such as online car hailing, shared autonomous cars, etc.) to provide an innovative solution for passenger and cargo co-transportation networks. The flexibility and rapid response capability of unmanned aerial vehicles enable them to timely deliver time-sensitive lightweight packages, while shared cars can handle cargo delivery tasks while mainly completing passenger transport tasks. Unmanned aerial vehicles and shared cars can independently perform their respective transport tasks or be jointly carried by shared cars. This complementarity significantly improves the efficiency of the hybrid transportation framework, especially in areas with high transportation demand.

[0005] The object of the present application can be achieved by the following technical solutions:

[0006] The present application provides a shared car passenger and cargo co-transportation method based on vehicle-mounted cargo unmanned aerial vehicle technology, which combines unmanned aerial vehicles and shared cars for passenger and cargo co-transportation. The unmanned aerial vehicles are used to handle time-sensitive lightweight packages, and the shared cars assist the unmanned aerial vehicles in completing package delivery tasks while transporting passengers. The method comprises the following steps:

[0007] According to the unmanned aerial vehicle-shared car passenger and cargo co-transportation method, an unmanned aerial vehicle-shared car passenger and cargo co-transportation path optimization objective function and constraints are constructed, and decision variables are selected. The objective function includes a minimum operating cost function and a minimum transportation time function.

[0008] Based on the objective function and constraints, a shared car path is planned, and an unmanned aerial vehicle-shared car passenger and cargo co-transportation scheme is generated based on the results of the path planning.

[0009] As a preferred technical solution, the unmanned aerial vehicle-shared car passenger and cargo co-transportation method includes a single-car-single-machine mapping mode, i.e., the shared cars and unmanned aerial vehicles have a one-to-one fixed pairing relationship, and the shared cars only provide goods for the unmanned aerial vehicles and do not perform delivery duties.

[0010] As a preferred technical solution, in the single-car-single-machine mapping mode, the constraints include:

[0011] Shared car demand matching limit constraint: Where K represents the total number of shared cars, k represents the kth shared car, j represents the end point of passenger transport demand, and P represents the total set of passenger transport demand points. Indicates whether the kth shared car is from the starting point i to the destination j. If yes, If no,

[0012] Cargo demand point service frequency constraint: Wherein, T represents the total unmanned aerial vehicle, t represents the t unmanned aerial vehicle; j' represents the end point of freight demand, P' represents the total freight demand point set; represents whether the t unmanned aerial vehicle is from the starting point i' to the destination j', if yes, then if no, then

[0013] Car sharing start and stop parking lot sequence constraints: and Wherein, h represents any node in the road network node set V; represents the starting point of the parking lot to the node h; g represents any node g in the road network node set V; 2n+1 represents the end point of the parking lot; represents whether the shared car k is from the node g to the end point of the parking lot, if yes, then if no, then

[0014] Road network node flow balance constraints: Wherein, represents the path distance of the k shared car from the node h to the node g; represents whether the k shared car is from the node g to the node h, if yes, then if no, then

[0015] Shared car path time constraints: Wherein, is the time when the shared car k arrives at g; d g is the service time when the shared car launches the unmanned aerial vehicle at the node g; represents the time when the shared car k travels from g to h in the road network; M represents a constant of infinity;

[0016] Unmanned aerial vehicle path time constraints: Wherein, is the time when the unmanned aerial vehicle t arrives at g; d' g is the service time when the unmanned aerial vehicle is launched at the node g, is the time when the unmanned aerial vehicle t travels from g to h in the road network; T represents the unmanned aerial vehicle set;

[0017] Shared car arrival time constraints: Wherein, represents the time when the shared car arrives at the starting point of passenger demand i; represents the time when the shared car arrives at the end point of passenger demand j; represents the time when the shared car arrives at the meeting point w with the unmanned aerial vehicle; I is the demand starting point set; J is the demand end point set;

[0018] UAV arrival time constraints: where, denotes the time of UAV arrival at the origin of the freight demand i'; denotes the time of UAV arrival at the meeting point of the freight demand w; denotes the time of UAV arrival at the end of the freight demand j';

[0019] Freight demand processing time constraints: where, denotes the time of UAV arrival at the actual origin of the freight demand i'1; denotes the time of shared car arrival at the virtual origin of the freight demand i'2; denotes the time of UAV arrival at the actual end of the freight demand j'1; denotes the time of shared car arrival at the virtual end of the freight demand j'2; where, denotes the latest service time of the freight demand; denotes the latest delivery time of the freight demand;

[0020] Shared car capacity constraints: and where, denotes the number of passengers in the shared car k after it leaves the origin of the demand i; denotes the number of passengers in the shared car k after it leaves any node v; p ij denotes the number of passengers of the demand; and the shared car passenger volume at any node in the road network is less than or equal to the shared car capacity;

[0021] UAV capacity constraints: and where, denotes the cargo capacity of the UAV t after it leaves the origin of the demand i'; denotes the cargo capacity of the UAV t after it leaves the node v; q i′j′ denotes the amount of cargo of the demand; and the UAV cargo volume at any node in the road network is less than or equal to the UAV capacity;

[0022] UAV endurance constraints: where, denotes the path distance between the actual origin of the demand i'1and the virtual origin of the demand i'2; denotes the path distance between the actual origin of the demand i'1and the meeting point of the UAV w; B denotes the endurance flight distance of the UAV.

[0023] As a preferred technical scheme, in the single-car-single-machine mapping mode, the shared car path planning method is used for solving a shared car path problem with time window and capacity constraints.

[0024] As a preferred technical scheme, the UAV-shared car passenger and cargo co-transport mode further includes a multi-car-multi-machine mapping mode, that is, the shared car and the UAV are in a one-to-one cooperation without a fixed pairing relationship, allowing the UAV to flexibly connect to all shared cars in the system, and the shared car can directly leave after delivering the goods to the UAV take-off point after the UAV is launched, and the UAV does not need to return to the same shared car.

[0025] As a preferred technical scheme, in the multi-car-multi-machine mapping mode, the constraints include a primary network constraint, a secondary network constraint, and a connection constraint, wherein the primary network constraint includes:

[0026] Shared car demand matching limit constraint: wherein K represents the total number of shared cars, k represents the kth shared car, j represents the starting point of passenger demand, and P represents the total passenger demand. represents whether the kth shared car is from the starting point i to the destination j, if yes, then if no, then

[0027] Shared car start-stop parking lot sequence constraint: and wherein h represents the hth node in the road network node set V. represents the starting point of the parking lot to the node h, and g represents the gth node in the road network node set V; 2n+1 represents the end point of the parking lot. represents the node g to the end point of the parking lot, if yes, then if no, then

[0028] Road network node flow balance constraint: wherein represents the kth shared car from the node h to the node g. represents the kth shared car from the node g to the node h, if yes, then if no, then

[0029] Shared car path time constraint: wherein is the time when the shared car k arrives at g. g is the service time when the shared car launches the UAV at the node g. denotes the travel time of shared vehicle k from g to h in the road network; M denotes an infinite constant;

[0030] Shared vehicle arrival time constraints: wherein, denotes the arrival time at passenger demand origin j; I denotes the set of demand origins; J denotes the set of demand destinations; denotes the arrival time at passenger demand origin j; I denotes the set of demand origins; J denotes the set of demand destinations;

[0031] Shared vehicle capacity constraints: and wherein, denotes the number of passengers in shared vehicle k after it leaves demand origin i; denotes the number of passengers in shared vehicle k after it leaves any node v; p ij denotes the number of passengers in shared vehicle k after it leaves any node v; p

[0032] The secondary network constraints include:

[0033] Drone path time constraints: wherein, denotes the arrival time of drone t at g; d' g denotes the service time of drone t at node g to launch a drone, denotes the travel time of drone t from g to h in the road network; T denotes the set of drones;

[0034] Drone arrival time constraints: wherein, denotes the arrival time of drone at freight demand origin i'; denotes the arrival time of drone at freight demand destination j';

[0035] Demand processing time constraints: wherein, denotes the arrival time of drone at actual demand origin i'1; denotes the arrival time of shared vehicle at virtual demand origin i'2; denotes the arrival time of drone at actual demand destination j'1; denotes the arrival time of shared vehicle at virtual demand origin j2; wherein, denotes the latest service time of demand; denotes the latest delivery time of demand;

[0036] Drone secondary network node flow balance constraints: wherein, denotes whether the t-th UAV has flown from node h to node g, and if yes, then if no, then denotes whether the t-th UAV has flown from node g to node h, and if yes, then if no, then

[0037] UAV landing point UAV quantity constraint: wherein t g denotes the UAV capacity of node g, which is a fixed constant;

[0038] UAV capacity constraint: and wherein denotes the cargo compartment capacity of UAV t after leaving the demand starting point i'; denotes the cargo compartment capacity of UAV t after leaving node v; q i′j′ denotes the demand cargo amount; and the UAV cargo amount at any node in the road network is less than or equal to the UAV capacity;

[0039] UAV endurance constraint: wherein denotes the path distance between the actual demand starting point i'1 and the virtual demand starting point i'2; B denotes the UAV endurance flight distance;

[0040] The connection constraint includes: two-level network cargo flow balance constraint, i.e. wherein is the cargo amount transported by shared car k from node g to node h, is the cargo amount transported by UAV t from node g to node h.

[0041] As a preferred technical solution, in the multi-car multi-machine mapping mode, the shared car path planning method is a double-objective capacity-constrained shared car path problem with a time window.

[0042] As a preferred technical solution, the decision variable is a 0-1 variable of whether the shared car has passed through two nodes in the road network and a 0-1 variable of whether the UAV has moved.

[0043] As a preferred technical solution, the minimum operation cost function is:

[0044]

[0045] wherein C e |K| represents the fixed use cost of K shared cars, which is determined by the number of shared cars; C e|T| represents the fixed use cost of the T-architecture unmanned aerial vehicle, which is determined by the number of cargo unmanned aerial vehicles; |T| represents the fixed use cost of the T-architecture unmanned aerial vehicle, which is determined by the number of cargo unmanned aerial vehicles; |T| represents the fixed use cost of the T-architecture unmanned aerial vehicle, which is determined by the number of cargo unmanned aerial vehicles; |T| represents the fixed use cost of the T-architecture unmanned aerial vehicle, which is determined by the number of cargo unmanned aerial vehicles; |T| represents the fixed use cost of the T-architecture unmanned aerial vehicle, which is determined by the number of cargo unmanned aerial vehicles; |T| represents the fixed use cost of the T-architecture unmanned aerial vehicle, which is determined by the number of cargo unmanned aerial vehicles.

[0046] As a preferred technical scheme, the minimum transportation time function is:

[0047]

[0048] wherein, is the travel time of shared car k from node g to h; is the travel time of unmanned aerial vehicle t from node g to h; k represents any shared car in the shared car set K; g and h represent any node in the road network node set V.

[0049] Compared with the prior art, the present application provides a shared car-unmanned aerial vehicle passenger and cargo co-transportation method, which minimizes the operation cost and the overall transportation time as a double objective function and constructs appropriate constraints according to the actual operation mode to plan the path, ensuring that the logistics efficiency is improved without increasing the time cost of shared cars to complete passenger transportation; Compared with the prior art which only allows shared cars and unmanned aerial vehicles to jointly transport goods or only allows shared cars to transport passengers and goods, the method provided by the present application takes into account the number of express delivery requirements and flexibility, and also avoids the problem of shared cars detouring during passenger and cargo co-transportation, maximizes the use of unmanned aerial vehicles and improves the service efficiency of passenger and cargo co-transportation. And in the whole process of passenger and cargo co-transportation, the last delivery mileage is completed by the unmanned aerial vehicle, reducing the situation that the vehicle detours to complete the cargo delivery task and reduces the passenger transportation efficiency, making the whole process more flexible and suitable for a wider range of scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 is the flow chart of the method of the present application;

[0051] Figure 2 is the scene schematic diagram of the passenger and cargo co-transportation system under the single car-single machine mapping mode of the present application;

[0052] Figure 3 is the logic flow chart of the shared car-unmanned aerial vehicle processing passenger and cargo demand under the single car-single machine mapping mode of the present application;

[0053] Figure 4 A scene schematic diagram of a passenger and cargo co-transportation system in a multi-vehicle and multi-machine mapping mode of the present application;

[0054] Figure 5 A logic flow chart of a shared car processing passenger and cargo demand in a multi-vehicle and multi-machine mapping mode of the present application;

[0055] Figure 6 A logic flow chart of a UAV processing passenger and cargo demand in a multi-vehicle and multi-machine mapping mode of the present application. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0057] The details of one or more embodiments of the present application are presented in the following drawings and description to make other features, objects and advantages of the present application more apparent.

[0058] In the present application, "embodiment" means that the specific features, structures or characteristics described in conjunction with the embodiment can be included in at least one embodiment of the present application. The appearance of this phrase at various places in the specification does not necessarily mean the same embodiment, nor is it an independent or alternative embodiment to other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described in the present application can be combined with other embodiments without conflict.

[0059] From the perspective of the way unmanned aerial vehicles are integrated into the logistics transportation system, several common ways, such as unmanned aerial vehicles alone forming a freight network, unmanned aerial vehicles combined with large trucks, and unmanned aerial vehicles combined with public transportation networks, cannot simultaneously consider the number of express delivery and flexibility. The combination of unmanned aerial vehicles and shared cars (such as online car hailing, shared autonomous cars, etc.) provides an innovative solution for passenger and cargo co-transportation networks.

[0060] The flexibility and rapid response capability of unmanned aerial vehicles enable them to timely deliver time-sensitive lightweight packages, while shared cars can handle longer delivery tasks along the way while completing passenger transportation tasks. Unmanned aerial vehicles and shared cars can independently perform their respective delivery tasks, or be jointly carried by shared cars. This complementarity significantly improves the efficiency of the hybrid transportation framework, especially in areas with high transportation demand.

[0061] Therefore, the application provides a shared car passenger and cargo co-transportation method based on a vehicle-mounted cargo unmanned aerial vehicle technology, which combines an unmanned aerial vehicle and a shared car for passenger and cargo co-transportation, wherein the unmanned aerial vehicle is used to handle lightweight packages with time-sensitive delivery, and the shared car assists the unmanned aerial vehicle to complete package delivery while completing passenger transportation. Before path planning, the passenger and cargo co-transportation mode operation process details are planned, specifically including:

[0062] a) Determination of passenger and cargo delivery scenario components.

[0063] The subject participating in mode operation, the task of the subject in the passenger and cargo co-transportation system, and the mutual relationship between the subjects are determined. Specifically, the subjects include a shared car, a vehicle-mounted cargo unmanned aerial vehicle, cargo, a cargo delivery task demand point, a passenger task demand point, an unmanned aerial vehicle take-off and landing point, a delivery center, and a road network.

[0064] The shared car must be at the unmanned aerial vehicle take-off and landing point to realize the launch and acceptance of the vehicle-mounted cargo unmanned aerial vehicle. The shared car gives priority to completing passenger orders. The attribute parameters of the shared car include passenger capacity, cargo capacity, vehicle speed, maximum driving distance, single vehicle cost, single vehicle unit mileage power consumption, and carbon emission, etc.

[0065] The vehicle-mounted cargo unmanned aerial vehicle only goes back and forth between the take-off and landing point and the delivery task demand point to complete the cargo delivery demand. The attribute parameters of the vehicle-mounted cargo unmanned aerial vehicle include cargo capacity, unit load endurance capability, single machine cost, and flight speed, etc.

[0066] Cargo is the main body of the entire delivery service, and its attributes include weight, volume, type, etc. The "cargo" in the passenger and cargo co-transportation system includes two types - passengers and cargo. When the demand of a demand point is greater than the single vehicle loading capacity, the cargo needs to be split and transported multiple times.

[0067] The demand point usually refers to the location point of the service object and is the object that the shared car must serve in the delivery task. It can be regarded as a node on the corresponding road. In actual life, each demand point has corresponding attributes, generally including location, demand, serviceable time, and service priority, etc. According to different demands, the cargo demand point may be accessed multiple times, or it may only need to be accessed once. The passenger demand point defaults to only need to be accessed once.

[0068] The unmanned aerial vehicle take-off and landing point is a service point for the shared car to launch or recover the unmanned aerial vehicle and is an important functional node in the road network. It is the terminal point of the shared car delivery cargo and the starting point of the unmanned aerial vehicle delivery cargo.

[0069] The distribution center is the starting point of each shared car. The traditional solution to the VRP (Vehicle Routing Problem) problem requires all shared cars to start from the distribution center, and after completing all deliveries, return to the distribution center. Generally, there can be one or more distribution centers, and the attributes of each distribution center include: location, service start time, service end time, number of shared cars owned, etc. In this scenario, the distribution center is a courier warehouse. Although there are multiple warehouses in a city, each warehouse corresponds to a fixed community service point, so from a regional perspective, it can be considered as a single-car VRP problem. In addition, all shared cars in this application start from the warehouse and return to the nearest warehouse after completing the task.

[0070] The road network is the carrier of the shared car, and the shared car must pass through the road network to complete the delivery service. The road network contains information such as road congestion, connection relationship between nodes, transportation distance, etc., which can directly determine the optimal delivery path.

[0071] b) System constraints for building a passenger and cargo sharing system.

[0072] The carrying capacity of the shared car and the unmanned aerial vehicle is not allowed to exceed the rated carrying capacity, and the passenger comfort and shared time cost are considered. Each shared car is only allowed to serve one passenger order at a time in the passenger and cargo sharing mode, i.e. different passengers are not allowed to share.

[0073] The number of shared cars in the road network is sufficient to meet the immediate travel needs of all passengers. The starting point of all shared orders is the cargo warehouse, and the speed is constant during the trip.

[0074] After the passenger submits the order, he needs to wait at the unmanned aerial vehicle take-off and landing point, i.e. the passenger demand point can only be selected from the unmanned aerial vehicle take-off and landing point.

[0075] All passengers agree to share with the goods by default, and passengers are not allowed to get off during the trip. The total time of passengers getting on and off is fixed. After the shared order is successfully matched, the goods are immediately delivered after being loaded in the transfer field. The loading and unloading time of the goods is a fixed time.

[0076] The flight speed of the unmanned aerial vehicle is constant, and it flies straight between two points. After returning to the shared car each time, it automatically changes the full battery, and can complete the battery replacement before the next take-off.

[0077] c) Determination of car-machine cooperation mode and operation process design in passenger and cargo delivery scenario.

[0078] According to the characteristics of the road network density of the applicable scenario, the car-machine mapping mode of the passenger and cargo sharing scenario can be determined, as well as the corresponding passenger and cargo delivery demand processing sequence, task allocation logic, and unmanned aerial vehicle delivery range division. This application adopts single-car-single-machine mapping mode and multi-car-multi-machine mapping mode, and according toFigure 1 The flowchart shows the shared car-unmanned aerial vehicle passenger and cargo co-transport design, and the steps include:

[0079] S1, according to the unmanned aerial vehicle-shared car passenger and cargo co-transport mode (single vehicle and single machine or multiple vehicles and multiple machines), the unmanned aerial vehicle-shared car passenger and cargo co-transport path optimization objective function and constraint are constructed, and the decision variable is selected; the objective function includes minimizing the operation cost function and minimizing the transportation time function.

[0080] S2, based on the objective function and constraint, the shared car path planning is carried out, and the unmanned aerial vehicle-shared car passenger and cargo co-transport scheme is generated according to the path planning result.

[0081] In detail, the flowcharts of single vehicle-single machine mapping mode and multiple vehicles-multiple machine mapping mode are shown in embodiment 1 and embodiment 2.

[0082] Embodiment 1

[0083] This embodiment provides a single vehicle-single machine shared car-unmanned aerial vehicle passenger and cargo co-transport mode, that is, the shared car and the unmanned aerial vehicle have a one-to-one fixed pairing relationship, and the shared car only provides goods for the unmanned aerial vehicle and does not perform the distribution duty, thereby minimizing the load driving distance of the unmanned aerial vehicle, and regarding the shared car as a mobile warehouse of the unmanned aerial vehicle. This mode is shown in Figure 2 The unmanned aerial vehicle basically follows the shared car, only temporarily leaves the shared car when picking up and delivering goods, and independently performs the task. After picking up the goods, the unmanned aerial vehicle takes the goods back to the shared car. After the shared car is transported to the nearby take-off and landing point of the goods demand point, the unmanned aerial vehicle leaves the shared car to perform the delivery task, and finally returns to the shared car. This organization mode is highly related to the paths of the shared car and the unmanned aerial vehicle. Once a delivery task is performed, the shared car needs to visit the take-off and landing point of the unmanned aerial vehicle at most four times. The execution logic is shown in Figure 3 The specific steps are as follows:

[0084] Determine whether the shared car accepts a new passenger order. The determination only depends on the current state of the shared car. If the shared car is completing a passenger pickup or delivery task, it does not accept a new order. If the shared car has no passenger task, it accepts a new passenger order, and the shared car does not need to process the cargo order demand as the main body, that is, the shared car only delivers one passenger task at a time.

[0085] When there is a cargo order request, the passenger and cargo co-transport system checks the state of the unmanned aerial vehicle. Whether the unmanned aerial vehicle accepts the cargo order also only depends on the task state of the unmanned aerial vehicle. However, whether the unmanned aerial vehicle accepts the delivery task will affect the subsequent path selection of the shared car. The shared car will select the nearest take-off and landing point to launch or recover the unmanned aerial vehicle within the time allowed by the passenger order.

[0086] If the UAV state is delivery or pickup, the new freight order request will be rejected, and the UAV is launched during the execution of the passenger transport task.

[0087] If the UAV has no task, the freight order request is accepted, and the UAV is launched during the execution of the passenger transport task.

[0088] If the UAV has finished picking up goods, the new freight order request will be rejected, and the UAV is recovered during the execution of the passenger transport task.

[0089] The above process ensures the orderliness and efficiency of the UAV during the execution of the task, and also ensures the response mode of the UAV in different task states.

[0090] Step S1 is performed to first confirm the objective function in this mode:

[0091] To explore the operation effect of the new passenger and freight combined transport mode introduced by the vehicle-mounted freight UAV, both the operation cost (from the perspective of the operation platform) and the service level (from the perspective of social benefits and user experience) need to be considered. Considering that the operation cost is related to the number of shared cars, the number of UAVs, the mileage of shared cars, and the flight mileage of UAVs, the service level of the transport mode is measured by the average time of completing an order in the transport system, that is, the minimum total transport time is taken as the target, so the objective function is a double objective function of minimizing the operation cost and the overall transport time. Specifically, the minimum operation cost function is:

[0092]

[0093] Among them, C e |K| represents the fixed use cost of K shared cars, which is determined by the number of shared cars; C e |T| represents the fixed use cost of T UAVs, which is determined by the number of freight UAVs; represents the unit shared car unit travel cost, which is determined by the number of shared cars and the shared car path, and g and h are any points in the node set V of the road network; represents the use cost of the shared car from node g to node h; represents the path distance of the shared car from node g to node h; represents the use cost of the UAV from node g to node h; represents the path distance of the UAV from node g to node h.

[0094] The minimum transport time function is:

[0095]

[0096] Among them, The travel time of the shared car k from node g to h; The travel time of the unmanned aerial vehicle t from node g to h; k represents any shared car in the shared car set K; g and h represent any node in the road network node set V.

[0097] Secondly, confirm the decision variables in this mode, as shown in Table 1.

[0098] Table 1 Decision variable table in single-car-single-machine mapping mode

[0099]

[0100] Finally, the path planning constraints in single-car-single-machine mode are constructed, including:

[0101] Shared car demand matching constraint: Wherein, K represents the total number of shared cars, k represents the kth shared car; j represents the terminal point of passenger demand, and P represents the total passenger demand point set; Indicates whether the kth shared car is from the starting point i to the destination j, if yes, then If no, then

[0102] Freight demand point service frequency constraint: Wherein, T represents the total number of unmanned aerial vehicles, t represents the tth unmanned aerial vehicle; j' represents the terminal point of freight demand, and P' represents the total freight demand point set; Indicates whether the tth unmanned aerial vehicle is from the starting point i' to the destination j', if yes, then If no, then

[0103] Shared car parking lot sequence constraint: And Wherein, h represents any node in the road network node set V; Indicates the starting point of the car park to node h; g represents any node g in the road network node set V; 2n+1 represents the terminal car park; Indicates whether the shared car k is from the node g to the terminal car park, if yes, then If no, then

[0104] Road network node flow balance constraint: Wherein, Indicates the path distance of the kth shared car from node h to node g; Indicates whether the kth shared car is from node g to node h, if yes, then If no, then

[0105] Shared vehicle path time constraints: where, is the time that shared vehicle k arrives at g; d g is the service time for shared vehicle to launch drone at node g; denotes the time that shared vehicle k travels from g to h in the road network; M denotes a constant of infinity.

[0106] Drone path time constraints: where, is the time that drone t arrives at g; d' g is the service time for drone to be launched at node g, is the time that drone t travels from g to h in the road network; T denotes the set of drones.

[0107] Shared vehicle arrival time constraints: where, denotes the time of arrival at passenger demand origin i; denotes the time of arrival at passenger demand destination j; denotes the time that shared vehicle arrives at the meeting point w with drone; I denotes the set of demand origins; J denotes the set of demand destinations.

[0108] Drone arrival time constraints: where, denotes the time that drone arrives at freight demand origin i'; denotes the time that drone arrives at freight demand meeting point w; denotes the time that drone arrives at freight demand destination j'.

[0109] Freight demand processing time constraints: where, denotes the time that drone arrives at actual freight demand origin i'1; denotes the time that shared vehicle arrives at virtual freight demand origin i'2; denotes the time that drone arrives at actual freight demand destination j'1; denotes the time that shared vehicle arrives at virtual freight demand origin j'2; where, denotes the latest service time for freight demand; denotes the latest delivery time for freight demand.

[0110] Shared vehicle capacity constraints: and where, denotes the number of passengers in shared vehicle k after shared vehicle k leaves demand origin i; denotes the number of passengers in the shared car after the shared car k leaves any node v; p ij denotes the number of passengers in the shared car after the shared car k leaves any node v; p

[0111] UAV capacity constraint: and wherein, denotes the cargo capacity of UAV t after leaving the demand origin i'; denotes the cargo capacity of UAV t after leaving node v; q i′j′ denotes the number of passengers in the shared car after the shared car k leaves any node v; p

[0112] UAV endurance constraint: wherein, denotes the path distance between the actual demand origin i'1and the virtual demand origin i'2; denotes the path distance between the actual demand origin i'1and the UAV- shared car meeting point w; B denotes the UAV endurance flight distance.

[0113] Step S2 is performed, i.e., a shared car path problem solving method with time window and capacity constraint, i.e., a CVRPTW problem solving method is solved based on the above-mentioned objective function, constraint and decision variable, and the method is a common means for those skilled in the art, and will not be described here.

[0114] Embodiment 2

[0115] In this embodiment, a multi-car multi-machine shared car-UAV passenger and cargo co-transportation mode is provided, i.e., the shared car and the UAV are a one-to-one cooperative non-fixed pairing relationship, allowing the UAV to flexibly connect to all shared cars in the system, the shared car delivers goods to the UAV take-off point, and after the UAV is launched, it can directly leave, and the UAV does not need to return to the same shared car. Such a mode is as shown in Figure 4As shown, the shared car and the drone are one-to-one collaborative and have no fixed pairing relationship, allowing the drone to flexibly connect to all shared cars in the system, and the shared car will deliver goods to the drone landing point, and can directly leave after the drone is launched, and the drone does not need to return to the same shared car, and in the execution process, a delivery task only needs the vehicle to access the drone landing point at most twice, and the selection of the landing point also needs to ensure that the drone takes and delivers the goods path is the shortest, while trying to prevent the vehicle from deviating too far from the shortest passenger path. Compared with the single-car-single-drone mapping mode in embodiment 1, this mode does not need to consider the turn of the shared car and the drone, and the shared car does not need to specially recover the drone after the drone performs the delivery task, and two levels of path planning are involved in the process of passenger and cargo co-transport of multiple cars and multiple drones, a first-level network: the shared car is responsible for transporting goods from the warehouse to each drone landing point; a second-level network, the drone further delivers the goods from the landing point to the specific freight demand point. The logic executed by the first-level network is as shown in Figure 5 The logic executed by the second-level network is as shown in Figure 6 As shown, the logic executed by the second-level network is as shown in

[0116] Whether the shared car accepts a new passenger order depends only on the current state of the shared car, if the shared car is completing a passenger pickup or drop-off task, it does not accept a new order, if the shared car has no passenger task, it accepts a new passenger order, and the shared car needs to handle the freight order demand as the main body, that is, the shared car only delivers one passenger task at a time.

[0117] Whether the shared car accepts a new freight order also depends on the current state of the shared car, if the shared car is executing a freight task, it does not accept a new order, if the shared car has no freight task, it accepts a new passenger order, that is, the shared car only delivers one freight task at a time.

[0118] The shared car completes the task of picking up goods from the warehouse site and delivering them to the drone landing point within the passenger order time.

[0119] The drone is only responsible for delivering goods from the landing point to the freight demand point, and when the drone is on the way back or has no task, it accepts a new delivery demand, that is, the drone only delivers one freight task at a time.

[0120] The above process ensures the orderliness and efficiency of the drone in executing tasks, and also ensures the response mode of the drone in different task states.

[0121] Step S1 is performed, and a target function under this mode is first constructed, in this embodiment, the same as in embodiment 1, the minimization of operating cost and the minimization of total transportation time are taken as the target functions of two-level path planning.

[0122] Secondly, the decision variables in this mode are confirmed, as shown in Table 2.

[0123] Table 2 Decision variable table in multi-vehicle and multi-drone mapping mode

[0124]

[0125] Finally, the path planning constraints in the multi-vehicle and multi-drone mode are constructed. In this mode, the constraints include primary network constraints, secondary network constraints and connection constraints. The primary network constraints include:

[0126] Shared car demand matching limit constraint: Wherein, K represents the total number of shared cars, k represents the kth shared car; j represents the origin of passenger demand, and P represents the total passenger demand; represents whether the kth shared car is from the origin i to the destination j, if yes, then if no, then

[0127] Shared car start and stop parking lot sequence constraint: and Wherein, h represents the hth node in the road network node set V; represents the origin parking lot to node h, and g represents the gth node in the road network node set V; 2n+1 represents the terminal parking lot; represents node g to the terminal parking lot, if yes, then if no, then

[0128] Road network node flow balance constraint: Wherein, represents the kth shared car from node h to node g; represents the kth shared car from node g to node h, if yes, then if no, then

[0129] Shared car path time constraint: Wherein, is the time when shared car k arrives at g; d g is the service time when shared car emits drone at node g; represents the time when shared car k travels from g to h in the road network; M represents a constant infinity.

[0130] Shared car arrival time constraint: Wherein, represents the time when it arrives at the destination i of passenger demand; denotes the time of arrival at the passenger demand origin j; I is the set of demand origins; J is the set of demand destinations.

[0131] Shared car capacity constraint: and wherein, denotes the number of passengers in the shared car k after it leaves the demand origin i; denotes the number of passengers in the shared car k after it leaves any node v; p ij denotes the number of passengers of the demand; and the shared car passenger volume at any node in the road network is less than or equal to the shared car capacity.

[0132] The secondary network constraint includes:

[0133] Drone path time constraint: wherein, is the time of arrival of drone t at g; d' g is the service time of the drone at node g to launch a drone, is the time of drone t from g to h in the road network; T denotes the set of drones;

[0134] Drone arrival time constraint: wherein, denotes the time of arrival of the drone at the freight demand origin i'; denotes the time of arrival of the drone at the freight demand destination j';

[0135] Demand processing time constraint: wherein, denotes the time of arrival of the drone at the actual demand origin i'1; denotes the time of arrival of the shared car at the virtual demand origin i'2; denotes the time of arrival of the drone at the actual demand destination j'1; denotes the time of arrival of the shared car at the virtual demand destination j2; wherein, denotes the latest service time of the demand; denotes the latest delivery time of the demand;

[0136] Drone secondary network node flow balance constraint: wherein, denotes whether the tth drone is from node h to node g, if yes then if no, then denotes whether the tth drone is from node g to node h, if yes then if no, then

[0137] The UAV landing point UAV quantity constraint: Wherein, t g represents the UAV capacity of node g, which is a fixed constant;

[0138] The UAV capacity constraint: And Wherein, represents the cargo compartment capacity of UAV t after leaving the demand starting point i'; represents the cargo compartment capacity of UAV t after leaving node v; q i′j′ represents the demand cargo quantity; and the UAV cargo quantity at any node in the road network is less than or equal to the UAV capacity;

[0139] The UAV endurance constraint: Wherein, represents the path distance between the actual demand starting point i'1 and the virtual demand starting point i'2; B represents the UAV endurance flight range;

[0140] The connection constraint includes: two-level network cargo flow balance constraint, that is, Wherein, is the cargo quantity transported by the shared car k from node g to node h, is the cargo quantity transported by the UAV t from node g to node h.

[0141] Step S2 is performed, that is, a two-objective capacity-constrained shared car path problem with a time window is solved, that is, a 2E-CVRPTW problem solving method is solved based on the above-mentioned objective function, constraint and decision variable, and the method is a common means for those skilled in the art, and will not be described here.

[0142] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1.A method for sharing a car for passenger and freight transportation based on a vehicle freight unmanned aerial vehicle technology, characterized in that, The method combines unmanned aerial vehicles and shared cars for passenger and cargo co-transport, wherein the unmanned aerial vehicles are used to handle lightweight packages with time-sensitive delivery, and the shared cars assist the unmanned aerial vehicles in completing package delivery tasks while completing passenger transport, including: According to the unmanned aerial vehicle-shared car passenger and cargo co-transport method, an unmanned aerial vehicle-shared car passenger and cargo co-transport path optimization objective function and constraints are constructed, and decision variables are selected; the objective function includes a minimum operating cost function and a minimum transportation time function; the minimum operating cost function is: wherein, C e |K| represents the fixed use cost of K shared cars, which is determined by the number of shared cars; C e |T| represents the fixed use cost of T drones, which is determined by the number of cargo drones; represents the unit shared car unit travel cost, which is determined by the number of shared cars and the shared car path, g and h are any points in the node set V of the road network; represents the use cost of the shared car from node g to node h; represents the path distance of the shared car from node g to node h; represents the use cost of the drone from node g to node h; represents the path distance of the drone from node g to node h; The minimum transportation time function is: wherein, is the travel time of a shared car k from node g to h; is the travel time of a drone t from node g to h; k denotes any shared car in the shared car set K; g and h denote any node in the road network node set V. Based on the objective function and constraints, shared car path planning is performed, and an unmanned aerial vehicle-shared car passenger and cargo co-transport scheme is generated according to the results of the path planning. 2.The method of claim 1, wherein, The unmanned aerial vehicle-shared car passenger and cargo co-transport method includes a single-car-single-machine mapping mode, i.e., the shared car and the unmanned aerial vehicle have a one-to-one fixed pairing relationship, and the shared car only provides goods for the unmanned aerial vehicle and does not perform delivery duties. 3.The method of claim 2, wherein, In the single-car-single-machine mapping mode, the constraints include: Shared car demand matching constraints: Wherein, K represents the total number of shared cars, k represents the kth shared car; j represents the end point of passenger demand, P represents the total set of passenger demand points; Indicates whether the kth shared car goes from the starting point i to the destination j, if yes, then If no, then The service frequency constraint of the freight demand point is as follows: Wherein, T represents the total number of drones, t represents the tth drone; j' represents the terminal point of the freight demand, and P' represents the total set of freight demand points; The tth drone whether from the starting point i' to the destination j' is represented, and if yes, then If no, then Shared car start-stop parking lot sequence constraints: and wherein h represents any node in the road network node set V; represents the start point parking lot to node h; g represents any node g in the road network node set V; 2n+1 represents the end point parking lot; represents whether the shared car k goes from node g to the end point parking lot, if yes, then if no, then Road network node flow balance constraint: wherein, denotes the path distance of the kth shared car from node h to node g; denotes whether the kth shared car has a path distance from node g to node h, if yes then if no then Shared car path time constraints: where, is the time that shared car k arrives at g; d g is the service time for shared car k to launch a drone at node g; denotes the time that shared car k takes to travel from g to h in the road network; M denotes a constant that is infinite. Unmanned aerial vehicle path time constraints: wherein, t is the time of arrival of the unmanned aerial vehicle g; d' g is the service time of the unmanned aerial vehicle at node g, t is the time of arrival of the unmanned aerial vehicle g; d' T represents a set of unmanned aerial vehicles; Shared car arrival time constraints: where, denotes the time of arrival at the origin i of the passenger demand; denotes the time of arrival at the destination j of the passenger demand; denotes the time of arrival of the shared car at the meeting point w with the autonomous vehicle; I is the set of demand origins; J is the set of demand destinations; UAV arrival time constraints: wherein, denotes the time of the UAV arrival at the origin of the freight demand i'; denotes the time of the UAV arrival at the meeting point w of the freight demand; denotes the time of the UAV arrival at the end point j' of the freight demand. Cargo demand processing time constraints: where, denotes the time when the UAV arrives at the actual cargo demand start point i'1; denotes the time when the shared car arrives at the virtual cargo demand start point i'2; denotes the time when the UAV arrives at the actual cargo demand end point j'1; denotes the time when the shared car arrives at the virtual cargo demand end point j'2; where, denotes the latest service time of the cargo demand; denotes the latest delivery time of the cargo demand; Shared car capacity constraints: and where, denotes the number of passengers in the shared car k after it leaves the demand origin i; denotes the number of passengers in the shared car k after it leaves any node v; p ij denotes the number of passengers of demand; and the shared car passenger volume at any node in the road network is less than or equal to the shared car capacity; UAV capacity constraints: and where, denotes the cargo capacity of UAV t after leaving demand origin i'; denotes the cargo capacity of UAV t after leaving node v; q i′j′ denotes the demand cargo amount; and the UAV cargo amount at any node in the road network is less than or equal to the UAV capacity; Unmanned aerial vehicle endurance constraint: wherein, represents the path distance between the actual demand origin i'1 and the virtual demand origin i'2; represents the path distance between the actual demand origin i'1 and the vehicle-machine convergence point w; and B represents the flight range of the unmanned aerial vehicle. 4.The method of claim 2, wherein, In the single-car-single-machine mapping mode, the method for shared car path planning is a shared car path problem solution with time window and capacity constraints. 5.The method of claim 1, wherein, The unmanned aerial vehicle-shared car passenger and cargo co-transport method also includes a multi-car-multi-machine mapping mode, i.e., the shared car and the unmanned aerial vehicle have a one-to-one cooperative non-fixed pairing relationship, allowing the unmanned aerial vehicle to flexibly connect to all shared cars in the system, the shared car delivers goods to the unmanned aerial vehicle take-off point, and after the unmanned aerial vehicle is launched, it can directly leave, and the unmanned aerial vehicle does not need to return to the same shared car. 6.The method of claim 5, wherein, In the multi-car-multi-machine mapping mode, the constraints include primary network constraints, secondary network constraints, and connection constraints, wherein the primary network constraints include: Shared car demand matching constraints: wherein K represents the total number of shared cars, k represents the kth shared car; j represents the origin of passenger demand, and P represents the total passenger demand; represents whether the kth shared car goes from the origin i to the destination j, and if yes, then if no, then Shared car start-stop parking lot sequence constraints: and wherein h represents the hth node in the node set V of the road network; represents the start point parking lot to node h; g represents the gth node in the node set V of the road network; and 2n+1 represents the end point parking lot; represents node g to the end point parking lot, if yes, then if no, then Road network node flow balance constraints: wherein, denotes the kth shared car from node h to node g; denotes the kth shared car from node g to node h, if yes then if no, then Shared car path time constraints: where, is the time that shared car k arrives at node g; d g is the service time for shared car k to launch a drone at node g; denotes the time that shared car k travels from g to h in the road network; M denotes a constant infinity. Shared car arrival time constraints: wherein, denotes the time of arrival at the passenger demand destination i; denotes the time of arrival at the passenger demand origin j; I is the set of demand origins; J is the set of demand destinations; Shared car capacity constraints: and wherein, denotes the number of passengers in the shared car k after it leaves the demand origin i; denotes the number of passengers in the shared car k after it leaves any node v; p ij denotes the number of passengers of the demand; and the shared car passenger volume at any node in the road network is less than or equal to the shared car capacity; The secondary network constraints include: Unmanned aerial vehicle path time constraints: wherein, is the time of arrival of the unmanned aerial vehicle t at g; d' g is the service time of the unmanned aerial vehicle at node g to launch an unmanned aerial vehicle, is the time of the unmanned aerial vehicle t in the road network from g to h; T denotes a set of unmanned aerial vehicles; UAV arrival time constraints: wherein, denotes the time of the UAV arrival at the origin of the freight demand i'; denotes the time of the UAV arrival at the destination of the freight demand j'. Demand handling time constraints: wherein, denotes the time when the UAV arrives at the actual demand start point i'1; denotes the time when the shared car arrives at the virtual demand start point i'2; denotes the time when the UAV arrives at the actual demand end point j'1; denotes the time when the shared car arrives at the virtual demand end point j'2; wherein, denotes the latest service time of the demand; denotes the latest delivery time of the demand; UAV second level network node flow balance constraint: wherein, denotes whether the tth UAV goes from node h to node g, if yes then if no, then denotes whether the tth UAV goes from node g to node h, if yes then if no, then The UAV landing point UAV quantity constraint: where t g represents the UAV capacity of the node g, which is a fixed constant; UAV capacity constraints: and where, denotes the cargo capacity of UAV t after leaving demand origin i'; denotes the cargo capacity of UAV t after leaving node v; q i′j′ denotes the demanded cargo amount; and the UAV cargo amount at any node in the road network is less than or equal to the UAV capacity; UAV endurance constraint: wherein, represents the path distance between the actual demand starting point i'1 and the virtual demand starting point i'2 of the UAV; B represents the flight range of the UAV. The connection constraints include two-level network freight flow balance constraints, i.e. wherein, is the amount of goods transported by the shared car k from node g to node h, is the amount of goods transported by the drone t from node g to node h. 7.The method of claim 5, wherein, In the multi-car-multi-machine mapping mode, the method for shared car path planning is a two-objective capacity-constrained shared car path problem solution with a time window. 8.The method of claim 1, wherein, The decision variables are 0-1 variables for whether the shared car has passed through two nodes in the road network and 0-1 variables for whether the unmanned aerial vehicle has moved.

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