Passenger and freight co-transportation method of shared automobile based on vehicle-mounted freight unmanned aerial vehicle technology

Abstract

The invention relates to a shared automobile passenger and freight co-transport method based on a vehicle-mounted freight unmanned aerial vehicle technology, the method combines an unmanned aerial vehicle and a shared automobile to carry out passenger and freight co-transport, the unmanned aerial vehicle is used for processing delivery time-sensitive lightweight packages, and the shared automobile assists the unmanned aerial vehicle in completing a package distribution task while completing passenger transport. Comprising the following steps: constructing an unmanned aerial vehicle-shared automobile passenger and freight co-transport path optimization objective function and constraint according to an unmanned aerial vehicle-shared automobile passenger and freight co-transport mode, and selecting a decision variable; and carrying out shared automobile path planning based on the target function and the constraint, and generating an unmanned aerial vehicle-shared automobile passenger and freight co-transportation scheme according to a path planning result. Compared with the prior art, the invention provides a logistics form of shared automobile-unmanned aerial vehicle passenger and freight co-transportation, and realizes freight network flow expansion cost minimization on the premise of ensuring passenger transportation service efficiency and level.

Description

Technical Field

[0001] The present invention relates to the field of shared car dispatching and logistics distribution, and in particular to a shared car passenger and cargo transportation method based on vehicle-mounted cargo drone technology. Background Art

[0002] As a new mode of transportation, passenger and cargo sharing has received extensive attention in recent years. This concept of connecting the mobility of people and goods by using a common mode of transportation has great potential in the urban transportation system. It is based on shared car resources, and meets the needs of passenger transport while completing the needs of freight, so as to improve transportation efficiency and reduce transportation costs. Because the integration of passenger and cargo flows into a single system can reduce the number of shared cars running in the city, and better synchronize the flow of passengers and goods. The passenger and cargo sharing mode does bring solutions to the increasing demand for express delivery, but on the other hand, due to the inconsistent demand points for passenger and freight, the shared car detours, the contradiction in the allocation of passenger and cargo space, and other problems, the promotion of this mode is still hindered. Therefore, some scholars have turned their attention to the end-to-end logistics delivery direction of drones, hoping to improve the flexibility of the distribution system by adding drones to the passenger and cargo sharing system, solve the problem of order delays caused by the detour of shared cars, and realize the construction of the urban passenger and cargo sharing mode. From the perspective of the way drones are integrated into the logistics and transportation system, there are several common ways, such as drones forming a freight network alone, drones combined with large trucks, and drones combined with public transportation lines. Research on the above three focuses on path planning to improve the delivery rate of goods. For example, China's patent application "CN113139678A" is a combination of drones and large trucks. It provides a method for joint delivery of drones and shared cars, allocating as many customer points as possible to drones for delivery, and drones can deliver multiple packages at a time under the load and flight distance restrictions. Shared cars can carry drones for delivery or deliver with drones at the same time. The two work together to complete the delivery task, which improves the efficiency of cargo delivery and reduces the length of the total delivery path; China's patent application "CN113359821A" provides a path planning method and system based on the collaborative operation of shared cars and drones, which solves the problem that drones and shared cars cannot collaborate in path planning in the prior art; although both of the above realize the efficient delivery of drones and shared cars, they cannot guarantee the flexibility of the collaborative process of drones and shared cars and can only realize joint freight, that is, they cannot take into account the quantity demand and flexibility of express delivery at the same time.

[0003] Therefore, providing a method that can take into account both the quantity demand and flexibility of express delivery is a technical problem that needs to be solved. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a shared car passenger and cargo transportation method based on vehicle-mounted cargo drone technology. The combination of drones and shared cars (such as online car-hailing, shared self-driving cars, etc.) provides an innovative solution for the passenger and cargo transportation network; the flexibility and rapid response capabilities of drones enable them to deliver time-sensitive lightweight packages in a timely manner, while shared cars can participate in handling freight delivery tasks while mainly completing passenger transportation tasks; drones and shared cars can independently perform their respective transportation tasks, or they can be carried together by shared cars. This complementarity significantly improves the efficiency of the mixed transportation framework, especially in areas with intensive transportation demand.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] The present invention provides a shared car passenger and cargo transport method based on vehicle-mounted cargo drone technology, wherein the method combines drones and shared cars to transport passengers and cargo, wherein the drones are used to handle lightweight packages with sensitive delivery time, and the shared car assists the drones in completing the package delivery task while completing the transportation of passengers, including:

[0007] According to the UAV-shared car passenger and cargo transport mode, the UAV-shared car passenger and cargo transport path optimization objective function and constraints are constructed, and decision variables are selected; the objective function includes minimizing the operating cost function and minimizing the transportation time function;

[0008] Based on the objective function and constraints, the shared car path planning is carried out, and the drone-shared car passenger and cargo transportation plan is generated according to the result of the path planning.

[0009] As a preferred technical solution, the drone-shared car passenger and cargo transportation method includes a single-car single-machine mapping mode, that is, the shared car and the drone have a one-to-one fixed pairing relationship, and the shared car only provides goods for the drone but does not perform delivery duties.

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

[0011] Car-sharing demand matching constraints: Among them, K represents the total number of shared cars, k represents the kth shared car; j represents the end point of passenger demand, and P represents the total set of passenger demand points; Indicates whether the kth shared car goes from starting point i to destination j. If yes, then If no, then

[0012] Freight demand point service frequency constraints: Where T represents the total number of drones, t represents the t-th drone; j′ represents the destination of the freight demand, and P′ represents the total set of freight demand points; Indicates whether the tth UAV goes from starting point i′ to destination j′. If yes, then If otherwise

[0013] The order constraints of shared cars starting parking lots: and Where h represents any node in the road network node set V; represents the starting parking lot to node h; g represents any node g in the road network node set V; 2n+1 represents the terminal parking lot; Indicates whether the shared car k goes from node g to the terminal parking lot. If yes, then If otherwise

[0014] Road network node flow balance constraints: in, represents the path distance of the kth shared car from node h to node g; Indicates whether the kth shared car has the path distance from node g to node h. If yes, then If otherwise

[0015] Time constraints for shared car routes: in, is the time it takes for shared car k to arrive at g; d g The service time for the shared car to launch the drone at node g; represents the time it takes for a shared car k to go from g to h in the road network; M represents an infinite constant;

[0016] Drone path time constraints: in, is the time when UAV t arrives at g; d′ g is the service time when the UAV is launched at node g, is the time it takes for UAV t to go from g to h in the road network; T represents the set of UAVs;

[0017] Shared car arrival time constraints: in, represents the time to arrive at the starting point i of the passenger demand; represents the time to reach the passenger demand destination j; represents the time when the shared car arrives at the meeting point w with the drone; I is the set of demand starting points; J is the set of demand end points;

[0018] UAV arrival time constraints: in, represents the time it takes for the drone to reach the starting point i' of the freight demand; represents the time it takes for the drone to reach the freight demand meeting point w; represents the time it takes for the drone to reach the freight demand destination j';

[0019] Freight demand processing time constraints: in, represents the time when the drone arrives at the starting point i′1 of the actual freight demand; represents the time when the shared car arrives at the starting point i′2 of the virtual freight demand; represents the time when the UAV arrives at the actual freight demand destination j′1; represents the time when the shared car arrives at the starting point j′2 of the virtual freight demand; in, Indicates the latest service time for freight demand; Indicates the latest delivery time of freight demand;

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

[0021] Drone capacity constraints: and in, represents the cargo hold capacity of drone t after it leaves the demand starting point i'; represents the cargo hold capacity of drone t after it leaves node v; q i′j′ Indicates the amount of cargo required; and the drone cargo volume at any node in the road network is less than or equal to the drone capacity;

[0022] Drone endurance constraints: in, Represents the path distance between the actual demand starting point i′1 of the UAV and the virtual demand starting point i′2; represents the path distance between the actual demand starting point i′1 and the vehicle-machine meeting point w; B represents the UAV’s endurance flight mileage.

[0023] As an optimal technical solution, in the single-vehicle single-machine mapping mode, the shared car path planning method is to solve the shared car path problem with time windows and capacity constraints.

[0024] As a preferred technical solution, the drone-shared car passenger and cargo transportation method also includes a multi-vehicle multi-machine mapping mode, that is, the shared car and the drone are a one-to-one collaborative relationship with no fixed pairing relationship, allowing the drone to flexibly connect to all shared cars in the system. The shared car delivers the goods to the drone take-off and landing point, and can leave directly after the drone is launched, and the drone does not need to return to the same shared car.

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

[0026] Car-sharing demand matching constraints: Where 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; Indicates whether the kth shared car goes from starting point i to destination j. If yes, then If no, then

[0027] The order constraints of shared cars starting parking lots: and Wherein, h represents the hth node in the road network node set V; represents the distance from the starting parking lot to the node h; g represents the g-th node in the road network node set V; 2n+1 represents the terminal parking lot; Indicates that node g is at the terminal parking lot. If yes, then If no, then

[0028] Road network node flow balance constraints: in, Indicates that the kth shared car goes from node h to node g; Indicates that the kth shared car goes from node g to node h. If yes, then If no, then

[0029] Time constraints for shared car routes: in, is the time it takes for shared car k to arrive at g; d g The service time for the shared car to launch the drone at node g; represents the time it takes for a shared car k to go from g to h in the road network; M represents an infinite constant;

[0030] Shared car arrival time constraints: in, represents the time to arrive at the passenger demand destination i; represents the time to arrive at the starting point j of the passenger demand; I is the set of demand starting points; J is the set of demand destinations;

[0031] Shared car capacity constraints: and in, represents the number of passengers in the shared car after the shared car k leaves the demand starting point i; represents the number of passengers in the shared car after the shared car k leaves any node v; p ij Indicates the number of passengers required; and the shared car passenger volume at any node in the road network is less than or equal to the shared car capacity;

[0032] The secondary network constraints include:

[0033] Drone path time constraints: in, is the time when UAV t arrives at g; d′ g is the service time of launching the UAV at node g, is the time it takes for UAV t to go from g to h in the road network; T represents the set of UAVs;

[0034] UAV arrival time constraints: in, represents the time it takes for the drone to reach the starting point i' of the freight demand; represents the time it takes for the drone to reach the freight demand destination j';

[0035] Demand processing time constraints: in, It represents the time when the UAV arrives at the actual demand starting point i′1; represents the time when the shared car arrives at the virtual demand starting point i′2; represents the time when the UAV arrives at the actual required destination j′1; represents the time when the shared car arrives at the virtual demand starting point j2; in, Indicates the latest service time of the demand; Indicates the latest delivery time required;

[0036] UAV secondary network node flow balance constraints: in, Indicates whether the tth drone is from node h to node g. If yes, then If no, then Indicates whether the tth drone is from node g to node h. If yes, then If no, then

[0037] Drone quantity restrictions at drone take-off and landing points: Among them, t g represents the drone capacity of node g, which is a fixed constant;

[0038] Drone capacity constraints: and in, represents the cargo hold capacity of drone t after it leaves the demand starting point i'; represents the cargo hold capacity of drone t after it leaves node v; q i′j′ Indicates the amount of cargo required; and the drone cargo volume at any node in the road network is less than or equal to the drone capacity;

[0039] Drone endurance constraints: in, represents the path distance between the actual demand starting point i′1 of the UAV and the virtual demand starting point i′2; B represents the flight mileage of the UAV;

[0040] The connection constraints include: the two-level network freight flow balance constraint, that is, in, is the amount of cargo transported by shared car k from node g to node h, is the amount of cargo transported by drone t from node g to node h.

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

[0042] As a preferred technical solution, the decision variables are 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 drone has moved.

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

[0044]

[0045] Among them, C e |K| represents the fixed cost of using K shared cars, which is determined by the number of shared cars; C e|T| represents the fixed cost of using T drones, which is determined by the number of cargo drones; represents the unit driving cost of a shared car, which is determined by the number of shared cars and the shared car routes. g and h are any points in the road network node set V; represents the cost of using 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 cost of using the drone from node g to node h; Represents the path distance from node g to node h of the drone.

[0046] As a preferred technical solution, the function of minimizing transportation time is:

[0047]

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

[0049] Compared with the prior art, the present invention proposes a shared car-drone passenger and cargo transport method, which takes the minimization of operating costs and the minimization of overall transportation time as dual objective functions and constructs appropriate constraints according to the actual operation mode to perform path planning, ensuring that the time cost of shared cars to complete passenger transportation will not be increased while improving logistics efficiency; compared with the two modes of the prior art that only allow shared cars and drones to jointly carry out freight work or only shared cars to carry out passenger and cargo transport, the method provided by the present invention takes into account both the quantity demand and flexibility of express delivery, and can also avoid the problem of shared cars detouring during passenger and cargo transport, maximize the use of drones and improve the service efficiency of passenger and cargo transport. In addition, during the entire passenger and cargo transport process, the last delivery mileage is completed by drones, which reduces the situation where vehicles detour to complete freight delivery tasks, resulting in reduced passenger transportation efficiency, making the entire process more flexible and applicable to a wider range of scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0051] Figure 2 It is a schematic diagram of a passenger-freight transport system scenario in a single-vehicle single-machine mapping mode of the present invention;

[0052] Figure 3 It is a logic flow chart of shared cars-drones processing passenger and cargo demands in the single-car single-machine mapping mode of the present invention;

[0053] Figure 4 It is a schematic diagram of a passenger and freight transport system scenario under a multi-vehicle and multi-machine mapping mode of the present invention;

[0054] Figure 5 It is a logic flow chart of the shared car processing passenger and cargo demands in the multi-car multi-machine mapping mode of the present invention;

[0055] Figure 6 This is a logical flow chart of the UAV processing passenger and cargo demands in the multi-vehicle multi-machine mapping mode of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0057] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent.

[0058] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0059] From the perspective of integrating drones into the logistics and transportation system, the existing common methods, such as drones alone forming a freight network, drones combined with large trucks, and drones combined with public transportation lines, cannot take into account both the quantity and flexibility of express delivery. The combination of drones and shared cars (such as online car-hailing, shared self-driving cars, etc.) provides an innovative solution for the passenger and freight transport network.

[0060] The flexibility and quick response capabilities of drones enable them to deliver time-sensitive lightweight packages in a timely manner, while shared cars can handle longer-distance delivery tasks while completing passenger transportation tasks. Drones and shared cars can perform their respective delivery tasks independently or be transported together by shared cars. This complementarity significantly improves the efficiency of the mixed transportation framework, especially in areas with intensive transportation demand.

[0061] Therefore, the present application provides a shared car passenger and cargo transport method based on vehicle-mounted cargo drone technology, combining drones and shared cars for passenger and cargo transport, wherein drones are used to handle lightweight packages with sensitive delivery time, and shared cars assist drones in completing package delivery tasks while completing passenger transport. Before performing route planning, the details of the passenger and cargo transport mode operation process are planned first, specifically including:

[0062] a) Determination of the components of passenger and freight delivery scenarios.

[0063] Determine the entities involved in the operation of the model, their tasks in the passenger and freight transport system, and the relationships between the entities. Specifically, the entities involved include: shared cars, vehicle-mounted cargo drones, cargo, freight delivery task demand points, passenger transport task demand points, drone take-off and landing points, distribution centers, and road networks.

[0064] Among them, shared cars must arrive at the drone take-off and landing point before they can launch and receive the on-board cargo drones. Shared cars give priority to completing passenger orders. The attribute parameters of shared cars include passenger capacity, freight capacity, vehicle speed, maximum driving distance, vehicle cost, vehicle power consumption per unit mileage, and carbon emissions.

[0065] The vehicle-mounted cargo drone only travels between the take-off and landing points and the delivery mission demand points to complete the cargo delivery needs. Its attribute parameters include cargo capacity, unit load endurance, unit cost and flight speed.

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

[0067] The demand point usually refers to the location of the service object. It is the object that the shared car must serve in the delivery task and can be regarded as a node on the corresponding road. In real life, each demand point has corresponding attributes, generally including: location, demand, service time and service priority, etc. Depending on the demand, the freight demand point may be visited multiple times or only once, while the passenger demand point only needs to be visited once by default.

[0068] The drone take-off and landing point is the service point for shared cars to launch or recover drones. It is also an important functional node in the road network. It is the end point for shared cars to deliver goods and the starting point for drones to deliver goods.

[0069] The distribution center is the starting point of each shared car. The traditional solution to the VRP (Vehicle Routing Problem) problem requires that all shared cars must start from the distribution center and return to the distribution center after delivering all the goods. Generally, there can be one or more distribution centers. The attributes of each distribution center include: location, service start time, service end time, number of shared cars, etc. The distribution center in this scenario is an express storage station. Although there are multiple storage stations in a city, each storage station corresponds to a fixed community service point, so from a regional perspective, it can be regarded as a single-park VRP problem. In addition, all shared cars for passengers and cargo in this application start from the storage station and return to the nearest storage station after completing the task.

[0070] The road network is the carrier of shared cars. Shared cars must pass through the road network to complete delivery services. The information contained in the road network includes: road congestion, connection relationships between nodes, transportation distances, etc., which can directly determine the optimal delivery route.

[0071] b) System constraints for building a passenger and freight transport system.

[0072] The cargo capacity of shared cars and drones is not allowed to exceed their rated cargo capacity. Taking into account passenger comfort and the time cost of carpooling, each shared car is only allowed to serve one passenger order at a time in the passenger and cargo carpooling mode, that is, different passengers are not allowed to carpool.

[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 ride orders is the cargo storage station, and the speed of the car is constant during the journey.

[0074] After submitting the order, the passenger needs to wait at the drone take-off and landing point, that is, the passenger demand point can only be selected from the drone take-off and landing points.

[0075] All passengers agree to share the ride with the goods by default. Passengers are not allowed to get off the bus during the ride. The total time for passengers to get on and off the bus is fixed. After the shared ride is successfully matched, the goods will be loaded in the transfer yard and depart immediately. The loading and unloading time of the goods is a fixed time.

[0076] The drone flies at a constant speed and travels back and forth in a straight line between two points. Every time it returns to the shared car, it automatically replaces the battery with a full one, and the battery replacement can be completed before the next takeoff.

[0077] c) Determine the vehicle-machine collaboration mode and design the operational process in passenger and freight delivery scenarios.

[0078] According to the road network density and other characteristics of the applicable scenario, the vehicle-machine mapping mode of the passenger-cargo co-transport scenario can be determined, as well as the corresponding passenger and cargo delivery demand processing sequence, task allocation logic, drone delivery range division, etc. This application adopts a single-vehicle single-machine mapping mode and a multi-vehicle multi-machine mapping mode, and follows Figure 1 The process shown in the figure is used to design shared car-drone passenger and cargo transportation, and its steps include:

[0079] S1. Based on the UAV-shared car passenger and cargo transport mode (single vehicle or multiple vehicles and multiple machines), construct the UAV-shared car passenger and cargo transport path optimization objective function and constraints, and select decision variables; the objective function includes minimizing the operating cost function and minimizing the transportation time function.

[0080] S2. Carry out shared car path planning based on the objective function and constraints, and generate a UAV-shared car passenger and cargo transportation plan based on the results of the path planning.

[0081] For details, the processes of the single-vehicle single-machine mapping mode and the multi-vehicle multi-machine mapping mode refer to the contents of Example 1 and Example 2.

[0082] Example 1

[0083] This embodiment provides a single-car single-machine shared car-drone passenger and cargo transport mode, that is, the shared car and the drone have a one-to-one fixed pairing relationship, and the shared car only provides goods to the drone without performing delivery duties, thereby minimizing the load driving distance of the drone, and the shared car is regarded as a mobile warehouse for the drone. Figure 2 As shown in the figure, the drone basically follows the shared car and only leaves the shared car briefly when picking up and delivering goods to perform tasks independently. After picking up the goods, the drone brings the goods back to the shared car. After the shared car transports the goods to the take-off and landing point near the demand point, the drone leaves the shared car to perform the delivery task and finally returns to the shared car. In this organizational model, the paths of shared cars and drones are highly correlated. One delivery task will cause the shared car to visit up to four drone take-off and landing points. Its execution logic is as follows Figure 3 As shown, specifically:

[0084] Whether a shared car accepts a new passenger transport order depends only on the current status of the shared car. If the shared car is completing a passenger pick-up or drop-off task, it will not accept a new order. If the shared car has no passenger transport task at the moment, it will accept a new passenger transport order, and the shared car does not need to handle freight order requirements as the main body, that is, the shared car only delivers one passenger transport task at a time.

[0085] When there is a freight order request, the passenger and freight transport system will check the status of the drone. Whether the drone accepts the freight order depends only on the drone mission status. However, whether the drone accepts the delivery mission will affect the subsequent route selection of the shared car. The shared car will choose the nearest take-off and landing point to launch or recover the drone if the passenger order time allows.

[0086] If the drone status is delivery or pickup, the new freight order request will be rejected, and the shared car will launch the drone during the passenger transport mission.

[0087] If the drone has no mission at the moment, the freight order request will be accepted and the shared car will launch the drone while performing the passenger transport mission.

[0088] If the drone has finished picking up the goods, it will reject the new freight order request, and the shared car will recover the drone during the passenger transport mission.

[0089] The above process ensures the orderliness and efficiency of the drone in performing tasks, and also ensures the response mode of the drone in different task states.

[0090] Execute step S1 and first confirm the objective function in this mode:

[0091] In order to explore the operational effect of the new passenger and cargo transport mode that introduces vehicle-mounted cargo drones, it is necessary to take into account both the operating cost (from the perspective of the operating platform) and the service level (from the perspective of social benefits and user experience). Considering that the operating cost is related to the number of shared cars, the number of drones, the mileage of shared cars, and the flight mileage of drones; the service level of the transportation mode is based on the average time to complete orders in the transportation system, that is, the minimum total transportation time is the goal, so the objective function is a dual objective function of minimizing operating costs and minimizing overall transportation time. Specifically, the function to minimize operating costs is:

[0092]

[0093] Among them, C e |K| represents the fixed cost of using K shared cars, which is determined by the number of shared cars; C e |T| represents the fixed cost of using T drones, which is determined by the number of cargo drones; represents the unit driving cost of a shared car, which is determined by the number of shared cars and the shared car routes. g and h are any points in the road network node set V; represents the cost of using 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 cost of using the drone from node g to node h; Represents the path distance from node g to node h of the drone.

[0094] The function that minimizes the transportation time is:

[0095]

[0096] in, is the travel time of shared car k from node g to h; is the travel time of drone t from node g to h; k represents any shared car in the shared car set K; g and h represent any nodes in the road network node set V.

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

[0098] Table 1 Decision variables table in single-vehicle single-machine mapping mode

[0099]

[0100] Finally, construct the path planning constraints in the single-vehicle stand-alone mode, including:

[0101] Car-sharing demand matching constraints: Among them, K represents the total number of shared cars, k represents the kth shared car; j represents the end point of passenger demand, and P represents the total set of passenger demand points; Indicates whether the kth shared car goes from starting point i to destination j. If yes, then If no, then

[0102] Freight demand point service frequency constraints: Where T represents the total number of drones, t represents the t-th drone; j′ represents the destination of the freight demand, and P′ represents the total set of freight demand points; Indicates whether the tth UAV goes from starting point i′ to destination j′. If yes, then If otherwise

[0103] The order constraints of shared cars starting parking lots: and Where h represents any node in the road network node set V; represents the starting parking lot to node h; g represents any node g in the road network node set V; 2n+1 represents the terminal parking lot; Indicates whether the shared car k goes from node g to the terminal parking lot. If yes, then If otherwise

[0104] Road network node flow balance constraints: in, represents the path distance of the kth shared car from node h to node g; Indicates whether the kth shared car has the path distance from node g to node h. If yes, then If otherwise

[0105] Time constraints for shared car routes: in, is the time it takes for shared car k to arrive at g; d g The service time for the shared car to launch the drone at node g; represents the time it takes for a shared car k to travel from g to h in the road network; M represents an infinite constant.

[0106] Drone path time constraints: in, is the time when UAV t arrives at g; d′ g is the service time when the UAV is launched at node g, is the time it takes for UAV t to go from g to h in the road network; T represents the set of UAVs.

[0107] Shared car arrival time constraints: in, represents the time to arrive at the starting point i of the passenger demand; represents the time to reach the passenger demand destination j; It represents the time when the shared car arrives at the meeting point w with the drone; I is the set of demand starting points; J is the set of demand end points.

[0108] UAV arrival time constraints: in, represents the time it takes for the drone to reach the starting point i' of the freight demand; represents the time it takes for the drone to reach the freight demand meeting point w; It represents the time it takes for the drone to reach the freight demand destination j'.

[0109] Freight demand processing time constraints: in, represents the time when the drone arrives at the starting point i′1 of the actual freight demand; represents the time when the shared car arrives at the starting point i′2 of the virtual freight demand; represents the time when the UAV arrives at the actual freight demand destination j′1; represents the time when the shared car arrives at the starting point j′2 of the virtual freight demand; in, Indicates the latest service time for freight demand; Indicates the latest delivery time of freight demand.

[0110] Shared car capacity constraints: and in, represents the number of passengers in the shared car after the shared car k leaves the demand starting point i; represents the number of passengers in the shared car after the shared car k leaves any node v; p ij Represents the number of passengers in demand; and the shared car passenger volume at any node in the road network is less than or equal to the shared car capacity.

[0111] Drone capacity constraints: and in, represents the cargo hold capacity of drone t after it leaves the demand starting point i'; represents the cargo hold capacity of drone t after it leaves node v; q i′j′ Represents the required cargo volume; and the drone cargo volume at any node in the road network is less than or equal to the drone capacity.

[0112] Drone endurance constraints: in, Represents the path distance between the actual demand starting point i′1 of the UAV and the virtual demand starting point i′2; represents the path distance between the actual demand starting point i′1 and the vehicle-machine meeting point w; B represents the UAV’s endurance flight mileage.

[0113] Execute step S2, and use the shared car routing problem solving method with time window and capacity constraint, that is, the CVRPTW problem solving method to solve the problem based on the above objective function, constraints and decision variables. This method is a common means for those skilled in the art and will not be described in detail here.

[0114] Example 2

[0115] In this embodiment, a shared car-drone passenger and cargo transport mode with multiple vehicles and multiple drones is provided, that is, the shared cars and drones are one-to-one cooperative and have no fixed pairing relationship, allowing drones to flexibly connect to all shared cars in the system. The shared cars deliver the goods to the drone take-off and landing points, and can leave directly after the drone is launched. The drone does not need to return to the same shared car. This mode is like Figure 4As shown, shared cars and drones are in a one-to-one collaborative relationship with no fixed pairing, allowing drones to flexibly connect to all shared cars in the system. Shared cars deliver goods to drone take-off and landing points, and can leave directly after the drone is launched. The drone does not need to return to the same shared car. During the execution process, a delivery mission only requires the vehicle to visit the drone take-off and landing point at most twice. The selection of take-off and landing points also needs to ensure that the drone takes the shortest delivery path while trying to prevent the vehicle from deviating too far from the shortest passenger path. Compared with the single-vehicle single-machine mapping mode in Example 1, this mode does not need to consider the rounds of shared cars and drones. Shared cars do not need to specifically recover drones after the drones perform delivery tasks. In the process of multi-vehicle and multi-machine transportation of passengers and goods, two levels of path planning are involved. The first-level network: shared cars are responsible for transporting goods from storage points to each drone take-off and landing point; the second-level network, drones then deliver goods from their respective take-off and landing points to specific freight demand points for path planning. Among them, the logic executed by the first-level network is as follows: Figure 5 As shown, the logic executed by the secondary network is as follows Figure 6 As shown, including:

[0116] Whether a shared car accepts a new passenger order depends only on the current status of the shared car. If the shared car is completing a passenger pick-up or drop-off task, it will not accept a new order. If the shared car has no passenger transport task, it will accept a new passenger transport order. At the same time, the shared car needs to handle the freight order demand as the main body, that is, the shared car only delivers one passenger transport task at a time.

[0117] Whether a shared car accepts a new freight order also depends on the current status of the shared car. If the shared car is performing a freight task, it will not accept new orders. If the shared car has no freight task at the moment, it will accept new passenger orders, that is, the shared car only delivers one freight task at a time.

[0118] When the passenger order time permits, the shared car will complete the task of picking up the goods from the storage site and delivering them to the drone take-off and landing point.

[0119] The drone is only responsible for delivering the cargo from the take-off and landing point to the freight demand point. When the drone is on the way back from a delivery or has no mission, it accepts new delivery demands, that is, the drone only delivers one freight mission at a time.

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

[0121] Execute step S1, first construct the objective function in this mode. In this embodiment, as in embodiment 1, minimizing the operating cost and minimizing the overall transportation time are used as the objective functions of the two-level path planning.

[0122] Secondly, confirm the decision variables under this model, as shown in Table 2.

[0123] Table 2 Decision variables table under multi-vehicle multi-machine mapping mode

[0124]

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

[0126] Car-sharing demand matching constraints: Where 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; Indicates whether the kth shared car goes from starting point i to destination j. If yes, then If no, then

[0127] The order constraints of shared cars starting parking lots: and Wherein, h represents the hth node in the road network node set V; represents the distance from the starting parking lot to the node h; g represents the g-th node in the road network node set V; 2n+1 represents the terminal parking lot; Indicates that node g is at the terminal parking lot. If yes, then If no, then

[0128] Road network node flow balance constraints: in, Indicates that the kth shared car goes from node h to node g; Indicates that the kth shared car goes from node g to node h. If yes, then If no, then

[0129] Time constraints for shared car routes: in, is the time it takes for shared car k to arrive at g; d g The service time for the shared car to launch the drone at node g; represents the time it takes for a shared car k to travel from g to h in the road network; M represents an infinite constant.

[0130] Shared car arrival time constraints: in, represents the time to arrive at the passenger demand destination i; It represents the time to arrive at the starting point j of the passenger demand; I is the set of demand starting points; J is the set of demand destinations.

[0131] Shared car capacity constraints: and in, represents the number of passengers in the shared car after the shared car k leaves the demand starting point i; represents the number of passengers in the shared car after the shared car k leaves any node v; p ij Represents the number of passengers in 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] Secondary network constraints include:

[0133] Drone path time constraints: in, is the time when UAV t arrives at g; d′ g is the service time of launching the UAV at node g, is the time it takes for UAV t to go from g to h in the road network; T represents the set of UAVs;

[0134] UAV arrival time constraints: in, represents the time it takes for the drone to reach the starting point i' of the freight demand; represents the time it takes for the drone to reach the freight demand destination j';

[0135] Demand processing time constraints: in, It represents the time when the UAV arrives at the actual demand starting point i′1; represents the time when the shared car arrives at the virtual demand starting point i′2; represents the time when the UAV arrives at the actual required destination j′1; represents the time when the shared car arrives at the virtual demand starting point j2; in, Indicates the latest service time of the demand; Indicates the latest delivery time required;

[0136] UAV secondary network node flow balance constraints: in, Indicates whether the tth drone is from node h to node g. If yes, then If no, then Indicates whether the tth drone is from node g to node h. If yes, then If no, then

[0137] Drone quantity restrictions at drone take-off and landing points: Among them, t g represents the drone capacity of node g, which is a fixed constant;

[0138] Drone capacity constraints: and in, represents the cargo hold capacity of drone t after it leaves the demand starting point i'; represents the cargo hold capacity of drone t after it leaves node v; q i′j′ Indicates the amount of cargo required; and the drone cargo volume at any node in the road network is less than or equal to the drone capacity;

[0139] Drone endurance constraints: in, represents the path distance between the actual demand starting point i′1 of the UAV and the virtual demand starting point i′2; B represents the flight mileage of the UAV;

[0140] The connection constraints include: the two-level network freight flow balance constraint, that is, in, is the amount of cargo transported by shared car k from node g to node h, is the amount of cargo transported by drone t from node g to node h.

[0141] Execute step S2, and use the dual-objective capacity-constrained shared car routing problem solving method with time window, that is, the 2E-CVRPTW problem solving method to solve the problem based on the above-mentioned objective function, constraints and decision variables. This method is a common means for those skilled in the art and will not be described in detail here.

[0142] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A shared car passenger and cargo transportation method based on vehicle-mounted cargo drone technology, characterized in that: The method combines drones and shared cars to carry out passenger and cargo transportation, wherein the drones are used to handle lightweight packages with sensitive delivery time, and the shared cars assist the drones in completing the package delivery task while completing the passenger transportation, including: According to the UAV-shared car passenger and cargo transport mode, the UAV-shared car passenger and cargo transport path optimization objective function and constraints are constructed, and decision variables are selected; the objective function includes minimizing the operating cost function and minimizing the transportation time function; Based on the objective function and constraints, the shared car path planning is carried out, and the drone-shared car passenger and cargo transportation plan is generated according to the result of the path planning.

2. According to claim 1, a shared car passenger and cargo transportation method based on vehicle-mounted cargo drone technology is characterized in that: The drone-shared car passenger and cargo transport mode includes a single-car single-machine mapping mode, that is, the shared car and the drone have a one-to-one fixed pairing relationship, and the shared car only provides goods for the drone but does not perform delivery duties.

3. The method for shared transportation of passengers and goods based on vehicle-mounted cargo drone technology according to claim 2 is characterized in that: In the single-vehicle single-machine mapping mode, the constraints include: Car-sharing demand matching constraints: Among them, K represents the total number of shared cars, k represents the kth shared car; j represents the end point of passenger demand, and P represents the total set of passenger demand points; Indicates whether the kth shared car goes from starting point i to destination j. If yes, then If no, then Freight demand point service frequency constraints: Where T represents the total number of drones, t represents the t-th drone; j′ represents the destination of the freight demand, and P′ represents the total set of freight demand points; Indicates whether the tth UAV goes from starting point i′ to destination j′. If yes, then If otherwise 0; The order constraints of shared cars starting parking lots: and K, where h represents any node in the road network node set V; represents the starting parking lot to node h; g represents any node g in the road network node set V; 2n+1 represents the terminal parking lot; Indicates whether the shared car k goes from node g to the terminal parking lot. If yes, then If otherwise Road network node flow balance constraints: in, represents the path distance of the kth shared car from node h to node g; Indicates whether the kth shared car has the path distance from node g to node h. If yes, then If otherwise Time constraints for shared car routes: in, is the time it takes for shared car k to arrive at g; d g The service time for the shared car to launch the drone at node g; represents the time it takes for a shared car k to go from g to h in the road network; M represents an infinite constant; Drone path time constraints: in, is the time when UAV t arrives at g; d′ g is the service time when the UAV is launched at node g, is the time it takes for UAV t to go from g to h in the road network; T represents the set of UAVs; Shared car arrival time constraints: in, represents the time to arrive at the starting point i of the passenger demand; represents the time to reach the passenger demand destination j; represents the time when the shared car arrives at the meeting point w with the drone; I is the set of demand starting points; J is the set of demand end points; UAV arrival time constraints: in, represents the time it takes for the drone to reach the starting point i' of the freight demand; represents the time it takes for the drone to reach the freight demand meeting point w; represents the time it takes for the drone to reach the freight demand destination j'; Freight demand processing time constraints: in, represents the time when the drone arrives at the starting point i′1 of the actual freight demand; represents the time when the shared car arrives at the starting point i′2 of the virtual freight demand; represents the time when the UAV arrives at the actual freight demand destination j′1; represents the time when the shared car arrives at the starting point j′2 of the virtual freight demand; in, Indicates the latest service time for freight demand; Indicates the latest delivery time of freight demand; Shared car capacity constraints: and in, represents the number of passengers in the shared car after the shared car k leaves the demand starting point i; represents the number of passengers in the shared car after the shared car k leaves any node v; p ij Indicates the number of passengers required; and the shared car passenger volume at any node in the road network is less than or equal to the shared car capacity; Drone capacity constraints: and in, represents the cargo hold capacity of drone t after it leaves the demand starting point i'; represents the cargo hold capacity of drone t after it leaves node v; q i′j′ Indicates the amount of cargo required; and the drone cargo volume at any node in the road network is less than or equal to the drone capacity; Drone endurance constraints: in, Represents the path distance between the actual demand starting point i′1 of the UAV and the virtual demand starting point i′2; represents the path distance between the actual demand starting point i′1 and the vehicle-machine meeting point w; B represents the UAV’s endurance flight mileage.

4. According to claim 2, a shared car passenger and cargo transportation method based on vehicle-mounted cargo drone technology is characterized in that: In the single-car single-machine mapping mode, the shared car path planning method is to solve the shared car path problem with time windows and capacity constraints.

5. According to claim 1, a shared car passenger and cargo transportation method based on vehicle-mounted cargo drone technology is characterized in that: The drone-shared car passenger and cargo transportation method also includes a multi-vehicle multi-machine mapping mode, that is, the shared car and the drone are a one-to-one collaborative relationship with no fixed pairing relationship, allowing the drone to flexibly connect to all shared cars in the system. The shared car delivers the goods to the drone take-off and landing point, and can leave directly after the drone is launched, and the drone does not need to return to the same shared car.

6. The method for shared transportation of passengers and cargo based on vehicle-mounted cargo drone technology according to claim 5 is characterized in that: In the multi-vehicle multi-machine mapping mode, the constraints include primary network constraints, secondary network constraints and connection constraints, wherein the primary network constraints include: Car-sharing demand matching constraints: Where 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; Indicates whether the kth shared car goes from starting point i to destination j. If yes, then If no, then The order constraints of shared cars starting parking lots: and Wherein, h represents the hth node in the road network node set V; represents the distance from the starting parking lot to the node h; g represents the g-th node in the road network node set V; 2n+1 represents the terminal parking lot; Indicates that node g is at the terminal parking lot. If yes, then If no, then Road network node flow balance constraints: in, Indicates that the kth shared car goes from node h to node g; Indicates that the kth shared car goes from node g to node h. If yes, then If no, then Time constraints for shared car routes: in, is the time it takes for shared car k to arrive at node g; d g The service time for the shared car to launch the drone at node g; represents the time it takes for a shared car k to go from g to h in the road network; M represents an infinite constant; Shared car arrival time constraints: in, represents the time to arrive at the passenger demand destination i; represents the time to arrive at the starting point j of the passenger demand; I is the set of demand starting points; J is the set of demand destinations; Shared car capacity constraints: and in, represents the number of passengers in the shared car after the shared car k leaves the demand starting point i; represents the number of passengers in the shared car after the shared car k leaves any node v; p ij Indicates the number of passengers required; 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: Drone path time constraints: in, is the time when UAV t arrives at g; d′ g is the service time of launching the UAV at node g, is the time it takes for UAV t to go from g to h in the road network; T represents the set of UAVs; UAV arrival time constraints: in, represents the time it takes for the drone to reach the starting point i' of the freight demand; represents the time it takes for the drone to reach the freight demand destination j'; Demand processing time constraints: in, It represents the time when the UAV arrives at the actual demand starting point i′1; represents the time when the shared car arrives at the virtual demand starting point i′2; represents the time when the UAV arrives at the actual required destination j′1; represents the time when the shared car arrives at the virtual demand starting point j2; in, Indicates the latest service time of the demand; Indicates the latest delivery time required; UAV secondary network node flow balance constraints: in, Indicates whether the tth drone is from node h to node g. If yes, then If no, then Indicates whether the tth drone is from node g to node h. If yes, then If no, then Drone quantity restrictions at drone take-off and landing points: Among them, t g represents the drone capacity of node g, which is a fixed constant; Drone capacity constraints: and in, represents the cargo hold capacity of drone t after it leaves the demand starting point i'; represents the cargo hold capacity of drone t after it leaves node v; q i′j′ Indicates the amount of cargo required; and the drone cargo volume at any node in the road network is less than or equal to the drone capacity; Drone endurance constraints: in, represents the path distance between the actual demand starting point i′1 of the UAV and the virtual demand starting point i′2; B represents the flight mileage of the UAV; The connection constraints include: the two-level network freight flow balance constraint, that is, in, is the amount of cargo transported by shared car k from node g to node h, is the amount of cargo transported by drone t from node g to node h.

7. The method for shared transportation of passengers and cargo based on vehicle-mounted cargo drone technology according to claim 5 is characterized in that: In the multi-vehicle multi-machine mapping mode, the shared car path planning method is to solve the dual-objective capacity-constrained shared car path problem with a time window.

8. The method for shared transportation of passengers and goods based on vehicle-mounted cargo drone technology according to claim 1 is characterized in that: The decision variables are a 0-1 variable indicating whether the shared car has passed through two nodes in the road network and a 0-1 variable indicating whether the drone has moved.

9. The method for shared transportation of passengers and goods based on vehicle-mounted cargo drone technology according to claim 1 is characterized in that: The minimization operation cost function is: Among them, C e |K| represents the fixed cost of using K shared cars, which is determined by the number of shared cars; C e |T| represents the fixed cost of using T drones, which is determined by the number of cargo drones; represents the unit driving cost of a shared car, which is determined by the number of shared cars and the shared car routes. g and h are any points in the road network node set V; represents the cost of using 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 cost of using the drone from node g to node h; Represents the path distance from node g to node h of the drone.

10. The method for shared transportation of passengers and goods based on vehicle-mounted cargo drone technology according to claim 1, characterized in that: The function to minimize transportation time is: in, is the travel time of shared car k from node g to h; is the travel time of drone t from node g to h; k represents any shared car in the shared car set K; g and h represent any nodes in the road network node set V.

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