Transportation network optimization method and device

Abstract

The invention discloses a transportation network optimization method and device, and relates to the technical field of computers. The method comprises the following steps: determining basic transportation data according to historical transportation data in a preset time period; determining a plurality of candidate routes according to an origin and a destination in the transportation network; wherein the candidate route indicates a corresponding transit hub, a transportation route and a transportation mode between the starting place and the destination; inputting the basic transportation data and the candidate route into a preset joint optimization model, and outputting to obtain a target route and a target transit hub corresponding to the target route; wherein the preset joint optimization model takes the minimum sum of the transit cost, the transportation cost and the transit hub construction cost as a target function. According to the embodiment, the transit hub is synchronously considered in the candidate route, the route and the transit hub are synchronously optimized through the joint optimization model, the target route and the target transit hub corresponding to the target route are obtained, and the overall transportation efficiency is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and device for optimizing a transportation network. Background Art

[0002] In the existing transportation network optimization process, usually only a single factor can be optimized, such as optimizing the transportation route when the transit hub is fixed, or optimizing the transit hub along the way when the transportation route is fixed. It is impossible to optimize the transportation route and the transit hub as a whole at the same time, and the transportation efficiency of the overall transportation network is still low. Summary of the invention

[0003] In view of this, an embodiment of the present invention provides a method and device for optimizing a transportation network, which simultaneously considers the transit hub in the candidate routes, and through a joint optimization model, takes the minimum sum of transit cost, transportation cost and transit hub construction cost as the objective function, and simultaneously optimizes the routes and transit hubs to obtain the target route and the target transit hub corresponding to the target route, thereby effectively improving the overall transportation efficiency.

[0004] To achieve the above objective, according to one aspect of an embodiment of the present invention, a method for optimizing a transportation network is provided.

[0005] A method for optimizing a transportation network in an embodiment of the present invention includes: determining basic transportation data based on historical transportation data within a preset time period; determining multiple candidate routes based on an origin and a destination in the transportation network; wherein the candidate routes indicate a transit hub, a transportation route, and a transportation method corresponding to the origin and the destination; inputting the basic transportation data and the candidate routes into a preset joint optimization model, and outputting a target route and a target transit hub corresponding to the target route; wherein the preset joint optimization model takes the minimization of the sum of transit cost, transportation cost, and transit hub construction cost as an objective function.

[0006] Optionally, determining a plurality of candidate routes according to an origin and a destination in the transportation network includes:

[0007] For each group of origin and destination in the transportation network: determine the optional intermediate transfer points; based on the intermediate transfer points and whether the origin and / or the destination are equipped with transfer hubs, exhaustively obtain all optional routes; according to the limited number of transfers and total mileage, filter out the candidate routes from the optional routes.

[0008] Optionally, the screening of the candidate routes from the optional routes based on the limited number of transfers and the total mileage includes: from all the optional routes, screening out the candidate routes whose number of intermediate transfer points is less than the preset limited number of transfers and whose total mileage is less than a preset distance.

[0009] Optionally, determining the basic transportation data based on the historical transportation data within a preset time period includes: calculating an average value of a plurality of historical transportation data within the preset time period; and using the average value as the basic transportation data.

[0010] Optionally, the basic transportation data and the candidate routes are input into a preset joint optimization model, and a target route and a target transit hub corresponding to the target route are output, including: determining an objective function value corresponding to each candidate route based on the basic transportation data; and determining the target route from a plurality of candidate routes based on the size of the objective function value.

[0011] Optionally, the constraints of the preset joint optimization model include at least one of the following: the target route corresponding to each of the origins to each of the destinations is unique, the total capacity of the transport vehicles corresponding to the target route is not less than the cargo volume corresponding to the target route, the type of transport vehicles corresponding to the target route corresponds to the number of transport vehicles, the type of outgoing vehicles corresponding to the target route is unique, the state variable of the target route is 1 when there is cargo flow in the target route, the cargo volume corresponding to the target route is greater than the preset open-line cargo volume, and the sum of the production capacities of multiple transit hubs belonging to a city is greater than the average daily cargo volume of the city.

[0012] To achieve the above objective, according to another aspect of an embodiment of the present invention, a device for optimizing a transportation network is provided.

[0013] An optimization device for a transportation network according to an embodiment of the present invention includes: a data determination module, which is used to determine basic transportation data based on historical transportation data within a preset time period; a route determination module, which is used to determine multiple candidate routes based on the origin and destination in the transportation network; wherein the candidate routes indicate the corresponding transit hubs, transportation routes, and transportation methods between the origin and the destination; an output module, which is used to input the basic transportation data and the candidate routes into a preset joint optimization model, and output a target route and a target transit hub corresponding to the target route; wherein the preset joint optimization model takes minimizing the sum of transit costs, transportation costs, and transit hub construction costs as an objective function.

[0014] To achieve the above objective, according to another aspect of an embodiment of the present invention, an electronic device for optimizing a transportation network is provided.

[0015] An electronic device for optimizing a transportation network according to an embodiment of the present invention comprises: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement a method for optimizing a transportation network according to an embodiment of the present invention.

[0016] To achieve the above objective, according to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided.

[0017] A computer-readable storage medium according to an embodiment of the present invention stores a computer program, and when the program is executed by a processor, a method for optimizing a transportation network according to an embodiment of the present invention is implemented.

[0018] An embodiment of the above invention has the following advantages or beneficial effects: the transit hub is simultaneously considered in the candidate routes, and through a joint optimization model, the minimum sum of transit cost, transportation cost and transit hub construction cost is used as the objective function to simultaneously optimize the route and the transit hub, so as to obtain the target route and the target transit hub corresponding to the target route, thereby effectively improving the overall transportation efficiency.

[0019] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0021] Figure 1 is a schematic diagram of the main flow of the method for optimizing a transportation network according to an embodiment of the present invention;

[0022] Figure 2 is a schematic diagram of a main process of determining basic transportation data according to an embodiment of the present invention;

[0023] Figure 3 is a schematic diagram of a main process of determining multiple candidate routes according to an embodiment of the present invention;

[0024] Figure 4 is a schematic diagram of determining optional routes according to whether a transit hub is set at an origin and a destination according to an embodiment of the present invention;

[0025] Figure 5 is a schematic diagram of a main process of determining a target route based on a target function according to an embodiment of the present invention;

[0026] Figure 6 is a schematic diagram of main modules of a transportation network optimization device according to an embodiment of the present invention;

[0027] Figure 7 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0028] Figure 8 It is a schematic diagram of the structure of a computer system of a terminal device or a server suitable for implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0030] It should be pointed out that the embodiments of the present invention and the technical features therein may be combined with each other without conflict.

[0031] It should be noted that the collection, collection, updating, analysis, processing, use, transmission, storage and other aspects of user personal information involved in the technical solution of this disclosure are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information, network security and national security.

[0032] Figure 1 It is a schematic diagram of main steps of a method for optimizing a transportation network according to an embodiment of the present invention.

[0033] like Figure 1 As shown, the method for optimizing the transportation network of the embodiment of the present invention mainly includes the following steps:

[0034] Step S101: determining basic transportation data according to historical transportation data within a preset time period;

[0035] Step S102: determining a plurality of candidate routes according to the origin and destination in the transportation network; wherein the candidate routes indicate the corresponding transit hubs, transportation routes, and transportation modes between the origin and the destination;

[0036] Step S103: input the basic transportation data and candidate routes into a preset joint optimization model, and output the target route and the target transit hub corresponding to the target route; wherein the preset joint optimization model takes the minimization of the sum of transit cost, transportation cost and transit hub construction cost as the objective function.

[0037] Among them, in the logistics link, the transit hub can be understood as a sorting center, which has the ability to sort and transit goods. In the prior art, the optimization process of the route often assumes that each city corresponds to a transit hub, so as to optimize the cities passed between the origin and the destination. In the present invention, the corresponding transit hubs in the candidate routes are taken into account, and the construction cost of the transit hubs is taken into account in the objective function of the preset joint optimization model, so that the transit hubs can be optimized while optimizing the routes, achieving the technical effect of overall optimization.

[0038] Specifically, the historical transportation data in step S101 may include, in an optional embodiment, transportation vehicle data, transit hub data, transportation route data, and transportation demand data. The above-mentioned various historical transportation data are explained below:

[0039] (I) Transport vehicle data refers to data related to transport vehicles, which may include at least one of the following: the type of transport vehicle, the carrying capacity of each type of transport vehicle, the unit transport cost, and the transport speed; the types of transport vehicles may include small trucks, medium trucks, large trucks, and cold chain transport vehicles, etc. According to the actual transportation needs, different routes correspond to different road sections, and the types of transport vehicles required are also different. For example, some sections of the road are narrow, and large trucks are subject to transportation time restrictions and cannot meet the distribution needs, so only medium trucks can be selected for transportation.

[0040] (ii) Transit hub data refers to data related to transit hubs, which may include at least one of the following: the level of the transit hub, the construction cost corresponding to each level, and the average daily capacity; among them, transit hubs can be divided into different levels according to the scale of construction, and the construction cost of each level is different, such as land, building, equipment purchase and other costs. At the same time, transit hubs set up in different cities have different average daily capacities according to geographical conditions, surrounding population density, and economic development levels. It is understandable that the more goods a transit hub can handle (that is, the higher the average daily capacity), the more meaningful its setting is. If the level and construction cost of the transit hub are very high, but the average daily capacity is very low, the utilization rate of the transit hub will be very low, and there will be a waste of cost.

[0041] (III) Transport route data refers to data related to transport routes, including at least one of the following: origin and destination, optional vehicle types and number of vehicles, line opening cost, and minimum transport capacity; each transport route must correspond to an origin and destination, and no matter how the intermediate route is optimized, the origin and destination are fixed and unchanged. As mentioned above, according to the actual transportation needs, different transport routes correspond to different road sections and transportation needs, and the types of transport vehicles required are also different. Line opening cost refers to the fixed cost required to open each transport route, such as road usage fees, tolls, etc. Minimum transport capacity refers to the minimum transport capacity required to be achieved after each transport route is opened to ensure the economic benefits of the transport route.

[0042] (IV) Transportation demand data refers to data related to the supply and demand relationship of logistics transportation, including at least one of the following: the first-end transit hub and the last-end transit hub, information on the geographical area to which each transit hub belongs, and the transportation demand between the transit hubs. Among them, according to the supply and demand relationship in different geographical locations, it can be determined which cities must build transit hubs. For example, provincial-level administrative regions such as municipalities can determine the first-end transit hub and the last-end transit hub based on the transportation route. The transportation demand between the transit hubs can be understood as the total number or total weight of packages transported between the transit hubs over a period of time.

[0043] Typically, the above historical transportation data can be counted on a daily basis, and the preset time period can be divided into months. Therefore, in an optional embodiment, the process of determining the basic transportation data in step S101 can be as follows: Figure 2 As shown, including:

[0044] Step S201: Calculate the average value of multiple historical transportation data within a preset time period;

[0045] Step S202: taking the average value as basic transportation data.

[0046] It is understandable that the basic transportation data can be obtained relatively accurately by taking the average value, but if the daily historical transportation data differ greatly, it is not suitable for the process of taking the average value for determination. For this, the basic transportation data can be determined by curve fitting or setting weights. In a further optional embodiment, the historical transportation data can be cleaned before determining the basic transportation data to delete the historical transportation data that is obviously abnormal, so as to obtain more accurate basic transportation data.

[0047] It should be noted that the basic transportation data can be understood as the data required in the process of calculating the transit cost, transportation cost and transit hub construction cost. However, only when the candidate routes are determined can the transit cost, transportation cost and transit hub construction cost be added to obtain the total cost of the candidate routes, and then the candidate routes can be further selected. Therefore, the process of determining the candidate routes in the present invention is more important. For this purpose, the process of determining multiple candidate routes in step S102 can be as follows: Figure 3 As shown, including:

[0048] For each set of origins and destinations in the transportation network, perform the following process:

[0049] Step S301: Determine an optional intermediate transit point;

[0050] Step S302: Based on the intermediate transfer point and whether the origin and / or destination are equipped with a transfer hub, all optional routes are obtained exhaustively;

[0051] Step S303: According to the limited number of transfers and the total mileage, a candidate route is obtained from the optional routes.

[0052] Among them, the cities that can be used for logistics transfer in the transportation network are generally fixed, that is, they are determined during the construction of the transportation network. Therefore, in the process of determining all optional routes, it is only necessary to select and combine cities that can be used for logistics transfer. For example, the transportation network includes 5 cities, namely City A, City B, City C, City D, and City E. When the origin is City A and the destination is City E, the optional routes include:

[0053] One stopover:

[0054] (1) City A-City B-City E

[0055] (2) City A - City C - City E

[0056] (3) City A - City D - City E

[0057] Two transfers:

[0058] (4) City A - City B - City C - City E

[0059] (5) City A - City B - City D - City E

[0060] (6) City A - City C - City B - City E...

[0061] By analogy, all optional routes are obtained by exhaustively enumerating the different transfer times and the different combinations of origin and destination, among which all intermediate cities except the origin and destination are intermediate transfer points. However, the above exhaustive process assumes that each city has a transfer hub, such as Figure 4 As shown. However, the present invention takes into account whether it is necessary to build a transit hub, and further divides whether to set up a transit hub at the origin and destination. Taking the optional route of city A-city B-city C-city E as an example, it is actually divided into three situations, such as Figure 4 As shown. The first type is that city A does not set up a transfer hub, and transports to city B through long-distance transfer, and other cities have transfer hubs and use conventional routes for transportation; the second type is that city E does not set up a transfer hub, and transports from city C to city E through long-distance transfer, and other cities have transfer hubs and use conventional routes for transportation; the third type is that neither city A nor city E has a transfer hub, that is, the two ends use long-distance transfer, and city B to city C uses conventional routes for transportation.

[0062] Through the above process, all optional routes can be obtained in an exhaustive manner. However, taking into account the fact that there are detours and the number of transfers in all optional routes is not in line with reality, the present invention screens all optional routes in terms of number and total mileage through step S303. Specifically, step S303 includes: from all optional routes, screening out candidate routes whose number of intermediate transfer points is less than the preset limited number of transfers and whose total mileage is less than the preset distance. Specifically, the preset distance can be set to a multiple of the straight-line distance from the origin to the destination, for example, between 1 and 1.5 times. It can be seen that based on the screening of the number of transfers and the total mileage, candidate routes that can be used as model inputs are obtained. After being subsequently input into the model, several calculations are performed on the candidate routes to determine the target route with the minimum total cost.

[0063] For the process of step 103, in an optional embodiment, it can be as follows Figure 5 As shown, including:

[0064] Step S501: determining the objective function value corresponding to each candidate route according to basic transportation data;

[0065] Step S502: Determine a target route from multiple candidate routes according to the value of the target function.

[0066] The objective function value represents the sum of the transit cost, transportation cost and transit hub construction cost. According to the size of the objective function value, the target route with the minimum sum of the transit cost, transportation cost and transit hub construction cost can be determined. For example, the objective function can be expressed by the following formula (1):

[0067] Formula (1)

[0068] Among them, r represents the number of the candidate route, Z represents the unit transit cost, Indicates the number of transfers on route r. represents the transportation cost of vehicle type v on route l, s They represent the number of the city and the set of all cities respectively, K represents the set of transit hubs of different sizes, k represents the kth type of transit hub in the set of transit hubs, represents the number of k-th transfer hubs built in city s, Represents the construction cost of the k-th transfer hub.

[0069] In a further optional embodiment, the above objective function may be further refined, as shown in formula (2):

[0070] Formula (2)

[0071] Where r represents the number of the candidate route. A 0-1 variable representing whether the candidate route r is used, Z represents the unit transit cost, Indicates the number of transfers on route r. represents the cargo weight on route r, l and L represent the route number and the set of all routes respectively, v and They represent the model v and the set of optional models on route l, is a continuous variable representing the number of vehicles of type v on route l, represents the transportation cost of vehicle type v on route l, s They represent the number of the city and the set of all cities respectively. K represents the set of transit hubs of different sizes (for example, small transit hubs: 200,000 parcels per day, medium transit hubs: 400,000 parcels per day, large transit hubs: 600,000 parcels per day). k represents the kth type of transit hub in the set of transit hubs. represents the number of k-th transfer hubs built in city s, Represents the construction cost of the k-th transfer hub.

[0072] By comparing formula (2) with formula (1), it can be seen that formula (2) further considers the variables of candidate routes and the continuous variable of the number of vehicles of type v on route l on the basis of formula (1), so as to obtain more accurate transfer cost and transportation cost.

[0073] In an optional embodiment, the model also needs to set multiple constraints during the solution process to obtain the optimal solution under the multiple constraints. Specifically, the constraint conditions of the joint optimization model preset in the embodiment of the present invention include at least one of the following:

[0074] (I) The target route from each origin to each destination is unique, as shown in the following formula (3):

[0075] Formula (3)

[0076] in, represents the set of candidate routes between cities i and j;

[0077] (ii) The total capacity of the transport vehicles corresponding to the target route shall not be less than the cargo volume corresponding to the target route, as shown in the following formula (4):

[0078] Formula (4)

[0079] in, Indicates the loading capacity of vehicle type v;

[0080] (III) The type of transport vehicle corresponding to the target route corresponds to the number of transport vehicles, that is, only when a certain vehicle type is selected on route l can the number of that vehicle type be greater than 0. The formula is as follows (5):

[0081] Formula (5)

[0082] in, A 0-1 variable indicating whether vehicle type v is used on line l. represents the upper limit of the number of vehicle types v on route l;

[0083] (IV) The type of outbound vehicle corresponding to the target route is unique, as shown in the following formula (6):

[0084] Formula (6)

[0085] in, A 0-1 variable indicating whether line l is used. represents the set of candidate routes via line l;

[0086] (V) When there is cargo flow in the target route, the state variable of the target route is 1, as shown in the following formula (7):

[0087] Formula (7)

[0088] Among them, M is an infinite natural number. represents the set of all routes passing through line l;

[0089] (VI) The cargo volume corresponding to the target route is greater than the preset open-line cargo volume, as shown in the following formula (8):

[0090] Formula (8)

[0091] in, Indicates the cargo volume opening standard that needs to be met for line l to be opened. Represents the set of all lines;

[0092] (VII) The sum of the production capacities of the multiple transfer hubs in a city is greater than the average daily cargo volume of the city, as shown in the following formulas (9) and (10):

[0093] Formula (9)

[0094] Formula (10)

[0095] in, represents the capacity of the k-th transit hub, represents the set of all regular routes from city s (excluding Figure 4 Long-distance transmission stations in It represents the average daily cargo volume transferred to the station in city s.

[0096] Based on the constraints mentioned above, the optimal solution for the objective function can be solved. An optional method is to use the Gurobi solver for automatic solution. By traversing all the variables in the model that reflect whether the candidate route r is used, the usage of the candidate route is analyzed. By traversing all the variables that reflect the number of various types of transit hubs built in the city, the number of transit hubs built in each city is obtained, and finally a model result indicating the target route and the target transit hub corresponding to the target route is generated. Among them, the target transit hub can be understood as which city in the target route is to build the transit hub.

[0097] In an optional embodiment, the results calculated by the above model can be stored, that is, a data structure (such as a dictionary or a table) is created to store the final optimization results of the transportation network, and the target route and the target transit hub corresponding to the target route are stored in the data structure.

[0098] According to the method for optimizing the transportation network of an embodiment of the present invention, the transit hub is simultaneously considered in the candidate routes, and through a joint optimization model, the minimum sum of transit cost, transportation cost and transit hub construction cost is used as the objective function to simultaneously optimize the routes and transit hubs, so as to obtain the target route and the target transit hub corresponding to the target route, thereby effectively improving the overall transportation efficiency.

[0099] Figure 6 4 is a schematic diagram of main modules of a device for optimizing a transportation network according to an embodiment of the present invention.

[0100] like Figure 6 As shown, the transport network optimization device 600 of the embodiment of the present invention includes:

[0101] The data determination module 601 is used to determine the basic transportation data according to the historical transportation data within a preset time period;

[0102] The route determination module 602 is used to determine a plurality of candidate routes according to the origin and the destination in the transportation network; wherein the candidate routes indicate the corresponding transit hubs, transportation routes, and transportation modes between the origin and the destination;

[0103] The output module 603 is used to input the basic transportation data and the candidate routes into a preset joint optimization model, and output a target route and a target transit hub corresponding to the target route; wherein the preset joint optimization model takes minimizing the sum of transit cost, transportation cost and transit hub construction cost as an objective function.

[0104] In an optional embodiment of the present invention, the route determination module 602 is also used to: determine an optional intermediate transfer point for each group of origin and destination in the transportation network; exhaustively obtain all optional routes based on the intermediate transfer point and whether a transit hub is set at the origin and / or the destination; and filter the candidate route from the optional routes based on a limited number of transits and total mileage.

[0105] In an optional embodiment of the present invention, the route determination module 602 is further used to screen out the candidate routes from all the optional routes, the candidate routes having the number of intermediate transfer points less than the preset limited number of transfers and the total mileage less than the preset distance.

[0106] In an optional embodiment of the present invention, the data determination module 601 is further used to calculate an average value of a plurality of historical transportation data within a preset time period; and use the average value as basic transportation data.

[0107] In an optional embodiment of the present invention, the output module 603 is further used to determine the objective function value corresponding to each candidate route according to the basic transportation data; and determine the target route from the multiple candidate routes according to the size of the objective function value.

[0108] In an optional embodiment of the present invention, the constraints of the preset joint optimization model include at least one of the following: the target route corresponding to each of the origins to each of the destinations is unique, the total capacity of the transport vehicles corresponding to the target route is not less than the cargo volume corresponding to the target route, the type of transport vehicles corresponding to the target route corresponds to the number of transport vehicles, the type of outgoing vehicles corresponding to the target route is unique, the state variable of the target route is 1 when there is cargo flow in the target route, the cargo volume corresponding to the target route is greater than the preset open-line cargo volume, and the sum of the production capacities of multiple transit hubs belonging to a city is greater than the average daily cargo volume of the city.

[0109] In an optional embodiment of the present invention, the historical transportation data includes: transportation vehicle data, transit hub data, transportation route data, and transportation demand data; wherein the transportation vehicle data includes at least one of the following: the type of transportation vehicle, the carrying capacity corresponding to each type of transportation vehicle, the unit transportation cost, and the transportation speed; the transit hub data includes at least one of the following: the level of the transit hub, the construction cost and the average daily production capacity corresponding to each level; the transportation route data includes at least one of the following: the origin and destination, optional vehicle types and the number of vehicles, line opening cost, and minimum transportation capacity; the transportation demand data includes at least one of the following: the first-end transit hub and the terminal transit hub, information on the geographical area to which each of the transit hubs belongs, and the transportation demand between each transit hub.

[0110] According to the optimization device of the transportation network of the embodiment of the present invention, the transit hub is simultaneously considered in the candidate routes, and through a joint optimization model, the minimum sum of transit cost, transportation cost and transit hub construction cost is used as the objective function, and the routes and transit hubs are simultaneously optimized to obtain the target route and the target transit hub corresponding to the target route, thereby effectively improving the overall transportation efficiency.

[0111] Figure 7 An exemplary system architecture 700 is shown to which the method for optimizing a transportation network or the device for optimizing a transportation network according to the embodiments of the present invention can be applied.

[0112] like Figure 7 As shown, system architecture 700 may include terminal devices 701, 702, 703, network 704 and server 705. Network 704 is used to provide a medium for communication links between terminal devices 701, 702, 703 and server 705. Network 704 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0113] The user can use the terminal devices 701, 702, 703 to interact with the server 705 through the network 704 to receive or send data, etc. Various communication client applications can be installed on the terminal devices 701, 702, 703, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0114] The terminal devices 701 , 702 , and 703 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0115] The server 705 may be a server that provides various services, such as a backend management server that provides support for shopping websites browsed by users using the terminal devices 701, 702, and 703. The backend management server may analyze and process the received historical transportation data and other data, and feed back the processing results (such as the target route and the target transit hub corresponding to the target route) to the terminal device.

[0116] It should be noted that the transportation network optimization method provided in the embodiment of the present invention is generally executed by the server 705 , and accordingly, the transportation network optimization device is generally disposed in the server 705 .

[0117] It should be understood that Figure 7 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0118] Reference below Figure 8 , which shows a schematic diagram of the structure of a computer system 800 of a terminal device suitable for implementing an embodiment of the present invention. Figure 8 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0119] like Figure 8 As shown, the computer system 800 includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage part 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the system 800 are also stored. The CPU 801, the ROM 802, and the RAM 803 are connected to each other through a bus 804. An input / output (I / O) first interface 805 is also connected to the bus 804.

[0120] The following components are connected to the I / O first interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network first interface card such as a LAN card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O first interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed, so that a computer program read therefrom is installed into the storage section 808 as needed.

[0121] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the central processing unit (CPU) 801, the above-mentioned functions defined in the system of the present invention are executed.

[0122] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0123] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0124] The modules involved in the embodiments of the present invention may be implemented in software or hardware. The modules described may also be set in a processor, for example, it may be described as: a processor includes a data determination module, a route determination module and an output module. The names of these modules do not constitute a limitation on the modules themselves in some cases, for example, the data determination module may also be described as a "module for determining basic transportation data based on historical transportation data within a preset time period".

[0125] As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes: determining basic transportation data based on historical transportation data within a preset time period; determining multiple candidate routes based on the origin and destination in the transportation network; wherein the candidate routes indicate the corresponding transit hub, transportation route, and transportation mode between the origin and the destination; inputting the basic transportation data and the candidate routes into a preset joint optimization model, and outputting the target route and the target transit hub corresponding to the target route; wherein the preset joint optimization model takes the minimum sum of transit cost, transportation cost and transit hub construction cost as the objective function.

[0126] According to the technical solution of the embodiment of the present invention, the transit hub is simultaneously considered in the candidate routes, and through a joint optimization model, the minimum sum of transit cost, transportation cost and transit hub construction cost is used as the objective function to simultaneously optimize the route and the transit hub to obtain the target route and the target transit hub corresponding to the target route, thereby effectively improving the overall transportation efficiency.

[0127] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing a transportation network, characterized in that: include: Determine basic transportation data based on historical transportation data within a preset time period; Determine a plurality of candidate routes according to the origin and the destination in the transportation network; wherein the candidate routes indicate the corresponding transit hubs, transportation routes, and transportation modes between the origin and the destination; The basic transportation data and the candidate routes are input into a preset joint optimization model, and the target route and the target transit hub corresponding to the target route are output; wherein the preset joint optimization model takes the minimization of the sum of transit cost, transportation cost and transit hub construction cost as an objective function.

2. The method according to claim 1, characterized in that The step of determining a plurality of candidate routes according to the origin and the destination in the transportation network comprises: For each set of origins and destinations in the transportation network: Identify optional intermediate transit points; Based on the intermediate transfer place and whether the origin and / or the destination are equipped with a transfer hub, all optional routes are obtained exhaustively; The candidate route is obtained by screening from the optional routes according to the limited number of transfers and the total mileage.

3. The method according to claim 2, characterized in that The step of selecting the candidate route from the optional routes according to the limited number of transfers and the total mileage includes: From all the optional routes, the candidate routes are selected whose number of intermediate transfer points is less than the preset limited number of transfers and whose total mileage is less than the preset distance.

4. The method according to claim 1, characterized in that: Determining basic transportation data according to historical transportation data within a preset time period includes: Calculate the average value of multiple historical transportation data within a preset time period; The average values ​​were taken as basic transport data.

5. The method according to claim 1, characterized in that The step of inputting the basic transportation data and the candidate routes into a preset joint optimization model and outputting a target route and a target transit hub corresponding to the target route includes: Determine the objective function value corresponding to each of the candidate routes according to the basic transportation data; The target route is determined from the plurality of candidate routes according to the size of the target function value.

6. The method according to claim 1, characterized in that The constraint conditions of the preset joint optimization model include at least one of the following: The target route corresponding to each of the origins and each of the destinations is unique, the total capacity of the transport vehicles corresponding to the target routes is not less than the cargo volume corresponding to the target routes, the type of transport vehicles corresponding to the target routes corresponds to the number of transport vehicles, the type of outgoing vehicles corresponding to the target routes is unique, the state variable of the target route is 1 when there is cargo flow in the target route, the cargo volume corresponding to the target route is greater than the preset open-line cargo volume, and the sum of the production capacities of multiple transit hubs belonging to a city is greater than the average daily cargo volume of the city.

7. The method according to claim 1, characterized in that The historical transportation data includes: transportation vehicle data, transit hub data, transportation route data, and transportation demand data; The transport vehicle data includes at least one of the following: the type of transport vehicle, the carrying capacity corresponding to each type of transport vehicle, the unit transport cost, and the transport speed; The transit hub data includes at least one of the following: the level of the transit hub, the construction cost and the average daily capacity corresponding to each level; The transportation route data includes at least one of the following: an origin and a destination, optional vehicle types and vehicle quantities, line opening costs, and minimum transportation capacity; The transportation demand data includes at least one of the following: the first-end transit hub and the last-end transit hub, information on the geographical area to which each of the transit hubs belongs, and the transportation demand between each transit hub.

8. A transport network optimization device, characterized in that: include: A data determination module, used to determine basic transportation data based on historical transportation data within a preset time period; A route determination module, used to determine a plurality of candidate routes according to an origin and a destination in a transportation network; wherein the candidate routes indicate a corresponding transit hub, transportation route, and transportation mode between the origin and the destination; An output module is used to input the basic transportation data and the candidate routes into a preset joint optimization model, and output a target route and a target transit hub corresponding to the target route; wherein the preset joint optimization model takes minimizing the sum of transit cost, transportation cost and transit hub construction cost as an objective function.

9. An electronic device for optimizing a transportation network, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

11. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method according to any one of claims 1 to 7.

Images