Method, device and equipment for determining day and night parking lot stations of vehicle route

Abstract

The invention provides a method, a device and equipment for determining day and night parking lot stations of vehicle routes. The method comprises the following steps: acquiring data of a plurality of vehicle routes and geographic position data of a plurality of parking lot stations; respectively constructing a daytime distance matrix and a nighttime distance matrix based on the data of all the vehicle routes and the geographic position data of the parking lot stations; constructing a linear programming model based on preset constraint conditions, the daytime distance matrix and the night distance matrix; and based on a preset objective function, solving the linear programming model to determine day and night parking lot stations of each vehicle route. The day-and-night operation environment of a traffic or logistics scheduling system can be taken into consideration, the day-and-night parking lot station of the vehicle route is effectively planned, the distribution efficiency of the parking lot station in the day-and-night operation environment is improved, the resource utilization rate of the parking lot station is improved, and the cost is reduced. And the flexibility of dynamic adjustment of the corresponding day and night parking lot station can be realized in combination with the change of the vehicle route.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic or logistics scheduling, and in particular to a technology for determining a daytime and nighttime parking station for a vehicle route. Background Art

[0002] In the existing transportation or logistics field, the transportation or logistics scheduling system usually plans fixed vehicle routes and parking lots. For vehicles operating on the vehicle routes, there are deficiencies or defects when allocating their parking lots: Lack of day and night parking lot allocation optimization capabilities: Failure to effectively distinguish between different needs during the day and at night may lead to low resource utilization of parking lots; Unable to adjust dynamically and lack flexibility: Faced with changes in actual operations, such as traffic conditions and / or parking lot location adjustments, parking lot capacity adjustments, etc., it is difficult to adjust the allocation plan in a timely and effective manner. The above deficiencies or defects limit the operating efficiency and adjustment flexibility of the existing transportation or logistics systems, resulting in the technical problem of low efficiency in parking lot allocation in the existing transportation or logistics systems under day and night operation environments. Summary of the invention

[0003] The purpose of the present invention is to provide a method, device and equipment for determining the day and night parking stations of a vehicle route, so as to at least partially solve the technical problem of low efficiency in allocating parking stations in existing traffic or logistics scheduling systems under day and night operation environments.

[0004] According to one aspect of the present invention, there is provided a method for determining day and night parking stations of a vehicle route, wherein the method comprises:

[0005] Obtaining data of a number of vehicle routes and geographic location data of a number of parking stations;

[0006] Based on the data of all vehicle routes and the geographical location data of parking lots, a daytime distance matrix and a nighttime distance matrix are constructed respectively;

[0007] Constructing a linear programming model based on preset constraints, the daytime distance matrix and the nighttime distance matrix;

[0008] Based on a preset objective function, the linear programming model is solved to determine the day and night parking stations for each vehicle route.

[0009] Optionally, the vehicle route data includes:

[0010] The starting point geographic location data and the ending point geographic location data of the morning shift of the vehicle route;

[0011] The starting point geographic location data and the ending point geographic location data of the evening shift of the vehicle route.

[0012] Optionally, the step of constructing a daytime distance matrix and a nighttime distance matrix based on all vehicle route data and parking lot station data comprises:

[0013] Calculate the first driving distance from the terminal point of the morning shift of each vehicle route to each parking lot station according to the geographical location data of the terminal point of the morning shift of each vehicle route and the geographical location data of each parking lot station;

[0014] Calculate a second driving distance from each parking lot station to the starting point of the evening shift of each vehicle route based on the geographical location data of each parking lot station and the geographical location data of the starting point of the evening shift of each vehicle route;

[0015] Calculate the third driving distance from the end point of the evening shift of each vehicle route to each parking lot station according to the geographical location data of the end point of the evening shift of each vehicle route and the geographical location data of each parking lot station;

[0016] Calculate a fourth driving distance from each parking lot station to the starting point of the morning shift of each vehicle route based on the geographical location data of each parking lot station and the geographical location data of the starting point of the morning shift of each vehicle route;

[0017] The sum of a first driving distance from the end point of a morning shift of a vehicle route to a parking station and a second driving distance from the parking station to the starting point of a night shift of the vehicle route is taken as an element of the daytime distance matrix, and each vehicle route and each parking station are traversed to construct the daytime distance matrix;

[0018] The sum of the third driving distance from the end point of the evening shift of a vehicle route to a parking lot station and the fourth driving distance from the parking lot station to the starting point of the morning shift of the vehicle route is taken as an element of the night distance matrix, and each vehicle route and each parking lot station are traversed to construct the night distance matrix.

[0019] Optionally, calculating the driving distance between two geographic locations includes:

[0020] The shortest road network distance between two geographical locations determined based on preset road network planning data.

[0021] Optionally, the constraint conditions include:

[0022] Each vehicle route is assigned only one daytime parking station and one nighttime parking station.

[0023] Optionally, the constraint condition further includes at least one of the following:

[0024] The type of each parking station;

[0025] The parking space capacity and charging pile capacity of each parking station.

[0026] Optionally, the objective function includes:

[0027] The sum of the driving distances assigned to all vehicle routes is minimized, wherein the driving distance assigned to each vehicle route is determined based on the vehicle route and its corresponding assigned daytime parking station and nighttime parking station.

[0028] According to another aspect of the present invention, there is provided a device for determining the day and night parking stations of a vehicle route, wherein the device comprises:

[0029] The first module is used to obtain data of several vehicle routes and geographical location data of several parking stations;

[0030] The second module is used to construct a daytime distance matrix and a nighttime distance matrix based on the data of all vehicle routes and the geographical location data of parking lots;

[0031] The third module is used to construct a linear programming model based on preset constraints, the daytime distance matrix and the nighttime distance matrix;

[0032] The fourth module is used to solve the linear programming model based on a preset objective function to determine the day and night parking stations for each vehicle route.

[0033] Compared with the prior art, the present invention provides a method, device and equipment for determining the day and night parking stations of vehicle routes, and the method includes: A. obtaining data of several vehicle routes and geographical location data of several parking stations; B. constructing a daytime distance matrix and a nighttime distance matrix based on the data of all vehicle routes and the geographical location data of parking stations; C. constructing a linear programming model based on preset constraints, the daytime distance matrix and the nighttime distance matrix; D. solving the linear programming model based on a preset objective function to determine the daytime and night parking stations of each vehicle route. The present invention can take into account the overall daytime and nighttime operating environment of the traffic or logistics scheduling system, effectively plan the daytime and nighttime parking stations of the vehicle routes, improve the allocation efficiency of the parking stations in the daytime and nighttime operating environment, improve the resource utilization of the parking stations, and can realize the flexibility of dynamic adjustment of the corresponding daytime and night parking stations in combination with the changes in the vehicle routes. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0035] Figure 1 A schematic flow chart of a method for determining day and night parking stations of a vehicle route according to one aspect of the present invention is shown;

[0036] Figure 2 A schematic diagram showing a device for determining a day and night parking station for a vehicle route according to another aspect of the present invention;

[0037] The same or similar reference numerals in the drawings represent the same or similar components. DETAILED DESCRIPTION

[0038] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0039] In a typical configuration of each embodiment of the present invention, the execution subject of the method, each trusted party of the system and / or each module of the device includes one or more processors (CPU), input / output interface, network interface and memory.

[0040] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0041] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory media such as modulated data signals and carrier waves.

[0042] In the field of transportation or logistics, operators usually invest a certain number of vehicles and operate them on planned routes. With the development of new energy vehicle technology, more and more new energy vehicles are put into operation. Whether it is a traditional fuel vehicle or a new energy vehicle, when it stops after completing the operation or needs to refuel / charge, it needs to enter the nearest parking station, which is usually allocated according to the parking station of the corresponding vehicle route through the transportation or logistics scheduling system. The existing transportation or logistics system has the technical problem of low efficiency in parking station allocation in a day and night operation environment, lacks the ability to optimize the allocation of parking stations during the day and night, and it is difficult to adjust the allocation plan in a timely and effective manner in the face of changes in actual operations.

[0043] The present invention provides a method for determining the day and night parking stations of a vehicle route, which is implemented through a traffic or logistics scheduling system. First, the data of the vehicle route and the geographical location data of the parking stations are obtained; then, based on the above data, a daytime distance matrix and a nighttime distance matrix are constructed respectively; then, based on preset constraints, a linear programming model is constructed; finally, based on a preset objective function, the linear programming model is solved to determine the day and night parking stations corresponding to the vehicle route. The present invention can take into account the overall day and night operating environment of the traffic or logistics scheduling system, effectively plan the day and night parking stations of the vehicle route, improve the allocation efficiency of the parking stations under the day and night operating environment, and can realize the flexibility of dynamic adjustment of the corresponding day and night parking stations in combination with changes in the vehicle route.

[0044] In order to further illustrate the technical means adopted by the present invention and the effects achieved, the technical scheme of the present invention is clearly and completely described below in conjunction with the accompanying drawings and various embodiments.

[0045] Figure 1 A schematic diagram of a method for determining a day and night parking lot station of a vehicle route according to one aspect of the present invention is shown, wherein the method of an embodiment includes:

[0046] S101 obtains data of a plurality of vehicle routes and geographic location data of a plurality of parking lots;

[0047] S102 constructs a daytime distance matrix and a nighttime distance matrix based on the data of all vehicle routes and the geographical location data of parking lots;

[0048] S103 constructs a linear programming model based on preset constraints, the daytime distance matrix and the nighttime distance matrix;

[0049] S104 solves the linear programming model based on a preset objective function to determine the daytime and nighttime parking lots for each vehicle route.

[0050] The method of this embodiment is implemented by a traffic or logistics dispatching system 100 deployed by an enterprise providing traffic or logistics dispatching services. A commuting service system generally includes a computer device that deploys the necessary software and hardware environment. The computer device includes, but is not limited to, a personal computer, a laptop computer, an industrial computer, a server, a network host, a single network server, or a network server cluster. Here, the computer device is only an example, and other existing or future devices and / or resource platforms that may appear should also be included in the scope of protection of the present invention if they are applicable to the present invention, and are hereby included by reference.

[0051] In this embodiment, in step S101 , the system 100 may obtain data of a number of vehicle routes and geographic location data of a number of parking stations.

[0052] Operators providing transportation or logistics dispatching services usually plan the operation routes of transportation or logistics vehicles and parking stations that can provide parking, refueling or charging services for vehicles operating on the vehicle routes in combination with market demand and / or business needs. The system 100 can obtain data on several planned vehicle routes and geographic location data of several parking stations that can provide parking, refueling or charging services from relevant databases or data files.

[0053] In this application, vehicles operating on vehicle routes include morning shift vehicles operating during the day, and also include evening shift vehicles operating at night. Usually, vehicles start operating from a parking lot station for parking, refueling or charging to the starting point of the route, and return to a parking lot station from the end point of the route to park, refuel or charge after completing the operation. Among them, the morning shift vehicle will drive out from a night parking lot station to the starting point of the morning shift of the route to start the morning shift operation (without limiting the number of shifts). After completing the morning shift operation, it will drive from the end point of the morning shift of the route to a daytime parking lot station to park, refuel or charge. The evening shift vehicle will drive out from a daytime parking lot station to the starting point of the evening shift of the route to start the evening shift operation (without limiting the number of shifts). After completing the evening shift operation, it will drive from the end point of the evening shift of the route to a night parking lot station to park, refuel or charge. Among them, the starting point of the morning shift of the route can be different from the starting point of the evening shift, and the end point of the morning shift of the route can also be different from the end point of the evening shift.

[0054] Optionally, in step S101, the vehicle route data includes:

[0055] The starting point geographic location data and the ending point geographic location data of the morning shift of the vehicle route;

[0056] The starting point geographic location data and the ending point geographic location data of the evening shift of the vehicle route.

[0057] The data of each vehicle route acquired by the system 100 may include the starting point geographic location data and the ending point geographic location data of the morning shift of the vehicle route, and the starting point geographic location data and the ending point geographic location data of the evening shift of the vehicle. The geographic location data includes the longitude and latitude coordinate information of the geographic location, which can be obtained through software tools such as map services.

[0058] After obtaining the data of several vehicle routes and the geographic location data of several parking stations, the system 100 continues in this embodiment. In step S102, the system 100 can construct a daytime distance matrix and a nighttime distance matrix based on the data of all vehicle routes and the geographic location data of the parking stations.

[0059] Among them, the system 100 can determine the driving distance between the starting point of the morning shift of each vehicle route and each parking lot station, the driving distance between the end point of the morning shift of each vehicle route and each parking lot station, the driving distance between the starting point of the evening shift of each vehicle route and each parking lot station, and the driving distance between the end point of the evening shift of each vehicle route and each parking lot station based on the data of each vehicle route and the geographic location data of each parking lot station, and then construct a daytime distance matrix and a nighttime distance matrix respectively according to each driving distance. As an example, assuming that the system 100 obtains data on N vehicle routes and geographic location data on M parking lots, a daytime distance matrix X and a nighttime distance matrix Y can be constructed respectively, where X[i][j] represents the sum of the driving distance between the end point of the morning shift of the ith vehicle route and the jth parking lot as a daytime parking lot, and the driving distance between the starting point of the evening shift of the vehicle route and the daytime parking lot, where i=1,…,N, j=1,…,M; Y[u][v] represents the sum of the driving distance between the end point of the evening shift of the uth vehicle route and the vth parking lot as a nighttime parking lot, and the driving distance between the starting point of the morning shift of the vehicle route and the nighttime parking lot, where u=1,…,N, v=1,…,M.

[0060] Optionally, the step S102 includes:

[0061] Calculate the first driving distance from the terminal point of the morning shift of each vehicle route to each parking lot station according to the geographical location data of the terminal point of the morning shift of each vehicle route and the geographical location data of each parking lot station;

[0062] Calculate a second driving distance from each parking lot station to the starting point of the evening shift of each vehicle route based on the geographical location data of each parking lot station and the geographical location data of the starting point of the evening shift of each vehicle route;

[0063] Calculate the third driving distance from the end point of the evening shift of each vehicle route to each parking lot station according to the geographical location data of the end point of the evening shift of each vehicle route and the geographical location data of each parking lot station;

[0064] Calculate a fourth driving distance from each parking lot station to the starting point of the morning shift of each vehicle route based on the geographical location data of each parking lot station and the geographical location data of the starting point of the morning shift of each vehicle route;

[0065] The sum of a first driving distance from the end point of a morning shift of a vehicle route to a parking station and a second driving distance from the parking station to the starting point of a night shift of the vehicle route is taken as an element of the daytime distance matrix, and each vehicle route and each parking station are traversed to construct the daytime distance matrix;

[0066] The sum of the third driving distance from the end point of the evening shift of a vehicle route to a parking lot station and the fourth driving distance from the parking lot station to the starting point of the morning shift of the vehicle route is taken as an element of the night distance matrix, and each vehicle route and each parking lot station are traversed to construct the night distance matrix.

[0067] Among them, the system 100 can first calculate the first driving distance from the end point of the morning shift of each vehicle route to each parking lot station according to the geographical location data of the end point of the morning shift of each vehicle route and the geographical location data of each parking lot station; calculate the second driving distance from each parking lot station to the starting point of the evening shift of each vehicle route according to the geographical location data of each parking lot station and the geographical location data of the starting point of the evening shift of each vehicle route; calculate the third driving distance from the end point of the evening shift of each vehicle route to each parking lot station according to the geographical location data of the end point of the evening shift of each vehicle route and the geographical location data of each parking lot station; calculate the fourth driving distance from each parking lot station to the starting point of the morning shift of each vehicle route according to the geographical location data of each parking lot station and the geographical location data of the starting point of the morning shift of each vehicle route. Then, for each vehicle route and each parking lot, the sum of the first driving distance from the end point of the morning shift of a vehicle route to a parking lot and the second driving distance from the parking lot to the starting point of the evening shift of the vehicle route is taken as an element of the daytime distance matrix, and each vehicle route and each parking lot are traversed to construct a daytime distance matrix; and for each vehicle route and each parking lot, the sum of the third driving distance from the end point of the evening shift of a vehicle route to a parking lot and the fourth driving distance from the parking lot to the starting point of the morning shift of the vehicle route is taken as an element of the nighttime distance matrix, and each vehicle route and each parking lot are traversed to construct a nighttime distance matrix. Continuing with the above example, if the first driving distance between the end point of the morning shift of the i-th vehicle route and the j-th parking lot as a daytime parking lot is Ldij1, and the second driving distance between the starting point of the evening shift of the vehicle route and the daytime parking lot is Ldij2, then X[i][j]=Ldij1+Ldij2. If the third driving distance between the end point of the evening shift of the u-th vehicle route and the v-th parking lot station which is a night parking lot station is Lnuv1, and the driving distance between the starting point of the morning shift of the vehicle route and the night parking lot station is Lnuv2, then Y[u][v]=Lnuv1+Lnuv2.

[0068] Optionally, calculating the driving distance between two geographic locations includes:

[0069] The shortest road network distance between two geographical locations determined based on preset road network planning data.

[0070] The calculation of the driving distance between the starting point / end point of the morning / evening shift of the vehicle route and a parking lot can be determined based on the shortest network distance between the starting point / end point and the parking lot determined by the network planning data preset in the system 100, wherein the network planning data preset in the system 100 can be network planning data determined by an integrated map software tool (e.g., Baidu Maps, Amap, etc.).

[0071] Continuing with this embodiment, in step S103, the system 100 may construct a linear programming model based on preset constraints, the daytime distance matrix and the nighttime distance matrix.

[0072] Among them, the constraints can be preset in combination with the application scenario and business requirements, and then a linear programming model can be constructed based on the constraints and the constructed daytime distance matrix and nighttime distance matrix. As an example, the `MPSolver` of Google OR-Tools can be used to create a linear programming solver, and the SCIP solver can be selected as the backend to complete the construction of the linear programming model.

[0073] Optionally, in step S103, the constraint conditions include:

[0074] Each vehicle route is assigned only one daytime parking station and one nighttime parking station.

[0075] Among them, the preset constraints include: for each vehicle route, no matter it is an early shift or an evening shift, the vehicle route is only allocated one daytime parking station and one nighttime parking station, wherein the daytime parking station and the nighttime parking station allocated to the vehicle route can be different or the same.

[0076] In order to better meet application scenarios and business requirements, optionally, the constraint condition also includes at least one of the following:

[0077] The type of each parking station;

[0078] The parking space capacity and charging pile capacity of each parking station.

[0079] In actual application scenarios and business needs, there are several types of parking stations: some may include charging piles (can be assigned to vehicles that need to be charged), some may not include charging piles (cannot be assigned to vehicles that need to be charged), some may not have refueling conditions (cannot be assigned to fuel vehicles that need to be refueled), etc. Therefore, the type of each parking station can be used as a constraint. Furthermore, each parking station has different site conditions and environmental conditions. Each parking station has a parking capacity limit. If it includes rechargeable parking spaces, there is also a charging pile capacity limit (if a vehicle has a charging demand, being assigned to a parking station that cannot be charged is an invalid allocation).

[0080] Continuing with this embodiment, in step S104 , the system 100 may solve the linear programming model based on a preset objective function to determine the day and night parking stations for each vehicle route.

[0081] Among them, the objective function of the linear programming model can be set according to the actual application scenario and business needs, and then based on the objective function, the optimal solution of the linear programming model is solved under the above constraints to determine a daytime parking station and a nighttime parking station for each vehicle route. Continuing the above example, the selected SCIP solver can be called to solve and obtain the optimal solution of the linear programming model.

[0082] Optionally, in step S104, the objective function includes:

[0083] The sum of the driving distances assigned to all vehicle routes is minimized, wherein the driving distance assigned to each vehicle route is determined based on the vehicle route and its corresponding assigned daytime parking station and nighttime parking station.

[0084] The objective function can be set in combination with the actual application scenario and business needs. In this optional embodiment, the objective function can be set to: the sum of the driving distances assigned to all vehicle routes is minimized, wherein the driving distance assigned to each vehicle route is determined based on the vehicle route and its corresponding assigned one daytime parking station and one nighttime parking station. Based on the objective function, under the constraint condition, one daytime parking station and one nighttime parking station corresponding to each vehicle route can be determined, so that the total driving distance of the morning and evening shifts of all vehicle routes before and after the start of operation is the shortest.

[0085] In the above embodiments and / or optional embodiments, the system 100 can construct a linear programming model based on the data of several vehicle routes and the geographical location data of several parking stations, and obtain the optimal solution under the constraints according to the preset objective function, thereby optimizing the allocation of day and night parking stations of vehicle routes, improving the efficiency of traffic or logistics scheduling, and improving the resource utilization rate of parking stations. In actual application scenarios, the system 100 can flexibly allocate day and night parking stations of each vehicle route based on the actual number of vehicle routes and parking stations, and can adapt to alternating or logistics scheduling systems of different scales and needs, such as corporate commuter shuttles, shared buses, express delivery, etc. Based on the same business needs, the more the number of vehicle routes and parking stations obtained, the more obvious the optimization effect of the allocation of day and night parking stations of vehicle routes.

[0086] Different objective functions can also be set according to actual application scenarios and business needs, such as minimizing idle time, etc. The allocation can be adjusted dynamically according to actual traffic conditions and parking lot usage, which is very flexible.

[0087] Figure 2 A device for determining a day and night parking station of a vehicle route according to another aspect of the present invention is shown, wherein the device of one embodiment includes:

[0088] The first module 210 is used to obtain data of a plurality of vehicle routes and geographic location data of a plurality of parking stations;

[0089] The second module is used to construct a daytime distance matrix and a nighttime distance matrix based on the data of all vehicle routes and the geographical location data of parking lots;

[0090] The third module is used to construct a linear programming model based on preset constraints, the daytime distance matrix and the nighttime distance matrix;

[0091] The fourth module is used to solve the linear programming model based on a preset objective function to determine the day and night parking stations for each vehicle route.

[0092] The device of this embodiment is integrated into the aforementioned system 100 .

[0093] In this embodiment, the first module 210 of the device can obtain data of several planned vehicle routes and geographic location data of several parking stations that can provide parking, refueling or charging services from relevant databases or data files.

[0094] Continuing with this embodiment, through the second module 220 of the device, the driving distance between the starting point of the morning shift of each vehicle route and each parking station, the driving distance between the end point of the morning shift of each vehicle route and each parking station, the driving distance between the starting point of the evening shift of each vehicle route and each parking station, and the driving distance between the end point of the evening shift of each vehicle route and each parking station can be determined based on the data of each vehicle route and the geographical location data of each parking station, and then the daytime distance matrix and the nighttime distance matrix are constructed respectively according to the respective driving distances.

[0095] Continuing in this embodiment, through the third module 230 of the device, constraints can be preset in combination with application scenarios and business requirements, and then a linear programming model is constructed based on the constraints and the constructed daytime distance matrix and nighttime distance matrix.

[0096] Continuing with this embodiment, through the fourth module 240 of the device, the objective function of the linear programming model can be set according to the actual application scenario and business needs, and then based on the objective function, the optimal solution of the linear programming model is solved under the above constraints to determine one daytime parking station and one nighttime parking station for each vehicle route.

[0097] In the various embodiments and / or optional embodiments of the above-mentioned system, the matters not mentioned in the method steps executed by each module are the same as those of the aforementioned related method embodiments and / or optional embodiments, and will not be repeated here.

[0098] According to yet another aspect of the present invention, a computer-readable medium is provided, wherein the computer-readable medium stores computer-readable instructions, and the computer-readable instructions can be executed by a processor to implement part or all of the above method.

[0099] It should be noted that each method embodiment and / or optional embodiment of the present invention may be partially or completely implemented in software and / or a combination of software and hardware. The software program involved in the present invention may be executed by a processor to implement some or all of the steps or functions of the above-mentioned embodiments and / or optional embodiments. Similarly, the software program of the present invention (including related data structures) may be stored in a computer-readable recording medium.

[0100] In addition, a part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide part or all of the method and / or technical solution according to the present invention through the operation of the computer. The program instruction for calling the method of the present invention may be stored in a fixed or removable recording medium, and / or transmitted through a data stream in a broadcast or other signal-bearing medium, and / or stored in a working memory of a computer device that runs according to the program instruction.

[0101] According to another aspect of the present invention, there is provided a device for determining day and night parking lots of vehicle routes, wherein the device comprises: a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the device is triggered to execute part or all of the methods and / or technical solutions of the aforementioned embodiments and / or optional embodiments.

[0102] It is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments and / or optional embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim can also be implemented by one unit or device through software and / or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

Claims

1. A method for determining the day and night parking stations of a vehicle route, characterized in that: The method comprises: Obtaining data of a number of vehicle routes and geographic location data of a number of parking stations; Based on all vehicle route data and parking lot station data, a daytime distance matrix and a nighttime distance matrix are constructed respectively; Constructing a linear programming model based on preset constraints, the daytime distance matrix and the nighttime distance matrix; Based on a preset objective function, the linear programming model is solved to determine the day and night parking stations for each vehicle route.

2. The method according to claim 1, characterized in that The vehicle route data includes: The starting point geographic location data and the ending point geographic location data of the morning shift of the vehicle route; The starting point geographic location data and the ending point geographic location data of the evening shift of the vehicle route.

3. The method according to claim 2, characterized in that The daytime distance matrix and the nighttime distance matrix are constructed based on all vehicle route data and parking lot station data, respectively, including: Calculate the first driving distance from the terminal point of the morning shift of each vehicle route to each parking lot station according to the geographical location data of the terminal point of the morning shift of each vehicle route and the geographical location data of each parking lot station; Calculate a second driving distance from each parking lot station to the starting point of the evening shift of each vehicle route based on the geographical location data of each parking lot station and the geographical location data of the starting point of the evening shift of each vehicle route; Calculate the third driving distance from the end point of the evening shift of each vehicle route to each parking lot station according to the geographical location data of the end point of the evening shift of each vehicle route and the geographical location data of each parking lot station; Calculate a fourth driving distance from each parking lot station to the starting point of the morning shift of each vehicle route based on the geographical location data of each parking lot station and the geographical location data of the starting point of the morning shift of each vehicle route; The sum of a first driving distance from the end point of a morning shift of a vehicle route to a parking station and a second driving distance from the parking station to the starting point of a night shift of the vehicle route is taken as an element of the daytime distance matrix, and each vehicle route and each parking station are traversed to construct the daytime distance matrix; The sum of the third driving distance from the end point of the evening shift of a vehicle route to a parking lot station and the fourth driving distance from the parking lot station to the starting point of the morning shift of the vehicle route is taken as an element of the night distance matrix, and each vehicle route and each parking lot station are traversed to construct the night distance matrix.

4. The method according to claim 3, characterized in that Calculate the driving distance between two geographic locations, including: The shortest road network distance between two geographical locations determined based on preset road network planning data.

5. The method according to claim 3, characterized in that: The constraints include: Each vehicle route is assigned only one daytime parking station and one nighttime parking station.

6. The method according to claim 5, characterized in that The constraints also include at least one of the following: The type of each parking station; The parking space capacity and charging pile capacity of each parking station.

7. The method according to claim 5, characterized in that The objective function includes: The sum of the driving distances assigned to all vehicle routes is minimized, wherein the driving distance assigned to each vehicle route is determined based on the vehicle route and its corresponding assigned one daytime parking station and one nighttime parking station.

8. A device for determining the day and night parking stations of a vehicle route, characterized in that: The device comprises: The first module is used to obtain data of several vehicle routes and geographical location data of several parking stations; The second module is used to construct a daytime distance matrix and a nighttime distance matrix based on the data of all vehicle routes and the geographical location data of parking lots; The third module is used to construct a linear programming model based on preset constraints, the daytime distance matrix and the nighttime distance matrix; The fourth module is used to solve the linear programming model based on a preset objective function to determine the day and night parking stations for each vehicle route.

9. A computer-readable medium, characterized in that Computer-readable instructions are stored thereon, and the computer-readable instructions are executed by a processor to implement part or all of the method according to any one of claims 1 to 7.

10. A device for determining a day and night parking station for a vehicle route, characterized in that: The device comprises: one or more processors; and A memory storing computer-readable instructions, wherein when the computer-readable instructions are executed, the processor is caused to perform part or all of the operations of the method according to any one of claims 1 to 7.

Images