阅读说明：本技术 基于gps技术的智慧物流园交通管理系统和方法 (Intelligent logistics park traffic management system and method based on GPS technology ) 是由 蒋林霞 于 2021-06-02 设计创作，主要内容包括：本申请公开了基于GPS技术的智慧物流园交通管理系统和方法,该方法包括：在无人驾驶运货车辆沿路径运送货物完毕之后,从无人驾驶运货车辆在路径上行驶记录的日志文件中获取无人驾驶运货车辆停车等待的地理位置；对于所有路径中的每一条路径,分别统计预定时间段内该路径中停车等待次数最多的地理位置；再次规划从起点到终端的路径时,确定再次规划的路径是否经过等待次数最多的地理位置,如果未经过则确定使用该路径,如果经过等待次数最多的地理位置则重新规划路径。通过本申请解决相关技术中根据距离最短原则确定无人车运送路线有可能导致运送效率降低的问题,在一定程度上提高了无人车运送效率。(The application discloses wisdom logistics garden traffic management system and method based on GPS technique, the method includes: after the unmanned freight vehicle finishes transporting goods along the path, acquiring the geographical position of the unmanned freight vehicle for parking and waiting from a log file recorded by the unmanned freight vehicle in the traveling process on the path; for each path in all paths, respectively counting the geographical position with the maximum number of parking waiting times in the path within a preset time period; when the path from the starting point to the terminal is planned again, whether the planned path passes through the geographical position with the largest waiting times is determined, if not, the path is determined to be used, and if the geographical position with the largest waiting times passes, the path is planned again. The problem that the conveying efficiency is possibly reduced due to the fact that the conveying route of the unmanned vehicle is determined according to the shortest distance principle in the related technology is solved, and the conveying efficiency of the unmanned vehicle is improved to a certain extent.)

1. A GPS technology-based intelligent logistics park traffic management method is characterized by comprising the following steps:

acquiring a path for transporting goods by an unmanned freight vehicle in a logistics park, wherein the path is planned according to a starting point and an end point of the transported goods;

after the unmanned cargo vehicle finishes transporting the cargo along the path, acquiring the geographic position of the unmanned cargo vehicle for parking and waiting from a log file recorded by the unmanned cargo vehicle in the path;

for each path in all paths, respectively counting the geographical position with the maximum number of parking waiting times in the path within a preset time period;

and when the path from the starting point to the terminal is planned again, determining whether the planned path passes through the geographical position with the maximum waiting times, if not, determining to use the path, and if so, re-planning the path.

2. The method of claim 1, further comprising:

and sending the routes which do not comprise the geographical position with the largest waiting times to the unmanned cargo vehicle as the running routes of the unmanned cargo vehicle.

3. The method of claim 1 or 2, wherein the geographic location is obtained using a GPS mounted on the unmanned cargo vehicle.

4. The method of any one of claims 1 to 3, wherein the geographic location is obtained using a Beidou positioning System mounted on the unmanned cargo vehicle.

5. The utility model provides an wisdom logistics park traffic management system based on GPS technique which characterized in that includes:

the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring a path for the unmanned freight vehicle to transport cargos in a logistics park, and the path is planned according to a starting point and an end point for transporting cargos;

the second acquisition module is used for acquiring the geographic position of the unmanned cargo vehicle for parking and waiting from the log file of the driving record of the unmanned cargo vehicle on the path after the unmanned cargo vehicle finishes conveying the cargo along the path;

the statistical module is used for respectively counting the geographical position with the maximum parking waiting times in each path within a preset time period;

and the planning module is used for determining whether the re-planned path passes through the geographical position with the maximum waiting times or not when the path from the starting point to the terminal is planned again, determining to use the path if the path does not pass through the geographical position with the maximum waiting times, and re-planning the path if the path passes through the geographical position with the maximum waiting times.

6. The system of claim 5, further comprising:

a sending module, configured to send a route that does not include the geographic location with the highest number of waiting times among the plurality of driving routes to the unmanned cargo vehicle, as a running route of the unmanned cargo vehicle.

7. The system of claim 5 or 6, wherein the geographic location is obtained using a Beidou positioning System mounted on the unmanned cargo vehicle.

8. The system of any one of claims 5 to 7, wherein the geographic location is obtained using a GPS mounted on the unmanned cargo vehicle.

9. A memory for storing software, wherein the software is configured to perform the method of any one of claims 1 to 4.

10. A processor configured to execute software, wherein the software is configured to perform the method of any one of claims 1 to 4.

Technical Field

The application relates to the field of traffic management, in particular to an intelligent logistics park traffic management system and method based on a GPS technology.

Background

The logistics park is a place where various logistics facilities and different types of logistics enterprises are arranged in a spatially centralized manner in a plurality of transportation modes in a region where logistics operations are concentrated, and is also an aggregation point of the logistics enterprises with a certain scale and a plurality of service functions. With the development of times, logistics gardens will play an increasingly important role in the modern logistics industry.

The circulation of goods is also required in the logistics park, and with the development of unmanned technology, the transportation of the goods is basically carried out by using unmanned freight vehicles (hereinafter referred to as unmanned vehicles) in the logistics park. For the unmanned vehicles, the planning of the paths affects the transportation efficiency of the unmanned vehicles, in the related art, the transportation paths or routes of the unmanned vehicles are generally determined according to the principle that the distance is the shortest, and the planning method may cause congestion of the unmanned vehicles, so that the waiting time is prolonged, and the transportation efficiency is reduced.

Disclosure of Invention

The application provides an intelligent logistics park traffic management system and method based on GPS technology, and aims to solve the problem that the transportation efficiency is possibly reduced when an unmanned vehicle transportation route is determined according to the shortest distance principle in the related technology.

According to one aspect of the invention, an intelligent logistics park traffic management method based on GPS technology is provided, which comprises the following steps: acquiring a path for transporting goods by an unmanned freight vehicle in a logistics park, wherein the path is planned according to a starting point and an end point of the transported goods; after the unmanned cargo vehicle finishes transporting the cargo along the path, acquiring the geographic position of the unmanned cargo vehicle for parking and waiting from a log file recorded by the unmanned cargo vehicle in the path; for each path in all paths, respectively counting the geographical position with the maximum number of parking waiting times in the path within a preset time period; and when the path from the starting point to the terminal is planned again, determining whether the planned path passes through the geographical position with the maximum waiting times, if not, determining to use the path, and if so, re-planning the path.

Further, still include: and sending the routes which do not comprise the geographical position with the largest waiting times to the unmanned cargo vehicle as the running routes of the unmanned cargo vehicle.

Further, the geographic location is obtained using a GPS mounted on the unmanned cargo vehicle.

Further, the geographic location is obtained using a Beidou positioning system installed on the unmanned cargo vehicle.

According to another aspect of the present invention, there is also provided an intelligent logistics park traffic management system based on GPS technology, including: the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring a path for the unmanned freight vehicle to transport cargos in a logistics park, and the path is planned according to a starting point and an end point for transporting cargos; the second acquisition module is used for acquiring the geographic position of the unmanned cargo vehicle for parking and waiting from the log file of the driving record of the unmanned cargo vehicle on the path after the unmanned cargo vehicle finishes conveying the cargo along the path; the statistical module is used for respectively counting the geographical position with the maximum parking waiting times in each path within a preset time period; and the planning module is used for determining whether the re-planned path passes through the geographical position with the maximum waiting times or not when the path from the starting point to the terminal is planned again, determining to use the path if the path does not pass through the geographical position with the maximum waiting times, and re-planning the path if the path passes through the geographical position with the maximum waiting times.

Further, still include: a sending module, configured to send a route that does not include the geographic location with the highest number of waiting times among the plurality of driving routes to the unmanned cargo vehicle, as a running route of the unmanned cargo vehicle.

Further, the geographic location is obtained using a Beidou positioning system installed on the unmanned cargo vehicle.

Further, the geographic location is obtained using a GPS mounted on the unmanned cargo vehicle.

According to another aspect of the present application, there is also provided a memory for storing software for performing the above method.

According to another aspect of the present application, there is also provided a processor for executing software, wherein the software is configured to perform the above method.

The method comprises the following steps: acquiring a path for transporting goods by an unmanned freight vehicle in a logistics park, wherein the path is planned according to a starting point and an end point of the transported goods; after the unmanned cargo vehicle finishes transporting the cargo along the path, acquiring the geographic position of the unmanned cargo vehicle for parking and waiting from a log file recorded by the unmanned cargo vehicle in the path; for each path in all paths, respectively counting the geographical position with the maximum number of parking waiting times in the path within a preset time period; and when the path from the starting point to the terminal is planned again, determining whether the planned path passes through the geographical position with the maximum waiting times, if not, determining to use the path, and if so, re-planning the path. The problem that the conveying efficiency is possibly reduced due to the fact that the conveying route of the unmanned vehicle is determined according to the shortest distance principle in the related technology is solved, and the conveying efficiency of the unmanned vehicle is improved to a certain extent.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:

fig. 1 is a flowchart of an intelligent logistics park traffic management method based on GPS technology according to an embodiment of the present application.

Detailed Description

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, 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 disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

In this embodiment, a method for intelligent logistics park traffic management based on GPS technology is provided, and fig. 1 is a flowchart of a method for intelligent logistics park traffic management based on GPS technology according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:

step S102, obtaining a path for transporting goods by an unmanned cargo vehicle in a logistics garden, wherein the path is planned according to a starting point and an end point of the transported goods;

step S104, after the unmanned cargo vehicle finishes cargo transportation along the path, acquiring the geographic position of the unmanned cargo vehicle for parking and waiting from the log file of the driving record of the unmanned cargo vehicle on the path;

step S106, for each path in all paths, respectively counting the geographical position with the maximum number of parking waiting times in the path within a preset time period; for example, the geographical position with the largest number of parking wait times in each path within 30 days is counted by day, so that the geographical position can be avoided when planning again currently.

The system acquires information of the parking geographic position within 30 days from the current time point of starting to work every day, then traverses all starting points and end points according to the information, plans all possible path information, does not pass through the geographic position with the largest parking waiting times, and sends all the planned path information to the unmanned vehicle. In this case, the unmanned vehicles need to be networked only once to obtain all the route information. When the unmanned vehicle works, only the information of the terminal point and the starting point needs to be received. The unmanned vehicle is provided with an input device, a user can directly input the destination and the starting point through the input device, and the unmanned vehicle can directly obtain the path according to the starting point and the destination to start working at the moment.

As a preferred embodiment, in a route in which the a vehicle is parked at the first position and the second position respectively for waiting for the passage of another vehicle, the waiting times are T11 and T12 respectively, in the route, the B vehicle is parked at the first position and the third position respectively for waiting for the passage of another vehicle, the waiting times are T21 and T22, and the C vehicle is parked at the second position and the third position respectively for waiting for the passage of another vehicle, the waiting times are T31 and T32. At this time, the number of times of waiting at the first position, the second position, and the third position is two, and in this case, it may be determined according to the waiting time length, where the waiting time length at the first position is T11+ T21, the waiting time length at the second position is T12+ T31, and the waiting time length at the third position is T22+ T32, and the three positions with the shortest waiting time are taken as the geographical positions with the largest number of times of waiting.

The number of waits on each path in each hour during the last thirty days may be counted, for example, the number of waits in all paths from a to B is the first position at the maximum from 9 to 10. At this point the planned path avoids the first location during that time period. From 10 to 11, the second position is the most waiting time of all paths from A to B, and the planned path avoids the second position in the time period. The method comprises the steps of obtaining the running time of the unmanned vehicle, planning a path according to the position with the largest waiting times in the time range of the time, and planning the obtained path to avoid the position with the largest waiting times in the time range. The time frame is hourly.

Step S108, when the path from the starting point to the terminal is planned again, whether the planned path passes through the geographical position with the maximum waiting times is determined, if not, the path is determined to be used, and if the geographical position with the maximum waiting times passes, the path is planned again.

As an embodiment that can be added, if all the re-planned paths pass through the geographic position with the largest number of waiting times, the total number of the geographic positions that need to wait on each of all the re-planned paths is obtained, and the path with the smallest total number is selected from all the paths as the driving path.

In yet another embodiment, a model may be trained through machine learning, where the model is a neural network model and is obtained by training using multiple sets of training data, where each set of training data includes input data and output data, the input data is path information, and the output data is time required for completing a path corresponding to the path information; after training, the model can be used, all the re-planned paths are input into the model, the driving time of each re-planned path is obtained, and then the path with the shortest driving time is selected as the driving path.

Optionally, judging whether the path with the least total number and the path with the shortest time are the same driving path, and if so, taking the path as the driving path; and if the paths are not the same driving paths, selecting the path with the shortest distance from the two paths as the driving path.

The problem that the conveying efficiency is possibly reduced due to the fact that the conveying route of the unmanned vehicle is determined according to the shortest distance principle in the related technology is solved through the steps, and the conveying efficiency of the unmanned vehicle is improved to a certain extent.

Preferably, the method further comprises the following steps: and sending the routes which do not comprise the geographical position with the largest waiting times to the unmanned cargo vehicle as the running routes of the unmanned cargo vehicle.

Preferably, the geographic location is obtained using a GPS mounted on the unmanned cargo vehicle.

Preferably, the geographic location is obtained using a beidou positioning system mounted on the unmanned cargo vehicle.

In this embodiment, a system is further provided, where modules in the system correspond to the steps of the method described above, which have already been described in the above embodiments and are not described herein again.

The embodiment provides an wisdom logistics garden traffic management system based on GPS technique, includes: the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring a path for the unmanned freight vehicle to transport cargos in a logistics park, and the path is planned according to a starting point and an end point for transporting cargos; the second acquisition module is used for acquiring the geographic position of the unmanned cargo vehicle for parking and waiting from the log file of the driving record of the unmanned cargo vehicle on the path after the unmanned cargo vehicle finishes conveying the cargo along the path; the statistical module is used for respectively counting the geographical position with the maximum parking waiting times in each path within a preset time period; and the planning module is used for determining whether the re-planned path passes through the geographical position with the maximum waiting times or not when the path from the starting point to the terminal is planned again, determining to use the path if the path does not pass through the geographical position with the maximum waiting times, and re-planning the path if the path passes through the geographical position with the maximum waiting times.

Preferably, the method further comprises the following steps: a sending module, configured to send a route that does not include the geographic location with the highest number of waiting times among the plurality of driving routes to the unmanned cargo vehicle, as a running route of the unmanned cargo vehicle.

Preferably, the geographic location is obtained using a beidou positioning system mounted on the unmanned cargo vehicle.

Preferably, the geographic location is obtained using a GPS mounted on the unmanned cargo vehicle.

In this embodiment, a memory is provided for storing software for performing the above-described method.

In this embodiment, a processor is provided for executing software for performing the above-described method.

It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.

An embodiment of the present invention provides a storage medium on which a program or software is stored, the program implementing the above method when executed by a processor. The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.

The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.