CN118071124B

CN118071124B - Dispatching method, device and equipment for intelligent guiding trolley of large-sized automatic wharf

Info

Publication number: CN118071124B
Application number: CN202410480000.XA
Authority: CN
Inventors: 周少锐; 赵敏; 陈继红; 刘浩; 黄炳林; 廖麒杰; 刘家国; 李树沛
Original assignee: Sun Yat Sen University
Current assignee: Sun Yat Sen University
Priority date: 2024-04-22
Filing date: 2024-04-22
Publication date: 2024-07-02
Anticipated expiration: 2044-04-22
Also published as: CN118071124A

Abstract

The application discloses a dispatching method, a dispatching device and dispatching equipment for intelligent guided vehicles of a large-scale automatic wharf, which are used for acquiring first configuration information, wherein the first configuration information comprises information of the number of intelligent guided vehicles, a bank bridge, a pile area, a charging area and position information of the bank bridge, the pile area and the charging area; acquiring second configuration information of the intelligent guiding trolley, wherein the second configuration information comprises initial electric quantity, charging threshold electric quantity, full electric quantity, first power consumption and first speed when the intelligent guiding trolley is loaded, second power consumption and second speed when the intelligent guiding trolley is not loaded, and standard consumed time for loading and unloading boxes at a quay bridge; determining first distance data of a quay crane and a pile area, and second distance data between the pile area and a charging area; and acquiring task indexes, establishing a scheduling model of the intelligent guiding trolley under the multi-constraint condition, and solving to obtain a corresponding scheduling scheme. The method is beneficial to improving the intelligentization and the fine degree of scheduling, and has better practicability. The method and the device can be widely applied to the technical field of equipment scheduling.

Description

Dispatching method, device and equipment for intelligent guiding trolley of large-sized automatic wharf

Technical Field

The application relates to the technical field of equipment scheduling, in particular to a scheduling method, device and equipment for an intelligent guide trolley of a large-scale automatic wharf.

Background

Currently, with the rapid development of informatization technology and intelligent control technology, related applications have gradually been integrated into the life of people, and various services are provided for people. For example, the intelligent guided vehicle is a main device in a logistics transportation system, and in the context of a port and a dock, the intelligent guided vehicle connects a quay bridge at the front of the dock and a heap inside the dock, and is a main transportation device of the dock.

In the related art, the existing intelligent guided vehicle dispatching strategy technology is generally applied to urban logistics or warehouse factory background, and the running logic is simpler. With the attention of carbon emission and environmental protection problems around the world, the intelligent guiding trolley is changed from a form of taking petroleum fuel as a main energy source to a form of taking electric energy as a main energy source, and the existing intelligent guiding trolley scheduling strategy is always used for optimizing and scheduling only on route problems, so that the limitation of energy consumption is ignored, the practical application effect is poor, and complex and high-strength wharf operation scenes are difficult to deal with.

In summary, the technical problems in the related art are to be improved.

Disclosure of Invention

The embodiment of the application provides a dispatching method, a dispatching device and dispatching equipment for intelligent guiding trolleys of a large-scale automatic wharf.

An aspect of the embodiments of the present application provides a dispatching method for intelligent guided vehicles of a large-scale automated dock, where the method includes:

Acquiring first configuration information of a wharf; the first configuration information comprises first number information of a quay, second number information of a pile area, third number information of a charging area, fourth number information of an intelligent guiding trolley and position information of each quay, the pile area and the charging area, wherein the first configuration information comprises first number information of the quay, second number information of the pile area, third number information of the charging area, fourth number information of the intelligent guiding trolley and position information of each quay, the pile area and the charging area;

Acquiring second configuration information of each intelligent guiding trolley; the second configuration information comprises initial electric quantity, charging threshold electric quantity, full electric quantity, first power consumption and first speed of a preset unit distance when the box is loaded, second power consumption and second speed of a preset unit distance when the box is not loaded, and standard consumed time for loading and unloading the box at the shore bridge of the intelligent guiding trolley;

determining first distance data between each quay crane and the heap area and second distance data between each heap area and the charging area according to the position information;

Acquiring a task index, and establishing a scheduling model of the intelligent guided vehicle under a multi-constraint condition by taking the shortest sum of the running distances of all the intelligent guided vehicles as an optimized objective function according to the task index, the first configuration information, the second configuration information, the first distance data and the second distance data;

and solving the scheduling model to obtain a corresponding scheduling scheme, and scheduling each intelligent guiding trolley according to the scheduling scheme.

On the other hand, the embodiment of the application provides a dispatching device of the intelligent guiding trolley of the large-scale automatic wharf, which comprises the following components:

the first acquisition unit is used for acquiring first configuration information of the wharf; the first configuration information comprises first number information of a quay, second number information of a pile area, third number information of a charging area, fourth number information of an intelligent guiding trolley and position information of each quay, the pile area and the charging area, wherein the first configuration information comprises first number information of the quay, second number information of the pile area, third number information of the charging area, fourth number information of the intelligent guiding trolley and position information of each quay, the pile area and the charging area;

a second obtaining unit for obtaining second configuration information of each intelligent guiding trolley; the second configuration information comprises initial electric quantity, charging threshold electric quantity, full electric quantity, first power consumption and first speed of a preset unit distance when the box is loaded, second power consumption and second speed of a preset unit distance when the box is not loaded, and standard consumed time for loading and unloading the box at the shore bridge of the intelligent guiding trolley;

A calculation unit for determining first distance data between each of the quads and the heap area and second distance data between each of the heap areas and the charging area according to the position information;

The establishing unit is used for acquiring task indexes, and establishing a scheduling model of the intelligent guiding trolley under the multi-constraint condition by taking the shortest sum of the running distances of all the intelligent guiding trolley as an optimized objective function according to the task indexes, the first configuration information, the second configuration information, the first distance data and the second distance data;

And the processing unit is used for solving the scheduling model to obtain a corresponding scheduling scheme, and scheduling each intelligent guiding trolley according to the scheduling scheme.

In another aspect, an embodiment of the present application provides an electronic device, including a processor and a memory;

the memory is used for storing a computer program;

And the processor executes the computer program to realize the dispatching method of the intelligent guide trolley of the large-scale automatic wharf.

In another aspect, an embodiment of the present application provides a computer readable storage medium storing a computer program executed by a processor to implement the foregoing method for scheduling a large automated dock intelligent guided vehicle.

The embodiment of the application at least comprises the following beneficial effects: the application provides a dispatching method, a dispatching device and dispatching equipment for intelligent guiding trolleys of a large-scale automatic wharf, and first configuration information of the wharf is obtained; the first configuration information comprises first number information of a quay, second number information of a pile area, third number information of a charging area, fourth number information of an intelligent guiding trolley and position information of each quay, the pile area and the charging area, wherein the first configuration information comprises first number information of the quay, second number information of the pile area, third number information of the charging area, fourth number information of the intelligent guiding trolley and position information of each quay, the pile area and the charging area; acquiring second configuration information of each intelligent guiding trolley; the second configuration information comprises initial electric quantity, charging threshold electric quantity, full electric quantity, first power consumption and first speed of a preset unit distance when the box is loaded, second power consumption and second speed of a preset unit distance when the box is not loaded, and standard consumed time for loading and unloading the box at the shore bridge of the intelligent guiding trolley; determining first distance data between each quay crane and the heap area and second distance data between each heap area and the charging area according to the position information; acquiring a task index, and establishing a scheduling model of the intelligent guided vehicle under a multi-constraint condition by taking the shortest sum of the running distances of all the intelligent guided vehicles as an optimized objective function according to the task index, the first configuration information, the second configuration information, the first distance data and the second distance data; and solving the scheduling model to obtain a corresponding scheduling scheme, and scheduling each intelligent guiding trolley according to the scheduling scheme. The intelligent dispatching method can realize intelligent dispatching of the intelligent guiding trolley under the condition of considering the electric energy consumption of the intelligent guiding trolley, can be suitable for complex and high-strength wharf operation scenes, is beneficial to improving the intellectualization and the fineness of dispatching, and has better practicability.

Drawings

The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate and do not limit the application.

FIG. 1 is a schematic diagram of an implementation environment of a dispatching method for intelligent guided vehicles of a large-scale automated dock provided in an embodiment of the present application;

Fig. 2 is a schematic flow chart of a dispatching method of a large-scale automated dock intelligent guiding trolley provided in the embodiment of the application;

Fig. 3 is a schematic diagram of a movement track of an intelligent guiding trolley according to an embodiment of the present application;

Fig. 4 is a schematic diagram of a search solution according to an embodiment of the present application.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with embodiments of the application, but are merely examples of apparatuses and methods consistent with aspects of embodiments of the application as detailed in the accompanying claims.

It is to be understood that the terms "first," "second," and the like, as used herein, may be used to describe various concepts, but are not limited by these terms unless otherwise specified. These terms are only used to distinguish one concept from another. For example, the first information may also be referred to as second information, and similarly, the second information may also be referred to as first information, without departing from the scope of embodiments of the present application. The words "if", as used herein, may be interpreted as "at … …" or "at … …" or "in response to a determination", depending on the context.

The terms "at least one", "a plurality", "each", "any" and the like as used herein, at least one includes one, two or more, a plurality includes two or more, each means each of the corresponding plurality, and any one means any of the plurality.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the application only and is not intended to be limiting of the application.

Before describing embodiments of the present application in detail, some of the terms and expressions that are referred to in the embodiments of the present application will be described first, and the terms and expressions that are referred to in the embodiments of the present application are applicable to the following explanation.

An intelligent guided vehicle (INTELLIGENT GUIDED VEHICLE, IGV) is an automated navigation vehicle, also known as AGV (Automated Guided Vehicle). Intelligent guided vehicles are commonly used in industry and logistics, and can automatically move articles or goods in factories, warehouses, hospitals, and the like. Intelligent guided vehicles are often equipped with lidar, sensors, cameras, etc. devices for sensing the surrounding environment and avoiding obstacles. They can also navigate their own travel routes through preset paths, landmarks or navigation systems. The intelligent guiding trolley can carry out tasks such as autonomous transportation, carrying and sorting as required, improves logistics efficiency, and reduces labor cost and logistics time. Intelligent guided vehicles play an important role in industrial settings, being an important component of intelligent manufacturing and logistics automation. The system can be in networking cooperation with other equipment, robots and systems to realize optimization and coordination of automatic production and logistics processes. With the continuous development of technology, the application range and the functions of the intelligent guiding trolley are wider and wider, and a more efficient, flexible and intelligent solution is brought to the modern industry and logistics field.

In view of the above, the embodiment of the application provides a dispatching method, a device and equipment for intelligent guided vehicles of a large-sized automatic wharf, and the method can realize intelligent dispatching of the intelligent guided vehicles under the condition of considering the electric energy consumption of the intelligent guided vehicles, can be suitable for a complex and high-strength wharf operation scene, is beneficial to improving the intellectualization and the fineness of dispatching, and has better practicability.

The following describes in detail the implementation of the embodiments of the present application with reference to the drawings. First, a dispatching method for intelligent guided vehicles of a large-scale automated dock provided in the embodiment of the application is described with reference to the accompanying drawings.

Referring to fig. 1, fig. 1 is a schematic diagram illustrating an implementation environment of a dispatching method for intelligent guided vehicles of a large-scale automated dock according to an embodiment of the present application. In this implementation environment, the main hardware and software body includes a terminal device 110 and a background server 120. The terminal device 110 and the background server 120 are in communication connection.

Specifically, the dispatching method of the intelligent guided vehicle of the large-scale automated dock provided by the embodiment of the application can be independently executed on the side of the terminal equipment 110, can be independently executed on the side of the background server 120, or can be executed based on data interaction between the terminal equipment 110 and the background server 120.

The terminal device 110 of the above embodiment may include, but is not limited to, a mobile phone, a computer, an intelligent voice interaction device, an intelligent home appliance, a vehicle-mounted terminal, an aircraft, and the like.

The background server 120 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs (Content Delivery Network, content delivery networks), basic cloud computing services such as big data and artificial intelligence platforms, and the like.

In addition, the background server 120 may also be a node server in a blockchain network.

A communication connection may be established between the terminal device 110 and the background server 120 through a wireless network or a wired network. The wireless network or wired network may be configured as the internet, using standard communication techniques and/or protocols, or any other network including, for example, but not limited to, a local area network (Local Area Network, LAN), metropolitan area network (Metropolitan Area Network, MAN), wide area network (Wide Area Network, WAN), mobile, wired or wireless network, a private network, or any combination of virtual private networks. The software and hardware main bodies can adopt the same communication connection mode or different communication connection modes, and the application is not particularly limited.

Of course, it can be understood that the implementation environment in fig. 1 is only some optional application scenarios of the scheduling method of the intelligent guided vehicle for a large-scale automated dock provided in the embodiment of the present application, and the actual application is not fixed to the software and hardware environment shown in fig. 1.

The following describes and describes in detail a scheduling method for intelligent guided vehicles of a large-scale automated dock provided in the embodiment of the present application in combination with the above description of the implementation environment.

As shown in fig. 2, in the embodiment of the present application, a dispatching method for a large-scale automated dock intelligent guidance trolley is provided, and the dispatching method for the large-scale automated dock intelligent guidance trolley can be applied to the terminal device 110 or the background server 120 shown in fig. 1. Referring to fig. 2, the dispatching method for the intelligent guided vehicle of the large-scale automated dock provided in the embodiment of the present application specifically includes, but is not limited to, steps 210 to 250:

Step 210, obtaining first configuration information of a wharf; the first configuration information comprises first number information of a quay, second number information of a pile area, third number information of a charging area, fourth number information of an intelligent guiding trolley and position information of each quay, the pile area and the charging area, wherein the first configuration information comprises first number information of the quay, second number information of the pile area, third number information of the charging area, fourth number information of the intelligent guiding trolley and position information of each quay, the pile area and the charging area;

In this step, when the intelligent guided vehicle is applied to dispatching, first, the configuration information of the dock where the intelligent guided vehicle to be dispatched is located may be obtained. For a dock of a port, an intelligent guiding trolley is used for connecting a quay bridge of a dock front edge and a stacking area in the dock, for a shipping process, containers in the stacking area are loaded onto the intelligent guiding trolley through a field bridge, then the intelligent guiding trolley runs near the quay bridge of the dock front edge, and the quay bridge lifts the containers on the intelligent guiding trolley onto a cargo ship; for the process of unloading, after the cargo ship is on shore, the shore bridge lifts the boxes on the cargo ship onto the intelligent guiding trolley, the intelligent guiding trolley runs to a designated stacking area of a storage yard, and the stacking area places the boxes on the intelligent guiding trolley to a designated position through the yard bridge.

Meanwhile, it should be noted that, since the intelligent guiding trolley generally adopts an electromotive device, energy needs to be supplemented, that is, when the electric quantity of the intelligent guiding trolley is consumed to a certain threshold value due to work, charging operation needs to be performed to a designated position, and the designated position is marked as a charging area. Therefore, it will be appreciated that various facilities included at the dock may include a quay bridge, a heap area, a charging area, and intelligent lead trolleys that travel between the quay bridge and the heap area, transport boxes, and complete energy replenishment in the charging area. In the embodiment of the application, the acquired first configuration information may include number information of the shore bridge, number information of the heap area, number information of the charging area, and number information of the intelligent guiding trolley, wherein the number information of the shore bridge is recorded as first number information, the number information of the heap area is recorded as second number information, the number information of the charging area is recorded as third number information, and the number information of the intelligent guiding trolley is recorded as fourth number information. In addition to the number information, position information of each quay crane, heap area and charging area is acquired.

Step 220, obtaining second configuration information of each intelligent guiding trolley; the second configuration information comprises initial electric quantity, charging threshold electric quantity, full electric quantity, first power consumption and first speed of a preset unit distance when the box is loaded, second power consumption and second speed of a preset unit distance when the box is not loaded, and standard consumed time for loading and unloading the box at the shore bridge of the intelligent guiding trolley;

In this step, when the dispatching task of the intelligent guiding trolley is performed, configuration information of each intelligent guiding trolley is also acquired and recorded as second configuration information. Here, because the energy consumption and the energy supplementing situation of the intelligent guiding trolley need to be considered, the second configuration information acquired in the embodiment of the application can include the initial electric quantity, the charging threshold electric quantity and the full electric quantity of the intelligent guiding trolley, wherein the initial electric quantity refers to the electric quantity of the intelligent guiding trolley when the scheduling task is currently executed; the charging threshold electric quantity refers to the lower limit electric quantity of the intelligent guiding trolley which needs to be subjected to energy supplementing, namely when the intelligent guiding trolley reaches the charging threshold electric quantity, the intelligent guiding trolley needs to be charged in a charging area; the full electric quantity is the highest electric quantity which can be achieved by the intelligent guiding trolley, and when the intelligent guiding trolley is charged to the full electric quantity in the charging area, the intelligent guiding trolley is continuously put into a transportation task.

In the embodiment of the application, the acquired second configuration information may further include power consumption and speed of the intelligent guiding trolley at a predetermined unit distance when the intelligent guiding trolley loads the boxes, where the default intelligent guiding trolley loads the boxes with identical specifications, that is, the same weight, and the predetermined unit distance can be flexibly set according to actual requirements, which is not limited by the application. The power consumption of the intelligent guiding trolley when the box is loaded is recorded as first power consumption, and the corresponding speed is recorded as first speed. In contrast, the power consumption and the speed of the intelligent guiding trolley, which travel a preset unit distance when the box is not loaded, can be respectively recorded as second power consumption and second speed.

In addition, it should be noted that when the intelligent guiding trolley works in the pile area, the operation time of the field bridge in the pile area is faster, and it can be considered that the intelligent guiding trolley can immediately execute related requirements when reaching the pile area, and at the shore bridge, the intelligent guiding trolley and the goods for the service of the shore bridge are relatively more, which often needs to consume longer time. Therefore, in the embodiment of the application, the standard consumption time for loading and unloading the box at the shore bridge by the intelligent guiding trolley can be counted and is recorded as a part of the second configuration information.

Step 230, determining first distance data between each quay bridge and the pile area and second distance data between each pile area and the charging area according to the position information;

In the step, for the obtained position information of each quay crane, heap area and charging area, the moving distance of the intelligent guiding trolley under each possible task can be calculated. Here, it should be noted that, generally, the charging area of the intelligent guiding trolley is disposed in the yard or is located closer to the yard, and the intelligent guiding trolley tends to pass through the stacking area during the process of going from the shore bridge to the charging area. Therefore, referring to fig. 3, fig. 3 is a schematic diagram of a movement track of an intelligent guiding trolley according to an embodiment of the present application, and in an embodiment of the present application, the movement track of the intelligent guiding trolley at each predetermined period may be simplified into six types: from the heap to the quay, from the quay to the heap, from the heap to the charging area, charging continues in the charging area, from the charging area to the heap, and intelligent guidance of the virtual tasks of the trolley. In fig. 3, task a represents a task of the intelligent guiding trolley to perform from a heap area to a quay bridge, task b represents a task of the intelligent guiding trolley to perform from the quay bridge to the heap area, task c represents a task of the intelligent guiding trolley to perform from the heap area to a charging area, task e represents a task of the intelligent guiding trolley to perform from the charging area to the heap area, task m represents a task of the intelligent guiding trolley to perform charging continuously in the charging area, and task w represents a virtual task of the intelligent guiding trolley, namely the intelligent guiding trolley directly performs the next task, and the task is virtual.

In the embodiment of the present application, as can be seen from the schematic diagram of the movement track shown in fig. 3, for each intelligent guiding trolley, the possible movement distance between each bridge and the pile area and the distance between each pile area and the charging area are the distances, so in the embodiment of the present application, the distance data between each bridge and the pile area can be determined according to the previously obtained position information, and the distance data between each pile area and the charging area is determined and the distance data is recorded as the first distance data, and the distance data between each pile area and the charging area is determined and the second distance data. Specifically, in the embodiment of the application, when the first distance data and the second distance data are determined, the positions of the midpoints of the quay crane, the heap area and the charging area can be determined according to the position information, and then the first distance data and the second distance data are determined based on the positions of the midpoints.

Step 240, acquiring a task index, and establishing a scheduling model of the intelligent guided vehicle under a multi-constraint condition by taking the shortest sum of the running distances of all the intelligent guided vehicles as an optimized objective function according to the task index, the first configuration information, the second configuration information, the first distance data and the second distance data;

in this step, after the foregoing various information and data are obtained, a predetermined task index is also required to be obtained when a specific scheduling task is executed. In the embodiment of the application, the task index can be the number of boxes to be loaded and unloaded in each stack area. After the task index is obtained, a scheduling model of the intelligent guided vehicle under the multi-constraint condition can be established by taking the shortest sum of the running distances of all the intelligent guided vehicles as an optimized objective function based on the task index according to the obtained first configuration information, second configuration information, first distance data and second distance data.

Specifically, in the embodiment of the present application, when determining the objective function and establishing the scheduling model, the model variables and parameters need to be defined first, where the parameters of the part may be determined according to the first configuration information and the second configuration information, for example, the first number information of the quay bridge may be expressed asThe quads are numbered sequentially according to the first number information, and the obtained quay set can be expressed asWhich can pass throughIndexing, i.e. using symbolsTo represent a quay bridge; the second number of heap areas may be expressed asThe heap areas are numbered sequentially according to the second number information, and the obtained heap area set can be expressed asThe heap may be passed throughIndexing, i.e. using symbolsTo represent the heap area; the third number information of the charging region can be expressed asThe charging areas are numbered sequentially according to the third number information, and the obtained charging area set can be expressed asThe charging area can pass throughIndexing, i.e. using symbolsTo represent a charging region; the fourth number information of the intelligent guided vehicle can be expressed asThe intelligent guided vehicles are numbered sequentially according to the fourth number information, and the obtained intelligent guided vehicle set can be expressed asIntelligent guiding trolley can pass throughIndexing, i.e. using symbolsTo represent an intelligent guided vehicle.

In the embodiment of the application, four types of parameters are determined according to the first distance data and the second distance data, which are respectively，，，Wherein, the method comprises the steps of, wherein,Representing slave heap areasLanded bridgeIs used for the distance of (a),Representing a slave quay bridgeTo the heap areaIs used for the distance of (a),Representing slave heap areasTo the charging areaIs used for the distance of (a),Representing the secondary charging areaTo the heap areaIs a distance of (3). In addition to the above parameters, task segments are definedEach of which isRepresents an intelligent guiding trolleyIs a task selection of the same. For example, the number of the cells to be processed,Can represent intelligent guiding trolleyIn the first placeSelective execution of individual task segments from heapLanded bridgeIs a travel distance corresponding to the task. The predetermined task index may be expressed as. In the embodiment of the application, the following steps are providedDefined as the total number of tasks executed by the intelligent guided vehicle, then the tasks are numbered according to the order of task execution, and the task set corresponding to the intelligent guided vehicle can be determined to be expressed asEach task can pass throughTo index.

In the embodiment of the application, a plurality of variable parameters related to the intelligent guiding trolley can be determined according to the second configuration information, for example, the initial electric quantity of the intelligent guiding trolley can be expressed asThe charge threshold level may be expressed asThe full charge can be expressed asThe first power consumption of the case when loaded and traveling a predetermined unit distance can be expressed asThe first speed may be expressed asThe second power consumption for traveling a predetermined unit distance without loading the box can be expressed asThe second speed may be expressed asThe standard time consumption for loading and unloading boxes at the quay bridge can be expressed asThe charge amount per charge can be expressed asAfter the intelligent guiding trolley reaches the charging area, the charging time can be expressed as。

In the embodiment of the application, some decision variables and intermediate auxiliary variables are also introduced for constructing the model. Specifically, these variables include:

Parameters (parameters) A sufficiently large number greater than a predetermined first threshold; a parameter N, a sufficient fraction, less than a predetermined second threshold;

a binary variable, if the intelligent guiding trolley In the first placeIn the secondary action, the task from the heap area beta to the quay bridge theta is selectedTaking 1, otherwise taking 0;

a binary variable, if the intelligent guiding trolley In the first placeIn the secondary action, the task from the quay bridge theta to the heap zone beta is selectedTaking 1, otherwise taking 0;

a binary variable, if the intelligent guiding trolley In the first placeIn the secondary action, the range from the stack region beta to the charging region is selectedIs to (1)Taking 1, otherwise taking 0;

a binary variable, if the intelligent guiding trolley In the first placeIn the secondary action, the secondary charging area is selectedTo the task of heap area betaTaking 1, otherwise taking 0;

a binary variable, if the intelligent guiding trolley In the first placeIn the secondary action, the charging area is selectedTask of charging, thenTaking 1, otherwise taking 0;

a binary variable, if the intelligent guiding trolley In the first placeWhen acting next time, the virtual task is selected to be carried out, namely the next task is directly carried out, thenTaking 1, otherwise taking 0.

Intermediate auxiliary variables of binary type, comprising、、、、Floating-point type intermediate auxiliary variableThese variables are mainly used to make the model build in a linearized manner, without practical significance.

Based on the above description of the variables, in the embodiment of the present application, the shortest sum of the running distances of all intelligent guiding trolleys is used as the optimized objective function, and the objective function may be expressed as:

In the embodiment of the application, constraint conditions of a scheduling model may include running state constraint of the intelligent guiding trolley, task index constraint, electric quantity constraint of the intelligent guiding trolley and first-come first-serve constraint of the intelligent guiding trolley, and specifically, constraint conditions of the intelligent guiding trolley may be represented by the following formula:

In the constraints represented by the above respective formulas, Is different fromIs the number of the other intelligent guiding trolley,Is different fromIs the number of another task segment. The constraint condition expressed by the formula (2) belongs to the operation state constraint of the intelligent guiding trolley, and is used for ensuring that the intelligent guiding trolley is in one of six operation states, namely from a heap area to a shore bridge, from the shore bridge to the heap area, from the heap area to a charging area, charging is continued in the charging area, and virtual tasks of the intelligent guiding trolley from the charging area to the heap area and the intelligent guiding trolley are performed in any task section. The constraint represented by the formula (3) is a task index constraint for ensuring that the loading and unloading tasks of the respective heap areas are satisfied. The constraints represented by formulas (4) - (7) belong to the operational state constraints of the intelligent guided vehicle, which are used to ensure that the intelligent guided vehicle does not repeatedly perform tasks from the heap to the quay, from the quay to the heap, from the heap to the charging area, and from the charging area to the heap in the task section before and after the intelligent guided vehicle. The constraint conditions expressed by the formulas (8) - (10) belong to the running state constraint of the intelligent guiding trolley and are used for ensuring that the front and back secondary tasks of the intelligent guiding trolley meet the logic relationship. Formulas (11) - (15) are used to initialize the initial mission segment of the intelligent guided vehicle. The constraint conditions represented by the formulas (16) and (17) belong to the electric quantity constraint of the intelligent guiding trolley, and the electric quantity of the intelligent guiding trolley is defined. The constraint conditions represented by formulas (18) - (21) belong to the electric quantity constraint of the intelligent guiding trolley, and are used for ensuring that the intelligent guiding trolley is charged to a charging area when the electric quantity is lower than a charging threshold electric quantity under the condition that the intelligent guiding trolley is empty after executing a certain task. The constraints represented by formulas (22) - (26) pertain to the power constraint of the intelligent guided vehicle for ensuring that the intelligent guided vehicle in the charging area is fully charged. Formulas (27) - (29) define the time spent on intelligent guided vehicles without regard to queuing. The constraints represented by formulas (30) - (37) pertain to a first-come first-served constraint of the intelligent guided vehicle for ensuring that the intelligent guided vehicle meets the first-come first-served condition and updating the time consumption of the vehicle.

And 250, solving the scheduling model to obtain a corresponding scheduling scheme, and scheduling each intelligent guiding trolley according to the scheduling scheme.

In the step, after the scheduling model is established, the scheduling model can be solved to obtain a corresponding scheduling scheme, and each intelligent guiding trolley is scheduled according to the scheduling scheme. It can be appreciated that in the embodiment of the application, the intelligent dispatching of the intelligent guiding trolley can be realized under the condition of considering the electric energy consumption of the intelligent guiding trolley, the intelligent dispatching method and the intelligent dispatching device can be suitable for complex and high-strength wharf operation scenes, are beneficial to improving the intellectualization and the fineness of dispatching, and have better practicability.

Specifically, in some embodiments, the solving the scheduling model to obtain a corresponding scheduling scheme, and scheduling each intelligent guiding trolley according to the scheduling scheme includes:

solving the scheduling model to obtain an initial feasible solution;

According to the initial feasible solution, carrying out iterative optimization solution by calling a constructed algorithm based on large-area search to obtain a target optimal solution;

and obtaining a corresponding scheduling scheme according to the target optimal solution, and scheduling each intelligent guiding trolley according to the scheduling scheme.

In the embodiment of the application, when solving the scheduling model, an algorithm (Large Neighborhood Search, LNS) based on large-area search can be used, and the LNS algorithm is a heuristic search algorithm for solving the combination optimization problem, and searches for a better solution by searching a neighborhood of the current optimal solution in a large solution space based on the concept of large-area search. The LNS algorithm generally includes the steps of: 1. initializing: an initial solution is selected and an initial search radius is set. 2. Iterative search: searching in the neighborhood of the current solution to find a better solution and updating the current solution. 3. Disturbance: introducing some random disturbance in the neighborhood of the current solution, and jumping out of the local optimal solution. 4. Acceptance criteria: whether to accept the new solution is determined based on the acceptance criteria. 5. Termination condition: termination conditions, such as time limit or number of iterations, are set. The LNS algorithm has the advantage of being able to search within a large solution space, so that it is more likely to find a globally optimal solution. Meanwhile, the method can jump out of the local optimal solution and avoid sinking into the local optimal solution.

Specifically, in the embodiment of the present application, the scheduling model may be first solved to obtain an initial feasible solutionObjective function value corresponding to initial feasible solutionThen, the scheduling model is defined asThenRepresenting the objective function value of the model.Representing a random number between [0,1] definingFunction, function value is letThe vehicle intelligent guidance cart does not access the maximum of possible reductions generated by point P (point P is a heap). Function ofThe heap access point is removed, specifically as equation (38). Similarly, the shore bridge and the charging area can be removed, and the charging area is represented by a formula (40) and a formula (42) respectively.

It will be appreciated that removing the heap access point involves both the quay and the charging area, and removing either the quay or the charging area involves only the relationship of the two. Thus removing heap access points from the solution space is more variable, and the function is taken into account for computational convenienceBy a function ofTo represent.

In the embodiment of the application, the search can be realized by using a simulated annealing algorithm. The initial feasible solution is acquired first, and can be solved by using a CPLEX solver or by using other methods. The initial feasible solution is recorded asThen initializing parameters, andAssignment toObjective function value corresponding to initial feasible solutionAssignment to. Setting the initial problem temperature of simulated annealing to be. Then a loop is executed until a stop condition is satisfied, one is selected at the beginning of the loopBy the method ofTransforming to obtain a new feasible solutionCalculated according to the above. If a new optimal solutionIs found and usedMethod to make itThe domain of the solution searches until it iteratesInstead, no new optimal solution is found. Otherwise, if the new solution is better than the current solution, or worse than the current solution but meets the acceptance criteria for simulated annealing, the new solution is accepted. The search process is shown in fig. 4. Fig. 4 shows the search process in one iteration from an initial solution to accepting a new solution. Furthermore, the temperature of the simulated annealing is updated last in each iteration, whereinIs the cooling rate and is usedTo record the initial pairA method of transformation. At the end of the algorithm, the optimal solution after iterationAnd the corresponding optimal objective function valueIs returned.

In the embodiment of the application, useTo represent the transformation of the solution in its solution space.The method is that let theThe greatest possible reduction in the occurrence of vehicle intelligent guided vehicles not accessing point P, whereinAnd P is a group of numbers, which are in combination withThe methods are in one-to-one correspondence. Specifically, an array is first createdWherein the elements and classesAssociated, classStore let intelligent guide dollySpecific information of P-point is not accessed. Then ordering is performed so that the groupsClass corresponding to the first element of (a)A kind of electronic deviceThe function value is the largest, i.e. let the arrayIs pressed by information inThe function values range from large to small. A loop is then performed until a stop condition is met. In the cycle, let firstClass with largest function valueCorresponding stored intelligent guiding trolleyWithout accessing the P-Point, this condition is added to the constraint, and then this class is removedAnd arrange the arraysThe corresponding element is cleared, then the solution is adjusted so that it meets all constraints, making it a viable solution, if the adjustment is still not met, then the loop is continued, using the new classAnd obtain the corresponding feasible solution.

The embodiment of the application also provides a dispatching device of the intelligent guiding trolley of the large-scale automatic wharf, which comprises the following components:

It can be understood that the content in the dispatching method of the intelligent guided vehicle of the large-sized automated dock shown in fig. 2 is applicable to the embodiment of the device, the specific functions of the embodiment of the device are the same as those of the dispatching method of the intelligent guided vehicle of the large-sized automated dock shown in fig. 2, and the achieved beneficial effects are the same as those of the dispatching method of the intelligent guided vehicle of the large-sized automated dock shown in fig. 2.

The embodiment of the application also discloses an electronic device, which comprises:

At least one processor;

At least one memory for storing at least one program;

The at least one program, when executed by the at least one processor, causes the at least one processor to implement a scheduling method for a large automated dock intelligent guided vehicle as shown in fig. 2.

It can be understood that the content in the dispatching method of the intelligent guided vehicle of the large-sized automated dock shown in fig. 2 is suitable for the embodiment of the electronic device, the functions specifically realized by the embodiment of the electronic device are the same as those of the dispatching method of the intelligent guided vehicle of the large-sized automated dock shown in fig. 2, and the beneficial effects achieved by the dispatching method of the intelligent guided vehicle of the large-sized automated dock shown in fig. 2 are the same as those achieved by the dispatching method of the intelligent guided vehicle of the large-sized automated dock shown in fig. 2.

The electronic device of the embodiment of the application can be a terminal device, a computer device or a server device.

The embodiment of the application also discloses a computer readable storage medium, wherein a program executable by a processor is stored, and the program executable by the processor is used for realizing the dispatching method of the intelligent guided vehicle of the large-scale automatic wharf shown in fig. 2 when being executed by the processor.

It can be understood that the content in the dispatching method of the intelligent guided vehicle for the large-sized automated dock shown in fig. 2 is applicable to the embodiment of the computer readable storage medium, the specific function of the embodiment of the computer readable storage medium is the same as that of the dispatching method of the intelligent guided vehicle for the large-sized automated dock shown in fig. 2, and the achieved beneficial effect is the same as that of the dispatching method of the intelligent guided vehicle for the large-sized automated dock shown in fig. 2.

In some alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present application are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed, and in which sub-operations described as part of a larger operation are performed independently.

Furthermore, while the application is described in the context of functional modules, it should be appreciated that, unless otherwise indicated, one or more of the functions and/or features may be integrated in a single physical device and/or software module or may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary to an understanding of the present application. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be apparent to those skilled in the art from consideration of their attributes, functions and internal relationships. Accordingly, one of ordinary skill in the art can implement the application as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative and are not intended to be limiting upon the scope of the application, which is to be defined in the appended claims and their full scope of equivalents.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable storage medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.

In the foregoing description of the present specification, reference has been made to the terms "one embodiment/example", "another embodiment/example", "certain embodiments/examples", and the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the application, the scope of which is defined by the claims and their equivalents.

While the preferred embodiment of the present application has been described in detail, the present application is not limited to the embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present application, and the equivalent modifications or substitutions are intended to be included in the scope of the present application as defined in the appended claims.

Claims

1. A method for scheduling intelligent guided vehicles for a large automated dock, the method comprising:

Solving the scheduling model to obtain a corresponding scheduling scheme, and scheduling each intelligent guiding trolley according to the scheduling scheme;

The objective function is expressed as:

；

In the method, in the process of the invention, The number of the stack area is indicated,The value set of (2) is，The second number of information is represented by a second number,The number of the quay bridge is indicated,The value set of (2) is Q=，The first number of information is represented by a first number,A number indicating the charging area is provided,The value set of (C) is C =，The information indicating the third number of pieces of information,Representing the task segment of the intelligent guided vehicle,The value set of (2) is t=，Representing the total number of tasks,The number of the intelligent guiding trolley is represented,The value set of (2) is，Representing fourth number information; Represent the first The intelligent guiding trolley is at the firstSelect execution of task segment from the firstTo the heap areaThe distance travelled by the mission of the quay bridge,Represent the firstThe intelligent guiding trolley is at the firstIndividual task segment selection execution slaveShore bridge to the firstThe travel distance corresponding to the task of each stack area,Represent the firstThe intelligent guiding trolley is at the firstSelect execution of task segment from the firstStack area to the firstThe distance travelled by the task of the individual charging areas,Represent the firstThe intelligent guiding trolley is at the firstSelect execution of task segment from the firstThe charging areas to the firstThe driving distance corresponding to the task of each pile area; Is a binary variable, if The intelligent guiding trolley is at the firstFrom the first task segmentTo the heap areaThe task of the quay bridge is thatTaking 1, otherwise taking 0; Is a binary variable, if The intelligent guiding trolley is at the firstWith each task segment, a slave is selectedShore bridge to the firstTasks of individual heap areasTaking 1, otherwise taking 0; Is a binary variable, if The intelligent guiding trolley is at the firstFrom the first task segmentStack area to the firstTasks of the charging areasTaking 1, otherwise taking 0; Is a binary variable, if The intelligent guiding trolley is at the firstFrom the first task segmentThe charging areas to the firstTasks of individual heap areasTaking 1, otherwise taking 0;

Constraint conditions of the scheduling model comprise running state constraint, task index constraint, electric quantity constraint and first-come first-serve constraint of the intelligent guide trolley; the running state constraint is used for constraining the intelligent guiding trolley to be in any task section, from a pile area to a quay bridge, from the quay bridge to the pile area, from the pile area to a charging area, continuously charging in the charging area, from the charging area to the pile area and one of virtual tasks of the intelligent guiding trolley, and the running state constraint is also used for constraining the intelligent guiding trolley not to repeatedly execute the tasks from the pile area to the quay bridge, from the quay bridge to the pile area, from the pile area to the charging area and from the charging area to the pile area in the front and back task sections; the task index constraint is used for constraining the intelligent guiding trolley to meet loading and unloading tasks of each pile area; the electric quantity constraint is used for constraining the intelligent guiding trolley to be charged in a charging area when the electric quantity is lower than a charging threshold electric quantity under the condition that the intelligent guiding trolley is empty after executing a certain task, and the electric quantity constraint is also used for constraining the intelligent guiding trolley in the charging area to be fully charged; the first-come first-serve constraint is used for constraining the intelligent guiding trolley to meet the first-come first-serve condition;

Solving the scheduling model to obtain a corresponding scheduling scheme, and scheduling each intelligent guiding trolley according to the scheduling scheme, wherein the method comprises the following steps:

solving the scheduling model to obtain an initial feasible solution;

according to the initial feasible solution, carrying out iterative optimization solution by calling a constructed large neighborhood search algorithm to obtain a target optimal solution;

Obtaining a corresponding scheduling scheme according to the target optimal solution, and scheduling each intelligent guiding trolley according to the scheduling scheme;

In the process of carrying out iterative optimization solving by calling the constructed large neighborhood search algorithm, transforming the space of the solution by removing the access point, and determining the maximum value which is possibly reduced and is generated by removing the access point, wherein the formula for determining the maximum value which is possibly reduced and is generated by removing the access point comprises the following steps:

；

In the method, in the process of the invention, Represent the firstThe intelligent guiding trolley does not access the firstA maximum value of possible reductions generated by the individual stack areas;

；

In the method, in the process of the invention, Represent the firstThe intelligent guiding trolley does not access the firstThe maximum value of the possible reduction produced by the individual quay bridges;

；

In the method, in the process of the invention, Represent the firstThe intelligent guiding trolley does not access the firstA maximum value of possible reduction generated by the individual charging areas.

2. The method for dispatching intelligent guided vehicles for large automated docks according to claim 1, wherein determining first distance data between each of the quay and the heap area and second distance data between each of the heap area and the charging area according to the position information comprises:

Determining a first coordinate of a center point of each shore bridge, a second coordinate of a center point of each pile area and a third coordinate of a center point of each charging area according to the position information;

Determining first distance data between the quay crane and the heap area according to the first coordinates and the second coordinates;

and determining second distance data between the pile area and the charging area according to the second coordinate and the third coordinate.

3. The method for scheduling large automated dock intelligent guided vehicles of claim 1, wherein the task index is determined by:

inquiring the number of boxes to be loaded and unloaded in each pile area under the current task stage;

and establishing task indexes according to the number of boxes to be loaded and unloaded in each pile area.

4. The method for scheduling large automated dock intelligent guided vehicles of claim 1, wherein the large neighborhood search algorithm comprises a simulated annealing algorithm.

5. A dispatching device for intelligent guided vehicles of large-scale automated docks, the device comprising:

The processing unit is used for solving the scheduling model to obtain a corresponding scheduling scheme, and scheduling each intelligent guiding trolley according to the scheduling scheme;

The objective function is expressed as:

；

In the method, in the process of the invention, The number of the stack area is indicated,The value set of (2) is，The second number of information is represented by a second number,The number of the quay bridge is indicated,The value set of (2) is Q=，The first number of information is represented by a first number,A number indicating the charging area is provided,The value set of (C) is C =，The information indicating the third number of pieces of information,Representing the task segment of the intelligent guided vehicle,The value set of (2) is t=，Representing the total number of tasks,The number of the intelligent guiding trolley is represented,Is a value set of (a)，Representing fourth number information; Represent the first The intelligent guiding trolley is at the firstSelect execution of task segment from the firstTo the heap areaThe distance travelled by the mission of the quay bridge,Represent the firstThe intelligent guiding trolley is at the firstIndividual task segment selection execution slaveShore bridge to the firstThe travel distance corresponding to the task of each stack area,Represent the firstThe intelligent guiding trolley is at the firstSelect execution of task segment from the firstStack area to the firstThe distance travelled by the task of the individual charging areas,Represent the firstThe intelligent guiding trolley is at the firstSelect execution of task segment from the firstThe charging areas to the firstThe driving distance corresponding to the task of each pile area; Is a binary variable, if The intelligent guiding trolley is at the firstFrom the first task segmentTo the heap areaThe task of the quay bridge is thatTaking 1, otherwise taking 0; Is a binary variable, if The intelligent guiding trolley is at the firstWith each task segment, a slave is selectedShore bridge to the firstTasks of individual heap areasTaking 1, otherwise taking 0; Is a binary variable, if The intelligent guiding trolley is at the firstFrom the first task segmentStack area to the firstTasks of the charging areasTaking 1, otherwise taking 0; Is a binary variable, if The intelligent guiding trolley is at the firstFrom the first task segmentThe charging areas to the firstTasks of individual heap areasTaking 1, otherwise taking 0;

solving the scheduling model to obtain an initial feasible solution;

；

6. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the method of any of claims 1 to 4 when executing the computer program.

7. A computer readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method of any one of claims 1 to 4.