Dispatching system for warehouse intelligent trolley

Abstract

The application discloses a scheduling system for warehouse intelligent trolley, relates to the optimal scheduling technical field of warehouse carrying, and comprises a warehouse information collection module, a real-time data collection module, a task data receiving module, a task distribution module and a task scheduling module. The warehouse information collection module is arranged to collect road information in the warehouse in advance. The real-time data collection module is arranged to collect basic information of the warehouse intelligent trolley in a working state in real time. The task data receiving module is arranged to receive a new goods carrying task in real time. The task distribution module is arranged to distribute one warehouse intelligent trolley to each new carrying task. The task scheduling module is arranged to re-distribute a scheduling route when each warehouse intelligent trolley receives a new carrying task. The optimal route scheduling problem of the warehouse intelligent trolley in the warehouse is solved.

Description

Technical Field

[0001] This invention belongs to the field of warehousing and relates to task scheduling technology, specifically a scheduling system for intelligent warehousing vehicles. Background Technology

[0002] Most warehouses have large spaces, and goods are stacked in multiple locations according to their type. When intelligent warehouse vehicles move goods, they need to know the starting and ending points of the goods in advance in order to move them automatically. Obviously, the load-bearing capacity of the intelligent warehouse vehicles is limited, and the current position of the intelligent warehouse vehicle may be far from the starting point of the goods. If the moving tasks of the intelligent warehouse vehicles are arbitrarily arranged, it may lead to a significant reduction in moving efficiency. Therefore, a suitable moving task scheduling system is particularly important.

[0003] Therefore, a scheduling system for intelligent warehouse vehicles is proposed. Summary of the Invention

[0004] This invention aims to solve at least one of the technical problems existing in the prior art. To this end, this invention proposes a scheduling system for intelligent warehouse vehicles. This system includes: a warehouse information collection module to pre-collect road information in the warehouse; a real-time data collection module to collect basic information of intelligent warehouse vehicles in operation; a task data receiving module to receive new cargo handling tasks in real time; a task allocation module to allocate one intelligent warehouse vehicle to each new handling task; and a task scheduling module to reallocate the scheduling route when each intelligent warehouse vehicle receives a new handling task. This solves the problem of optimal route scheduling for intelligent warehouse vehicles within a warehouse.

[0005] To achieve the above objectives, an embodiment of the first aspect of the present invention provides a scheduling system for intelligent warehouse vehicles, comprising a warehouse information collection module, a real-time data collection module, a task data receiving module, a task allocation module, and a task scheduling module; wherein the modules are connected to each other via electrical and / or wireless network means.

[0006] The warehouse information collection module is mainly used to collect road information in the warehouse in advance;

[0007] The warehouse information collection module collects road information in the warehouse by: using a drone to traverse the warehouse and photograph the roads; using real-scene 3D modeling technology to model the photographed warehouse road images; and labeling the roads in the modeled image; further, by abstracting the roads, cargo handling start points, and destinations into an undirected graph, the Dijkstra algorithm is used to calculate the shortest route from the start point to the destination; the warehouse information collection module sends the collected warehouse road information and shortest route information to the task allocation module and the task scheduling module.

[0008] The real-time data collection module is mainly used to collect basic information of the intelligent warehouse vehicle in operation in real time.

[0009] The real-time data collection module collects basic information about the intelligent warehouse vehicle by installing a weight sensor and a positioning device on each intelligent warehouse vehicle. The weight sensor and positioning device transmit the weight of the goods carried by the intelligent warehouse vehicle and the location of the intelligent warehouse vehicle to the task allocation module wirelessly in real time.

[0010] The task data receiving module is mainly used to receive new cargo handling tasks in real time.

[0011] The task data receiving module receives new cargo handling tasks in the following way: whenever a new handling task occurs in the warehouse, a handling task is generated by the management personnel or intelligent machinery; the handling task includes the starting position, ending position, and weight of the cargo; the task data receiving module sends the received new handling task to the task allocation module.

[0012] The task allocation module is mainly used to allocate a warehouse intelligent vehicle for each new handling task.

[0013] The task allocation module assigns a warehouse intelligent vehicle to each new handling task, including the following steps:

[0014] Step S1: The task allocation module collects the maximum load capacity of each intelligent warehouse vehicle in advance;

[0015] Step S2: The task allocation module receives new handling tasks and the location and current load capacity of each warehouse intelligent vehicle; wherein, the current load capacity includes the weight of handling tasks for which the warehouse intelligent vehicle has received the task but has not yet gone to pick up the goods;

[0016] Step S3: The task allocation module removes warehouse intelligent vehicles that do not meet the load conditions from all warehouse intelligent vehicles; the load conditions are that the sum of the current load capacity of the warehouse vehicle and the weight of the handling task is not greater than the maximum load capacity.

[0017] Step S4: The task allocation module calculates the sum of the route distances for each remaining warehouse smart car to first go to the starting point of the handling task and then to the ending point of the handling task;

[0018] Step S5: Obviously, for a task allocation module that does not have global information, the greedy algorithm that selects the one with the shortest travel distance is the most effective. The task allocation module selects the warehouse intelligent vehicle with the smallest sum of distances as the vehicle to perform the handling task and sends the handling task to the task scheduling module of the corresponding warehouse intelligent vehicle.

[0019] The task scheduling module is mainly used to reallocate scheduling routes when each intelligent warehouse vehicle receives a new handling task.

[0020] The task scheduling module adjusts the scheduling order of the transport task queue, including the following steps:

[0021] Step P1: Collect all current destinations of the warehouse smart vehicle, including the starting addresses of handling tasks that have not yet been picked up and the destinations of handling tasks that have not yet been transported.

[0022] Step P2: Divide the handling tasks of the warehouse intelligent vehicle into two categories: picked-up goods and unpicked goods; for handling tasks with picked-up goods, mark the task endpoint as dw; for handling tasks with unpicked goods, mark the task start point as sw and the task endpoint as dw; where w represents the task number; it can be understood that for handling tasks with unpicked goods, the warehouse intelligent vehicle must first go to its corresponding start point before it can go to its endpoint.

[0023] Step P3: The warehouse intelligent vehicle first goes to the nearest task start point sw or task end point dw; it should be noted that if it goes to the task end point dw, it should first determine whether it is a handling task for which goods have not been picked up. If so, it needs to find the nearest task start point or task end point again.

[0024] Step P4: Obtain the shortest route between each starting point and ending point in the real-world model; mark the starting point of each trip of the storage intelligent car as i, the ending point as j, and the distance traveled from starting point i to ending point j as Kij; use n to represent the nth trip of the storage intelligent car; set the variable xnij to indicate whether the storage intelligent car travels from starting point i to ending point j in the nth trip; if xnij = 1, it means yes; xnij = 0, it means no.

[0025] Then the total distance traveled by the intelligent vehicle is ∑ i ∑ j Kij*xnij; This formula is the optimization objective;

[0026] The constraints that the storage-enabled intelligent vehicle needs to be subject to during operation include:

[0027]

[0028]

[0029]

[0030] ∑ j x1sj=1; (4)

[0031]

[0032] The above planning problem is solved using planning methods such as genetic algorithm and ant colony algorithm to obtain the optimal solution set X; the solution set X contains the driving route of the storage intelligent car in the nth time, that is, the solution set X is the scheduling route of the storage intelligent car.

[0033] Compared with the prior art, the beneficial effects of the present invention are:

[0034] 1. This invention allocates tasks to the intelligent warehouse vehicle by summing the distances between the intelligent warehouse vehicle and the starting and ending points of each new handling task, thereby improving task allocation efficiency.

[0035] 2. This invention transforms the task scheduling problem of each intelligent warehouse vehicle into a planning problem, and obtains the optimal planned route by using a planning solution tool; thus greatly improving the cargo handling efficiency of each intelligent warehouse vehicle. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0037] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] like Figure 1 As shown, the scheduling system for intelligent warehouse vehicles includes a warehouse information collection module, a real-time data collection module, a task data receiving module, a task allocation module, and a task scheduling module; wherein, the modules are connected to each other via electrical and / or wireless network means.

[0039] Understandably, most warehouses have large spaces, and goods are stacked in multiple locations depending on their type. When intelligent warehouse vehicles move goods, they need to know the starting and ending points of the goods in advance before they can automatically move them. Obviously, the load-bearing capacity of the intelligent warehouse vehicles is limited, and the current position of the intelligent warehouse vehicle may be far from the starting point of the goods, resulting in a significant reduction in handling efficiency. Therefore, a suitable handling task scheduling system is particularly important.

[0040] The warehouse information collection module is mainly used to collect road information in the warehouse in advance;

[0041] In a preferred embodiment, the warehouse information collection module collects road information in the warehouse by: using a drone to traverse the warehouse and photograph the roads; using real-scene 3D modeling technology to model the photographed warehouse road images; and labeling the roads in the modeled image; further, by abstracting the roads, cargo handling start points, and destinations into an undirected graph, the Dijkstra algorithm is used to calculate the shortest route from the start point to the destination; the warehouse information collection module sends the collected warehouse road information and shortest route information to the task allocation module and the task scheduling module.

[0042] The real-time data collection module is mainly used to collect basic information of the intelligent warehouse vehicle in operation in real time.

[0043] It is understandable that each intelligent warehouse vehicle has a limited weight capacity, and the working state of the intelligent warehouse vehicle is to move around in various locations in the warehouse to transport goods.

[0044] In a preferred embodiment, the real-time data collection module collects basic information about the intelligent warehouse vehicle by installing a weight sensor and a positioning device on each intelligent warehouse vehicle; the weight sensor and positioning device transmit the weight of the goods carried by the intelligent warehouse vehicle and the location of the intelligent warehouse vehicle to the task allocation module wirelessly in real time.

[0045] The task data receiving module is mainly used to receive new cargo handling tasks in real time.

[0046] It is understandable that warehouse handling tasks are often not generated at the same location or simultaneously, but rather in different locations and in different batches, due to limitations in the speed of goods production and the carrying capacity of intelligent warehouse vehicles; therefore, there is a possibility that new handling tasks may be generated at any time.

[0047] In a preferred embodiment, the task data receiving module receives new cargo handling tasks in the following manner: whenever a new handling task occurs in the warehouse, a handling task is generated by the management personnel or intelligent machinery; the handling task includes the starting position, ending position, and weight of the cargo; the task data receiving module sends the received new handling task to the task allocation module.

[0048] The task allocation module is mainly used to allocate a warehouse intelligent vehicle for each new handling task.

[0049] In a preferred embodiment, the task allocation module assigns a warehouse smart cart to each new handling task, including the following steps:

[0050] Step S1: The task allocation module collects the maximum load capacity of each intelligent warehouse vehicle in advance;

[0051] Step S2: The task allocation module receives new handling tasks and the location and current load capacity of each warehouse intelligent vehicle; wherein, the current load capacity includes the weight of handling tasks for which the warehouse intelligent vehicle has received the task but has not yet gone to pick up the goods;

[0052] Step S3: The task allocation module removes warehouse intelligent vehicles that do not meet the load conditions from all warehouse intelligent vehicles; the load conditions are that the sum of the current load capacity of the warehouse vehicle and the weight of the handling task is not greater than the maximum load capacity.

[0053] Step S4: The task allocation module calculates the sum of the route distances for each remaining warehouse smart car to first go to the starting point of the handling task and then to the ending point of the handling task;

[0054] Step S5: Obviously, for a task allocation module that does not have global information, the greedy algorithm that selects the one with the shortest travel distance is the most effective. The task allocation module selects the warehouse intelligent vehicle with the smallest sum of distances as the vehicle to perform the handling task and sends the handling task to the task scheduling module of the corresponding warehouse intelligent vehicle.

[0055] The task scheduling module is mainly used to reallocate scheduling routes when each intelligent warehouse vehicle receives a new handling task.

[0056] Understandably, when each intelligent warehouse vehicle receives a new handling task, it may already have several other handling tasks; therefore, it is necessary to adjust the scheduling order of the new handling task queue.

[0057] In a preferred embodiment, the task scheduling module adjusts the scheduling order of the transport task queue by including the following steps:

[0058] Step P1: Collect all current destinations of the warehouse smart vehicle, including the starting addresses of handling tasks that have not yet been picked up and the destinations of handling tasks that have not yet been transported.

[0059] Step P2: Divide the handling tasks of the warehouse intelligent vehicle into two categories: picked-up goods and unpicked goods; for handling tasks with picked-up goods, mark the task endpoint as dw; for handling tasks with unpicked goods, mark the task start point as sw and the task endpoint as dw; where w represents the task number; it can be understood that for handling tasks with unpicked goods, the warehouse intelligent vehicle must first go to its corresponding start point before it can go to its endpoint.

[0060] Step P3: The warehouse intelligent vehicle first goes to the nearest task start point sw or task end point dw; it should be noted that if it goes to the task end point dw, it should first determine whether it is a handling task for which goods have not been picked up. If so, it needs to find the nearest task start point or task end point again.

[0061] Step P4: Obtain the shortest route between each starting point and ending point in the real-world model; mark the starting point of each trip of the storage intelligent car as i, the ending point as j, and the distance traveled from starting point i to ending point j as Kij; use n to represent the nth trip of the storage intelligent car; set the variable xnij to indicate whether the storage intelligent car travels from starting point i to ending point j in the nth trip; if xnij = 1, it means yes; xnij = 0, it means no.

[0062] Then the total distance traveled by the intelligent vehicle is ∑ i ∑ j Kij*xnij; This formula is the optimization objective;

[0063] The constraints that the storage-enabled intelligent vehicle needs to be subject to during operation include:

[0064]

[0065]

[0066]

[0067] ∑ j x1sj=1; (4)

[0068]

[0069] In this context, (1) is used to restrict each route to be traveled only once; in (2), j is the starting point of the handling task that has not yet picked up the goods, and j' is the end point corresponding to j; (2) is used to restrict the warehouse intelligent vehicle to go to the starting point of the handling task to pick up the goods before it can go to the corresponding end point; (3) is used to restrict the warehouse intelligent vehicle to start from the end point of the previous trip each time; in (4), s is the initial position of the warehouse intelligent vehicle; (4) is used to restrict the warehouse intelligent vehicle to start from the initial position; and (5) is used to restrict the warehouse intelligent vehicle to travel only one route each time.

[0070] The above planning problem is solved using planning methods such as genetic algorithm and ant colony algorithm to obtain the optimal solution set X; the solution set X contains the driving route of the storage intelligent car in the nth time, that is, the solution set X is the scheduling route of the storage intelligent car.

[0071] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A dispatching system for warehouse intelligent trolleys, characterized in that, The warehouse information collection module, the real-time data collection module, the task data receiving module, the task allocation module, and the task scheduling module are connected through electrical and / or wireless network modes; The warehouse information collection module is used for collecting road information in the warehouse in advance; the warehouse information collection module sends the collected warehouse road information and shortest route information to the task allocation module and the task scheduling module; The real-time data collection module is used for collecting the basic information of the intelligent warehouse car in a working state in real time; the real-time data collection module sends the weight of the goods carried by the intelligent warehouse car and the position of the intelligent warehouse car to the task allocation module in real time through a wireless mode; The task data receiving module is used for receiving new goods carrying tasks in real time; the task data receiving module sends the received new carrying tasks to the task allocation module; The task allocation module is used for allocating an intelligent warehouse car for each new carrying task and sending the carrying task to the task scheduling module of the corresponding intelligent warehouse car; The task scheduling module is used for constructing a planning model and solving the planning model to re-allocate and schedule a route when each intelligent warehouse car receives a new carrying task; The task scheduling module includes the following steps: Step P1: collecting all current destinations of the intelligent warehouse car, the destinations including a carrying task starting address without taking goods and a carrying task ending address without transporting goods; Step P2: dividing the carrying tasks of the intelligent warehouse car into two categories: taking goods and not taking goods; for the carrying task of taking goods, marking the task ending address as dw; for the carrying task of not taking goods, marking the task starting address as sw and the task ending address as dw; wherein w represents the task number; Step P3: the intelligent warehouse car first goes to the nearest task starting address sw or task ending address dw; Step P4: obtaining the shortest route between each starting address and ending address in the real scene model; marking the starting address of each travel of the intelligent warehouse car as i, marking the ending address as j, and marking the distance from the starting address i to the ending address j as Kij; using n to represent the nth travel of the intelligent warehouse car; setting the variable xnij to represent whether the intelligent warehouse car travels from the starting address i to the ending address j in the nth time; if xnij = 1, it means yes; if xnij = 0, it means no; The total travel distance of the warehouse intelligent trolley is ; the formula is an optimization target; The constraints that the intelligent warehouse car needs to meet during travel include: ； ； ； ； ； wherein j' is the ending address corresponding to j, and s is the initial position of the intelligent warehouse car; using a planning solving method to solve the above planning model to obtain an optimal solution set X; the solution set X contains the travel route of the intelligent warehouse car in the nth time, that is, the solution set X is the scheduling route of the intelligent warehouse car.

2. The dispatch system for warehouse intelligent trolleys of claim 1, wherein, The warehouse information collection module collects the road information in the warehouse in the following manner: traversing the warehouse by using a drone, and taking pictures of the roads in the warehouse; using a real scene three-dimensional modeling technology to model the photographed warehouse road images; and labeling the roads in the modeled model; by abstracting the roads, the starting point and the end point of the goods carrying into the mode of an undirected graph, using Dijkstra algorithm to calculate the shortest route from the starting position to the end position.

3. The dispatch system for warehousing intelligent trolleys of claim 1, wherein, The real-time data collection module collects the basic information of the warehouse intelligent trolley in the following manner: a weight sensor and a positioning device are installed on each warehouse intelligent trolley.

4. The dispatch system for warehousing intelligent trolleys of claim 1, wherein, The task data receiving module receives a new goods carrying task in the following manner: a carrying task is generated by the management personnel or the intelligent machine every time a new carrying task appears in the warehouse; the carrying task includes the starting position, the end position and the weight of the goods carrying.

5. The dispatch system for warehousing intelligent trolleys of claim 1, wherein, The task allocation module allocates a warehouse intelligent trolley for each new carrying task, which includes the following steps: Step S1: the task allocation module pre-collects the maximum load of each warehouse intelligent trolley; Step S2: the task allocation module receives a new carrying task, and the position and the current load of each warehouse intelligent trolley; wherein the current load includes the weight of the carrying task that the warehouse intelligent trolley has received but has not gone to pick up the goods; Step S3: the task allocation module eliminates the warehouse intelligent trolleys that do not meet the load condition from all the warehouse intelligent trolleys; the load condition is that the sum of the current carrying capacity of the warehouse intelligent trolley and the weight of the carrying task is not greater than the maximum load; Step S4: the task allocation module calculates the sum of the route distances of each remaining warehouse intelligent trolley from the carrying task starting point to the carrying task end point; Step S5: the task allocation module selects the warehouse intelligent trolley with the smallest distance sum as the carrying task execution vehicle, and sends the carrying task to the task scheduling module of the corresponding warehouse intelligent trolley.

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