CN118333525A - Intelligent logistics warehouse management system - Google Patents
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Abstract
The invention provides an intelligent logistics warehouse management system, which comprises: the scene construction unit is used for constructing a virtual scene in the warehouse; the article information acquisition unit is used for acquiring basic information of articles to be put in and put out; the path planning unit is used for constructing an initial moving path of the logistics vehicle in the virtual scene according to the basic information of the object; the path removing unit is used for removing the initial moving path influencing warehouse management; the path screening unit is used for screening the rest initial moving paths according to a sparrow searching algorithm and obtaining an optimal path; in path planning, the number of initial moving paths is reduced through path elimination so as to reduce the calculated amount of a path screening unit, the path screening unit adopts a sparrow searching algorithm to conduct optimizing, an optimal path can be obtained, a logistics vehicle moves according to the optimal path, article warehouse entry and article warehouse exit can be achieved, the moving paths are shortened, and article warehouse entry and article warehouse exit efficiency is improved.
Description
Technical Field
The invention relates to the technical field of intelligent logistics, in particular to an intelligent logistics warehouse management system.
Background
With the rapid development of electronic commerce and the increasing complexity of globalization supply chains, the logistics storage industry also faces greater challenges, in the traditional logistics storage industry, manual operation and management are mostly relied on, the efficiency is low, mistakes are easy to occur, and with the rapid development of technologies such as the Internet of things, big data and artificial intelligence in recent years, strong technical support is provided for intelligent transformation of the logistics storage industry, in the logistics storage industry, an intelligent logistics vehicle can be used for carrying out article warehouse entry and exit, the intelligent logistics vehicle can automatically plan a path according to articles which are put in and taken out in the warehouse, however, when the current intelligent logistics vehicle carries out path planning, the conventional logistics vehicle mainly refers to independently carrying out warehouse entry on articles or carrying out warehouse exit on articles, namely, when the logistics vehicle has a warehouse entry request and a warehouse exit request, the path planning is required to be carried out separately twice, the warehouse entry and the warehouse exit cannot be combined together at the same time, the route of the logistics vehicle is longer, the loss is increased, and the warehouse exit efficiency is also reduced.
Disclosure of Invention
In view of the above, the invention provides an intelligent logistics warehouse management system which can combine the warehouse in and warehouse out together to plan the path of a logistics vehicle, improve the warehouse in and warehouse out efficiency of articles and shorten the working time of the logistics vehicle.
The technical scheme of the invention is realized as follows:
An intelligent logistics warehouse management system, comprising:
The scene construction unit is used for constructing a virtual scene in the warehouse;
The article information acquisition unit is used for acquiring basic information of articles to be put in and put out;
the path planning unit is used for constructing an initial moving path of the logistics vehicle in the virtual scene according to the basic information of the object;
the path removing unit is used for removing the initial moving path influencing warehouse management;
The path screening unit is used for screening the rest initial moving paths according to a sparrow searching algorithm and obtaining an optimal path;
The scene construction unit, the path planning unit, the path eliminating unit and the path screening unit are sequentially connected in data, and the article information acquisition unit is connected with the path planning unit in data.
Preferably, the execution steps of the scene construction unit are as follows:
S11, acquiring basic information of a warehouse, wherein the basic information comprises warehouse areas and warehouse area division;
Step S12, generating a virtual scene by adopting virtual scene generating software based on basic information of a warehouse;
And S13, acquiring an entrance and an exit of the warehouse and an internal road, and adding the entrance and the internal road to the virtual scene.
Preferably, the basic information of the articles to be put in and taken out includes the storage area and the size of the storage area.
Preferably, the path planning unit performs the following steps:
Step S21, determining a plurality of warehousing positions of the articles to be warehoused in the virtual scene and a plurality of storage positions of the articles to be warehoused in the virtual scene according to the belonging warehousing areas;
s22, selecting a warehouse-in position closest to a warehouse entry as a starting point;
Step S23, randomly selecting the rest storage positions and the warehouse-in positions, and connecting the rest storage positions and the warehouse-in positions in the virtual scene to form a moving path;
and step S24, taking the warehouse outlet as an end point, and connecting with the moving path to form an initial moving path.
Preferably, in the step S23, when the storage position and the warehouse-in position are selected, the size of the article to be warehouse-out in the selected storage position needs to be smaller than the size of the article to be warehouse-in the previous warehouse-in position.
Preferably, the path rejection unit performs the steps of:
Step S31, determining the current path planning time;
Step S32, inputting path planning time into a trained neural network, and processing the path planning time by the neural network to obtain a busy area;
And step S33, eliminating the initial moving path passing through the busy area.
Preferably, the neural network is obtained by training historical activity records of all areas in a warehouse, the activity records comprise movement tracks of logistics vehicles, operation start time and operation end time, the activity records are divided into training sets and test sets, the neural network is trained according to the training sets, and the accuracy of the neural network is tested through the test sets.
Preferably, the path screening unit performs the steps of:
Step S41, initializing sparrow population, and setting searching times and scale;
step S42, randomly selecting an initial moving path, and calculating the adaptability value of the sparrow based on the shortest path as an index;
Step S43, selecting another initial moving path, calculating the fitness value, comparing with the previously calculated fitness value, and reserving the initial moving path with larger fitness value;
and S44, performing iterative search, and outputting the initial movement path with the maximum fitness value as an optimal path after obtaining the initial movement path.
Preferably, the number of searches is the number of initial moving paths after the elimination.
Compared with the prior art, the invention has the beneficial effects that:
The intelligent logistics warehouse management system is used for planning a moving path of an intelligent logistics vehicle in a warehouse, firstly, constructing a virtual scene of the warehouse so as to acquire the types of articles stored in each area in the warehouse, then acquiring basic information of the articles to be warehoused and ex-warehouse, determining the specific positions of the articles in the virtual scene, constructing a plurality of initial moving paths in the virtual scene, wherein each initial moving path passes through the articles to be warehoused and ex-warehouse, and some initial moving paths do not meet the requirements of warehouse management, so that after the articles are rejected, optimizing is performed by adopting a sparrow search algorithm, an optimal path is obtained, the intelligent logistics vehicle can move according to the optimal path to carry out warehouse-in and ex-warehouse of the articles, the intelligent logistics vehicle is prevented from moving for a plurality of times while realizing warehouse-in and ex-warehouse, the working time is shortened, the loss is reduced, and the article ex-warehouse efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only preferred embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an intelligent logistics warehouse management system of the present invention;
FIG. 2 is a flow chart of a scenario construction unit of the intelligent logistics warehouse management system of the present invention;
FIG. 3 is a flow chart of a path planning unit of the intelligent logistics warehouse management system of the present invention;
FIG. 4 is a flow chart of a path rejection unit of the intelligent logistics warehouse management system of the present invention;
FIG. 5 is a flow chart of a path screening unit of the intelligent logistics warehouse management system of the present invention;
in the figure, 1 is a scene construction unit, 2 is an article information acquisition unit, 3 is a path planning unit, 4 is a path eliminating unit, and 5 is a path screening unit.
Detailed Description
For a better understanding of the technical content of the present invention, a specific example is provided below, and the present invention is further described with reference to the accompanying drawings.
Referring to fig. 1 to 5, the intelligent logistics warehouse management system provided by the present invention includes:
A scene construction unit 1 for constructing a virtual scene in a warehouse;
an article information obtaining unit 2 for obtaining basic information of articles to be put in and put out;
a path planning unit 3, configured to construct an initial movement path of the logistics vehicle in the virtual scene according to the basic information of the object;
a path rejection unit 4 for rejecting an initial moving path affecting warehouse management;
The path screening unit 5 is used for screening the rest initial moving paths according to a sparrow searching algorithm and obtaining an optimal path;
The scene construction unit 1 is respectively and sequentially connected with the path planning unit 3, the path eliminating unit 4 and the path screening unit 5 in a data mode, and the article information acquisition unit 2 is connected with the path planning unit 3 in a data mode.
The invention relates to an intelligent logistics warehouse management system, which is used for planning a moving path of an intelligent logistics vehicle in a warehouse, wherein the intelligent logistics vehicle has an article warehouse-in task and a warehouse-out task, articles are required to be stored in a designated position in the warehouse and the articles in the designated position in the warehouse are required to be taken away, so when the moving path of the intelligent logistics vehicle is planned, firstly, a scene construction unit 1 acquires basic information of the warehouse and is used for constructing a virtual scene, the virtual scene comprises an area for storing the articles so as to take the articles in the corresponding area out of the warehouse, or the articles are placed in the corresponding area for warehouse-in, after the virtual scene of the warehouse is constructed, an article information acquisition unit 2 acquires the basic information of the articles to be warehoused and taken out of the warehouse so as to determine the specific position of the articles to be warehoused and taken out of the warehouse in the virtual scene, the path planning unit 3 may then plan an initial moving path according to a specific position, where the initial moving path covers a warehouse entry task and a warehouse exit task of the articles in the logistics vehicle, that is, the logistics vehicle may move in a single initial moving path to complete the warehouse entry task and the warehouse exit task, and in the initial moving path, the initial moving path may include an initial moving path affecting warehouse management, after the initial moving path is removed, the calculation amount of the subsequent path screening unit 5 may be reduced, after the path screening unit 5 adopts a sparrow search algorithm to optimize the remaining initial moving path, an optimal path may be obtained, the intelligent logistics vehicle may move in the warehouse based on the optimal path, and warehouse exit and warehouse entry may be performed on the articles in the moving process, so as to reduce the moving time of the logistics vehicle, and the article warehouse-in and warehouse-out efficiency is improved.
Preferably, the scene construction unit 1 performs the following steps:
S11, acquiring basic information of a warehouse, wherein the basic information comprises warehouse areas and warehouse area division;
Step S12, generating a virtual scene by adopting virtual scene generating software based on basic information of a warehouse;
And S13, acquiring an entrance and an exit of the warehouse and an internal road, and adding the entrance and the internal road to the virtual scene.
When the virtual scene of the warehouse is constructed, warehouse area division and the like of the warehouse are obtained, three-dimensional software such as openai is adopted to generate the virtual scene according to basic information of the warehouse, and then an entrance and an internal road of the warehouse are added into the virtual scene at the same time, so that all paths of a logistics vehicle moving from the entrance to the exit along the internal road in the virtual scene are obtained when path planning is carried out.
Preferably, the basic information of the articles to be put in and taken out includes the storage area and the size of the storage area.
When the articles to be put in and put out are put in and put out, the articles need to be stored in or taken out from a designated storage area, the cargo carrying capacity of the logistics vehicle is limited, and when the articles are put in and put out together, the articles need to be put in and put out before the articles are put in and put out, so that the articles to be put in and put out are stored in the original positions of the articles to be put in and put out, and therefore the sizes of the articles to be put in and put out need to be obtained.
Preferably, the path planning unit 3 performs the following steps:
Step S21, determining a plurality of warehousing positions of the articles to be warehoused in the virtual scene and a plurality of storage positions of the articles to be warehoused in the virtual scene according to the belonging warehousing areas;
s22, selecting a warehouse-in position closest to a warehouse entry as a starting point;
Step S23, randomly selecting the rest storage positions and the storage positions, connecting the rest storage positions and the storage positions in a virtual scene to form a moving path, and when the storage positions and the storage positions are selected, the sizes of the articles to be stored in the selected storage positions are required to be smaller than the sizes of the articles to be stored in the previous storage position;
and step S24, taking the warehouse outlet as an end point, and connecting with the moving path to form an initial moving path.
When a path planning is carried out, firstly determining a storage area to which an article to be stored and an article to be stored belong, wherein the position of the article to be stored in a virtual scene is a storage position, the position of the article to be stored in the virtual scene is a storage position, the number of the article to be stored and the number of the articles to be stored in the virtual scene are multiple, the storage positions and the storage positions are also multiple, after the storage positions and the storage positions are acquired, firstly determining a storage position according to an entrance of a warehouse so as to be convenient for storing the article to be stored, and then vacating a position on a logistics vehicle so as to be convenient for placing the article to be stored, thus firstly selecting the storage position closest to the entrance as a starting point, then connecting the rest storage position and the storage position into an initial moving path in a random selection mode, wherein the initial moving path comprises the entrance, all the storage positions and the outlets, and the logistics vehicle can carry out storage of the article according to the entrance of the initial moving path, and the articles to be stored in the random size is ensured not to be further selected according to the random size of the selected position when the articles to be stored in the logistics vehicle are required to be stored in the free position.
Preferably, the path rejection unit 4 performs the following steps:
Step S31, determining the current path planning time;
step S32, inputting path planning time into a trained neural network, processing the neural network to obtain a busy area, training the neural network by adopting historical activity records of all areas in a warehouse, wherein the activity records comprise movement tracks of logistics vehicles, operation starting time and operation ending time, dividing the activity records into a training set and a testing set, training the neural network according to the training set, and testing the accuracy of the neural network through the testing set;
And step S33, eliminating the initial moving path passing through the busy area.
After the initial moving paths are obtained, the number of the initial moving paths needs to be further reduced, wherein the path removing unit 4 can determine a busy area when planning a current path according to historical busy data in a warehouse, firstly determine the current path planning time, then identify the busy area in the current path planning time by using a neural network after training, determine the busy area according to the past historical data, and finally remove the initial moving paths passing through the busy area, so that the number of the initial moving paths is reduced, and the path screening unit 5 is convenient to reduce the calculation amount.
The neural network is trained by adopting a history record, wherein the history record comprises a history activity record of each area in a warehouse, the history activity record comprises a moving track of a logistics vehicle, operation starting time and operation ending time, the neural network is trained according to the history activity record, wherein the input is the operation starting time, and the output is the moving track of the logistics vehicle, so that the neural network can obtain a busy area according to the current path planning time so as to facilitate the elimination of an initial moving path.
Preferably, the path filtering unit 5 performs the following steps:
Step S41, initializing sparrow population, and setting searching times and scale, wherein the searching times are the number of initial moving paths after being removed;
step S42, randomly selecting an initial moving path, and calculating the adaptability value of the sparrow based on the shortest path as an index;
Step S43, selecting another initial moving path, calculating the fitness value, comparing with the previously calculated fitness value, and reserving the initial moving path with larger fitness value;
and S44, performing iterative search, and outputting the initial movement path with the maximum fitness value as an optimal path after obtaining the initial movement path.
When the path screening is carried out, the invention adopts a sparrow searching algorithm to carry out optimizing based on the searching habit of sparrows on food, wherein the optimizing is carried out by taking the shortest path as an index, and the optimal path can be obtained after calculating the fitness value of each initial moving path and comparing, and finally the commodity circulation car can carry out the warehouse-out and warehouse-in of the commodity when moving according to the optimal path, thereby shortening the total path of the commodity circulation car, improving the warehouse-out and warehouse-in efficiency of the commodity and reducing the abrasion of the commodity circulation car.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (9)
1. An intelligent logistics warehouse management system, comprising:
The scene construction unit is used for constructing a virtual scene in the warehouse;
The article information acquisition unit is used for acquiring basic information of articles to be put in and put out;
the path planning unit is used for constructing an initial moving path of the logistics vehicle in the virtual scene according to the basic information of the object;
the path removing unit is used for removing the initial moving path influencing warehouse management;
The path screening unit is used for screening the rest initial moving paths according to a sparrow searching algorithm and obtaining an optimal path;
The scene construction unit, the path planning unit, the path eliminating unit and the path screening unit are sequentially connected in data, and the article information acquisition unit is connected with the path planning unit in data.
2. The intelligent logistics warehouse management system of claim 1, wherein the scene construction unit performs the steps of:
S11, acquiring basic information of a warehouse, wherein the basic information comprises warehouse areas and warehouse area division;
Step S12, generating a virtual scene by adopting virtual scene generating software based on basic information of a warehouse;
And S13, acquiring an entrance and an exit of the warehouse and an internal road, and adding the entrance and the internal road to the virtual scene.
3. The intelligent logistics warehouse management system of claim 1, wherein the basic information of the articles to be warehoused and offloaded comprises the warehouse area and the size.
4. An intelligent logistics warehouse management system as set forth in claim 3, wherein the path planning unit performs the steps of:
Step S21, determining a plurality of warehousing positions of the articles to be warehoused in the virtual scene and a plurality of storage positions of the articles to be warehoused in the virtual scene according to the belonging warehousing areas;
s22, selecting a warehouse-in position closest to a warehouse entry as a starting point;
Step S23, randomly selecting the rest storage positions and the warehouse-in positions, and connecting the rest storage positions and the warehouse-in positions in the virtual scene to form a moving path;
and step S24, taking the warehouse outlet as an end point, and connecting with the moving path to form an initial moving path.
5. The intelligent logistics warehouse management system of claim 4, wherein the size of the selected storage location is smaller than the size of the prior storage location when the storage location and the storage location are selected in step S23.
6. The intelligent logistics warehouse management system of claim 1, wherein the path elimination unit comprises the following steps:
Step S31, determining the current path planning time;
Step S32, inputting path planning time into a trained neural network, and processing the path planning time by the neural network to obtain a busy area;
And step S33, eliminating the initial moving path passing through the busy area.
7. The intelligent logistics warehouse management system of claim 6, wherein the neural network is trained using historical activity records of each area in the warehouse, the activity records comprising movement tracks of the logistics vehicle, start and end times of the operation, the activity records are divided into a training set and a testing set, the neural network is trained according to the training set, and the accuracy of the neural network is tested by the testing set.
8. The intelligent logistics warehouse management system of claim 1, wherein the path screening unit comprises the following steps:
Step S41, initializing sparrow population, and setting searching times and scale;
step S42, randomly selecting an initial moving path, and calculating the adaptability value of the sparrow based on the shortest path as an index;
Step S43, selecting another initial moving path, calculating the fitness value, comparing with the previously calculated fitness value, and reserving the initial moving path with larger fitness value;
and S44, performing iterative search, and outputting the initial movement path with the maximum fitness value as an optimal path after obtaining the initial movement path.
9. The intelligent logistics warehouse management system of claim 8, wherein the number of searches is the number of initial movement paths after culling.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115392838A (en) * | 2022-09-06 | 2022-11-25 | 珠海格力电器股份有限公司 | Warehouse cargo entry and exit control method, device and storage system |
| CN115409256A (en) * | 2022-08-24 | 2022-11-29 | 吉林化工学院 | Route recommendation method for congestion area avoidance based on travel time prediction |
| CN116070997A (en) * | 2023-02-13 | 2023-05-05 | 烟台南山学院 | A logistics warehouse management method and system based on virtual reality |
| US20230177419A1 (en) * | 2021-12-07 | 2023-06-08 | Capital One Services, Llc | Systems and methods for customizing and displaying user learning paths |
| CN116611600A (en) * | 2023-06-13 | 2023-08-18 | 中国电建集团贵州工程有限公司 | Planning method for material distribution path of assembly workshop |
| CN117078148A (en) * | 2023-08-21 | 2023-11-17 | 北华航天工业学院 | Vehicle path optimization method based on intelligent warehouse transportation |
| CN117775582A (en) * | 2023-12-28 | 2024-03-29 | 北京京东乾石科技有限公司 | Method, device, system and computer storage medium for controlling warehouse-in and warehouse-out |
-
2024
- 2024-05-10 CN CN202410576050.8A patent/CN118333525A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20230177419A1 (en) * | 2021-12-07 | 2023-06-08 | Capital One Services, Llc | Systems and methods for customizing and displaying user learning paths |
| CN115409256A (en) * | 2022-08-24 | 2022-11-29 | 吉林化工学院 | Route recommendation method for congestion area avoidance based on travel time prediction |
| CN115392838A (en) * | 2022-09-06 | 2022-11-25 | 珠海格力电器股份有限公司 | Warehouse cargo entry and exit control method, device and storage system |
| CN116070997A (en) * | 2023-02-13 | 2023-05-05 | 烟台南山学院 | A logistics warehouse management method and system based on virtual reality |
| CN116611600A (en) * | 2023-06-13 | 2023-08-18 | 中国电建集团贵州工程有限公司 | Planning method for material distribution path of assembly workshop |
| CN117078148A (en) * | 2023-08-21 | 2023-11-17 | 北华航天工业学院 | Vehicle path optimization method based on intelligent warehouse transportation |
| CN117775582A (en) * | 2023-12-28 | 2024-03-29 | 北京京东乾石科技有限公司 | Method, device, system and computer storage medium for controlling warehouse-in and warehouse-out |
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