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CN117611053A - Clothing warehouse management system - Google Patents

Clothing warehouse management system Download PDF

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Publication number
CN117611053A
CN117611053A CN202311567638.9A CN202311567638A CN117611053A CN 117611053 A CN117611053 A CN 117611053A CN 202311567638 A CN202311567638 A CN 202311567638A CN 117611053 A CN117611053 A CN 117611053A
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Prior art keywords
clothing
module
warehouse
management system
rfid
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CN202311567638.9A
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Inventor
高坤峰
陈洋
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Zhejiang Xuanri Technology Co ltd
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Zhejiang Xuanri Technology Co ltd
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Priority to CN202311567638.9A priority Critical patent/CN117611053A/en
Publication of CN117611053A publication Critical patent/CN117611053A/en
Pending legal-status Critical Current

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/34—Sorting according to other particular properties
    • B07C5/342—Sorting according to other particular properties according to optical properties, e.g. colour
    • B07C5/3422—Sorting according to other particular properties according to optical properties, e.g. colour using video scanning devices, e.g. TV-cameras
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/36—Sorting apparatus characterised by the means used for distribution
    • B07C5/361—Processing or control devices therefor, e.g. escort memory
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K7/00—Methods or arrangements for sensing record carriers, e.g. for reading patterns
    • G06K7/10—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation
    • G06K7/10009—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation sensing by radiation using wavelengths larger than 0.1 mm, e.g. radio-waves or microwaves
    • G06K7/10297—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation sensing by radiation using wavelengths larger than 0.1 mm, e.g. radio-waves or microwaves arrangements for handling protocols designed for non-contact record carriers such as RFIDs NFCs, e.g. ISO/IEC 14443 and 18092
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/096—Transfer learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/255—Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/54—Extraction of image or video features relating to texture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/56—Extraction of image or video features relating to colour
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G06V10/765—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects using rules for classification or partitioning the feature space
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C2501/00—Sorting according to a characteristic or feature of the articles or material to be sorted
    • B07C2501/0063—Using robots

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Abstract

The invention discloses a clothing warehouse management system, which comprises an internet of things sensing module, a big data analysis module, an artificial intelligent classification algorithm, an automatic sorting system, an RFID module and an intelligent warehouse management system. The clothing warehouse management system improves the warehouse management efficiency, reduces the manual operation requirement, reduces the management cost, realizes the accurate positioning and intelligent classification of clothing commodities, reduces the risks of inventory errors and cargo loss, provides deeper insight for warehouse management through big data analysis, formulates a more scientific inventory strategy, introduces an RFID technology, realizes the real-time tracking and management of commodities, improves the safety of the commodities, can monitor warehouse conditions in real time at any time and any place, and improves the management flexibility.

Description

Clothing warehouse management system
Technical Field
The invention relates to the technical field of clothing warehouse management, in particular to a clothing warehouse management system.
Background
With the continuous development of the clothing industry, inventory management of clothing enterprises faces increasing challenges, and higher requirements are put on clothing warehouse management systems, and conventional clothing warehouse management systems generally face the problems of inaccurate positioning of articles in warehouses, low sorting speed, irregular data management and the like.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a clothing warehouse management system, which aims to solve the problems that the traditional clothing warehouse management system usually faces inaccurate positioning of articles in a warehouse, low sorting speed, irregular data management and the like.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
the clothing warehouse management system comprises an Internet of things sensing module, a big data analysis module, an artificial intelligent classification algorithm, an automatic sorting system, an RFID module and an intelligent warehouse management system;
the sensing module of the Internet of things comprises a sensor network, a wireless communication module, a data acquisition and processing unit, a real-time positioning system, a power management module, a security and privacy protection module and a fault detection and self-diagnosis module;
the big data analysis module comprises a data collector, a data storage system, a data cleaning and preprocessing module real-time data processing engine, a batch data processing engine, a visualization and report generating tool, a user query interface and a real-time alarm system;
the artificial intelligent classification algorithm comprises an image recognition model, a deep learning network, a feature extractor, a transfer learning module, a label speculation algorithm, a real-time updating module, a model evaluation and optimization module and a heterogeneous clothing classification module;
the automatic sorting system comprises a mechanical arm, a robot, an image recognition system, a conveyor belt system, a sorting bin, an electronic tag, a control system, a fault detection and alarm system, a man-machine interface and an energy management system;
the RFID module comprises an RFID tag, an RFID reader, an antenna, an RFID control unit and a data processing unit;
the intelligent warehouse management system comprises a warehouse layout optimization module, a real-time monitoring and alarming system, a user terminal, an inventory management system, a warehouse operation data analysis and report generation system and a supply chain integration system.
Further, the internet of things sensing module is provided with sensors inside the warehouse for sensing storage positions, quantity and temperature and humidity information of clothing commodities.
Further, the big data analysis module is used for collecting, storing and analyzing clothing commodity data in the warehouse.
Furthermore, the artificial intelligent classification algorithm is used for intelligently classifying clothing commodities, and accuracy and speed of commodity identification are improved.
Further, the automatic sorting system is used for quickly and accurately sorting clothing commodities.
Further, the RFID module applies RFID tags to clothing items for real-time tracking, inventory management and theft prevention of the items.
Further, the intelligent warehouse management system is used for comprehensively monitoring, dispatching and managing the articles of clothing in the warehouse.
The beneficial effects of the invention are as follows:
the clothing warehouse management system improves the warehouse management efficiency, reduces the manual operation requirement, reduces the management cost, realizes the accurate positioning and intelligent classification of clothing commodities, reduces the risks of inventory errors and cargo loss, provides deeper insight for warehouse management through big data analysis, formulates a more scientific inventory strategy, introduces an RFID technology, realizes the real-time tracking and management of commodities, improves the safety of the commodities, can monitor warehouse conditions in real time at any time and any place, and improves the management flexibility.
Drawings
FIG. 1 is a schematic diagram of a garment warehouse management system according to the present invention;
fig. 2 is a schematic structural diagram of an internet of things sensing module according to the present invention;
FIG. 3 is a schematic diagram of a big data analysis module according to the present invention;
FIG. 4 is a schematic diagram of an artificial intelligence classification algorithm according to the present invention;
FIG. 5 is a schematic diagram of an automated sorting system according to the present invention;
FIG. 6 is a schematic diagram of an RFID module structure of the present invention;
fig. 7 is a schematic structural diagram of the intelligent warehouse management system according to the present invention.
Detailed Description
The following description of the technical solution in the embodiments of the present invention is clear and complete.
As shown in FIG. 1, the clothing warehouse management system comprises an Internet of things sensing module, a big data analysis module, an artificial intelligent classification algorithm, an automatic sorting system, an RFID module and an intelligent warehouse management system.
As shown in fig. 2, the sensing module of the internet of things includes:
sensor network: the sensing module of the internet of things comprises various types of sensors for sensing various information in a warehouse, such as:
temperature and humidity sensor: for monitoring the temperature and humidity within the warehouse, ensuring that the items of clothing are stored in a suitable environment.
Illumination sensor: the light level is monitored for determining if additional illumination is required.
Shelf weight sensor: the method is used for detecting the weight of clothing goods on the goods shelf and realizing accurate management of inventory.
Door magnetic sensor: the status of the warehouse door is monitored for security and anti-theft purposes.
A wireless communication module: the sensor performs data transmission with the warehouse management system through the wireless communication module.
A data acquisition and processing unit: the method is used for acquiring the original data from the sensor and performing preliminary processing on the data.
Real-time positioning system: for locating specific locations of items of clothing, real-time tracking of each item of clothing by using RFID technology.
And a power management module: and power management is provided for the sensor, so that the normal operation of the sensor is ensured.
Security and privacy protection module: the security of the sensor data and the protection of the user privacy are ensured.
Fault detection and self-diagnosis module: sensor status is monitored, and sensor faults or anomalies are detected and reported.
The sensing module of the internet of things is internally provided with sensors for sensing storage positions, quantity and temperature and humidity information of clothing commodities and intelligently monitoring and managing the warehouse.
As shown in fig. 3, the big data analysis module includes:
data acquisition unit: the system is used for collecting a large amount of real-time data from a plurality of data sources such as a networking sensing module, an RFID module, a warehouse management system and the like, wherein the real-time data comprise the positions, the humiture, the inventory information, the traffic flow in the warehouse and the like of clothing commodities.
A data storage system: efficient data storage is provided to accommodate various data types within a warehouse, with large data storage solutions, such as distributed databases or cloud storage services, to ensure data scalability and fault tolerance.
The data cleaning and preprocessing module is used for real-time data processing engine: and cleaning, denoising and processing the original data to ensure the accuracy and consistency of the data.
Batch data processing engine: and processing large-scale historical data, performing complex data analysis, and revealing potential modes, trends and association relations in the warehouse.
Visualization and report generation tools: and the analysis result is presented to warehouse manager in the form of visual chart, report, etc.
User query interface: the warehouse manager is provided with a query interface that allows them to freely retrieve and analyze data as desired.
Real-time alarm system: and monitoring data in the warehouse in real time, and giving an alarm in time when abnormal conditions occur.
The big data analysis module is used for collecting, storing and analyzing clothing commodity data in the warehouse.
As shown in fig. 4, the artificial intelligence classification algorithm includes:
image recognition model: the image processing technology such as convolutional neural network is used for identifying the images of clothing commodities and is used for training a model so that the model can accurately identify clothing of different types, colors and styles.
Deep learning network: modeling the time sequence information of the clothing commodity by adopting a cyclic neural network or a long-short-time memory network, and particularly for clothing with dynamic properties.
Feature extractor: for extracting key features in the clothing merchandise image, such as color, texture, shape, etc., for distinguishing between different types of clothing.
And the migration learning module: the model parameter migration method is used for migrating model parameters trained on other clothing types to new types, and improves accuracy of models.
Tag speculation algorithm: the label of the clothing commodity image is automatically estimated, so that the dependence on labeling data is reduced.
And a real-time updating module: the algorithm is allowed to dynamically learn and adapt to the new clothing types during operation, and the system is ensured to have better recognition capability on the new styles and types of clothing.
Model evaluation and optimization module: and (3) using cross verification to monitor indexes such as accuracy, recall rate and the like of the model, and carrying out corresponding model adjustment to ensure the performance of the classification model.
Heterogeneous clothing classification module: the condition that a plurality of different types of clothes exist in the same image is effectively processed, and each piece of clothes can be correctly classified.
The artificial intelligent classification algorithm is used for intelligently classifying clothing commodities, improves the accuracy and speed of commodity identification, efficiently and accurately classifies clothing commodities in a warehouse, and improves the intelligent processing capacity of a warehouse management system on the clothing commodities.
As shown in fig. 5, the automated sorting system includes:
mechanical arm and robot: and automatically grabbing, moving and placing clothing articles according to the instruction of the algorithm.
An image recognition system: the device is used for shooting and identifying images of clothing commodities, and ensures that the machine can accurately distinguish clothing of different types, colors and styles.
Conveyor belt system: for transporting items of clothing from one location to another in a warehouse.
Sorting bin positions: the device is used for storing clothing articles of different types, and the robot arm and the robot can place the clothing articles on corresponding sorting bins according to requirements.
Electronic tag: each item of clothing may be attached with an electronic tag or RFID tag for uniquely identifying and tracking the item.
And (3) a control system: and the control system is used for integrating and coordinating all the components, and the control system realizes automatic execution of sorting tasks according to the guidance of an algorithm.
Fault detection and alarm system: and the running condition of the system is monitored, and an alarm is sent out in time, so that the stability and the reliability of the system are ensured.
Human-machine interface: and a visual interface is provided, so that warehouse management personnel can monitor the running condition of the automatic sorting system and perform real-time monitoring and intervention.
An energy management system: the system is used for managing the energy consumption of the automatic sorting system, ensuring the efficient operation of the system and reducing the energy cost.
The automatic sorting system is used for quickly and accurately sorting clothing commodities, can reduce labor cost and improve sorting efficiency and precision of a warehouse while improving sorting speed.
As shown in fig. 6, the RFID module includes:
RFID tag: each article of apparel is attached with an RFID tag that contains unique identification information for identifying the article.
RFID reader: a device for reading and writing information on the RFID tag.
An antenna: for generating radio frequency signals to activate nearby RFID tags and to collect information returned by the tags.
An RFID control unit: the RFID control unit is used for managing communication between the RFID reader and the antenna and coordinating operation of the RFID system.
A data processing unit: for processing the data read by the RFID modules, matching the tag information to a database in the warehouse management system, ensuring that each RFID tag is properly associated with the corresponding clothing item and inventory information.
The RFID module is used for real-time tracking, inventory management and theft prevention of the articles, high-efficiency management of the articles is achieved, inventory visibility and accuracy are improved, manual operation requirements are reduced, and efficiency of the whole warehouse management system is improved.
As shown in fig. 7, the intelligent warehouse management system includes:
warehouse layout optimization module: based on data analysis and an artificial intelligence algorithm, the layout and shelf arrangement of the warehouse are optimized, so that the space utilization rate and the sorting efficiency of the warehouse are improved to the greatest extent.
Real-time monitoring and alarm system: the operation condition of the warehouse is monitored in real time, and a corresponding alarm mechanism is set so as to take measures in time when abnormal conditions occur.
User terminal: a user-friendly interface is provided, allowing warehouse management personnel to view warehouse status, operation indexes and alarm information in real time.
Inventory management system: real-time monitoring of inventory, report generation, order processing, etc. are supported.
Warehouse operation data analysis and report generation system: by analyzing the warehouse operation data, detailed reports are generated, and management staff is helped to know the operation conditions of the warehouse in depth.
Supply chain integration system: and the system is integrated with a supply chain management system, so that the end-to-end visibility of the supply chain is realized, and the cooperative operation of the warehouse and other links of the supply chain is ensured.
The intelligent warehouse management system is used for comprehensively monitoring, scheduling and managing the articles of clothing in the warehouse, can realize the intellectualization and automation of warehouse management, improves the operation efficiency and precision, and reduces the management cost.
The clothing warehouse management system improves the warehouse management efficiency, reduces the manual operation requirement, reduces the management cost, realizes the accurate positioning and intelligent classification of clothing commodities, reduces the risks of inventory errors and cargo loss, provides deeper insight for warehouse management through big data analysis, formulates a more scientific inventory strategy, introduces an RFID technology, realizes the real-time tracking and management of commodities, improves the safety of the commodities, can monitor warehouse conditions in real time at any time and any place, and improves the management flexibility.
The above description is illustrative of the preferred embodiments of the present invention and is not intended to limit the scope of the present invention, but is to be accorded the full scope of the claims.

Claims (7)

1. The clothing warehouse management system is characterized by comprising an Internet of things sensing module, a big data analysis module, an artificial intelligent classification algorithm, an automatic sorting system, an RFID module and an intelligent warehouse management system;
the sensing module of the Internet of things comprises a sensor network, a wireless communication module, a data acquisition and processing unit, a real-time positioning system, a power management module, a security and privacy protection module and a fault detection and self-diagnosis module;
the big data analysis module comprises a data collector, a data storage system, a data cleaning and preprocessing module real-time data processing engine, a batch data processing engine, a visualization and report generating tool, a user query interface and a real-time alarm system;
the artificial intelligent classification algorithm comprises an image recognition model, a deep learning network, a feature extractor, a transfer learning module, a label speculation algorithm, a real-time updating module, a model evaluation and optimization module and a heterogeneous clothing classification module;
the automatic sorting system comprises a mechanical arm, a robot, an image recognition system, a conveyor belt system, a sorting bin, an electronic tag, a control system, a fault detection and alarm system, a man-machine interface and an energy management system;
the RFID module comprises an RFID tag, an RFID reader, an antenna, an RFID control unit and a data processing unit;
the intelligent warehouse management system comprises a warehouse layout optimization module, a real-time monitoring and alarming system, a user terminal, an inventory management system, a warehouse operation data analysis and report generation system and a supply chain integration system.
2. A clothing warehousing management system according to claim 1, wherein: the sensing module of the internet of things is internally provided with sensors for sensing storage positions, quantity and temperature and humidity information of clothing commodities.
3. A clothing warehousing management system according to claim 1, wherein: the big data analysis module is used for collecting, storing and analyzing clothing commodity data in the warehouse.
4. A clothing warehousing management system according to claim 1, wherein: the artificial intelligent classification algorithm is used for intelligently classifying clothing commodities, and accuracy and speed of commodity identification are improved.
5. A clothing warehousing management system according to claim 1, wherein: the automatic sorting system is used for quickly and accurately sorting clothing commodities.
6. A clothing warehousing management system according to claim 1, wherein: the RFID module is used for applying RFID tags to clothing commodities and is used for tracking the commodities in real time, managing inventory and preventing theft.
7. A clothing warehousing management system according to claim 1, wherein: the intelligent warehouse management system is used for comprehensively monitoring, dispatching and managing the articles of clothing in the warehouse.
CN202311567638.9A 2023-11-23 2023-11-23 Clothing warehouse management system Pending CN117611053A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118154105A (en) * 2024-05-13 2024-06-07 青岛云创智通科技有限公司 Intelligent warehouse management system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118154105A (en) * 2024-05-13 2024-06-07 青岛云创智通科技有限公司 Intelligent warehouse management system

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