LU500232B1 - Intelligent customization system and method for luggage industry based on big data drive - Google Patents

Intelligent customization system and method for luggage industry based on big data drive Download PDF

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Publication number
LU500232B1
LU500232B1 LU500232A LU500232A LU500232B1 LU 500232 B1 LU500232 B1 LU 500232B1 LU 500232 A LU500232 A LU 500232A LU 500232 A LU500232 A LU 500232A LU 500232 B1 LU500232 B1 LU 500232B1
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Luxembourg
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luggage
elements
image
big data
data drive
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LU500232A
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German (de)
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Fangjun Kuang
Wenjun Zhou
Siyang Zhang
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Wenzhou Business College
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • 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
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0282—Rating or review of business operators or products
    • 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
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations

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  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Development Economics (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Economics (AREA)
  • General Engineering & Computer Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Game Theory and Decision Science (AREA)
  • Human Computer Interaction (AREA)
  • Image Analysis (AREA)

Abstract

The invention discloses an intelligent customization system and method for luggage industry based on big data drive, which comprises an acquisition module used for collecting luggage images; The extraction module is use for extracting luggage element in that luggage image and classifying the luggage elements to obtain position coordinates and label of the luggage elements; The setting module is used for setting popular weight parameters according to the appearance times of each classified item in the luggage image; The recommendation module is used for personalizing the luggage and recommending it to customers according to the popular weight of the luggage elements, the location coordinates of the luggage elements and the labels based on the needs of users. By adopting the technical scheme of the invention, customized products can be displayed for customers, so that customers can obtain better experience.

Description

DESCRIPTION Intelligent customization system and method for luggage industry based on big data drive
TECHNICAL FIELD The invention belongs to the technical field of product recommendation, and particularly relates to an intelligent customization system and method for luggage industry based on big data drive.
BACKGROUND With the development and progress of the society, the personalized demand for luggage products is increasing day by day. How to face the trend and meet the personalized product demand of users is a problem that luggage producers need to face.
At present, there are domestic research reports on luggage product display and e- commerce, but there is no online customization, sharing and interaction of luggage products.
Domestic luggage products are monotonous, with serious homogenization and low market competitiveness, which can't meet the personalized needs of customers in time. The main reason is that the information communication channels between the production enterprises and the end consumers are not smooth. Enterprises should meet the diversified and personalized product needs of end consumers in the market, change the production mode of enterprises, and cater to and meet the personalized customization needs of end consumers. The intelligent customization system and method of luggage industry based on big data is born to meet such needs.
SUMMARY The technical problem to be solved by the present invention 1s to provide an intelligent customization system and method for luggage industry based on big data drive, which can display customized products for customers and enable customers to obtain better experience.
In order to achieve the above purpose, the invention adopts the following technical scheme: An intelligent customization system for luggage industry based on big data drive, comprising; The acquisition module is used for acquiring luggage images; The extraction module is use for extracting luggage element in that luggage image and classifying the luggage elements to obtain position coordinates and label of the luggage elements; The setting module is used for setting popular weight parameters according to the appearance times of each classified item in the luggage image; The recommendation module is use for personalizing that luggage and recommend it to customers according to the popular weight of the luggage elements, the position coordinate and labels of the luggage elements based on the needs of users.
Preferably, the acquisition module collects image data of luggage by web crawler software.
Further, the extraction module comprises the following components: The segmentation unit is used for semantically segmenting the luggage image to obtain a luggage image data set;
The extraction unit is used for classifying the luggage image data set according to the convolution neural network model to obtain the position coordinates and labels of the luggage elements.
Optimally, a data storage module for storing the luggage image is also concluded.
Furthermore, it also comprises a display module, which is used for virtually displaying personalized customized luggage by VR technology.
It also includes a feedback module for optimizing the fashion weight parameters according to the user satisfaction The invention also provides an intelligent customization method for luggage industry based on big data drive, comprising the following steps: S1. Collecting luggage images; S2. Extracting luggage elements in that luggage image, and classify the luggage elements to obtain position coordinates and labels of the luggage element; S3. Setting popular weight parameters according to the appearance times of each classified item in the luggage image; S4. Based on the needs of users, according to the popular weight of the luggage elements, the position coordinates and labels of the luggage elements, personalizing the luggage and recommending it to customers.
Preferably, in S1, luggage image data is collected by web crawler software.
Preferably, S2 comprises the following content: Semantic segmentation is carried out on the luggage image to obtain a luggage image data set;
Classifying the luggage image data set according to the convolution neural network model to obtain the position coordinates and labels of the luggage elements.
According to the invention, the image semantic segmentation algorithm is optimized with the convolutional neural network, the luggage products are customized, and the customized products are displayed for customers through the VR virtual display platform, so that customers can obtain better experience.
BRIEF DESCRIPTION OF THE FIGURES In order to explain the embodiments of the present invention or the technical scheme in the prior art more clearly, the drawings used in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained according to these drawings for ordinary technicians in the field without paying creative labor.
Fig. 1 is the structural diagram of an intelligent customization system for luggage industry driven by big data; Fig. 2 1s the flowchart of an intelligent customization method for luggage industry based on big data drive.
DESCRIPTION OF THE INVENTION In the following embodiments, the invention will be described in detail with reference to the drawings. In the drawings or descriptions, similar or identical parts are given the same reference numerals, and in practical applications, the shape, thickness or height of each part can be enlarged or reduced. The examples of the present invention are only used to illustrate the present invention, not to limit the scope of the present invention. Any obvious modifications or changes made to the present invention do not depart from the spirit and scope of the present invention.
As shown in Fig. 1, an intelligent customization system for luggage industry based on big data drive, comprising: The acquisition module is used for acquiring luggage images; The extraction module is use for extracting luggage element in that luggage image and classifying the luggage elements to obtain position coordinates and label of the luggage elements; The setting module is used for setting popular weight parameters according to the appearance times of each classified item in the luggage image; The recommendation module is use for personalizing that luggage and recommend it to customers according to the popular weight of the luggage elements, the position coordinate and labels of the luggage elements based on the needs of users.
The recommendation module is use for personalizing that luggage and recommend it to customers according to the popular weight of the luggage elements, the position coordinate and labels of the luggage elements based on the needs of users.
Preferably, the acquisition module collects image data of luggage by web crawler software.
Further, the extraction module comprises the following components: The segmentation unit is used for semantically segmenting the luggage image to obtain a luggage image data set;
The extraction unit is used for classifying the luggage image data set according to the convolution neural network model to obtain the position coordinates and labels of the luggage elements.
Further, the image semantic segmentation method comprises the following steps: P1. Provide an image semantic segmentation model, wherein the image semantic segmentation model comprises at least two basic semantic segmentation submodels and a fusion unit; P2. Input the image to be segmented into the image semantic segmentation model provided in step P1 to execute the following segmentation steps P21 to P23: P21. Semantically segment the image to be segmented through the at least two basic semantic segmentation submodels to obtain at least two feature maps containing semantic information corresponding to the image to be segmented; P22. Calculate the weight of each feature map of the image to be segmented according to at least two feature maps of the image to be segmented and their semantic information; P23. At least two feature maps of the image to be segmented are fused by the fusion unit according to the corresponding weights obtained in Step P23, so as to obtain a predictive semantic segmentation result of the image to be segmented.
Further, classifying the luggage image data set according to the convolution neural network model comprises the following steps: Preprocess the acquired luggage image data set to construct a data set; Construct a convolutional neural network, and training the convolutional neural network by using data in a data set, wherein the convolutional neural network at least comprises a convolutional layer, the convolutional layer compresses a feature matrix, and performs sparse matrix vector multiplication operation on the generated sparse matrix on a graphics processing unit for extracting local features; Preprocess that data to be classified, input the preprocessed data into a trained convolutional neural network model, and outputting position coordinate and labels of luggage elements; The feature matrix is segmented and compressed, which includes steps: determining the sub-matrix segmentation window, allocating a thread block to all sub-matrices in each group of rows, allocating threads in the thread block to process sub-matrices in the block, and performing multi-thread calculation by GPU; A new format is adopted for storage, specifically, the subsequent non-zero values of the matrix are placed in a continuous shared memory, and three vectors are created for a certain sub-matrix: the first vector is used to store the non-zero values in the sub-matrix, the second vector is used to store the convolution kernel values mapped by the non-zero values, and the third vector is used to store the number of non-zero values.
Further, it also comprises a data storage module for storing the luggage image Furthermore, the display module is used for displaying the personalized customized luggage through VR technology.
Further, it also includes a feedback module, which is used to optimize the popularity weight parameters according to the user satisfaction.
The invention also provides an intelligent customization method for luggage industry based on big data drive, which comprises the following steps: S1. Collect luggage images;
S2. Extract luggage elements in that luggage image, and classify the luggage elements to obtain position coordinates and labels of the luggage element; S3. Set popular weight parameters according to the appearance times of each classified item in the luggage image; S4. Based on the needs of users, according to the popular weight of the luggage elements, the position coordinates and labels of the luggage elements, personalizing the luggage and recommending it to customers.
Preferably, in S1, luggage image data is collected by web crawler software.
Preferably, S2 comprises the following content: Semantic segmentation is carried out on the luggage image to obtain a luggage image data set; Classifying the luggage image data set according to the convolution neural network model to obtain the position coordinates and labels of the luggage elements.
The invention first obtains the luggage image by the network crawler set, and then the luggage image semantic segmentation, obtains the luggage image data set, obtains the characteristics of the box element according to the convolution neural network model structure, and classifies it, and obtains the position coordinate system and label of the box element image. At the same time, according to the number of occurrences of each category item in the luggage image sample as the reference of popular weight parameter setting, the default trend of weight is obvious. Moreover, according to the user’s needs, the recommendation algorithm is used. According to the popular weight of the package elements, the package types of the relevant users are personalized recommended. The virtual picture of the package selected by the user is displayed in the VR virtual exhibition hall. The user can visit the package model without objects and select the desired package. Finally, according to user satisfaction, the popular weight parameters are optimized.
It should be understood that although this specification 1s described according to embodiments, each embodiment does not contain only one independent technical solution. The description of this specification is only for the sake of clarity. Those skilled in the art should take the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims (9)

CLAIMS:
1. An intelligent customization system for luggage industry based on big data drive, comprising: the acquisition module is used for acquiring luggage images; the extraction module is use for extracting luggage element in that luggage image and classifying the luggage elements to obtain position coordinates and label of the luggage elements; the setting module is used for setting popular weight parameters according to the appearance times of each classified item in the luggage image; the recommendation module is use for personalizing that luggage and recommend it to customers according to the popular weight of the luggage elements, the position coordinate and labels of the luggage elements based on the needs of users.
2. The intelligent customization system for luggage industry based on big data drive according to claim 1, wherein the acquisition module collects image data of luggage by web crawler software.
3. The intelligent customization system for luggage industry based on big data drive according to claim 1, wherein the extraction module comprises the following components: the segmentation unit is used for semantically segmenting the luggage image to obtain a luggage image data set; the extraction unit is used for classifying the luggage image data set according to the convolution neural network model to obtain the position coordinates and labels of the luggage elements.
4. The intelligent customization system for luggage industry based on big data drive according to claim 1, further comprising a data storage module for storing the luggage image.
5. The intelligent customization system for luggage industry based on big data drive according to claim 1, further comprising a display module, which is used for virtually displaying personalized customized luggage by VR technology.
6. The intelligent customization system for the luggage industry based on big data drive according to claim 1, wherein it also includes a feedback module for optimizing the fashion weight parameters according to the user satisfaction.
7. The intelligent customization method for luggage industry based on big data drive, comprising the following steps: S1. Collecting luggage images; S2. Extracting luggage elements in that luggage image, and classify the luggage elements to obtain position coordinates and labels of the luggage element; S3. Setting popular weight parameters according to the appearance times of each classified item in the luggage image; S4. Based on the needs of users, according to the popular weight of the luggage elements, the position coordinates and labels of the luggage elements, personalizing the luggage and recommending it to customers.
8. The intelligent customization method of luggage industry based on big data drive as claimed in claim 7, wherein in S1, the image data of luggage is collected by web crawler software.
9. The intelligent customization service system for luggage industry based on big data drive according to claim 7, wherein S2 comprises the following content: semantic segmentation is carried out on the luggage image to obtain a luggage image data set; classifying the luggage image data set according to the convolution neural network model to obtain the position coordinates and labels of the luggage elements.
LU500232A 2021-06-01 2021-06-01 Intelligent customization system and method for luggage industry based on big data drive LU500232B1 (en)

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Effective date: 20211201