KR-102962332-B1 - Intelligent Camping Equipment Rental Platform Based on AI-Driven CNN Learning Model

KR102962332B1KR 102962332 B1KR102962332 B1KR 102962332B1KR-102962332-B1

Abstract

The present invention relates to an intelligent camping equipment rental platform applying an artificial intelligence-based CNN learning model, comprising: an intelligent computing unit that analyzes images of camping equipment registered by a rental provider using a CNN-based artificial intelligence model to classify categories and product names and generates classification result information for rental items; a rental list providing unit that generates a list of rentalable camping equipment from the classification result information for rental items generated by the intelligent computing unit based on search keywords entered by a rental user, and outputs the list to a rental user terminal; a rental contract concluding unit that concludes a rental contract for camping equipment selected by the rental user from the list output by the rental list providing unit; a delivery management unit that generates delivery and return schedules for camping equipment based on contract information concluded by the rental contract concluding unit and tracks and manages them; and a settlement management unit that evaluates the condition of returned camping equipment and calculates a settlement or compensation amount. According to the present invention, by utilizing an image-based artificial intelligence classification function to automate the entire rental process, the item registration process of the rental provider and the search process of the rental user can be simplified, and the efficiency of service operation can be increased.

Inventors

정성식

Assignees

주식회사 캠터

Dates

Publication Date: 20260508
Application Date: 20250806

Claims (5)

An intelligent computing unit that analyzes images of camping equipment registered by a rental provider using a CNN-based artificial intelligence model to classify categories and product names, and generates classification result information for rental items; A rental list providing unit that generates a list of rentalable camping equipment from classification result information for rental items generated by the intelligent computing unit based on search keywords entered by the rental user, and outputs the list to the rental user terminal; A rental contract conclusion unit that concludes a rental contract for camping equipment selected by the rental user from the list output by the rental list providing unit; A delivery management unit that generates and tracks the delivery and return schedules for camping equipment based on contract information concluded in the above-mentioned rental contract conclusion unit; and It includes a settlement management unit that evaluates the condition of returned camping equipment and calculates the settlement or compensation amount, and The above intelligent computing unit is, An input receiving module that receives an image of camping equipment registered by the above-mentioned rental provider and generates original image data; An information extraction module that extracts feature information from the above original image data; A category classification module that classifies categories of camping equipment based on the above feature information and generates category classification information; A product name classification module that classifies product names of camping equipment based on the above feature information and the above category classification information, and generates product name classification information; and It includes a classification result providing module that generates classification result information by integrating the above category classification information and the above product name classification information, and provides this to the above rental list providing unit. The above category classification module is, Generate category classification information using the EfficientNet-B0 model, and The above product name classification module is, Product name classification information is generated using the above category classification information and above feature information by a ResNet-18 model, and The above intelligent computing unit is, It further includes a damage verification module that extracts damage status information from an image of camping equipment taken upon return in conjunction with the above-mentioned category classification module and product name classification module, and The above settlement management department, An intelligent camping equipment rental platform applying an AI-based CNN learning model, characterized by receiving damage status information from the damage verification module, calculating a compensation amount based on the damage status information, comparing the compensation amount with the used market price of the same camping equipment obtained from a used goods trading platform, and recommending a purchase to the rental user if the used market price is lower than the compensation amount.
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In claim 1, The above intelligent computing unit is, A collection module that collects images and metadata of camping equipment from external sources to generate training image data and training metadata; A preprocessing module that generates preprocessed image data by performing size normalization, color standardization, noise removal, and data augmentation on the above-mentioned training image data; A feature extraction module that extracts key visual features from the above-mentioned preprocessed image data and generates learning feature information; A training data generation module that generates a model training dataset by mapping the above-mentioned training feature information and corresponding training label information; and A learning control module that controls the training of CNN-based artificial intelligence models for each of the category classification module and product name classification module using the above model training dataset; An intelligent camping equipment rental platform applying an AI-based CNN learning model characterized by further including
In claim 1, The above rental list providing department, Receives search input in the form of paragraphs or lines provided by the above-mentioned rental user as natural language search input information, and The above natural language search input information is linked with an external Large Language Model (LLM) service to analyze its meaning, and search keyword information is extracted from the analysis results. An intelligent camping equipment rental platform applying an artificial intelligence-based CNN learning model, characterized by being configured to search for classification result information of the intelligent computing unit corresponding to the above search keyword information, generate rental list information, and provide it to a rental user terminal.
In claim 4, The above rental list providing department, Receive the camping equipment images provided by the above-mentioned rental user as original image data for search, and The above original image data for search is analyzed through the intelligent computing unit to obtain search category classification information and search product name classification information, and An intelligent camping equipment rental platform applying an artificial intelligence-based CNN learning model, characterized by being configured to search for classification result information of the intelligent computing unit corresponding to identical or similar camping equipment based on the above-mentioned search category classification information and the above-mentioned search product name classification information, generate rental list information, and provide it to a rental user terminal.

Description

Intelligent Camping Equipment Rental Platform Based on AI-Driven CNN Learning Model The present invention relates to an intelligent camping equipment rental platform applying an artificial intelligence-based CNN learning model, and more specifically, to an intelligent platform that automatically classifies images of camping equipment through an artificial intelligence-based CNN learning model and provides a user-customized list of camping equipment available for rent based on the classified results. As interest in camping has recently spread widely, the demand for various camping equipment is increasing. Consequently, camping equipment rental services, which allow users to rent only the necessary gear for a set period without purchasing expensive equipment, are gaining popularity. However, conventional camping equipment rental platforms have the following problems in terms of user convenience and automation. First, from the perspective of registrants wishing to list camping equipment, there is the inconvenience of having to manually enter product names or select appropriate categories when registering their gear on the platform. In particular, if a registrant is not fully aware of the names or classification standards of the equipment they use, inaccurate information may be registered, leading to reduced visibility in search results and consequently a decline in rental success rates. On the other hand, from the perspective of users looking to rent camping equipment, there is the inconvenience of having to repeatedly enter various keywords to find desired gear, as product names and keywords often mix Korean and English or are not properly categorized. In particular, keyword-based search systems experience a sharp decline in search efficiency when users do not know the exact name of the equipment; furthermore, given the nature of camping gear, which requires visual evaluation and selection, it is difficult to provide sufficient user satisfaction with a text-based search interface alone. Therefore, there is a need for technology that can enhance user convenience and rental success rates for both registrants and users by automatically classifying categories and product names from camping equipment images uploaded by registrants and providing users with customized rental lists based on these classification results. To solve these conventional problems, the present invention aims to provide an intelligent camping equipment rental platform that automatically classifies images of camping equipment using CNN-based image analysis technology and automatically generates and provides a user-customized rental list based on the classification results. FIG. 1 is a diagram showing the system configuration of an intelligent camping equipment rental platform applying an artificial intelligence-based CNN learning model according to an embodiment of the present invention, and FIG. 2 shows the overall configuration of an intelligent camping equipment rental platform applying an artificial intelligence-based CNN learning model according to an embodiment of the present invention, and FIG. 3 illustrates the detailed configuration of an intelligent computing unit of an intelligent camping equipment rental platform applying an artificial intelligence-based CNN learning model according to an embodiment of the present invention, and FIG. 4 illustrates the process of registering items by a rental provider of an intelligent camping equipment rental platform applying an artificial intelligence-based CNN learning model according to an embodiment of the present invention. FIG. 5 illustrates the model learning process of an intelligent computing unit of an intelligent camping equipment rental platform applying an artificial intelligence-based CNN learning model according to an embodiment of the present invention. FIG. 6 illustrates the process of renting items by a user of an intelligent camping equipment rental platform applying an artificial intelligence-based CNN learning model according to an embodiment of the present invention. Figure 7 is the result of a performance evaluation of an intelligent computing unit of an intelligent camping equipment rental platform that applies an artificial intelligence-based CNN learning model according to an embodiment of the present invention. Hereinafter, some embodiments of the present invention will be described in detail with reference to exemplary drawings. It should be noted that in assigning reference numerals to the components of each drawing, the same components are given the same reference numeral whenever possible, even if they are shown in different drawings. Furthermore, in describing embodiments of the present invention, if it is determined that a detailed description of related known configurations or functions would hinder understanding of the embodiments of the present invention, such detailed description is omitted. In addition, terms such as first, second, A, B, (a), (b), etc., may be