KR-102960264-B1 - AI-BASED UNMANNED STORE MANAGEMENT SYSTEM AND METHOD FOR OPERATING THE SAME

KR102960264B1KR 102960264 B1KR102960264 B1KR 102960264B1KR-102960264-B1

Abstract

In an AI-based unmanned store management system according to various embodiments of the present invention, the AI-based unmanned store management system comprises: a user terminal; and a server, wherein the server comprises: a communication module and a processor that perform data transmission and reception with the user terminal via a network, and a memory that stores instructions that the processor can execute, wherein the memory stores instructions that cause the processor to perform the following operations, and the processor comprises: receiving data related to the operating status of an unmanned store from the user terminal; inputting the received data related to the operating status of an unmanned store into a model trained with artificial intelligence to generate analysis result data regarding the sales and inventory status of the unmanned store and order recommendation data regarding order target items and order quantities; and providing the generated analysis result data and order recommendation data to the user terminal so that the operation management information of the unmanned store is displayed by the user terminal.

Inventors

채원석

Assignees

주식회사 키즈팩토리

Dates

Publication Date: 20260507
Application Date: 20260109

Claims (6)

In AI-based unmanned store management systems, The above AI-based unmanned store management system is: User terminal; and Including the server, The above server is: A communication module that performs data transmission and reception through the above-mentioned user terminal and network, and processor and, It includes memory that stores instructions that the above processor can execute, and The above memory stores instructions that cause the processor to perform the following operation, and The above processor is: Receiving data related to the operating status of an unmanned store from the above user terminal; Inputting the data related to the operating status of the unmanned store received above into a model trained with artificial intelligence to generate analysis result data regarding the sales and inventory status of the unmanned store and order recommendation data regarding order target items and order quantities; The generated analysis result data and order recommendation data are provided to the user terminal so that operation management information of the unmanned store is displayed by the user terminal, and The above processor is, Mathematical formula 1 Through this, based on the degree of discrepancy between payment record data and inventory change detection data included in the data related to the operating status of the unmanned store received above, items Data reliability regarding Calculate, In the above mathematical formula 1, is an item It is a value representing data reliability that quantifies the degree of agreement between the payment quantity aggregated from payment record data and the inventory decrease quantity aggregated from inventory change detection data, and is an item identifier for identifying individual items managed in an unmanned store, and items aggregated based on the above payment record data during a preset observation period It is a value representing the payment quantity of, and items aggregated based on the inventory change detection data during the aforementioned preset observation period It is a value representing the outgoing quantity, and the said outgoing quantity is an inventory reduction quantity calculated by accumulating the decrease amount of item i in the inventory quantity change event included in the inventory change detection data, and Is It is a value greater than 0 that is preset as a denominator stabilization constant to prevent the denominator from becoming excessively small when is small, and is a symbol representing an operation that calculates the absolute value of an input value, and is a function representing an operation that outputs the minimum value among the input values, and The above processor is, Mathematical formula 2 Through this, based on the inventory quantity per item included in the data related to the operating status of the unmanned store received above, the item Stock shortage index for Calculate, In the above mathematical formula 2, is an item It is a value representing an inventory shortage index that quantifies the extent to which the current inventory quantity is insufficient compared to the critical inventory quantity, and is an item identifier for identifying individual items managed in an unmanned store, and is an item aggregated based on the inventory quantity by item included in the data related to the operating status of the unmanned store received above. It is a value representing the current stock quantity of, and is an item It is a value representing the preset threshold stock quantity for, and Is It is a value greater than 0 that is preset as a denominator stabilization constant to prevent the denominator from becoming excessively small when is small, and is a function representing an operation that outputs the maximum value among the input values, and is a function representing an operation that outputs the minimum value among the input values, AI-based unmanned store management system.
In paragraph 1, Inputting the above-mentioned data related to the operating status of the unmanned store into a model trained with artificial intelligence to generate analysis result data and order recommendation data regarding the sales and inventory status of the unmanned store is, Based on the location information of the unmanned store included in the data related to the operating status of the unmanned store received above, commercial area information and competitor information within a preset distance range are retrieved, and An AI-based unmanned store management system further comprising inputting the above commercial area information, the above competitor information, and data related to the received operating status of the unmanned store into a model trained with the above artificial intelligence to generate the above analysis result data and the above order recommendation data.
In paragraph 1, Generating the above order recommendation data is, Compare the inventory quantity per item included in the data related to the operating status of the unmanned store received above with a preset threshold inventory quantity, and identify items that are less than or equal to the threshold inventory quantity as items to be ordered; Calculate a recommended order quantity for each of the above-identified order target items based on the difference between the target inventory quantity and the above inventory quantity; Including the above-mentioned order target items and the above-mentioned recommended order quantities in the above-mentioned order recommendation data; Providing order recommendation data to the user terminal so that order recommendation information including an inventory shortage notification for the above-mentioned order target item and the above-mentioned recommended order quantity is displayed by the user terminal. AI-based unmanned store management system.
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In paragraph 1, The above processor is, Mathematical formula 3 Through, items Recommended order quantity for Calculate, In the above mathematical formula 3, is an item It is a value representing the recommended order quantity calculated based on the difference between the target inventory quantity and the current inventory quantity, and the degree of discrepancy between payment record data and inventory change detection data, and is an item identifier for identifying individual items managed in an unmanned store, and is an item It is a value representing the preset target inventory quantity for, and is an item aggregated based on the inventory quantity by item included in the data related to the operating status of the unmanned store received above. It is a value representing the current stock quantity of, and items aggregated based on payment record data included in data related to the operating status of the unmanned store received during a preset observation period It is a value representing the payment quantity of, and items aggregated based on inventory change detection data included in the data related to the operating status of the unmanned store received during the aforementioned preset observation period. It is a value representing the outgoing quantity, and the said outgoing quantity is an inventory reduction quantity calculated by accumulating the decrease amount of item i in the inventory quantity change event included in the inventory change detection data, and is a preset value as a correction coefficient to reflect the uncertainty correction amount in the recommended order quantity when there is a discrepancy between the aggregated quantity based on payment record data and the aggregated quantity based on sensor data, and is a value representing the magnitude of the discrepancy between the aggregated quantity based on the above payment record data and the aggregated quantity based on the above sensor data, and is a symbol representing an operation that calculates the absolute value of an input value, and is a function representing an operation that outputs the maximum value among the input values, and is a symbol representing an operation that rounds up an input value to convert it to an integer, and The above processor is, Mathematical formula 4 Through, items Order recommendation priority score for Calculate, In the above mathematical formula 4, is an item It is a value representing an order recommendation priority score that quantifies the priority of order recommendations based on sales history, desired margin criteria, and current inventory quantity, and is an item identifier for identifying individual items managed in an unmanned store, and Items aggregated based on payment record data generated at unmanned stores during a preset observation period It is a value representing the payment quantity of, and is an item Regarding this, it is a preset value as the target margin standard value for the unmanned store operator, and is an item aggregated based on the inventory quantity by item included in the data related to the operating status of the unmanned store received above. It is a value representing the current stock quantity of, and at silver It is a preset value as a numerator stabilization constant to prevent the priority score from being fixed at 0 when α is 0 and the numerator becomes 0, and at silver A preset value as a denominator stabilization constant to prevent the denominator from becoming zero when α is 0, AI-based unmanned store management system.
In paragraph 2, The above processor Mathematical formula 5 Through this, a market opportunity coefficient that quantifies market area and competitor information using the location information of unmanned stores Calculate, In the above mathematical formula 5, is a value representing the market area opportunity coefficient calculated based on the location information of the unmanned store, and is the population included in the commercial area information retrieved within a preset distance range based on the location information of the unmanned store included in the data related to the operating status of the unmanned store received above, and Is It is a value greater than 0 that is preset as a criterion value for normalization, and is the number of competitors included in the competitor information retrieved within a preset distance range based on the location information of the unmanned store included in the data related to the operating status of the unmanned store received above, and is included in the above competitor information It is the distance between the location information of the th competitor and the location information of the unmanned store included in the data related to the operating status of the unmanned store received above, and is a preset value as a reference distance value for setting the degree of attenuation of competitive impact due to distance increase, and Is from It is a symbol representing an operation of summing terms up to, and The above processor is, Mathematical formula 6 Through this, based on data related to the operating status of the unmanned store and commercial area information received above, the target deviation index Calculate, In the above mathematical formula 6, is a value representing the target deviation index, which quantifies how much the actual purchase distribution of an unmanned store deviates from the target population distribution of a commercial area, and is the number of payments satisfying a preset target condition among payments aggregated based on payment record data included in the data related to the operating status of the unmanned store received during a preset observation period, and is the total number of payments aggregated based on payment record data included in the data related to the operating status of the unmanned store received during the aforementioned preset observation period, and is the ratio of the population satisfying the preset target conditions among the commercial area population included in the commercial area information retrieved within a preset distance range based on the location information of the unmanned store included in the data related to the operating status of the unmanned store received above, and Is It is a value greater than 0 that is preset as a denominator stabilization constant to prevent the denominator from becoming excessively small when this is small or close to zero, and is a symbol representing an operation that calculates the absolute value of an input value, and is a function representing an operation that outputs the minimum value among the input values, AI-based unmanned store management system.

Description

AI-Based Unmanned Store Management System and Method for Operating the Same The present invention relates to the field of operational management technology for unmanned stores. In particular, it relates to unmanned store management technology that analyzes sales and payment data, inventory and product data, customer purchase data, trend data, and location-based commercial area data received from an unmanned store using artificial intelligence to generate analysis result data regarding sales and inventory status and order recommendation data regarding order target items and order quantities, and provides the generated data to a store management dashboard of a user terminal. Unless otherwise indicated in this specification, the contents described in this section are not prior art for the claims of this application, and are not to be recognized as prior art simply because they are included in this section. Unmanned stores are becoming widespread in various forms due to reduced labor costs and the convenience of unmanned operation; however, data generated during operations—such as sales, payments, entry/exit, product sales, and inventory fluctuations—is often collected in a fragmented manner across different equipment and services. Consequently, store operators find it difficult to assess operational status at a given moment using consistent standards, leading to increased management burdens as they must switch between multiple screens or manually organize data to check on-site conditions. Furthermore, data delays or omissions can result in misinterpretations of operational status, hindering timely responses and potentially leading to lost sales opportunities or customer inconvenience. Furthermore, inventory management in unmanned stores is complex and subject to changes caused by items with significant fluctuations in sales volume, demand variations by time of day, promotional and seasonal factors, and shifts in foot traffic in nearby commercial areas. Traditionally, orders were often placed relying on sales history or managerial experience, leading to problems such as stockouts during surges in demand or the accumulation of excess inventory during declines. In particular, stockouts cause customer churn, while excess inventory increases disposal and storage costs, and in the case of products with expiration dates, it exacerbates losses. Meanwhile, due to the nature of unmanned stores where operational personnel are not stationed, the detection of abnormal situations and the identification of their causes are prone to delays. For instance, even if issues such as payment errors, product tagging errors, access control failures, theft or loss, poor display, or sensor or camera malfunctions occur, failure to immediately recognize them by the operator can cause the problem to persist for an extended period, leading to accumulated damage. Furthermore, if the evidence identifying the specific factors that caused the issue is not sufficiently documented even after an anomaly occurs, preventative measures may remain merely temporary responses, resulting in a decline in the consistency of operational quality. Figure 1 is a configuration diagram of an AI-based unmanned store management system according to an embodiment of the present invention. FIG. 2 is an internal configuration diagram of a server included in an AI-based unmanned store management system according to an embodiment of the present invention. FIG. 3 is an exemplary flowchart of a method for providing operational status analysis and order recommendation information performed in an AI-based unmanned store management system according to an embodiment of the present invention. FIGS. 4 to 6 are examples of unmanned store operation management screens displayed on a user terminal according to an embodiment of the present invention. The drawings above are provided as examples to ensure that the concept of the present invention is sufficiently conveyed to those skilled in the art. Accordingly, the present invention is not limited to the drawings presented below and may be embodied in other forms. In addition, the same reference numbers throughout the specification represent the same components. In addition, please note that in the drawings above, specific parts have been enlarged or reduced without proportion to the scale to aid understanding. Various embodiments are now described with reference to the drawings. In this specification, various descriptions are provided to facilitate understanding of the present invention. However, it is evident that these embodiments can be practiced without such specific descriptions. As used herein, terms such as “component,” “module,” “server,” etc. refer to computer-related entities, hardware, firmware, software, combinations of software and hardware, or executions of software. For example, a component may be, but is not limited to, a procedure executed on a processor, a processor, an object, an execution thread, a program, and/o