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WO2025206647A1 - Serveur pour le calcul d'une quantité à commander sur la base d'une consommation et d'une prédiction de la demande de produits d'un magasin sans personnel, et son procédé de commande - Google Patents

Serveur pour le calcul d'une quantité à commander sur la base d'une consommation et d'une prédiction de la demande de produits d'un magasin sans personnel, et son procédé de commande

Info

Publication number
WO2025206647A1
WO2025206647A1 PCT/KR2025/003608 KR2025003608W WO2025206647A1 WO 2025206647 A1 WO2025206647 A1 WO 2025206647A1 KR 2025003608 W KR2025003608 W KR 2025003608W WO 2025206647 A1 WO2025206647 A1 WO 2025206647A1
Authority
WO
WIPO (PCT)
Prior art keywords
unmanned store
data
order quantity
unmanned
machine learning
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/KR2025/003608
Other languages
English (en)
Korean (ko)
Inventor
지성원
문성환
민준기
이현규
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beat Corp Co Ltd
Original Assignee
Beat Corp Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beat Corp Co Ltd filed Critical Beat Corp Co Ltd
Publication of WO2025206647A1 publication Critical patent/WO2025206647A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/906—Clustering; Classification
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/10—Pre-processing; Data cleansing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2134—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on separation criteria, e.g. independent component analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2123/00—Data types
    • G06F2123/02—Data types in the time domain, e.g. time-series data

Definitions

  • This disclosure calculates the order quantity of an unmanned store, and more specifically, calculates the order quantity based on the material usage and demand forecast of the unmanned store.
  • the purpose of the embodiment disclosed in this disclosure is to provide a server that calculates an order quantity based on the material usage amount and demand forecast of an unmanned store.
  • the order quantity calculation server of the unmanned store comprises: a communication unit that receives material usage data of the unmanned store; a memory that stores at least one instruction related to order quantity calculation of the unmanned store; And a processor that performs an operation related to the at least one instruction, and calculates a result value including the order quantity of the unmanned store and the sales period for each product based on a machine learning model, wherein the processor trains the machine learning model based on the material usage data of the unmanned store, and calculates the result value based on a plurality of first predictive variables and second predictive variables using the learned machine learning model, wherein the first predictive variable is a variable related to the entire inventory in the unmanned store, and is for predicting the order quantity for a preset future date from the prediction time point based on the machine learning model, and the second predictive variable is a variable related to the remaining amount of food ingredients to be manufactured through a food manufacturing machine installed in the unmanned store, and is for predicting the food ingredient usage
  • the above portable terminal may include, for example, all kinds of handheld-based wireless communication devices such as PCS, GSM, PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminals, smart phones, etc., as well as wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMD).
  • handheld-based wireless communication devices such as PCS, GSM, PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminals, smart phones, etc., as well as wearable devices such as watches, rings
  • the predefined operation rules or artificial intelligence models are characterized by being created through learning.
  • being created through learning means that the basic artificial intelligence model is trained using a learning algorithm using a plurality of learning data, thereby creating the predefined operation rules or artificial intelligence models set to perform a desired characteristic (or purpose).
  • This learning may be performed on the device itself on which the artificial intelligence according to the present disclosure is performed, or may be performed through a separate server and/or system.
  • Examples of the learning algorithm include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.
  • An artificial intelligence model may be composed of multiple neural network layers.
  • Each of the multiple neural network layers has multiple weights, and performs neural network operations through operations between the operation results of the previous layer and the multiple weights.
  • the multiple weights of the multiple neural network layers may be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights may be updated so that the loss value or cost value obtained from the artificial intelligence model is reduced or minimized during the learning process.
  • a processor can implement artificial intelligence.
  • Artificial intelligence refers to a machine learning method based on an artificial neural network that mimics human neurons (biological neurons) to enable machines to learn.
  • Artificial intelligence methodologies can be categorized into supervised learning, where input and output data are provided together as training data, thereby determining the solution (output data) to a problem (input data); unsupervised learning, where only input data is provided without output data, so that the solution (output data) to a problem (input data) is not determined; and reinforcement learning, where a reward is provided from an external environment each time an action is taken in the current state, and learning proceeds in a direction that maximizes this reward.
  • artificial intelligence methodologies can be categorized by the architecture of the learning model.
  • the architectures of widely used deep learning technologies can be categorized into convolutional neural networks, recurrent neural networks, transformers, and generative adversarial networks.
  • the device may include an artificial intelligence model.
  • the artificial intelligence model may be a single artificial intelligence model or may be implemented as multiple artificial intelligence models.
  • the artificial intelligence model may be composed of a neural network (or artificial neural network) and may include statistical learning algorithms that mimic biological neurons in machine learning and cognitive science.
  • a neural network may refer to a model in general that has problem-solving capabilities by changing the binding strength of synapses through learning, formed by artificial neurons (nodes) that form a network by combining synapses.
  • the neurons of the neural network may include a combination of weights or biases.
  • the neural network may include one or more layers composed of one or more neurons or nodes.
  • the device may include an input layer, a hidden layer, and an output layer. The neural network constituting the device can infer a desired outcome from an arbitrary input by changing the weights of the neurons through learning.
  • the processor can create a neural network, train (or learn) a neural network, perform a calculation based on received input data, generate an information signal based on the calculation result, or retrain the neural network.
  • the models of the neural network can include various types of models such as CNN, R-CNN, RPN, RNN, S-DNN, S-SDNN, Deconvolution Network, DBN, RBM, Fully Convolutional Network, LSTM Network, Classification Network, etc., such as GoogleNet, AlexNet, VGG Network, etc., but are not limited thereto.
  • the processor can include one or more processors for performing calculations according to the models of the neural network.
  • the neural network can include a deep neural network.
  • Neural networks include CNN, RNN, perceptron, multilayer perceptron, Feed Forward (FF), Radial Basis Network (RBF), Deep Feed Forward (DFF), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Auto Encoder (AE), Variational Auto Encoder (VAE), Denoising Auto Encoder (DAE), Sparse Auto Encoder (SAE), MC (Markov Chain), HN (Hopfield Network), BM (Boltzmann Machine), RBM (Restricted Boltzmann Machine), DBN (Depp Belief Network), DCN (Deep Convolutional Network), DN (Deconvolutional Network), DCIGN (Deep Convolutional Inverse Graphics Network), GAN (Generative Adversarial Network), LSM (Liquid State Machine), ELM (Extreme Learning) Machine), ESN (Echo State Network), DRN (Deep Residual Network), DNC (Differentiable Network) It will be understood by
  • the processor may be configured to perform a process for generating a CNN (Convolution Neural Network) such as GoogleNet, AlexNet, VGG Network, Region with Convolution Neural Network (R-CNN), Region Proposal Network (RPN), Recurrent Neural Network (RNN), Stacking-based deep Neural Network (S-DNN), State-Space Dynamic Neural Network (S-SDNN), Deconvolution Network, Deep Belief Network (DBN), Restrcted Boltzman Machine (RBM), Fully Convolutional Network, Long Short-Term Memory (LSTM) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT, SP-BERT, MRC/QA for natural language processing, Text Analysis, Dialog System, GPT-3, GPT-4, Visual Analytics for vision processing, Visual Understanding, Video Synthesis, ResNet for data intelligence, Anomaly Detection, Prediction, Time-Series Forecasting, Various artificial intelligence
  • CNN Convolution Neural Network
  • Figures 1 and 2 are schematic diagrams of an order quantity calculation system (10) of an unmanned store according to an embodiment of the present disclosure.
  • the order quantity calculation system (10) of an unmanned store includes a server, a cloud server (200), a store management system (300), and an order server (400).
  • the order quantity calculation system (10) of an unmanned store may include fewer or more components than the components illustrated in FIG. 1.
  • the server can receive material usage data of each unmanned store from the store management system (300).
  • the server can use stored commands, algorithms, and programs to produce results that include the order quantity for each unmanned store and the sales period for each product.
  • the server can produce a result value using a machine learning model (210).
  • the machine learning model (210) may be stored in a cloud server (200), and at least some of the algorithms and programs may use those stored in the cloud server (200).
  • the server can set the data input to the machine learning model (210) by considering the material usage amount of each unmanned store, the predicted situation during the ordering period, etc.
  • the server can request an order to the ordering server (400) based on the produced result.
  • FIG. 3 is a block diagram of an order quantity calculation server (100) of an unmanned store according to an embodiment of the present disclosure.
  • the order quantity calculation server (100) of an unmanned store includes a processor (110), a communication unit (120), and a memory (130).
  • the order quantity calculation server (100) of the unmanned store may include fewer or more components than the components illustrated in FIG. 2.
  • the processor (110) may be implemented as a storage unit that stores data regarding an algorithm for controlling the operation of components within the device or a program that reproduces the algorithm, and at least one processor (110) that performs the aforementioned operation using the data stored in the storage unit.
  • the storage unit and the processor (110) may each be implemented as separate chips.
  • the storage unit and the processor (110) may be implemented as a single chip.
  • processor (110) can control any one or a combination of the components described above to implement various embodiments according to the present disclosure described in the drawings below on the device.
  • the processor (110) can typically control the overall operation of the device.
  • the processor (110) can process signals, data, information, etc. input or output through the components described above, or run application programs stored in the storage unit, thereby providing or processing appropriate information or functions to the user.
  • the processor (110) may control at least some of the components of the device to run an application program stored in the storage unit. Furthermore, the processor (110) may operate at least two or more of the components included in the device in combination to run the application program.
  • the communication unit (120) may include one or more modules that connect the order quantity calculation server (100) of the unmanned store to one or more networks.
  • the communication unit (120) may include one or more components that enable communication with an external device, and may include, for example, at least one of a broadcast reception module, a wired communication module, a wireless communication module, a short-range communication module, and a location information module.
  • the wired communication module may include various wired communication modules such as a Local Area Network (LAN) module, a Wide Area Network (WAN) module, or a Value Added Network (VAN) module, as well as various cable communication modules such as a Universal Serial Bus (USB), a High Definition Multimedia Interface (HDMI), a Digital Visual Interface (DVI), RS-232 (recommended standard 232), power line communication, or plain old telephone service (POTS).
  • LAN Local Area Network
  • WAN Wide Area Network
  • VAN Value Added Network
  • USB Universal Serial Bus
  • HDMI High Definition Multimedia Interface
  • DVI Digital Visual Interface
  • RS-232 recommended standard 232
  • POTS plain old telephone service
  • the wireless communication module may include a wireless communication module that supports various wireless communication methods such as GSM (global System for Mobile Communication), CDMA (Code Division Multiple Access), WCDMA (Wideband Code Division Multiple Access), UMTS (universal mobile telecommunications system), TDMA (Time Division Multiple Access), LTE (Long Term Evolution), 4G, 5G, and 6G, in addition to a WiFi module and a Wireless Broadband module.
  • GSM Global System for Mobile Communication
  • CDMA Code Division Multiple Access
  • WCDMA Wideband Code Division Multiple Access
  • UMTS universalal mobile telecommunications system
  • TDMA Time Division Multiple Access
  • LTE Long Term Evolution
  • 4G Long Term Evolution
  • 5G Fifth Generation
  • 6G Wireless Broadband
  • the wireless communication module may include a wireless communication interface including an antenna and a transmitter for transmitting communication signals. Furthermore, the wireless communication module may further include a signal conversion module that modulates a digital control signal output from the processor (110) through the wireless communication interface into an analog wireless signal under the control of the processor (110).
  • the short-range communication module is for short-range communication, and can support short-range communication using at least one of Bluetooth, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), UWB (Ultra-Wideband), ZigBee, NFC (Near Field Communication), Wi-Fi (Wireless-Fidelity), Wi-Fi Direct, and Wireless USB (Wireless Universal Serial Bus) technologies.
  • RFID Radio Frequency Identification
  • IrDA Infrared Data Association
  • UWB Ultra-Wideband
  • ZigBee Ultra-Wideband
  • ZigBee Wireless-Fi
  • Wi-Fi Direct Wireless USB (Wireless Universal Serial Bus) technologies.
  • the memory (130) can store data supporting various functions of the device.
  • the memory (130) can store a plurality of application programs (or applications) running on the device, data for the operation of the device, and commands. At least some of these application programs may exist for the basic functions of the device. Meanwhile, the application programs can be stored in the memory (130), installed on the device, and driven to perform operations (or functions) by the processor (110).
  • the memory (130) may include at least one type of storage medium among a flash memory (130) type, a hard disk type, an SSD (Solid State Disk type), an SDD (Silicon Disk Drive) type, a multimedia card micro type, a card type memory (e.g., an SD or XD memory (130)), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory (130), a magnetic disk, and an optical disk.
  • the memory (130) may be a database that is separate from the device but is connected by wire or wirelessly.
  • the memory (130) may be equipped with multiple processes for the order quantity calculation server (100) of the unmanned store.
  • order quantity prediction server of an unmanned store may further include components such as an input section, an output section, and an interface section.
  • the input unit is for inputting video information (or signal), audio information (or signal), data, or information input from a user, and may include at least one camera, at least one microphone, and at least one user input unit. Voice data or image data collected from the input unit may be analyzed and processed into a user control command.
  • the input unit is for receiving information from the user, and when information is input through the input unit, the processor (110) can control the operation of the device to correspond to the input information.
  • the input unit may include a hardware physical key (e.g., a button located on at least one of the front, rear, and side of the device, a dome switch, a jog wheel, a jog switch, etc.) and a software touch key.
  • the touch key may be a virtual key, a soft key, or a visual key displayed on a touch screen type display unit through software processing, or may be a touch key disposed on a part other than the touch screen.
  • the virtual key or visual key may have various forms and be displayed on the touch screen, and may be, for example, formed of a graphic, text, an icon, a video, or a combination thereof.
  • the output unit is for generating output related to visual, auditory, or tactile sensations, and may include at least one of a display unit, an audio output unit, a haptic module, and an optical output unit.
  • the display unit may be formed as a layer structure with a touch sensor or formed as an integral part, thereby implementing a touch screen.
  • Such a touch screen may function as a user input unit that provides an input interface between the device and a user, and at the same time, may provide an output interface between the device and the user.
  • the interface unit serves as a passageway for various types of external devices connected to the device.
  • the interface unit may include at least one of a wired/wireless headset port, an external charger port, a wired/wireless data port, a memory card port, a port for connecting a device equipped with an identification module (SIM), an audio I/O (Input/Output) port, a video I/O (Input/Output) port, and an earphone port.
  • SIM identification module
  • the device may perform appropriate control related to the external device connected to the interface unit.
  • Figure 4 is a flowchart of a method for calculating an order quantity in an unmanned store according to an embodiment of the present disclosure.
  • Figure 5 is a diagram illustrating a flow for creating a logistics order list for operating an unmanned store.
  • the processor (110) checks the inventory. (S530)
  • the processor (110) generates an order guide list based on CMS logistics management raw material attribute information (S570). (S580)
  • the order quantity calculation system (10) of an unmanned store is configured to include an order quantity calculation server (100) of an unmanned store, a CMS (300), REDIS (210), a BATCH SERVER (220), an RDB (230), Lambda (240), and an S3 Bucket (250), and can implement a machine learning system (620).
  • the order quantity calculation system (10) of an unmanned store includes a data collection system (710), the Internet (720), a platform (730), and a machine learning system (740).
  • the configuration indicated by M in FIG. 7 is a configuration corresponding to Management
  • the configuration indicated by A is a configuration corresponding to Automation
  • the configuration indicated by D is a configuration corresponding to Data.
  • the server (100) can provide a system (10) that can simultaneously perform presentation (view) of indicators that can be identified in the area of a general data analysis dashboard based on management and operation (Management) and management (action) through the same.
  • CMS refers to a store management system (300).
  • the order quantity calculation system (10) of an unmanned store can obtain prediction results using the following machine learning system (620).
  • S3 Bucket 250 is an object storage service that provides scalability, data availability, security, and performance, and can provide management functions that can optimize, structure, and configure access to data according to requests from the server.
  • Data processed in this way can be organized into a dataset as shown in Fig. 6, and can be classified as Target_Time_Series, Related_Time_Series, and Item_Metadata.
  • the processor (110) processes the dataset to configure a first predictor variable (Logistics Predictors) and a second predictor variable (Realtime Predictors), and can obtain a prediction result (Forecast) based on the predictor variable using a machine learning model (210).
  • a machine learning model can output a prediction result through statistical time series analysis by learning material usage data corresponding to time series data.
  • the machine learning model (210) can output prediction results based on a weight-based quantile loss function.
  • the processor (110) can obtain a prediction result by inputting one of a plurality of preset moods within the mood range into the machine learning model (210).
  • the processor (110) can preset a plurality of moods suitable for calculating the order quantity of an unmanned store among moods of P1 to P99, and can perform the above process to select one mood among them to obtain a prediction result.
  • the processor (110) may perform post-processing on the result value obtained from S300 based on additional data to obtain a final result value.
  • additional data may include at least one of the following: data other than the conditions preset for the operation of the unmanned store, such as a breakdown of a food preparation machine in the unmanned store, actual operating hours different from the preset operating hours for the unmanned store, and marketing information conducted in the unmanned store.
  • the processor (110) can also obtain a prediction result regarding the operating hours of an unmanned store corresponding to the third predictive variable.
  • An embodiment of predicting the operating hours of an unmanned store will be described below with reference to FIG. 13.
  • Figure 8 is a diagram illustrating the packaging specifications, predicted demand, inventory on hand, order quantity, and expected arrival date of each material item required in an unmanned store.
  • the order quantity calculation server (100) of the unmanned store automatically stores packaging specifications for each raw material item to proceed with the order application.
  • the processor (110) can obtain predicted demand based on a machine learning model (210) and calculate an order quantity using information such as that in FIG. 8 to automatically place an order.
  • the materials include raw materials and auxiliary materials for producing food (beverage).
  • the processor (110) can calculate the logistics order quantity for raw materials using the following mathematical equations 1 to 6.
  • Inventory on hand this Monday Inventory on hand last week + Orders placed two weeks ago - Actual usage on hand last week
  • Next week's required quantity Next week's expected consumption quantity - Expected inventory on hand next Monday + Safety stock
  • processor (110) can provide statistics on the number of orders placed using coupons by period.
  • the processor (110) may generate statistical data on whether a customer who used a free drink coupon subsequently purchased a paid drink and how many times the customer subsequently purchased a drink.
  • the processor (110) can calculate and provide the number of coupons issued during a preset period, the number of coupon users, and the number of users who subsequently converted to paid payment, and can also provide detailed statistical data for each coupon.
  • Figure 13 is a diagram illustrating a flow for predicting the sales availability time of an unmanned store.
  • the processor (110) obtains the demand prediction result of the unmanned store for the requested prediction period based on the prediction result of S1310. (S1320, S1330)
  • the processor (110) since the processor (110) knows the inventory of each material in the unmanned store at the time of prediction, it can predict/calculate the time for which each menu sold in the unmanned store can be provided based on the inventory and demand prediction results for each food menu, and the result can be directly applied to the operating hours of the unmanned store.
  • the operating hours of an unmanned store may include the number of items available for sale and the hours of sale for each menu sold in the unmanned store.
  • This forecast period can be set by considering the inventory status in the unmanned store or the specifications of the material storage containers (e.g., dispensers) of the food preparation machines installed in the unmanned store.
  • the material storage containers e.g., dispensers
  • the processor (110) calculates the operating hours of the unmanned store based on the predicted results obtained through S1330 and the predicted values that are consistent or have the minimum error compared to the actual demand of the previous week. (S1340)
  • the processor (110) controls a load cell (not shown) at preset time intervals to obtain a weight measurement value. (S1350) At this time, the processor (110) recalculates the operating time of the unmanned store based on the measurement value obtained through S1350, and if the recalculated result is outside the preset error range of the result of S1340, the processor updates the predicted result for the operating time.
  • the processor (110) displays the predicted operating time for the unmanned store through the output unit. (S1360)
  • a material storage container (e.g., dispenser) related to food production in an unmanned store may include a load cell capable of measuring weight, and the processor (110) may receive a weight value measured through the load cell at a preset time or timing.
  • the processor (110) may receive a weight value measured through a load cell at preset intervals, or may receive a weight value measured through a load cell whenever a food order is placed.
  • the processor (110) can predict demand by hour from 00:00:01 every Monday to 23:59:59 the following Wednesday (total of 240 hours), and check and update the operating time by receiving the weight measured by the load cell at 5-minute intervals.
  • the processor (110) can display the required time until the time when the expected demand at the time corresponding to the update point and the expected demand for each subsequent hour are equal to or greater than the load cell measured weight as the available operating time.
  • the method according to one embodiment of the present disclosure described above can be implemented as a program (or application) and stored in a medium to be executed in combination with a hardware server.
  • the above storage medium refers to a medium that stores data semi-permanently and can be read by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory.
  • examples of the storage medium include, but are not limited to, ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage device. That is, the program can be stored in various recording media on various servers that the computer can access or in various recording media on the user's computer.
  • the medium can be distributed across network-connected computer systems, so that computer-readable code can be stored in a distributed manner.
  • the steps of a method or algorithm described in connection with the embodiments of the present disclosure may be implemented directly in hardware, implemented as a software module executed by hardware, or implemented by a combination thereof.
  • the software module may reside in a random access memory (RAM), a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a hard disk, a removable disk, a CD-ROM, or any other form of computer-readable recording medium well known in the art to which the present disclosure pertains.

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Abstract

La présente divulgation concerne un serveur pour calculer une quantité à commander sur la base d'une consommation et d'une prédiction de la demande de produits d'un magasin sans personnel, et un procédé de commande associé, dans lesquels un modèle d'apprentissage automatique peut être entraîné sur la base de données de consommation de produits du magasin sans personnel, et la quantité à commander peut être calculée par calcul, sur la base d'une pluralité de premières variables de prédiction et de secondes variables de prédiction, d'une valeur de résultat comprenant la quantité à commander et une période de vente disponible pour chaque produit du magasin sans personnel, à l'aide du modèle d'apprentissage automatique entraîné.
PCT/KR2025/003608 2024-03-29 2025-03-20 Serveur pour le calcul d'une quantité à commander sur la base d'une consommation et d'une prédiction de la demande de produits d'un magasin sans personnel, et son procédé de commande Pending WO2025206647A1 (fr)

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KR1020240043644A KR102723880B1 (ko) 2024-03-29 2024-03-29 무인 매장의 자재 사용량과 수요 예측을 기반으로 발주량을 산출하는 서버, 방법 및 프로그램
KR10-2024-0043644 2024-03-29

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KR102723880B1 (ko) * 2024-03-29 2024-10-30 주식회사 비트코퍼레이션 무인 매장의 자재 사용량과 수요 예측을 기반으로 발주량을 산출하는 서버, 방법 및 프로그램

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KR20220043003A (ko) * 2020-09-28 2022-04-05 주식회사 굿플레이스 무인매장의 상품 자동발주 서비스 제공방법, 서버 및 컴퓨터프로그램
KR102388297B1 (ko) * 2021-04-26 2022-04-20 쿠팡 주식회사 발주와 관련된 정보를 제공하는 전자 장치의 동작 방법 및 이를 지원하는 전자 장치
KR20230053942A (ko) * 2021-10-15 2023-04-24 주식회사 아니벌써 수요와 공급을 적응적으로 반영한 상품 발주 방법 및 그 장치
KR102723880B1 (ko) * 2024-03-29 2024-10-30 주식회사 비트코퍼레이션 무인 매장의 자재 사용량과 수요 예측을 기반으로 발주량을 산출하는 서버, 방법 및 프로그램

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