Predictive defrosting for beverage stores

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predictive defrosting for beverage stores. An example method includes receiving amounts of ingredients consumed in one or more previous operation cycles; generating, based on the amounts of ingredients consumed in the one or more previous operation cycles, predicted amounts of ingredients needed in a next operation cycle; sending, to a defrosting device, the predicted amounts of the ingredients needed in the next operation cycle; and determining a target temperature curve for defrosting the ingredients needed in the next operation cycle.

1 . A computer-implemented method, comprising:
 receiving amounts of ingredients consumed in one or more previous operation cycles;   generating, based on the amounts of ingredients consumed in the one or more previous operation cycles, predicted amounts of ingredients needed in a next operation cycle;   sending, to a defrosting device, the predicted amounts of the ingredients needed in the next operation cycle; and   determining a target temperature curve for defrosting the ingredients needed in the next operation cycle.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein receiving the amounts of ingredient consumed in one or more previous operation cycles comprises:
 receiving, from a workstation, amounts of ingredients consumed in each operation cycle at an end of the operation cycle, wherein the workstation is in the same store as the defrosting device.   
     
     
         3 . The computer-implemented method of  claim 1 , wherein generating the predicted amounts of the ingredients needed in the next operation cycle comprises:
 generating initial predicted amounts of the ingredients needed in the next operation cycle;   determining a factor indicating whether the next operation cycle includes a workday or a non-workday; and   determining final predicted amounts of the ingredients needed in the next operation cycle by multiplying the initial predicted amounts with the factor.   
     
     
         4 . The computer-implemented method of  claim 3 , wherein determining the factor indicating whether the next operation cycle includes a workday or a non-workday comprises:
 in response to determining that a defrosting duration is longer than an operation cycle, determining the factor based on a future operation cycle that follows the defrosting duration, wherein the factor indicates whether the future operation cycle includes a workday or a non-workday.   
     
     
         5 . The computer-implemented method of  claim 3 , wherein generating the predicted amounts of the ingredients needed in the next operation cycle further comprises:
 determining whether a defrosted inventory is sufficient based on the final predicted amounts.   
     
     
         6 . The computer-implemented method of  claim 1 , wherein generating predicted amounts of ingredients needed in the next operation cycle comprises:
 determining historical demand and demand variation based on the amounts of ingredients consumed in the one or more previous operation cycles;   determining types of ingredients needed for the next operation cycle based on the demand variation; and   predicting an amount for each type of ingredient needed in the next operation cycle based on the types of ingredients and the demand variation.   
     
     
         7 . The computer-implemented method of  claim 1 , further comprising:
 receiving historical operation data of more than one store, wherein the historical operation data of each store comprises amounts of ingredients consumed in the corresponding store in one or more previous operation cycles;   generating first operation data by processing historical operation data;   generating second operation data based on a relationship graph indicating similarities among the more than one store;   training a prediction model using features derived from the first operation data and the second operation data; and   generating, by the prediction model, predicted amounts of ingredients needed in the next operation cycle in each of the more than one store.   
     
     
         8 . The computer-implemented method of  claim 7 , wherein generating the second operation data based on the relationship graph indicating similarities among the more than one store comprises:
 collecting store-level information, wherein the store-level information comprises at least one of scale, location, business district information, regional attributes and regional weather type of each store in a chain store system;   generating a feature matrix based on the relationship graph to indicate similarities among stores in the store chain system, performing clustering analysis on the stores in the chain store system based on the feature matrix, and determining a representative store for each store cluster; and   selecting the store-level information of target stores of each store cluster as the second operation data, wherein the target stores are selected based on representative stores.   
     
     
         9 . The computer-implemented method of  claim 1 , wherein sending the predicted amounts of ingredients to the defrosting device comprises:
 sending the predicted amounts of ingredients to the defrosting device via an Internet of Thing (IoT) network, wherein a controller of the defrosting device is connected to one or more central servers via the IoT network.   
     
     
         10 . The computer-implemented method of  claim 1 , further comprising:
 determining whether the defrosting device is connected to one or more central servers; and   in response to determining that the defrosting device is not connected to one or more central servers:
 scanning, by a scanner, a QR code comprising information on ingredients needed in the next operation cycle; and 
 sending the information on ingredients needed in the next operation cycle to the defrosting device, wherein the scanner and the defrosting device are connected via a personal area network (PAN). 
   
     
     
         11 . The computer-implemented method of  claim 10 , further comprising:
 receiving, by a workstation from the one or more central servers, the information on ingredients needed in the next operation cycle; and   generating the QR code by the workstation.   
     
     
         12 . The computer-implemented method of  claim 1 , wherein determining the target temperature curve comprises:
 sending a plurality of temperature curves to the defrosting device, wherein the plurality of temperature curves correspond to a plurality of ingredients;   storing the plurality of temperature curves in a memory of the defrosting device; and   selecting, by the defrosting device, the target temperature curve from the plurality of temperature curves.   
     
     
         13 . The computer-implemented method of  claim 12 , further comprising:
 preconfiguring the plurality of temperature curves based on defrosting tests on the plurality of ingredients.   
     
     
         14 . The computer-implemented method of  claim 12 , further comprises:
 defrosting ingredients by controlling a temperature and/or a defrosting duration in the defrosting device based on the target temperature curve.   
     
     
         15 . The computer-implemented method of  claim 12 , wherein the target temperature curve is selected based on:
 types of the ingredients needed in the next operation cycle; and/or   the amounts of the ingredients needed in the next operation cycle.   
     
     
         16 . The computer-implemented method of  claim 12 , wherein the plurality of temperature curves have the same fixed defrosting duration. 
     
     
         17 . The computer-implemented method of  claim 1 , further comprising:
 generating a relationship graph of a plurality of stores;   identifying stores that are similar to a first store based on the relationship graph, wherein the first store has the defrosting device; and   generating, based on amounts of ingredients consumed in the stores similar to the first store, predicted amounts of ingredients needed in the first store.   
     
     
         18 . The computer-implemented method of  claim 17 , wherein the relationship graph is generated based on at least one of:
 store sizes, store locations, regional attributes, and regional weather type.   
     
     
         19 . A computer-readable storage media storing one or more instructions that, when executable by one or more computers, cause the one or more computers to perform operations comprising:
 receiving amounts of ingredients consumed in one or more previous operation cycles;   generating, based on the amounts of ingredients consumed in the one or more previous operation cycles, predicted amounts of ingredients needed in a next operation cycle;   sending, to a defrosting device, the predicted amounts of the ingredients needed in the next operation cycle; and   determining a target temperature curve for defrosting the ingredients needed in the next operation cycle.   
     
     
         20 . A computer-implemented system, comprising:
 one or more computers; and   one or more computer memory devices interoperably coupled with the one or more computers and having computer-readable storage media storing one or more instructions that, when executed by the one or more computers, perform one or more operations comprising:
 receiving amounts of ingredients consumed in one or more previous operation cycles; 
 generating, based on the amounts of ingredients consumed in the one or more previous operation cycles, predicted amounts of ingredients needed in a next operation cycle; 
 sending, to a defrosting device, the predicted amounts of the ingredients needed in the next operation cycle; and 
 determining a target temperature curve for defrosting the ingredients needed in the next operation cycle.