KR20220076012A - System for providing artificial intelligence based price decision service for business-to-business market - Google Patents

Abstract

An artificial intelligence-based selling price determination service providing system for the B2B market is provided, and at least one affiliated store terminal and auction information for ordering and paying for at least one type of food material at a price derived by a pre-established selling price determination algorithm; An external information collection unit that collects external information including an external API for public use including news information, weather information, and data from the National Statistical Office, and a confidential API including food material information for each restaurant product and middleman purchase information, at least one accumulated over a preset period An internal information collection unit that collects internal information, including purchase price information, order information, and food material information for each restaurant product, a pre-established selling price determination algorithm to ensure a preset margin rate based on internal and external information and a determination service providing server including a determination unit for determining the selling price through the determination unit, and an order management unit receiving an order and payment from at least one affiliated store terminal at the selling price determined by the determination unit.

Description

SYSTEM FOR PROVIDING ARTIFICIAL INTELLIGENCE BASED PRICE DECISION SERVICE FOR BUSINESS-TO-BUSINESS MARKET

The present invention relates to an artificial intelligence-based selling price determination service providing system for the B2B market, and provides a platform that can accurately determine the selling price of food materials by learning and modeling an artificial intelligence algorithm using internal and external information as a dataset. .

The number of small business enterprises in Korea is close to 90% of all businesses, and small business owners account for 40% of all business operators. These are the backbone of the national economy, the retirement of the baby boomer generation, and relatively low barriers to entry. According to the survey of small business owners, the motivation to start a business for maintaining a livelihood was found to be the highest. Recently, due to the rapid growth of O2O (Online to Offline) services, the burden of fees for small business owners who have no choice but to depend on the service is increasing. The introduction of digital systems is required for the qualitative growth of small business owners. In particular, even in food materials, the production and price of vegetables that are highly affected by the climate occur.

At this time, a B2B (Business-to-Business) platform for ordering food materials and a platform for predicting and ordering food materials were researched and developed, respectively. November 25, 2011) and Korean Patent Registration No. 10-1994607 (announced on June 28, 2019), when a food material order request is received, price information is received from the food material supplier, and the received price information is used After creating a food material comparison quotation list and providing it to the orderer, when the orderer selects any one food material from the food material comparison quotation list, an online payment is made. Thereafter, a configuration is disclosed in which an order order is created by comparing the price and delivery conditions, and a simulation result of the delivery process is applied to each order order to combine the order order price and the delivery completion date information as a contract condition, respectively.

However, although the term prediction is used in the above configuration, it is not a prediction in a practical sense, but simply combining information like a reverse auction method and providing it to the operator, so it cannot be a prediction in the true sense. Because food materials are auctioned every day, prices fluctuate every day, and especially vegetables that are affected by climate change in production and price. Although fast and accurate market price identification is a major concern for governments and related companies as well as individual consumers, the identification of price indicators is delayed by days or weeks, leading to the absence of key data necessary for national policy making and investment potential. lowers the Official economic indicators announced by the government can only capture information about consumer prices in the past, not the present. Therefore, research and development of a platform that can predict the selling price and purchase and pay for food materials at the predicted selling price on a B2B basis is required.

An embodiment of the present invention collects the cost of sales by collecting the purchase price of the middleman, collects the past price change data to understand the market price information, collects information such as wholesale market auction data, weather and specific day information or news, and By modeling the artificial intelligence algorithm using the collected data as a dataset, accurate price prediction is made, and the final price is calculated by adding a preset margin to the median price. It is possible to provide a method of providing an artificial intelligence-based selling pricing service for the B2B market, which improves reliability by linking external APIs, and guarantees the middleman's margin by setting a minimum margin rate. However, the technical task to be achieved by the present embodiment is not limited to the technical task as described above, and other technical tasks may exist.

As a technical means for achieving the above-described technical problem, an embodiment of the present invention provides at least one affiliated store terminal for ordering and paying for at least one type of food material at a price derived by a pre-established selling price determination algorithm, and An external information collection unit that collects external information including an external API for public use including auction information, news information, weather information, and data from the National Statistical Office, and a confidential API including food material information for each restaurant product and purchase information from a middleman, accumulated over a preset period An internal information collection unit that collects internal information including purchase price information of at least one type of food material, order information, and food material information for each restaurant product, sales established to ensure a preset margin rate based on internal information and external information and a decision service providing server including a decision unit that determines a selling price through a price determination algorithm, and an order management unit that receives an order and payment from at least one affiliated store terminal at the selling price determined by the decision unit.

According to any one of the above-described problem solving means of the present invention, the cost of sales is collected by collecting the purchase price of the matchmaker, the price information is grasped by collecting the historical price fluctuation data, and the wholesale market auction data, weather and specific day information or news B2B as a practical sales price prediction by collecting information such as the above and using the collected data as a dataset to model an artificial intelligence algorithm to make an accurate price prediction, and to calculate the final price by adding a preset margin to the median price. It is possible to receive basic food material orders, improve reliability by linking external APIs, and guarantee the middleman's margin by setting a minimum margin rate.

1 is a view for explaining a system for providing an artificial intelligence-based selling price determination service for a B2B market according to an embodiment of the present invention.
FIG. 2 is a block diagram illustrating a decision service providing server included in the system of FIG. 1 .
3 and 4 are diagrams for explaining an embodiment in which an artificial intelligence-based selling price determination service for a B2B market according to an embodiment of the present invention is implemented.
5 is an operation flowchart illustrating a method of providing an artificial intelligence-based selling price determination service for a B2B market according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art can easily implement them. However, the present invention may be embodied in several different forms and is not limited to the embodiments described herein. And in order to clearly explain the present invention in the drawings, parts irrelevant to the description are omitted, and similar reference numerals are attached to similar parts throughout the specification.

Throughout the specification, when a part is "connected" with another part, this includes not only the case of being "directly connected" but also the case of being "electrically connected" with another element interposed therebetween. . In addition, when a part "includes" a certain component, it means that other components may be further included, rather than excluding other components, unless otherwise stated, and one or more other features However, it is to be understood that the existence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded in advance.

The terms "about", "substantially", etc. to the extent used throughout the specification are used in or close to the numerical value when manufacturing and material tolerances inherent in the stated meaning are presented, and are intended to enhance the understanding of the present invention. To help, precise or absolute figures are used to prevent unfair use by unconscionable infringers of the stated disclosure. As used throughout the specification of the present invention, the term "step of (to)" or "step of" does not mean "step for".

In this specification, a "part" includes a unit realized by hardware, a unit realized by software, and a unit realized using both. In addition, one unit may be implemented using two or more hardware, and two or more units may be implemented by one hardware. Meanwhile, '~ unit' is not limited to software or hardware, and '~ unit' may be configured to be in an addressable storage medium or to reproduce one or more processors. Thus, as an example, '~' denotes components such as software components, object-oriented software components, class components, and task components, and processes, functions, properties, and procedures. , subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and variables. The functions provided in the components and '~ units' may be combined into a smaller number of components and '~ units' or further separated into additional components and '~ units'. In addition, components and '~ units' may be implemented to play one or more CPUs in a device or secure multimedia card.

Some of the operations or functions described as being performed by the terminal, apparatus, or device in the present specification may be performed instead of by a server connected to the terminal, apparatus, or device. Similarly, some of the operations or functions described as being performed by the server may also be performed in a terminal, apparatus, or device connected to the server.

In this specification, some of the operations or functions described as mapping or matching with the terminal means mapping or matching the terminal's unique number or personal identification information, which is the identification data of the terminal. can be interpreted as

Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

1 is a view for explaining a system for providing an artificial intelligence-based selling price determination service for a B2B market according to an embodiment of the present invention. Referring to FIG. 1 , the artificial intelligence-based selling price determination service providing system 1 for the B2B market includes at least one affiliated store terminal 100 , a determination service providing server 300 , and at least one information providing server 400 . may include However, since the artificial intelligence-based selling price determination service providing system 1 for the B2B market of FIG. 1 is only an embodiment of the present invention, the present invention is not limitedly interpreted through FIG. 1 .

At this time, each component of FIG. 1 is generally connected through a network (Network, 200). For example, as shown in FIG. 1 , at least one affiliated store terminal 100 may be connected to the decision service providing server 300 through the network 200 . In addition, the determination service providing server 300 may be connected to at least one affiliated store terminal 100 and at least one information providing server 400 through the network 200 . In addition, the at least one information providing server 400 may be connected to the decision service providing server 300 through the network 200 .

Here, the network refers to a connection structure in which information exchange is possible between each node, such as a plurality of terminals and servers, and an example of such a network includes a local area network (LAN), a wide area network (WAN: Wide Area Network), the Internet (WWW: World Wide Web), wired and wireless data communication networks, telephone networks, wired and wireless television networks, and the like. Examples of wireless data communication networks include 3G, 4G, 5G, 3rd Generation Partnership Project (3GPP), 5th Generation Partnership Project (5GPP), Long Term Evolution (LTE), World Interoperability for Microwave Access (WIMAX), Wi-Fi (Wi-Fi) , Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), RF (Radio Frequency), Bluetooth (Bluetooth) network, NFC ( Near-Field Communication) networks, satellite broadcast networks, analog broadcast networks, Digital Multimedia Broadcasting (DMB) networks, and the like are included, but are not limited thereto.

In the following, the term at least one is defined as a term including the singular and the plural, and even if the at least one term does not exist, each element may exist in the singular or plural, and may mean the singular or plural. it will be self-evident In addition, that each component is provided in singular or plural may be changed according to embodiments.

The at least one affiliate store terminal 100 may be a terminal of an affiliate store that orders food materials using a web page, an app page, a program, or an application related to an artificial intelligence-based selling price determination service for the B2B market. In this case, the affiliated store terminal 100 may be a terminal that orders food materials at the selling price determined by the determination service providing server 300 .

Here, the at least one affiliated store terminal 100 may be implemented as a computer capable of accessing a remote server or terminal through a network. Here, the computer may include, for example, navigation, a laptop equipped with a web browser, a desktop, and a laptop. In this case, the at least one affiliated store terminal 100 may be implemented as a terminal capable of accessing a remote server or terminal through a network. The at least one affiliated store terminal 100 is, for example, a wireless communication device that ensures portability and mobility, including navigation, Personal Communication System (PCS), Global System for Mobile communications (GSM), Personal Digital Cellular (PDC), 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) ) terminal, a smart phone, a smart pad, a tablet PC, etc. may include all kinds of handheld-based wireless communication devices.

The decision service providing server 300 may be a server that provides an AI-based selling price determination service web page, an app page, a program, or an application for the B2B market. In addition, the decision service providing server 300 may be a server that generates a dataset by accumulating and collecting internal information and external information. In addition, the decision service providing server 300 may be a server that models an artificial intelligence algorithm for predicting the price of food materials using a data set by learning and testing. In addition, the decision service providing server 300 may be a server that builds the modeled artificial intelligence algorithm as a pricing algorithm and continuously re-learns the prediction in which an error occurs.

Here, the decision service providing server 300 may be implemented as a computer capable of accessing a remote server or terminal through a network. Here, the computer may include, for example, navigation, a laptop equipped with a web browser, a desktop, and a laptop.

The at least one information providing server 400 provides external information to the decision service providing server 300 with or without using an artificial intelligence-based selling price determination service-related web page, app page, program or application for the B2B market. It can be a server that Here, the at least one information providing server 400 may be implemented as a computer capable of accessing a remote server or terminal through a network. Here, the computer may include, for example, navigation, a laptop equipped with a web browser, a desktop, and a laptop.

Figure 2 is a block diagram for explaining a decision service providing server included in the system of Figure 1, Figures 3 and 4 are artificial intelligence-based selling price determination service for the B2B market according to an embodiment of the present invention is implemented It is a diagram for explaining an embodiment.

Referring to FIG. 2 , the determination service providing server 300 includes an external information collection unit 310 , an internal information collection unit 320 , a determination unit 330 , an order management unit 340 , a check unit 350 , and modeling. It may include a unit 360 , a big data conversion unit 370 , and an error detection unit 380 .

The decision service providing server 300 according to an embodiment of the present invention or another server (not shown) operating in conjunction with at least one affiliated store terminal 100 and at least one information providing server 400 for the B2B market When transmitting an artificial intelligence-based selling price determination service application, program, app page, web page, etc., at least one merchant terminal 100 and at least one information providing server 400, artificial intelligence-based sales for the B2B market You can install or open a pricing service application, program, app page, web page, etc. In addition, the service program may be driven in at least one affiliated store terminal 100 and at least one information providing server 400 by using a script executed in a web browser. Here, the web browser is a program that enables the use of a web (WWW: World Wide Web) service, and refers to a program that receives and displays hypertext written in HTML (Hyper Text Mark-up Language), for example, Netscape. , Explorer, Chrome, and the like. In addition, the application means an application on the terminal, for example, includes an app (App) executed in a mobile terminal (smartphone).

Referring to FIG. 2, the external information collection unit 310 includes an external API for public use including auction information, news information, weather information, and data from the National Statistical Office, and a confidential API including food material information for each restaurant product and matchmaker purchase information. We may collect external information. In this case, the external information collection unit 310 may use a web crawler when collecting external information. Web crawler for web crawling is a computer program that browses the World Wide Web in an organized and automated way. The work of a web crawler is called web crawling or spidering, and it is a type of bot or software agent. There are two main types of web crawlers: general web crawlers and distributed web crawlers.

When explaining the basic operation of a web crawler, it starts by fetching a URL from the URL Frontier module and fetching a web page of the URL using the HTTP protocol. Then, the Fetch module stores the web page in temporary storage, the Parser module extracts the text and links, and the text is sent to the Indexer. In the case of a link, whether it should be added to the URL frontier is determined by going through the Content Seen, URL Filter, and Duplication URL Element modules. At this time, since it is virtually impossible to crawl all web documents with a general web crawler, a distributed web crawler can be further used.

Distributed web crawler is largely divided into two types, one of which is centralized and the other is P2P (or Fully-Distributed). A centralized distributed web crawler is a structure in which the URL manager acts like a server and the crawler acts as a client. After downloading the document from the crawler, extracting the OutLink URL and passing it to the URL manager, the URL manager checks whether the URL of the downloaded document is the URL and removes the URL duplication. In other words, the URL manager takes over the URL duplication and URL management in the general web crawler. On the other hand, in the P2P method, each crawler has a completely independent structure. In the P2P method, each crawler operates like a general web crawler. Each crawler downloads documents, extracts OutLink URLs, and deduplicates URLs. Each crawler operates independently. To do this, the list of downloaded URLs managed by each crawler must be mutually exclusive. Otherwise, the phenomenon of downloading the same document from different crawlers will occur. As a way to solve this, each crawler can manage the download URL domains by dividing them exclusively. In other words, it is a method of managing only what belongs to the download domain and passing the rest of the URLs to other crawlers. If this method is used, each crawler can operate independently.

Next, web content needs to be extracted. Web content extraction technology provides a function to automatically extract content to be used for information analysis from a web document. A web content extraction system is a device that extracts only content by automatically producing rules for extracting content, a rule generator that automatically creates content extraction rules, and a navigation content eliminator that removes navigation content from a given web document. , it may be composed of a content extractor (Core Context Extractor) that extracts content through comparison of keyword similarity with content extraction rules. Of course, it is possible to collect real-time data by various methods other than the above-described crawling method, and is not limited to the listed ones, and is not excluded for reasons not listed.

The internal information collection unit 320 may collect internal information including purchase price information of at least one type of food material accumulated during a preset period, order information, and food material information for each restaurant product. At this time, the food material information for each restaurant product is, for example, if the product (menu) costs 20,000 won for 2 servings of stir-fried octopus, 1 octopus, 1/2 onion, 1/6 carrot, 1/4 piece of cabbage, 1 green onion , cooking oil 1T, sesame oil 3 drops, sesame 1T, soy sauce 3T, sugar 1T, cooking wine 1T, red pepper powder 2T, minced garlic 1T. it could be At this time, the reason for receiving the order information is that it is possible to extract the food material information to be included in the order information (menu) as described above by predicting the type and quantity of the next order information by knowing how many orders are received. to be. However, in most restaurants, recipes are kept secret due to business know-how, so it is impossible to disclose all of this information. Therefore, it is possible to create and branch one block constituting the private block chain, and access it when calculating only food materials after inquiring it from The 3rd Trust Party.

The determining unit 330 may determine the selling price through a pre-established selling price determination algorithm to ensure a preset margin rate based on internal information and external information. In this case, as the selling price determination algorithm, deep learning may be used as one type of artificial intelligence algorithm. Artificial intelligence is the broadest concept, and one of the important implementation methods of artificial intelligence is machine learning, and one of the methodologies of machine learning is deep learning. Deep learning is also called a deep neural network (DNN), and a neural network consists of an input layer, a hidden layer, and an output layer. A deep neural network is a neural network constructed by stacking two or more hidden layers. As libraries for machine learning or deep learning, there are many libraries such as TensorFlow, Torch, Caffe, MXNet, and Chainer, so you can use a library with good algorithm performance.

TensorFlow is an open source software library that performs numerical operations using data flow graphs. TensorFlow was originally developed by researchers and engineers from the Google Brain team, Google's artificial intelligence research organization, for the purpose of researching machine learning and deep neural networks. In addition, linear regression, one of the basic machine learning techniques, is called linear regression when there is one variable, and it is a method of predicting a value by modeling with a linear correlation. Hypotheses are expressed in Equation 1 below.

In a big data-based food material pricing platform, multiple variables must be linked to a single result. In this case, it is called multi-variate linear regression. For example, by implementing multi-variate linear regression using Tensorflow, which is used by many companies among open source deep learning frameworks, on data that changes prices depending on climate, deep for price prediction It becomes possible to design a learning learning model.

Since the big data is collected by the big data conversion unit 370, for example, if you want to understand the relationship between the weather data and food material price data, after building the weather data and food material price data as a dataset, a deep learning model is designed. and implement it. In this case, it is possible to induce an optimal price to be purchased in response to the frequently fluctuating food material prices through the Open API that can obtain both structured data and unstructured data. In order to analyze food material price fluctuations, we need weather data from previous years and food material prices. Data can be provided free of charge from the Korea Meteorological Administration’s meteorological data open portal and KAMIS agricultural product distribution information, which are provided as Open APIs in Korea. For example, the average monthly vegetable wholesale price for 10 years can be collected from an agricultural product distribution information site. Multiple types of food material information can be collected based on wholesale prices, and monthly weather average data for 10 years can be collected from the meteorological data open portal.

After merging and refining the weather data and the average price data, a data sheet for the composition of the analysis data can be created, for example, the average temperature, average maximum temperature, average minimum temperature, and precipitation affect the price Therefore, by assigning weights to each, hypotheses can be established as in Equation 2 below.

For convenience, in order to express the calculation conveniently except for bias, a matrix expression is used, and if expressed as a matrix product, it can be calculated simply as in Equation 3 below.

The above hypotheses can be tested by implementing a multivariate linear regression model in Python code using the TensorFlow library. For example, read the above data sheet, input four average weather data values into the x variable, input the average price data into the y variable, set hypotheses using the TensorFlow function, and optimize the gradient descent The method (Gradient Descent Optimizer) is used and, for example, in the process of repeating learning up to 100,000 epochs, the loss value (Cost) and hypotheses can be checked by outputting every 1000 epochs, in which case the hypothesis is the optimal price. (Price) can be In addition, it is also possible to create a graph that visualizes the loss value (Cost) derived through learning. The loss value gradually decreases and may show a form converging to 0.

After learning, the model is stored in a checkpoint file, weather data at the time when food material prices are required is input as a variable, and the model is called from the saved checkpoint file to predict the optimal price and perform an experiment. In this way, the quality of analysis results can be improved through the connection of various big data such as wholesale market price information and weather data, and the prediction will become more accurate if an algorithm that increases variables and learns time series data is added in the above method. can In addition, it is possible to secure the quantity at the lowest price of such food materials so that it can be delivered by lowering the delivery unit price. It can be grafted to a distribution platform that can be used to receive real-time weather forecasts, so that the predicted food material prices can be viewed visually easily and conveniently.

Here, the price prediction model can be further applied using a recurrent neural network suitable for time series data learning and prediction among machine learning techniques. It is an artificial neural network model developed to be transmitted to an input point that generates Accordingly, since time information between data can be considered, it is a model suitable for learning and predicting time series data. In particular, several transformation techniques, such as RNN-LSTM (Long Term Memory), a transformation model of a cyclic neural network developed to compensate for the problem of gradient loss during data learning of a cyclic neural network model, have been developed and applied to various problems. In the method according to the embodiment, it is possible to provide a price prediction comparison service by applying and evaluating various transformation techniques of the recurrent neural network.

In the cyclical neural network, the data input for each time step and the state of the previous time step are input to create a new state and use it to predict the price. This is used to predict the price. In this price prediction, the number of days in the past input for each time step has a significant effect on the price prediction performance. In addition to this, the learning rate and the number of learning times of the cyclic neural network model are also variables that affect learning. Accordingly, while changing the parameter (price fluctuation factor parameter) values, the value showing the lowest RMSE (Root Mean Square Error) used as a performance measure is set as the parameter value of the recurrent neural network model according to an embodiment of the present invention. can be selected In addition, in order to solve the gradient loss problem, one of the LSTM Cell and the GRU Cell, which is a recurrent neural network model, can be selected. Both techniques complement the long-term memory impairment problem of cyclic neural networks. In the case of GRU Cell, the greater the number of data types it learns, the better it is in terms of learning speed and prediction performance than LSTM. Of course, the method of modeling the artificial intelligence algorithm is not limited to the above-described method or variable, and is not excluded for reasons not listed.

The order management unit 340 may receive an order and payment from the at least one affiliated store terminal 100 at the selling price determined by the determination unit 330 . The at least one affiliated store terminal 100 may order and pay for at least one type of food material at a price derived by a pre-established selling price determination algorithm. At this time, the established selling price determination algorithm models an artificial intelligence algorithm that uses each item included in internal information and external information as a data set as a variable, and outputs the selling price when inputted as a query. is a result In this case, various items may be included in the internal information and the external information, and it is not limited to the items included in the aforementioned internal information and external information, so it may be changed, modified, or updated according to each embodiment. In addition, even when an error is identified in an example to be described later, it may be re-learned to correct the error and increase accuracy. In the beginning, the accumulated internal information may be relatively small, so there may be errors that occur because sufficient data sets are not collected. can increase

Accordingly, it is possible to eliminate the case where the exact price of food material A was not known because the time of ordering food material A was before the auction. For example, assuming that the auction is held every morning (Monday-Friday) at 5 o'clock, and the franchisee placing the order places the order at 9:00 pm the day before the closing date of the restaurant, the exact price of the ingredients that will arrive tomorrow may not be known. At this time, when the prediction system according to an embodiment of the present invention is used, food materials can be purchased at a price predicted with high accuracy even on the previous day, and it is possible to know when to buy the food materials with a graph, etc., so that the supply of food materials It can also be advantageous when securing a product or lowering the unit price.

The check unit 350 may check the accuracy of whether the sale price guarantees a preset minimum margin rate for the matchmaker when compared with the matchmaker purchase price for the same day. In this case, the minimum margin may be, for example, 20%, but may be changeable according to an embodiment.

The modeling unit 360 builds a database by inputting internal information and external information, and then performs database classification (DB Classification), opinion mining (Opinion Mining), and data processing (DataSet). , and after modeling the artificial intelligence algorithm through the learning segment and the judgment segment as a dataset, the selling price determination algorithm can be built. In this case, the learning segment may include a feature extraction and model learning, and the judgment segment may include a sensing element and a model judgment.

At this time, the sentiment score can be calculated by applying opinion mining to the preprocessed data, and at this time, the sentiment dictionary builder ( Sentiment Dictionary Builder). Based on this, sentiment analysis is carried out, and in this process, a detailed analysis algorithm for detailed analysis of sentiment may be added.

Online reviews are created in the form of multimedia such as text, images, and videos, and feature selection using machine learning may be used to select important words within the text. FS can filter out unnecessary words and improve classification performance in selecting important words. Alternatively, for an effective opinion mining model for sentiment analysis, a combination of a learning classifier and an attribute selection method FS may be used. Here, FS may be CFS (Correlation based Feature Selection), IG (Information gain), etc., and the classifier used for classification is Logistic Regression, Decision Tree, Neural Network, Support Vector. Machine (Support Vector Machine), Naive Bayesian Network, Random Forest, Bagging, Stacking, Random Subspace Any one or a combination of at least one can

FS, which appeared with the advent of data sets containing hundreds of thousands of attributes, can model the process of data creation and management and secure variables. to maintain or improve performance. For attribute selection, filter learning, in which the derivation algorithm learns as an independent preprocessing step, which depends on the general properties of the data, is used as a black box, and predictive performance is used to determine the relative usefulness of a subset of variables. Wrapper learning using a learning method as a subroutine that evaluates and calls a property selection algorithm to evaluate a subset of each feature (features) Subsets are displayed as included in the classification criteria construct, and may include embeddings that allow searching and finding dependencies in the combined space of subsets of properties and hypotheses.

IG is one of the single-variable methods of evaluation attributes. This method evaluates functions according to information acquisition and considers only one function at a time, and entropy measurement is as shown in Equation 4 below.

Here, p(y) is the limiting probability density function for the random variable (Y). When the observed Y value in the training data set S is partitioned according to the second attribute, the X value, if the entropy of Y for the partition induced by X is smaller than the entropy of Y before the division, then the There is a relationship between the features. At this time, after observing X, Y is the same as in Equation 5 below.

where p(y|x) is the conditional probability of a given x. Given the entropy relative to the impurity in the training set S, we can define a measure that reflects the additional information about Y provided by X representing the amount by which the entropy of Y decreases. This measurement is an index indicating the dependency between X and Y, and is referred to as IG, and the resulting equation is as follows. In this case, a threshold value for providing regular classification of all attributes and selecting the number of attributes according to the obtained order may be preset.

CFS is a multivariate filter algorithm that ranks a subset of attributes according to a correlation-based heuristic evaluation function. This is a method of evaluating the error of whether or not subsets of included properties are correlated with each other. Heuristic evaluation deals with low-relevance variables that impede the performance of classification on target variables. Attributes with low relevance are excluded because they have low correlation with classes. Attribute selection can be performed independently.

Logistic regression is a regression analysis method used for the purpose of linear or non-linear classification. In this method, the output of the training instance belonging to each class is set to 1, and the output of the non-member instance is set to 0, and regression is performed on the class. A linear equation is derived from this result, and then, when performing unknown class verification, it is a classifier that calculates the result of each linear equation and classifies it by selecting the largest value. The goal of naive Bayesian is to accurately predict the class of a test instance by learning a training instance that includes class information. In this case, the naive Bayesian network can be specialized in the form of a naive because it depends on two important simplification assumptions. In particular, it is assumed that the prediction properties are conditionally independent for each given class, and the identified or latent properties influence the prediction process.

The neural network is an algorithm that mimics the neurons of the human brain. It is a multilayer perceptron and consists of an input layer, a hidden layer, and an output layer. It is composed of nodes. Next, the hidden layer processes the variable values transmitted from the input layer as a non-linear function and transmits them to the output layer. The output layer has nodes corresponding to the target variables. Random forest is an ensemble learning method for performing regression and other tasks, in which a large number of decision trees are constructed during a training period, and can be operated by classification mode or class output, which is the average prediction of individual trees.

Random Forest is suitable for emotion classification because it is a more suitable classifier for sentiment classification compared to various ensemble classifiers due to the many overlapping values of text classification. In a random space, the training data set can be modified using algorithms such as bagging, where the modification can take place in the attribute space rather than the instance space. Random forest can use arbitrary subspaces to construct and aggregate base classifiers, and when the data set has a large number of redundant or unrelated properties, the base classifier is more effective in random subspaces compared to the original feature space. You can choose a classifier. The combined decision of this base classifier may outperform a single classifier built on the training data set of the entire set of attributes.

A decision tree is a non-parametric method that can be applied to classification or regression tasks. It is a hierarchical data structure for supervised learning in which the input space is divided into local regions to predict the dependent variable. The support vector machine provides an optimal separation hyperplane state for solving the classification task. Mathematical analysis can represent nonlinear problems related to the input space as linear problems in high-dimensional isolated hyperplanes.

Bagging is an abbreviation of Bootstrap aggregating. After repeatedly sampling according to a uniform probability distribution, classification prediction is made according to voting by combining a decision tree model. Bagging is mainly used in regression analysis and can improve stability and accuracy and reduce variance. Unlike bagging, which combines models of the same type, stacking is the task of combining predictions of different algorithms by learning various learning algorithms. In particular, it is possible to find and combine the classifier with the best performance after estimating the performance of which classifier is reliable using the meta learner.

The big data conversion unit 370 stores raw data including internal information and external information in parallel and distributedly, and unstructured data, structured data, and semi-structured data included in the stored raw data. (Semi-structured) is refined, preprocessing including classification is performed as meta data, analysis including data mining is performed on the preprocessed data, and the analyzed data can be visualized and output. At this time, data mining is a classification that predicts a new data class by learning a training data set with a known class by exploring the inherent relationship between preprocessed data, or grouping data based on similarity without class information. clustering) can be performed. Since the example of collecting big data is the same as described above, a redundant description will be omitted.

The error detection unit 380 performs error verification on the difference between the selling price determined by the established selling price determination algorithm and the selling price on the same day, and identifies the error part and error cause through error verification, and the identified error part and Deep learning using an artificial neural network is performed to discover patterns for error causes and predict error rates through classification, and errors can be identified so that the data derived from deep learning is reflected in the established selling price determination algorithm.

Hereinafter, an operation process according to the configuration of the determination service providing server of FIG. 2 will be described in detail with reference to FIGS. 3 and 4 as an example. However, it will be apparent that the embodiment is only one of various embodiments of the present invention and is not limited thereto.

Referring to Figure 3a, the platform according to an embodiment of the present invention is a wholesale market direct transaction artificial intelligence platform for alleviating the burden of purchasing food materials in the restaurant business, and one embodiment of the present invention among a plurality of systems is a second system, that is, an artificial intelligence algorithm. It is a system for determining the selling price. It determines the selling price with a guaranteed margin of 20% compared to the purchase price, and can be set based on internal and external information. 3b and 3c are system structural diagrams of the present invention. The data of FIG. 3c is information for deriving the results of the artificial intelligence algorithm, and is made based on the collection of internal information and external information, and the DB/DataSet stores the collected information. Classifying data for each visualization through mining after preprocessing, modeling is for deriving results through modeling by matching each algorithm with feature extraction, weights, and fixed variable values It is a visualization material that can be viewed by administrators or users (merchants) through pages, etc.

Referring to FIG. 3D , the service of the present invention is shown. In the wholesale market, an auction for food materials occurs every day, and accordingly, even for the same food material, the price difference depending on specific variables (weather, specific day, etc.) is extreme. is showing Accordingly, a pricing algorithm has been developed based on data and know-how accumulated over 7 years on the platform according to an embodiment of the present invention, guaranteeing a 20% matchmaker margin, and improving reliability through linking with external APIs such as wholesale markets. can The internal price determination algorithm is an algorithm based on internal and external information for price determination, applies a margin rate guarantee system, and uses internal information (past market price fluctuations, weather, specific days, know-how), etc. In addition, the price determination external API is an API for collecting external information, and external information, for example, public API of the Agricultural and Fishery Products Distribution Corporation or wholesale market auction data, may be included.

At this time, the margin rate guarantee accuracy is the accuracy of whether the selling price guarantees the matchmaker's minimum margin of 20% when compared with the matchmaker purchase price for the same day, and the determined price application rate is the price determined through the selling price determination system in the mobile application It is the accuracy of what appeared.

Referring to FIG. 4A , according to an embodiment of the present invention, direct transaction in the wholesale market is possible through a mobile application. It enables reasonable purchase of wholesale market food materials that were only available for offline purchase through the mobile application, and the sale price compared to the auction market price on the day A transparent transaction environment can be provided through disclosure. In addition, the efficiency of the wholesale market and restaurant can be maximized through the artificial intelligence algorithm, and price and demand prediction can be made based on the artificial intelligence algorithm, so the price in the wholesale market can be stabilized, and automatic ordering and quotation comparison can be performed. Through this, restaurant owners can help them focus on restaurant operations, and the wholesale market reduces resources for guaranteeing margins and sales and customer service.

Referring to FIG. 4B , the service according to an embodiment of the present invention has know-how acquired while operating offline distribution and vegetable stores, price fluctuations by product, product cause information, information on major purchase products by restaurant, major sales products by industry Data, major sales product information by region, sales volume information by product, sales status information by wholesale market middleman, order pattern and amount information by restaurant, and actual efficiency information of logistics routes by time period have been used to secure and build big data, so it is modeled. In other words, if modeling is performed to create an artificial intelligence algorithm, an algorithm with an accurate predictive degree can be created.

Referring to Figure 4c, market price fluctuations and weather data, product recommendation data by category, order pattern data by company, relationship analysis algorithm for agricultural products past data and environmental variables, relationship analysis algorithm for agricultural production and growth characteristics, products used by customers and purchase variables Order prediction algorithm, other distribution channel sales price collection and price relationship analysis algorithm, sales information and environment variable relationship prediction algorithm can be derived, and price information according to customer industry classification, product recommendation for each customer, and supplier inventory burden can be derived. Demand forecasting, order pattern analysis, sales estimation, and demand forecasting for

Referring to FIG. 4D , since the platform according to an embodiment of the present invention brings goods directly through the public wholesale market, it is not possible to go through an intermediate distribution stage and builds an infrastructure that can receive various fresh food or industrial products. Therefore, immediate supply and direct supply are possible. In addition, as shown in FIG. 4E, logistics infrastructure efficiency was secured across the country using four public wholesale markets as logistics bases.

The matters that have not been described with respect to the method of providing the artificial intelligence-based selling price determination service for the B2B market of FIGS. 2 to 4 are described above with respect to the method of providing the artificial intelligence-based selling price determination service for the B2B market through FIG. 1. Since it is the same as the content or can be easily inferred from the described content, the following description will be omitted.

5 is a diagram illustrating a process in which data is transmitted/received between components included in the artificial intelligence-based selling price determination service providing system for the B2B market of FIG. 1 according to an embodiment of the present invention. Hereinafter, an example of a process in which data is transmitted and received between each component will be described with reference to FIG. 5, but the present application is not limited to such an embodiment, and the example shown in FIG. 5 according to various embodiments described above will be described. It is apparent to those skilled in the art that the data transmission/reception process may be changed.

Referring to FIG. 5, the decision service providing server provides an external API for public use including auction information, news information, weather information, and data from the National Statistical Office, and external information including a confidential API including food material information for each restaurant product and middleman purchase information. Collect (S5100).

And, the determination service providing server collects internal information including purchase price information of at least one type of food material accumulated for a preset period, order information, and food material information for each restaurant product (S5200), and collects internal information and external information The selling price is determined through a pre-established selling price determination algorithm so as to ensure a preset margin rate based on it (S5300).

In addition, the determination service providing server receives the order and payment from at least one affiliated store terminal at the determined selling price (S5400).

The order between the above-described steps ( S5100 to S5400 ) is merely an example and is not limited thereto. That is, the order between the above-described steps ( S5100 to S5400 ) may be mutually changed, and some of these steps may be simultaneously executed or deleted.

Matters that have not been described with respect to the method of providing an artificial intelligence-based selling price determination service for the B2B market of FIG. Since it is the same as the content or can be easily inferred from the described content, the following description will be omitted.

The method for providing an artificial intelligence-based selling price determination service for the B2B market according to an embodiment described with reference to FIG. 5 is in the form of a recording medium including instructions executable by a computer, such as an application or program module executed by a computer. can also be implemented. Computer-readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. Also, computer-readable media may include all computer storage media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.

The method for providing an artificial intelligence-based selling price determination service for the B2B market according to an embodiment of the present invention described above includes an application basically installed in a terminal (which may include a program included in a platform or operating system basically installed in the terminal) ), and may be executed by an application (ie, a program) installed directly on the master terminal by a user through an application providing server such as an application store server, an application, or a web server related to the corresponding service. In this sense, the AI-based selling price determination service providing method for the B2B market according to an embodiment of the present invention described above is implemented as an application (that is, a program) installed basically in a terminal or directly installed by a user, and installed in the terminal. It may be recorded on a computer-readable recording medium, such as.

The above description of the present invention is for illustration, and those of ordinary skill in the art to which the present invention pertains can understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. For example, each component described as a single type may be implemented in a dispersed form, and likewise components described as distributed may be implemented in a combined form.

The scope of the present invention is indicated by the following claims rather than the above detailed description, and all changes or modifications derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention. do.

Claims (7)

at least one affiliated store terminal for ordering and paying for at least one type of food material at a price derived by a pre-established selling price determination algorithm; and
An external information collection unit that collects external information including an external API for public use including auction information, news information, weather information, and data from the National Statistical Office, and a confidential API including food material information for each restaurant product and purchase information from a middleman, accumulated over a preset period An internal information collection unit that collects internal information including purchase price information of at least one type of food material, order information, and food material information for each restaurant product, the pre-established construction to ensure a preset margin rate based on the internal information and external information a determination service providing server comprising: a determination unit for determining a selling price through the determined selling price determination algorithm;
An artificial intelligence-based selling pricing service provision system for the B2B market, including.
The method of claim 1,
The decision service providing server,
a check unit for checking the accuracy of whether the sale price guarantees a preset minimum margin rate for the matchmaker when compared with the matchmaker purchase price for the same day;
Artificial intelligence-based selling price determination service providing system for the B2B market, characterized in that it further comprises.
The method of claim 1,
The decision service providing server,
After building a database by inputting the internal information and external information, the database classification (DB Classification), opinion mining (Opinion Mining) and data processing (Data Processing) are performed to generate a dataset (DataSet), and the a modeling unit for constructing the selling price determination algorithm after performing modeling of an artificial intelligence algorithm through a learning segment and a judgment segment with a dataset;
Artificial intelligence-based selling price determination service providing system for the B2B market, characterized in that it further comprises.
4. The method of claim 3,
The training segment includes a feature extractor and model learning,
The judgment segment is an artificial intelligence-based selling price determination service providing system for the B2B market, characterized in that it includes a sensing element and a model judgment.
The method of claim 1,
The decision service providing server,
Raw data including the internal information and external information is stored in parallel and distributed, and unstructured data, structured data and semi-structured data included in the stored raw data are stored. a big data conversion unit that refines, performs preprocessing including classification as meta data, analyzes the preprocessed data including data mining, and visualizes and outputs the analyzed data;
Artificial intelligence-based selling price determination service providing system for the B2B market, characterized in that it further comprises.
6. The method of claim 5,
The data mining is a classification (Classification) that predicts a class of new data by learning a training data set with a known class by exploring the inherent relationship between the preprocessed data, or grouping data based on similarity without class information ( Clustering), an artificial intelligence-based selling pricing service providing system for the B2B market, characterized in that it includes performing.
The method of claim 1,
The decision service providing server,
Error verification is performed on the difference between the selling price determined by the established selling price determination algorithm and the selling price on the day, and the error part and cause of the error are identified through the error verification, and the error part and the cause of the error are identified. an error detection unit that performs deep learning using an artificial neural network to discover a pattern and predict an error rate through classification, and reflects the data derived from the deep learning into the established selling price determination algorithm;
Artificial intelligence-based selling price determination service providing system for the B2B market, characterized in that it further comprises.

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