KR102139620B1 - Method And Apparatus for Managing Logistics by Using Quick Response Code - Google Patents

Abstract

Disclosed are a method and device for managing logistics by using a QR code. An embodiment of the present invention provides the method and device for managing a QR code to collect product purchase information that a first QR code attached to a specific product is recognized from a consumer terminal and collect logistics location movement information that a second QR code attached to a specific product is recognized from a logistics base for gathering and matching the product purchase information and the logistics location movement information, and then generate optimal output information and optimal logistics distribution information to use the optimal output information and the optimal logistics distribution information supporting to make a decision for each specific product.

Description

Method and Apparatus for Managing Logistics by Using Quick Response Code}

One embodiment of the present invention relates to a method and apparatus for managing logistics using a QR code.

The contents described below merely provide background information related to the present embodiment, and do not constitute a prior art.

In order to determine whether a product is genuine, a genuine product authentication method is used to give a serial number, barcode, etc. to the product. Due to the development of copying technology, manufacturing technology, etc., if the identification means such as the serial number assigned to the product are copied, the consumer cannot easily determine whether the product is genuine.

In order to prevent duplication of the serial number-labeled label itself, a technique for reducing the possibility of duplication has been developed using a method using a light reflective film or the like.

When the goods are manufactured and there is an instruction for delivery, the goods are delivered from the place of production to the place of delivery, and there are several intermediate steps from the manufacture of goods to the place of delivery. easy. Therefore, in order to systematically manage logistics, a logistics management system that manages the entire process of delivery from production to delivery is introduced and used.

However, the conventional logistics management system does not manage the entire process from manufacturing of goods to the place of delivery, but only some processes that require management are managed independently, so inventory management other than logistics, or copying or illegally manufactured goods are distributed. There is a problem that cannot be managed by one system.

This embodiment collects product purchase information that recognizes a first QR code attached to a specific product from a consumer terminal, and collects and matches logistics location movement information that recognizes a second QR code attached to a specific product from a logistic base. The objective is to provide a logistics management method and apparatus using QR codes that are used to support decision-making by specific products by generating optimal production information and optimal distribution information after collecting them in a form.

According to an aspect of the present embodiment, a product purchase information management unit collecting product purchase information recognizing a first QR code (Quick Response Code) attached to a specific product from a consumer terminal and authenticating the product purchase information; A logistics information management unit collecting and authenticating logistics location movement information recognizing a second QR code attached to the specific product from the logistics base; An information collecting unit for generating raw data collected in a form of accumulating and matching the product purchase information and the logistics location movement information based on product identification information corresponding to the specific product; And generating optimum production information based on the raw data, generating optimal distribution information for classifying the optimum production information into a sales place, and making decisions for specific products based on the optimum production information and the optimal distribution information. It provides a logistics management device characterized in that it comprises a decision support unit to support.

As described above, according to the present embodiment, the consumer terminal collects product purchase information recognizing the first QR code attached to the specific product, and the logistics location recognizes the second QR code attached to the specific product from the logistics base. It has the effect of supporting decision-making by specific products by generating movement information and collecting it in a matching form, and then generating optimal production information and optimal distribution information.

1 is a view showing a logistics management system using a QR code according to this embodiment.
2 is a block diagram schematically showing a logistics management device according to the present embodiment.
3 is a diagram showing a list of issued QR codes according to the present embodiment.
4 is a diagram showing the authentication status of the registered product according to the present embodiment.
5 is a view showing the proportion of sales for registered products according to the present embodiment.
6 is a view showing the detailed certification details of the registered individual products according to the present embodiment.
7 is a view showing a country-specific certification history of individual products according to the present embodiment.
8 is a view showing a certification history of each individual product according to the present embodiment.
9 is a view showing a certification history of each device of each product according to the present embodiment.
10 is a view showing an insight composite analysis according to the present embodiment.
11 is a view showing detailed items for a selected product in the insight composite analysis according to the present embodiment.
12 is a view showing a period and time zone selection in the insight composite analysis according to the present embodiment.
13 is a diagram showing date selection in an insight composite analysis according to the present embodiment.
14 is a diagram showing spatial selection in the insight composite analysis according to the present embodiment.
15 is a view showing customer selection in the insight composite analysis according to the present embodiment.
16 is a conceptual diagram illustrating a method for predicting a sales rate according to the present embodiment.
17 is a conceptual diagram showing a method of generating an artificial intelligence learning model according to the present embodiment.
18 is a conceptual diagram illustrating a method of determining a scale according to a product line during learning according to the present embodiment.

Hereinafter, this embodiment will be described in detail with reference to the accompanying drawings.

1 is a view showing a logistics management system using a QR code according to this embodiment.

The logistics management system according to this embodiment is applicable to B2B (Business to Business) or B2C (Business to Customer).

The logistics management system using the QR code according to the present embodiment includes a product 110, a first QR code 122, a second QR code 124, a consumer terminal 132, a dedicated reader 134, and a logistics management device ( 140). Components included in the logistics management system are not necessarily limited thereto.

The first QR code 122 is attached to one side of the product 110, and the second QR code 124 is attached to the other side.

The first QR code 122 can be attached to one side of food, industrial products, etc. as well as cosmetics. The first QR code 122 includes product identification information corresponding to the corresponding product. The first QR code 122 includes information requesting to collect purchase location information, purchase date information, and purchase weather (humidity, temperature) information, terminal model information, subscriber gender information, subscriber age information, and SNS connection information. It contains information that you are asked to collect selectively.

The second QR code 124 is a location-only QR, and can be recognized by a dedicated reader. The second QR code 124 includes product identification information corresponding to the corresponding product. The second QR code 124 includes information requesting to collect the location information and the arrival date and time (year/month/day/time) at each node by checking the time point of cargo movement for each node.

The second QR code 124 can be attached to the other side of the product. Since the second QR code 124 includes only location information, it can be implemented in a smaller size than the first QR code 122. When the product to which the second QR code 124 is attached is passed through the logistic base, the dedicated reader 134 provided at the logistic base recognizes the second QR code 124 to determine the point of movement of cargo for each node. Check.

The product to which the second QR code 124 is attached can be used to locate the export product or the import product. When importing a product to which the second QR code 124 is attached, the logistics management device 140 may check the route of goods shipped from the origin to the customs via the logistics base. When exporting a product to which the second QR code 124 is attached, the logistics management device 140 may check the route of the product shipped from the origin to the destination via the logistics base.

The consumer terminal 132 refers to an electronic device that performs voice or data communication via a network according to a user's key operation. The consumer terminal 132 includes a memory for storing a program or protocol for communicating with the logistics management device 140 via a network, a microprocessor for executing a corresponding program, and calculating and controlling the program.

The consumer terminal 132 includes a smart phone, a tablet, a laptop, a personal computer (PC), a portable multimedia player (PMP), and a wireless communication terminal. ), a media player, or the like.

When the consumer terminal 132 photographs the first QR code 122 attached to the product 110, the product identification information corresponding to the first QR code 122 is extracted and transmitted to the logistics management device 140. If the consumer terminal 132 is authenticated that the product identification information corresponding to the first QR code 122 from the logistics management device 140 is genuine, the purchase location, purchase weather (humidity, temperature), terminal model, subscriber gender, The subscriber's age and the presence or absence of SNS connection are collected and transmitted to the logistics management device 140.

The dedicated reader 134 is installed in the logistics base and recognizes the second QR code 124 attached to the product 110 passing through the logistics base. The dedicated reader 134 extracts product identification information corresponding to the second QR code 124 and transmits it to the logistics management device 140. The dedicated reader 134 extracts location information and arrival date (year/month/day/time) from the logistics management device 140 when the product identification information corresponding to the second QR code 124 is recognized as a genuine logistic. Transfer to the management device 140.

The logistics management device 140 includes the same hardware module as a typical web server or network server in hardware. The logistics management device 140 may be implemented in the form of a web server or a network server. The logistics management device 140 generally communicates with an unspecified number of clients or other servers via an open computer network such as the Internet.

The logistics management device 140 refers to a computer system or computer software (web server program) that derives and provides a work result corresponding to a request to perform a work performed by a client or another web server. The logistics management device 140 should be understood as a broad concept including a series of application programs or various databases built in the device, in addition to the above-described web server program.

For example, if a customer purchases cosmetics using the consumer terminal 132 and then photographs the first QR code 122 attached to the cosmetics, the logistics management device 140 uses product identification information corresponding to the first QR code. For the genuine product activation, the customer identification information, purchase date (year/month/day/time), and weather at the time of purchase (humidity/temperature) are collected from the consumer terminal 132.

The logistics management device 140 can process and visualize the information at the time of purchase (customer identification information, date and time of purchase, time of purchase), and then output it. When the seller or manager accesses the system, the logistics management device 140 displays the sales product information, the number of sales, and the sales location (displayed on the map) to be visually processed. The logistics management device 140 is applicable to a company's decision support system.

Logistics management device 140, each time the product 110 is attached to the second QR code 124 is shipped through the logistics base, location information, arrival date and time (year / month / day / time) at each node After checking, it is possible to generate and output route information.

Logistics management device 140 reads the information that recognizes the first QR code 122 from the consumer terminal 132, and the information that recognizes the second QR code 124 from the dedicated reader 134 provided in the logistics base. Reads. The logistics management device 140 reads the customer identification information, the date of purchase, and the time of purchase at the time of purchase, which is the information recognized by the first QR code 122 from the consumer terminal 132. The logistics management device 140 reads the location information, which is the information recognized by the second QR code 124, from the dedicated reader 134 provided at the logistics base, and the date and time of arrival (year/month/day/time).

The logistics management device 140 processes the collected data using the first QR code 122 and the second QR code 124, and then calculates logistics distribution information and production quantity information using a preset algorithm and AI. . The logistics management device 140 selects a region in which a specific product (for example, A product) is sold a lot based on the collected information, and provides distribution information for quickly supplying a specific product (for example, A product) to the corresponding region. Logistics management device 140 calculates trend information based on an increase rate and a decrease rate for sales by a specific region Logistics management device 140 determines product information to be produced based on trend information.

The logistics management device 140 calculates the future sales rate by learning the increase rate and the decrease rate for sales by specific region. The logistics management device 140 may calculate a future sales rate using a learning model.

2 is a block diagram schematically showing a logistics management device according to the present embodiment.

The logistics management device 140 according to the present embodiment includes a product purchase information management unit 210, a logistics information management unit 220, an information collection unit 230, and a decision support unit 240. Components included in the logistics management device 140 are not necessarily limited thereto.

Each component included in the logistics management device 140 may be connected to a communication path connecting a software module or a hardware module inside the device to operate organically with each other. These components communicate using one or more communication buses or signal lines.

Each component of the logistics management device 140 shown in FIG. 2 refers to a unit that processes at least one function or operation, and may be implemented by a software module, a hardware module, or a combination of software and hardware.

The product purchase information management unit 210 collects product purchase information that recognizes the first QR code 122 attached to a specific product from the consumer terminal 132. The product purchase information management unit 210 authenticates product identification information included in the product purchase information.

When the consumer terminal 132 recognizes the first QR code, the product purchase information management unit 210 authenticates whether the product identification information corresponding to the first QR code 122 is genuine. The product purchase information management unit 210 essentially causes the consumer terminal 132 to collect purchase location information, purchase date information, and purchase weather (humidity, temperature) information when the product identification information is authenticated as a genuine product. The product purchase information management unit 210 selectively collects terminal model information, subscriber gender information, subscriber age information, and SNS connection information.

The logistics information management unit 220 collects logistics location movement information recognizing the second QR code 124 attached to the specific product from the dedicated reader 134 provided at the logistics base. The logistics information management unit 220 authenticates product identification information included in logistics location movement information.

When the dedicated reader 134 provided in the logistics base recognizes the second QR code, the logistics information management unit 220 authenticates whether the product identification information corresponding to the second QR code 124 is genuine. When the product identification information is authenticated as a genuine product, the logistics information management unit 220 is a dedicated reader provided at the logistics base whenever the product with the second QR code 124 is shipped at the time of export or import. The second QR code 124 is recognized from 134 to check the cargo movement time for each node.

The logistics information management unit 220 imports and exports the goods shipped from the origin to the customs via the logistics base based on the location information at each node and route information confirming the arrival date (year/month/day/time) through the logistics base. Create a path and print it.

The information collecting unit 230 generates raw data collected in a form of accumulating and matching product purchase information and logistics location movement information based on product identification information corresponding to a specific product. The information aggregator 230, based on the product identification information, purchase location information, purchase weather (humidity, temperature) information, terminal model information, subscriber gender information, subscriber age information, SNS connection presence information, location information at each node, arrival Match the date and time (year/month/day/time) in a cumulative form to generate raw data.

The decision support unit 240 generates optimal production amount information based on the raw data. The decision support unit 240 generates optimal logistics distribution information for classifying optimal production information into sales locations. The decision support unit 240 supports decision-making for a specific product based on optimal production information and optimal distribution information.

The decision support unit 240 calculates sales amount information for each region based on the purchase location information included in the raw data. Sales information by region includes sales information by country and sales information by city. The decision support unit 240 calculates sales amount information for each region including sales amount information for each country or sales amount for each city.

The decision support unit 240 selects a region in which a specific product (A) is frequently sold, and calculates optimal logistics distribution information for rapidly supplying a specific product (A) to the corresponding region based on sales information for each region.

The decision support unit 240 calculates an increase rate and a decrease rate for products sold in a specific region based on sales information for each region. The decision support unit 240 calculates trend information based on an increase rate and a decrease rate for a preset recent period of the entire period. The decision support unit 240 calculates optimal output information based on the trend information.

The decision support unit 240 includes a learning unit 252, a future sales rate calculation unit 254, and a logistics estimation unit 256. Components included in the decision support unit 240 are not necessarily limited thereto.

The learning unit 252 generates learning models that predict future sales rates by inputting an increase rate and a decrease rate for products sold by specific regions as learning data. The future sales rate calculation unit 254 calculates the future sales rate using a learning model.

The logistics estimator 256 calculates optimal production information based on the future sales rate. The logistics estimator 256 calculates optimal logistics distribution information for distributing optimum production information for each region.

The logistics estimator 256 extracts only product identification information certified to be genuine sales within a specific country selected based on raw data. The logistics estimator 256 extracts the sales city, the number of authentications, the cumulative sales volume, and the sales for the product identification information for which genuine sales are certified in a specific country. Logistics estimator 256 calculates sales volume increase/decrease rate information for each city based on sales city, number of authentications, cumulative sales volume, and sales.

The logistics estimator 256 calculates the optimum production information for each city based on the sales increase/decrease rate information for each city, and sums the optimum production information for each city in a specific country to generate total production information for a specific country. The logistics estimator 256 generates optimal logistics distribution information for each city that distributes total production information according to the optimum production information for each city.

The logistics estimator 256 extracts temperature information, humidity information, fine dust concentration information, and precipitation information about the city to be sold from the purchase weather information.

The logistics estimation unit 256 checks the sales amount of the product identification information sold when the temperature information included in the purchase weather information is greater than or equal to the temperature threshold and the humidity information is less than the humidity threshold. The logistics estimator 256 classifies product identification information in which the sales volume has risen from the lower region to the upper region as a temperature change product group. The logistics estimator 256 determines the temperature weight so that products with high sales have high weight by checking sales of the temperature change product line. For example, the logistic estimator 256 may determine that when the temperature is high on a sunny day and when the humidity is low, a high weight is applied to the sunscreen, which is a product whose sales increase rapidly. The logistics estimator 256 applies temperature weights when generating optimum production information for each city.

The logistics estimation unit 256 checks the sales amount of the product identification information sold when the precipitation information included in the purchase weather information is greater than or equal to the precipitation threshold. The logistics estimator 256 classifies product identification information in which the sales volume has risen from the lower region to the upper region as a precipitation change product group. The logistics estimator 256 determines the precipitation weights so that the products with high sales have high weights by checking the sales of the precipitation change product line. For example, the logistics estimator 256 may determine to give a high weight to an umbrella, which is a product whose sales increase rapidly on a rainy day. The logistics estimator 256 applies precipitation weights when generating optimum production information for each city.

The logistics estimating unit 256 checks the sales amount of the product identification information sold when the temperature information included in the purchase weather information is less than the cold limit. The logistics estimator 256 classifies product identification information in which the sales volume has risen from the lower right to the upper right as a cold protection product line. The logistics estimator 256 determines the cold wave weights so that products with high sales have high weights by checking sales of cold protection products. For example, the logistics estimator 256 may determine to give a high weight to a cold product, which is a product whose sales increase rapidly on a cold day. The logistics estimator 256 applies the cold wave weight when generating optimal production information for each city.

The logistics estimation unit 256 checks the sales amount of the product identification information sold when the fine dust concentration information included in the purchase weather information is greater than or equal to the concentration threshold. The logistics estimating unit 256 classifies product identification information in which the sales volume has risen from the lower rank to the upper rank as an air pollution product range. The logistics estimator 256 determines the air pollution degree weighting value by checking the sales of the air pollution degree product line so that the products having high sales have high weight. For example, the logistics estimator 256 may determine to give a high weight to a mask, which is a product whose sales increase rapidly on a day when the fine dust concentration is high. The logistics estimator 256 applies the weight of air pollution when generating optimum production information for each city.

The logistics estimator 256 generates information on optimum production for each city by applying temperature weights, precipitation weights, cold weights, and air pollution weights to each product group included in the sales status (increase/decrease) of each city. The logistics estimator 256 generates total production information by summing up optimal production information for each city included in a specific country. The logistics estimator 256 generates optimal logistics distribution information for each city that distributes total production information according to the optimum production information for each city.

The logistics estimator 256 checks whether or not a decision is made on optimal production information including total production information and optimal logistic distribution information by city, which is distributed according to optimal production information by city, and supports decision-making based on the verification result. .

The logistics estimation unit 256 extracts anniversary information, day information, and time zone information from the purchase date and time information.

The logistics estimator 256 checks the sales amount of the product identification information sold on the day corresponding to the anniversary information included in the purchase date and time information. The logistics estimating unit 256 classifies product identification information in which the sales volume has risen from the lower rank to the upper rank as an anniversary product line. The logistics estimator 256 determines the anniversary weights so that the products with high sales have high weights by checking the sales of anniversary products. For example, the logistics estimator 256 may determine to give a high weight to chocolate, which is a product whose sales increase rapidly on an anniversary such as Valentine's Day on a yearly basis. The logistics estimator 256 applies anniversary weights when generating total production information.

The logistics estimator 256 checks the sales amount of the product identification information sold for each day of the week information included in the purchase date and time information. The logistics estimator 256 classifies product identification information in which the sales volume has risen from the lower right to the upper right on a particular day according to the change of the day of the week. The logistics estimator 256 determines the weights for each day of the week so that the products with high sales have high weights by checking the sales of the product groups for each day of the week. For example, the logistics estimator 256 may determine to give a high weight to liquor, which is a product whose sales increase rapidly on Friday. The logistics estimator 256 applies weights for each day of the week when generating total production information.

The logistics estimator 256 checks the sales amount of the product identification information sold for each time zone information included in the purchase date and time information. The logistics estimator 256 classifies product identification information in which the sales volume has risen from the lower right to the upper right in a specific time zone according to the change of the time zone into a product group by time zone. The logistics estimator 256 determines the weights for each time slot so that the products with high sales have high weights by checking the sales of the product groups by time. For example, the logistics estimator 256 may determine to give high weight to alcoholic beverages, which are products whose sales increase rapidly in the evening hours. The logistics estimator 256 applies weights for each time zone when generating total production information.

3 is a diagram showing a list of issued QR codes according to the present embodiment.

The logistics management device 140 outputs the QR code details. The logistics management device 140 displays individual authentication status information of the issued QR code. The logistics management device 140 provides a history of QR code generation and a download of the history of QR code generation.

3, the logistics management device 140 outputs a QR code list. The logistics management device 140 displays the list of issued QR codes and the number of authentications, and when the QR code name is selected, displays the content in the individual QR detail area on the right.

3, the logistics management device 140 outputs an individual QR detail area. The logistics management device 140 displays the corresponding QR code image, authentication date, country, and city.

As shown in ③ of FIG. 3, the logistics management device 140 outputs a history of generating the entire QR code. The logistics management device 140 compresses and provides an image of the QR code when a request for downloading is made.

4 is a diagram showing the authentication status of the registered product according to the present embodiment.

The logistics management device 140 outputs an overview of the authentication status for the QR code. The logistics management device 140 provides the main authentication status values for the pre-registered products only for the products registered with the logged-in member account.

As shown in ① in FIG. 4, the logistics management device 140 displays the main numerical values in a summary form for the selected period. As shown in ② in FIG. 4, the logistics management device 140 displays cumulative numbers of selection items for a selection period in a graph.

As shown in (3) of FIG. 4, the logistics management device 140 displays (eg, a total of 5) in the order of products having a large total number of authentications per selection period. When the product name is selected, the logistics management device 140 moves to a product detail (authentication status-product-product detail) page. As shown in ④ of FIG. 4, the logistics management device 140 displays the total number of authentications per selection period in a country order (eg, 5 countries in total). When the country name is selected, the logistics management device 140 moves to the country detail (authentication status-space-country detail) page.

As shown in ⑤ of FIG. 4, the logistics management device 140 displays (eg, a total of 5 people) in the order of the number of total authentication times per selection period. The logistics management device 140 displays the customer name only as the customer's email address name (eg, the hosting address after @ is not displayed).

5 is a view showing the proportion of sales for registered products according to the present embodiment.

The logistics management device 140 provides authentication information for the registered product among the authentication status products. As shown in ① in FIG. 5, the logistics management device 140 displays the sales of certified products (eg, the top five) within a selected period. As shown in ② in FIG. 5, the logistics management device 140 displays the cumulative number of authentication of the selected product within the selected period. As shown in ③ of FIG. 5, the logistics management device 140 displays a list of related products and related information within a selected period. When the product name is selected, the logistics management device 140 moves to a product detail (authentication status-product-product detail) page.

6 is a view showing the detailed certification details of the registered individual products according to the present embodiment.

The logistics management device 140 outputs authentication status as product detailed information. The logistics management device 140 displays detailed authentication details of individual products.

6, the logistics management device 140 displays product main information. 6, the logistics management device 140 moves to a detailed page of the selected category-product. When the category is selected, the logistics management device 140 activates product selection. The logistics management device 140 displays only products and categories in which authentication has occurred. 6, the logistics management device 140 displays authentication status information of the corresponding product.

7 is a view showing a country-specific certification history of individual products according to the present embodiment.

The logistics management device 140 outputs country-specific information as product detailed information. The logistics management device 140 outputs the country-specific authentication details of individual products.

As shown in ① in FIG. 7, the logistics management device 140 displays the top countries (eg, 10 countries) as markers based on the total number of authentications during the selection period. As shown in ② in FIG. 7, the logistics management device 140 displays the top countries (eg, 5 countries) based on total sales during the selection period. As shown in ③ of FIG. 7, the logistics management device 140 displays a list of countries and information on which authentication occurred during the selection period.

8 is a view showing a certification history of each individual product according to the present embodiment.

The logistics management device 140 outputs weather-specific information as product detailed information. Logistics management device 140 outputs the certification of each product by weather. Here, weather items are provided through the API.

8, the logistics management device 140 visualizes and displays authentication information for each weather during a selection period. As shown in ② in FIG. 8, the logistics management device 140 displays authentication information for each weather during a selected period.

9 is a view showing a certification history of each device of each product according to the present embodiment.

The logistics management device 140 is product detailed information and outputs device-specific information. The logistics management device 140 displays authentication details for each device of each product. The logistics management device 140 provides three device items.

9, the logistics management device 140 visualizes and displays authentication information for each device during a selection period. 9, the logistics management device 140 displays authentication information for each device during a selection period.

10 is a view showing an insight composite analysis according to the present embodiment.

The logistics management device 140 outputs the results of the insight composite analysis. The logistics management device 140 provides authentication values according to conditions selected for all products registered on the platform in a download form. The logistics management device 140 defines access rights according to whether or not it is paid.

As shown in ① of FIG. 10, the logistics management device 140 receives the condition for each item. When the browse is selected, the logistics management device 140 displays a condition selection layer. 10, the logistics management device 140 displays the selected condition. The logistic management device 140 deletes the corresponding condition when X is selected. As shown in ③ of FIG. 10, the logistics management device 140 provides a result in a download form. The logistics management device 140 provides an Excel file in the form of a download for the authentication value result of the selected condition.

11 is a diagram showing detailed items for a selected product in the insight composite analysis according to the present embodiment.

The logistics management device 140 displays the selected products arranged in the X-axis, and displays the selection conditions in the Y-axis. The logistics management device 140 limits the value to be derived based on the received value.

As shown in ① of FIG. 11, the logistics management device 140 arranges and lists the products (mandatory selection) selected on the X axis. As shown in ② in FIG. 11, the logistics management device 140 sorts and arranges detailed items of the conditions selected on the Y-axis.

11, the logistics management device 140 outputs the result value. The logistics management device 140 displays the authentication value recorded in the detailed item of the condition selected for each product. As shown in ④ of FIG. 11, the logistics management device 140 displays the results for each selection condition for each sheet.

12 is a view showing a period and time zone selection in the insight composite analysis according to the present embodiment.

The logistics management device 140 is a period selection layer, which receives a period selection as a mandatory and selectively selects a time zone.

12, the logistics management device 140 receives a period selection. 12, the logistics management device 140 receives a time zone selection. As shown in ③ of FIG. 12, the logistics management device 140 receives a plurality of days as a day of the week selection.

13 is a diagram showing date selection in an insight composite analysis according to the present embodiment.

The logistics management device 140 provides a weather selection layer.

13, the logistics management device 140 is a weather selection layer, and selectively receives a plurality of weathers. The logistics management device 140 is provided with an API as a weather item. As shown in ② in FIG. 13, the logistics management device 140 is input in a manner of directly writing temperature information through temperature selection. As shown in ③ of FIG. 13, the logistics management device 140 receives the humidity information by directly inputting the humidity information by selecting the humidity.

14 is a diagram showing spatial selection in the insight composite analysis according to the present embodiment.

The logistics management device 140 provides a space selection layer.

As shown in ① of FIG. 14, the logistics management device 140 displays detailed countries in the area ② for country selection. The logistics management device 140 activates selection of cities belonging to the country. The logistics management device 140 selectively receives a plurality of countries by country selection. The logistics management device 140 displays the detailed city in the ② area by selecting a city. The logistics management device 140 enables a plurality of cities belonging to the corresponding country to be selected. Here, city selection is not essential information.

As shown in ② in FIG. 14, the logistic management device 140 deletes the corresponding country or city when an input to X exists. When deleting the parent item (eg, country), the logistics management device 140 deletes all of the child item (eg, city).

15 is a view showing customer selection in the insight composite analysis according to the present embodiment.

The logistics management device 140 provides a customer selection layer.

As shown in ① of FIG. 15, the logistics management device 140 receives customer ratings, customer age groups, and purchase frequency and extracts and outputs information corresponding to the conditions. The logistics management device 140 may receive a plurality of detailed yields included in the customer grade, customer age group, and purchase frequency.

16 is a conceptual diagram illustrating a method for predicting a sales rate according to the present embodiment.

16, a method for predicting a sales rate based on an increase rate and a decrease rate for sales in a specific region is disclosed.

Referring to FIG. 16, the future sales rate calculating unit 254 may adjust the range for predicting the sales rate based on the existing judgment result and the sales rate extraction sensitivity 1640. The determination result may include sales area location information 1610, sales increase rate information 1620, and sales decrease rate information 1630.

According to this embodiment, in the future, the sales rate calculating unit 254 may reflect relatively higher in the prediction range when the sales growth rate or the sales reduction rate fluctuates more than a predetermined threshold in a region where the sales frequency is low over the entire sales area. have.

Conversely, in the future, the sales rate calculating unit 254 may reflect a relatively small amount in the prediction range when the sales increase rate or the sales decrease rate fluctuates below a predetermined threshold in a region where the sales frequency is high in the entire sales area.

In the future, the sales rate calculating unit 254 may adjust the forecast range relatively larger as the size of the sales growth rate or sales reduction rate that fluctuates in a specific region is relatively large. Conversely, the future sales rate calculating unit 254 may adjust the forecast range relatively smaller as the size of the sales increase rate or the sales decrease rate that fluctuates in a specific region is relatively small.

In the future, as the sales rate calculator 254 has a relatively small effect on the increase/decrease rate generated in a specific region, the prediction range may be adjusted to be relatively large. Conversely, the future sales rate calculating unit 254 may have a relatively large influence on the increase/decrease rate, so that the prediction range may be adjusted to be smaller.

The future sales rate calculating unit 254 may speed up the future sales rate calculation and further increase the accuracy of the future sales rate through adjustment of the prediction range in consideration of regions and products that need more judgment in the above-described manner.

According to the present embodiment, the future sales rate calculating unit 254 is set as the prediction range is set relatively small as the sensitivity increase/extract sensitivity 1640 is set relatively high, on the contrary, the prediction range is set as the sensitivity increase/extract sensitivity 1640 is set relatively low. Can be set relatively large.

In addition, the logistics management device 140 may be set to have a relatively small prediction range for a corresponding portion as the reliability of the increase/decrease rate determination 1650 for the increase or decrease rate is lower. As a result of learning about the artificial intelligence model, the reliability of the increase/decrease rate judgment of the artificial intelligence model may be different from each other, and as the reliability of the increase/decrease rate judgment is relatively low, the prediction range may be set relatively small for more accurate judgment.

As described above, the adjustment of the prediction range for the entire sales area may be performed by continuously reflecting the determination result including the sales area location information, sales increase rate information, and sales decrease rate information.

17 is a conceptual diagram showing a method of generating an artificial intelligence learning model according to the present embodiment.

In FIG. 17, the learning unit 252 discloses a method for performing learning on an increase/decrease rate for each region based on classification. Referring to FIG. 17, the learning unit 252 may perform learning based on at least one learning model.

The first learning model 1710 may be a model learned based on the first to N regional sales growth rates and the first to N regional sales decrease rates for specific products that are actual learning data. The second learning model 1720 may not be an actual sale target product, but may be a model learned based on first to N regional sales growth rates and first to N regional sales decrease rates for products similar to the sales target products.

The actual learning data, the sales increase rate for a specific product and the sales reduction rate for a specific product, may be expressed in terms of the first learning data 1715. The sales increase rate for similar products similar to the selected sales product and the decrease rate for sales for similar products may be expressed in terms of second learning data 1725, although the sales product is not actually selected by the manager.

The third learning model 730 may be a model that performs learning based on the third learning data 735 in which the first learning data 1715 and the second learning data 1725 are mixed. The mixing ratio of the third learning data 1735 may be determined based on the similarity between the product to be sold and the product used when the second learning data 1725 is generated. As the similarity between the product to be sold and the product used when generating the second learning data 1725 is higher, the mixing ratio of the second learning data 1725 is relatively increased, so that the third learning data 1735 may be generated.

In the initial product inspection step, determination of the increase/decrease rate for each region may be performed based on the determination results for the first learning model 1710, the second learning model 1720, and the third learning model 1730.

Thereafter, in the step of confirming the increase and decrease rate for the first critical period, the accuracy of the increase and decrease rate for each region of the first learning model 1710, the second learning model 1720, and the third learning model 1730 may be determined. For example, the first learning model 1710 and the third learning model 1730 may show an accuracy of a threshold value or higher for a region-specific increase/decrease rate 1, and the learning model having the highest accuracy may be the third learning model 1730. . The first learning model 1710 and the second learning model 1720 may show an accuracy greater than or equal to a threshold for a region-specific increase and decrease rate 2, and the learning model having the highest accuracy may be the second learning model 1720.

In the initial increase/decrease rate checking step in which the increase/decrease rate for the first critical period has been checked, a model having a high accuracy can be extracted, and additional learning can be performed by inputting feedback for additional increase/decrease rate check results only for the corresponding model.

Thereafter, in the step of confirming the increase and decrease rate for the second critical period, only the learning model having the highest accuracy can be selected to more accurately determine the increase and decrease rate for each region.

18 is a conceptual diagram illustrating a method of determining a scale according to a product line during learning according to the present embodiment.

The learning unit 252 inputs an increase rate and a decrease rate for products sold by specific regions as learning data, and generates a learning model that predicts a future sales rate. You can learn by specifying the range of Scale). The learning unit 252 includes a product scale determining unit 1810, a continuous scale learning unit 1820, and a discontinuous scale learning unit 1830.

The information aggregator 230 basically generates raw data collected in a form of accumulating and matching product purchase information and logistic location movement information on a daily, weekly, monthly, or yearly basis. The product scale determination unit 1810 ) Calculates consumption patterns and sales tendencies for each product based on the entire period for the raw data generated by the information collecting unit 230.

The product scale determination unit 1810 checks the Sophie pattern or sales propensity for each product based on the entire period for the raw data, and continuously scales expensive products (for example, TV), frequently consumed products, and products with long consumption cycles. It is classified as a product group, and seasonal products (eg, cosmetics, electric fans), products that are sold frequently, and products with short consumption cycles are classified as a discontinuous scale product group.

In other words, the product scale determination unit 1810 calculates the sales propensity of each product based on the raw data, and adaptively determines the time scale of each product based on the sales propensity.

The product scale determination unit 1810 classifies a continuous scale product when a specific product is a product that continuously sells (products having a long consumption cycle). The product scale determination unit 1810 classifies the product as a continuous scale product family by determining the product that continuously sells based on the purchase date information and the sales amount information, based on the product identification information that is authenticated as the genuine product collected based on the first QR code.

The product scale determining unit 1810 is classified as a discontinuous scale family in the case of a product family in which sales (products with short consumption cycles) are discontinued. The product scale determining unit 1810 is a product in which non-continuous sales are made based on purchase date information and sales volume information for product identification information authenticated as a genuine product collected based on a first QR code (products in which sales volume occurs only in a specific period) And classify it as a discontinuous scale product line.

The continuous scale learning unit 1820 determines an increase rate and a decrease rate for a product in a state in which a time scale is set longer than a predetermined threshold (a specific period) when a specific product is classified as a continuous scale family (with a wide time scale). Input as learning data to predict future sales rate on a continuous scale. The continuous scale learning unit 1820 generates a continuous scale learning model 1822 based on the future sales rate of the continuous scale.

The discontinuous scale learning unit 1830 determines the rate of increase and decrease for a product in a state in which the time scale is set shorter than a preset threshold (a specific period) (a state having a narrow time scale) when a specific product is classified as a discontinuous scale family. Input as learning data to predict the future sales rate of the discontinuous scale. The discontinuous scale learning unit 1830 generates a discontinuous scale learning model 1822 based on the future sales rate of the discontinuous scale.

The future sales rate calculating unit 254 calculates the future sales rate using the continuous scale learning model 1822 and the discontinuous scale learning model 1822. The future sales rate calculating unit 254 calculates the future sales rate by reflecting different weights for each period in each of the continuous scale future sales rate and the discontinuous scale future sales rate.

The above description is merely illustrative of the technical idea of the present embodiment, and those skilled in the art to which this embodiment belongs may be capable of various modifications and variations without departing from the essential characteristics of the present embodiment. Therefore, the present embodiments are not intended to limit the technical spirit of the present embodiment, but to explain, and the scope of the technical spirit of the present embodiment is not limited by these embodiments. The protection scope of the present embodiment should be interpreted by the claims below, and all technical spirits within the equivalent range should be interpreted as being included in the scope of the present embodiment.

110: product
122: first QR code 124: second QR code
132: consumer terminal 134: dedicated reader
140: logistics management device
210: product purchasing information management unit 220: logistics information management unit
230: information collection unit 240: decision support unit

Claims (7)

A product purchase information management unit that collects product purchase information recognizing a first QR code (Quick Response Code) attached to a specific product from a consumer terminal, and authenticates the product purchase information;
A logistics information management unit collecting and authenticating logistics location movement information recognizing the second QR code attached to the specific product from the logistics base;
An information collecting unit for generating raw data collected in a form of accumulating and matching the product purchase information and the logistics location movement information based on product identification information corresponding to the specific product; And
Generate optimal production information based on the raw data, generate optimal logistics distribution information to classify the optimal production information into a sales place, and support decision-making for specific products based on the optimal production information and the optimal logistics distribution information Decision Support Department
Logistics management device comprising a. According to claim 1,
The product purchase information management unit,
When the consumer terminal recognizes the first QR code, authenticates whether the product identification information corresponding to the first QR code is genuine, and when the product identification information is authenticated as genuine, causes the consumer terminal to purchase location information , Logistics management device characterized in that it is essential to collect the purchase date and time, purchase weather information, and selectively collect terminal model information, subscriber gender information, subscriber age information, and SNS connection information. According to claim 2,
The logistics information management unit,
When the dedicated reader provided at the logistics base recognizes the second QR code, the product identification information corresponding to the second QR code is authenticated, and when the product identification information is authenticated as genuine, the second Whenever a product with a QR code is shipped at the time of export or import, it passes through the logistic base and recognizes the second QR code from the dedicated reader provided at the logistic base to check the timing of cargo movement for each node and checks each node. Logistics management device characterized in that the product is shipped from the origin based on the location information, route information confirming the arrival date and time to generate and output the import route and export route to the customs via the logistics base. According to claim 3,
The information collecting unit,
Accumulate the purchase location information, the purchase weather information, the terminal model information, the subscriber gender information, the subscriber age information, the SNS connection presence information, the location information at each node, and the arrival date based on the product identification information. Logistics management device, characterized in that to match the form to generate the raw data. According to claim 4,
The decision support unit,
The optimal logistics for calculating sales volume information for each region based on the purchase location information included in the raw data, selecting a region in which a specific product is sold a lot based on the sales information for each region, and rapidly supplying a specific product to the corresponding region Logistics management device characterized in that for calculating the distribution information. The method of claim 5,
The decision support unit,
Based on the sales information for each region, an increase rate and a decrease rate for a product sold in a specific region are calculated, and trend information is calculated based on an increase rate and a decrease rate for a recent period set in the entire period, and the optimality is based on the trend information Logistics management device, characterized in that for calculating the output information. The method of claim 6,
The decision support unit,
A learning unit generating learning models for predicting future sales rates by inputting an increase rate and a decrease rate for products sold for each specific region as learning data;
A future sales rate calculating unit for calculating a future sales rate using the learning model; And
A logistics estimation unit that calculates the optimal production information based on the future sales rate and calculates the optimal logistics distribution information that distributes the optimal production information for each region.
Logistics management device comprising a.

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