KR102588541B1 - Store management system - Google Patents

Abstract

Disclosed is an auto store setting and employee management system comprising: a mobile terminal; and a control server that input-receives net profit information generated in a store from the mobile terminal, and analyzes the net profit information to determine whether to switch a store operation mode. Therefore, the present invention can increase an efficiency of store operation.

Description

Auto store setting and employee management system {STORE MANAGEMENT SYSTEM}

The present invention relates to an automatic store setting and employee management system.

In general, conventional store management systems include a customer management and mileage provision system using wireless LAN, an intelligent smart shopping system based on RFID technology, a target advertising service provision system, and a reverse order payment system. Specifically, composition of product lists and menus for product introduction, price display for price information, delivery program for ordering or delivery, production or manufacturing instructions and related systems for orders, and payment system for payment of costs and fees. , programs provided for guidance and convenience related to the use of the store, advertising and marketing methods and related systems for the company's promotion targeting the store and users, or the store's own promotion and marketing, and all management systems necessary for the store, and There is a program.

Recently, the unmanned trend is spreading in the distribution industry. A variety of unmanned stores such as cafes, restaurants, clothing stores, health stores, salad shops, and cosmetic stores are appearing, and the distribution industry is achieving service innovation by converging with IT technology. What all unmanned stores at home and abroad have in common is that they provide not only a convenient experience to customers but also a unique and personally optimized experience based on cutting-edge technologies within the store, such as deep learning-based artificial intelligence, vein authentication payment service, QR code, and big data technology. With the introduction of various technologies such as , VR, etc., new customer experiences that are different from before are being created. Due to the coronavirus and the trend of unmanned stores, the non-contact culture is rapidly spreading, and there is a need for a way to receive services in consumer life without face-to-face with employees and without or minimizing contact with people through kiosks, chatbots, mobile devices, etc. is increasing. Accordingly, startup owners are required to research and develop a platform that can be operated unmanned without replacement personnel or replacement personnel.

One aspect of the present invention discloses a system that registers the entrepreneur as an administrator and sets up the conversion to an automatic store when the store's net profit exceeds a certain amount.

Another aspect of the present invention discloses an employee management system that manages employees, including managers, of an auto store to ensure systematic management.

The technical problem of the present invention is not limited to the technical problem mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below.

The system of the present invention includes a mobile terminal; And a control server that receives net profit information generated in the store from the mobile terminal, analyzes the net profit information, and determines whether to switch the store operation mode.

Meanwhile, the control server includes a profit monitoring unit that receives net profit information from the entrepreneur through the mobile terminal at a predetermined period and monitors whether the net profit generated in the store exceeds the standard profit; Auto store conversion setting that switches from offline store mode, which is a mode in which the entrepreneur resides and operates the store for at least part of the store operating hours, to auto store mode, which is an operation mode in which the entrepreneur does not reside in the store, according to the monitoring results from the profit monitoring department. wealth; When switching from offline store mode to auto store mode by the auto store conversion setting unit, store operation that receives monthly sales information through the mobile terminal, monitors the monthly sales information, and evaluates the operation score of the auto store mode. transition section; And an employee management unit that generates job assignment information for each of a plurality of employees in the store;

The employee management department inputs store employee information including name, gender, age, experience, self-introduction, and work hours, and store operation information including industry, size, employee position, employee work, and operating hours through the mobile terminal. Receive, generate store employee information and store operation information as input data, enter the input data into an artificial neural network learned to output job assignment information for the input data, obtain output data, and convert the output data to the tasks of each employee. It can be set with makeup information.

In addition, the automatic store conversion setting unit receives store information including store type, store size, store operation period, store location, initial investment, and average operating cost through the mobile terminal, and stores the store information in Equation 1 below. Apply to set the number of monitoring inspections, and if it is confirmed that the net profit information exceeds the standard profit more than the number of monitoring inspections calculated according to Equation 1 during a predetermined monitoring period, the store operation mode can be switched.

[Equation 1]

(In Equation 1, R is the percentage of store information that is the same as the store type entered by the founder among the plurality of store information stored in the database, A is the store size entered by the founder, and is a constant set according to the size ( For example, in the constant range from 1 to 10, the larger the store size, the larger the constant is set), P is the store operation period entered by the founder, and is a constant set according to the period (for example, from 1 to 10) In constants, the longer the store operation period, the larger the constant is set), D is the number of other stores located within a certain distance from the store location entered by the founder, B is the average operating cost entered by the founder, and is set according to the cost. A constant (e.g., a constant from 1 to 10, with a larger constant set as the cost increases), Ba is the initial investment entered by the founder, and a constant is set according to the cost (e.g., a constant from 1 to 10). In a constant, the higher the cost, the larger the constant is set.)

According to one aspect of the present invention described above, the offline store where the entrepreneur resides can be converted to an automatic store according to profit monitoring, and employee management services such as employee salary calculation and job division are provided to facilitate store management. And, the efficiency of store operation can be improved.

1 is a conceptual diagram of a system according to an embodiment of the present invention.
Figure 2 is a conceptual diagram of a management server according to an embodiment of the present invention.
Figure 3 is a conceptual diagram of a management server according to another embodiment of the present invention.

The advantages and features of the present invention and methods for achieving them will become clear by referring to the embodiments described in detail below along with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and will be implemented in various different forms. The present embodiments only serve to ensure that the disclosure of the present invention is complete and that common knowledge in the technical field to which the present invention pertains is not limited. It is provided to fully inform those who have the scope of the invention, and the present invention is only defined by the scope of the claims. Like reference numerals refer to like elements throughout the specification.

The terminology used herein is for describing embodiments and is not intended to limit the invention. As used herein, singular forms also include plural forms, unless specifically stated otherwise in the context. As used in the specification, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other components, steps and operations.

1 is a conceptual diagram of an automatic store setting and employee management system according to an embodiment of the present invention.

Referring to FIG. 1, the auto store setting and employee management system 1 according to an embodiment of the present invention may include a control server 100 and a mobile terminal 200.

The control server 100 can provide auto store setting and employee management services according to the present invention.

The control server 100 may be its own server, a cloud server, or a p2p (peer-to-peer) set of distributed nodes for providing the service according to the present invention.

The control server 100 may perform one or more of the calculation, storage, reference, input/output, and control functions of a general computer, and may include an artificial neural network to be described later based on input data.

Control server 100 may include a processor and memory. The processor can perform the auto store setting and employee management service according to the present invention, and may include devices capable of performing this. The processor may execute a program or control the control server 100. Program code executed by the processor may be stored in memory. The memory may store related information for performing the auto store setting and employee management service according to the present invention, or may store a program for implementing the method. The memory may be volatile memory or non-volatile memory.

The control server 100 may send data to or receive data from an external device using a network.

The control server 100 can train an artificial neural network or use a trained artificial neural network. The processor can train or execute an artificial neural network stored in memory, and the memory can store a trained artificial neural network. The electronic device that trains the artificial neural network and the electronic device that uses it may be the same, but may also be separate.

Artificial intelligence is a computer system that implements some of the functions of the human brain and can learn, guess, and make decisions on its own. As learning progresses, the probability of extracting an answer may increase. Artificial intelligence can be composed of learning and elemental technologies using it. Artificial intelligence learning is an algorithmic technology that classifies and learns features based on input data, and elemental technologies may be technologies that implement some of the functions of the human brain using learning algorithms.

Artificial intelligence is a technology that makes it easy to approach problems that can have multiple answers probabilistically, and can logically and probabilistically infer the optimal cycle, method, and plan according to any input data. Artificial intelligence's inference technology can include judging input data, optimization predictions, knowledge and probability-based reasoning, and preference-based planning.

Artificial neural network is one of the learning algorithms in the machine learning field and is a program that implements the connection between neurons and synapses in the brain. Artificial neural networks can be created through a program to create a neural network structure and then learn it to have the desired function. Although there may be errors, it is possible to learn based on huge data and output appropriate output data with input data. It has the advantage of being able to obtain output data with statistically good results and being similar to human reasoning.

The control server 100 can calculate the profit standard amount using an artificial intelligence algorithm built based on big data, and may include a plurality of artificial neural networks trained in advance for this purpose. In addition, the control server 100 can calculate the manager's salary using an artificial intelligence algorithm built based on big data, and may include a number of artificial neural networks trained in advance for this purpose. In addition, the control server 100 can calculate work distribution information for each employee using an artificial intelligence algorithm built on big data, and may include a number of artificial neural networks that have been trained in advance for this purpose.

The network is a communication network that is a high-speed backbone network of a large communication network capable of high-capacity, long-distance voice and data services, and may be a next-generation wired or wireless network to provide the Internet or high-speed multimedia services.

If the network is a mobile communication network, it may be a synchronous mobile communication network or an asynchronous mobile communication network. An example of an asynchronous mobile communication network may be a WCDMA (Wideband Code Division Multiple Access) type communication network. In this case, although not shown in the drawing, the network may include an RNC (Radio Network Controller). Meanwhile, although the WCDMA network was used as an example, it can be a 3G LTE network, a 4G network, other next-generation communication networks such as 5G, and other IP-based IP networks.

The mobile terminal 200 may include any terminal that can send and receive data through a network, such as a desktop computer, laptop, tablet, or smartphone.

The mobile terminal 200 may include one or more of the calculation function, storage function, reference function, input/output function, and control function of a computer in order to perform the service according to the present invention.

The mobile terminal 200 can access a website or install an application to receive services according to the present invention. The mobile terminal 200 can exchange data through a website or application.

The network is a communication network that is a high-speed backbone network of a large communication network capable of high-capacity, long-distance voice and data services, and may be a next-generation wired or wireless network to provide the Internet or high-speed multimedia services.

If the network is a mobile communication network, it may be a synchronous mobile communication network or an asynchronous mobile communication network. An example of an asynchronous mobile communication network may be a WCDMA (Wideband Code Division Multiple Access) type communication network. In this case, although not shown in the drawing, the network 300 may include a Radio Network Controller (RNC). Meanwhile, although the WCDMA network was used as an example, it can be a 3G LTE network, a 4G network, other next-generation communication networks such as 5G, and other IP-based IP networks.

The automatic store setting and employee management system (1) according to an embodiment of the present invention allows the offline store where the entrepreneur resides to be converted to an automatic store according to profit monitoring, and performs tasks such as calculating employee salaries and dividing work. By providing employee management services, you can facilitate store management and increase the efficiency of store operations.

Figure 2 is a conceptual diagram of a control server according to an embodiment of the present invention.

Referring to FIG. 2, the control server 100 may include a profit monitoring unit 110, an automatic store switching setting unit 120, and a store operation switching unit 130.

The profit monitoring unit 110 can monitor the net profit generated in the store. For example, the profit monitoring unit 110 may receive net profit information from the entrepreneur at a predetermined period through the mobile terminal 200.

The profit monitoring unit 110 may monitor whether the net profit generated in the store exceeds the standard profit. In this embodiment, if the net profit generated in the store exceeds the standard profit, the offline store mode can be switched to the automatic store mode. Offline store mode is a mode in which the founder resides and operates the store for at least part of the store's operating hours, and automatic store mode is a mode in which the founder does not reside in the store and is operated by employees.

When set to offline store mode, the profit monitoring unit 110 can monitor the net profit generated in the store to check whether it exceeds the standard profit.

The profit monitoring unit 110 can set a standard profit using an artificial intelligence neural network.

For example, the profit monitoring unit 110 generates a plurality of store information including store type, store size, store operation period, store location, initial investment, and average operating cost as input data, and net profit for the input data. Input data can be input to the learned artificial neural network to output the result data.

The profit monitoring unit 110 may obtain output data from the artificial neural network and set the output data as the reference profit.

In this embodiment, the profit monitoring unit 110 may include a plurality of pre-trained artificial neural networks to perform a machine learning algorithm. With machine learning, you can output output data based on input data and use the results to learn on your own, which can improve your own data processing ability. Artificial neural networks can extract features based on input data, infer regularities, and output result data. As this process accumulates, the reliability of the result data increases.

In this embodiment, the artificial neural network may be an algorithm that outputs a standard profit for store information input by the entrepreneur through the mobile terminal 200. Artificial neural networks can infer the best output data using big data as input data or after going through a processing process to organize unnecessary data.

ve Bayes Classifier, Hidden Markov Model, K-Means Clustering 등이 사용될 수 있다.Depending on the type of learning, artificial intelligence machine learning models include Super Viser Learning, UnSuper Viser Learning, Semisupervised Learning, and Reinforcement Learning. And machine learning algorithms include Decision Tree, K-Nearest Nearest Bor, Artificial Neural Network, Support Vector Machine, Ensemble Learning, Gradient Descent, Na

ve Bayes Classifier, Hidden Markov Model, K-Means Clustering, etc. can be used.

Artificial neural networks can stack and connect numerous artificial neurons in several layers. The input data of the artificial neural network is control numerical data and may include at least one of temperature, pressure, and pH concentration. The artificial neural network may be pre-trained on various input values that may be included in the input data. The artificial neural network can output output data by inferring control signals according to data input by the user.

An artificial neural network may be an artificial neural network that is learned according to reinforcement learning, one of the learning methods. Reinforcement learning is a method of gradually increasing the probability of obtaining the correct result by setting rewards and limits.

Artificial neural networks can also be modeled based on Convelutional Neural Network (CNN) or Recurrent Neural Network (RNN).

The automatic store conversion setting unit 120 may change the store operation mode from the offline store mode to the automatic store mode according to the profit monitoring results of the profit monitoring unit 110.

The automatic store conversion setting unit 120 may switch from the offline store mode to the automatic store mode when it is confirmed that the driver profit exceeds the standard profit during a predetermined monitoring period. In this case, the auto store conversion setting unit 120 can convert the founder information into manager information and register it.

Meanwhile, the automatic store conversion setting unit 120 can receive store information including store type, store size, store operation period, store location, initial investment, and average operating cost from the entrepreneur through the mobile terminal 200. The number of monitoring inspections can be set by applying store information to Equation 1 below.

[Equation 1]

In Equation 1, R is the number of monitoring checks, a is the percentage of store information that is the same as the store type entered by the founder among the plurality of store information stored in the database, and A is the store size entered by the founder, which is set according to the size. Constant (for example, a constant from 1 to 10, the larger the store size is set), P is the store operation period entered by the founder, and a constant set according to the period (for example, from 1 to 10 (The longer the store operation period is, the larger the constant is set), D is the number of other stores located within a certain distance from the store location entered by the founder, and B is the average operating cost entered by the founder, depending on the cost. A constant that is set (e.g., a constant from 1 to 10, the higher the cost, the larger the constant is set), Ba is the initial investment amount entered by the founder, and a constant that is set according to the cost (e.g., 1 to 10 It can be defined as (the higher the cost, the larger the constant is set).

For example, if a is 0.5, A is 10, P is 3, D is 6, B is 9, and Ba is 6, R can be calculated as 4.88, which can be rounded to 5. In this case, the automatic store switching setting unit 120 may switch the store operation mode when it is confirmed that the profit exceeds the standard profit more than five times during a predetermined monitoring period.

According to Equation 1, the number of monitoring inspections is shortened depending on whether the store operation period is short compared to the store size or the number of other stores located within a certain distance from the store location. The larger the difference between the initial investment and average operating cost, the longer the number of monitoring inspections. By doing so, the accuracy and efficiency of operating mode conversion can be increased to maximize the effect of increasing sales.

When the store operation switching unit 130 switches from the offline store mode to the automatic store mode, it can receive monthly sales information from the manager through the mobile terminal 200.

The store operation switching unit 130 monitors monthly sales information to evaluate the operation score of the auto store mode, and when the operation score of the auto store mode is below the standard score, it can switch to the offline store mode.

For example, the store sales conversion unit 130 can calculate the operation score of the automatic store mode using Equation 2 below.

[Equation 2]

In Equation 2, S is the operation score of the auto store mode, n is the operation month of the auto store mode, M(k) is the increase/decrease rate of monthly sales in the kth operation month of the auto store mode, sp is the standard profit, and sl is the previous The number of times monthly sales increased compared to the month, ss, refers to the months that fall within the preset peak season period among the total period n.

Here, when n=3, M(1)=0, M(2)=10, M(3)=-20, sl=1, ss=3, sq=100, S can be calculated as -5.3. there is.

According to Equation 2, the accuracy of understanding the operating performance of the automatic store mode can be increased by calculating the operation score by reflecting the monthly sales history, standard profit, and special situations during the operation period of the automatic store mode.

The store operation switching unit 130 may switch to the offline store mode when the operation score of the auto store mode is below the standard score.

Figure 3 is a conceptual diagram of a control server according to another embodiment of the present invention.

Referring to FIG. 3, the control server 101 according to this embodiment includes an employee management unit ( 140) may be further included.

The employee management unit 140 may generate job assignment information for each of a plurality of employees in the store.

The employee management unit 140 may receive store employee information and store operation information from the manager through the mobile terminal 200. Store employee information may include name, gender, age, experience, self-introduction, and working hours. Store operation information may include industry, size, employee position, employee duties, operating hours, etc.

The employee management unit 140 may use an artificial intelligence neural network to generate job assignment information for each of a plurality of employees in the store.

For example, the employee management unit 140 may generate store employee information and store operation information as input data, and input the input data into an artificial neural network trained to output job distribution information for the input data.

The employee management unit 140 may obtain output data from the artificial neural network and set the output data as job assignment information for each employee.

In this embodiment, the employee management unit 140 may include a plurality of pre-trained artificial neural networks to perform machine learning algorithms. With machine learning, you can output output data based on input data and use the results to learn on your own, which can improve your own data processing ability. Artificial neural networks can extract features based on input data, infer regularities, and output result data. As this process accumulates, the reliability of the result data increases.

In this embodiment, the artificial neural network may be an algorithm that outputs job distribution information for store operation information and store employee information input by the entrepreneur through the mobile terminal 200. Artificial neural networks can infer the best output data using big data as input data or after going through a processing process to organize unnecessary data.

ve Bayes Classifier, Hidden Markov Model, K-Means Clustering 등이 사용될 수 있다.Depending on the type of learning, artificial intelligence machine learning models include Super Viser Learning, UnSuper Viser Learning, Semisupervised Learning, and Reinforcement Learning. And machine learning algorithms include Decision Tree, K-Nearest Nearest Bor, Artificial Neural Network, Support Vector Machine, Ensemble Learning, Gradient Descent, Na

ve Bayes Classifier, Hidden Markov Model, K-Means Clustering, etc. can be used.

Artificial neural networks can stack and connect numerous artificial neurons in several layers. The input data of the artificial neural network is control numerical data and may include at least one of temperature, pressure, and pH concentration. The artificial neural network may be pre-trained on various input values that may be included in the input data. The artificial neural network can output output data by inferring control signals according to data input by the user.

An artificial neural network may be an artificial neural network that is learned according to reinforcement learning, one of the learning methods. Reinforcement learning is a method of gradually increasing the probability of obtaining the correct result by setting rewards and limits.

Artificial neural networks can also be modeled based on Convelutional Neural Network (CNN) or Recurrent Neural Network (RNN).

The employee management unit 140 may calculate employee salary information according to job assignment information.

The employee management unit 140 may calculate salary information for each employee according to a salary table according to pre-stored job assignment information.

The employee management unit 140 may add salary information for employees selected as managers.

The employee management unit 140 can receive manager evaluation information through the mobile terminal 200. Manager evaluation information may include manager work dates, manager self-evaluation questionnaire, number of event occurrences, etc.

[Equation 3]

In Equation 3, Pa is the added manager salary, n is the manager work date, E(n) is 1 or 0 depending on whether an event occurs, pt is salary according to the salary table, and bi is blank in the manager self-assessment question and answer table. 1, wi can be set to 1 if a specific keyword is included in the manager self-assessment question and answer table.

For example, n=30, , pt=50000, If calculated as , it can be calculated as 66,666.

In this way, in the case of the manager, the employee management unit 140 calculates the monthly salary according to the occurrence of events during the work day and the self-evaluation question and answer table. In order to add a high salary, the employee management department 140 improves the employee's work ability by encouraging the achievement of the blank parts of the self-assessment question and answer table. Improvement and further sales enhancement can be achieved.

Although embodiments of the present invention have been described above with reference to the attached drawings, those skilled in the art will understand that the present invention can be implemented in other specific forms without changing the technical idea or essential features. You will be able to understand it. Therefore, the embodiments described above should be understood in all respects as illustrative and not restrictive.

1: Auto store setting and employee management system
100: control server
200: mobile terminal

Claims (3)

mobile terminal; and
A control server that receives net profit information generated in the store from the mobile terminal, analyzes the net profit information, and determines whether to switch the store operation mode.
The control server is,
a profit monitoring unit that receives net profit information from the entrepreneur at a predetermined period through the mobile terminal and monitors whether the net profit generated by the store exceeds a standard profit;
Auto store conversion setting that switches from offline store mode, which is a mode in which the entrepreneur resides and operates the store for at least part of the store operating hours, to auto store mode, which is an operation mode in which the entrepreneur does not reside in the store, according to the monitoring results from the profit monitoring department. wealth;
When switching from offline store mode to auto store mode by the auto store conversion setting unit, store operation that receives monthly sales information through the mobile terminal, monitors the monthly sales information, and evaluates the operation score of the auto store mode. transition section; and
It includes an employee management unit that generates job assignment information for each of a plurality of employees in the store,
The staff management department said,
Through the mobile terminal, store employee information including name, gender, age, experience, self-introduction, and working hours, as well as store operation information including industry, size, employee position, employee work, and operating hours, are entered, and store employee information is received. and generate store operation information as input data, obtain output data by inputting the input data into an artificial neural network learned to output job assignment information for the input data, and set the output data as job assignment information for each employee; ,
The auto store conversion setting section,
Store information including store type, store size, store operation period, store location, initial investment, and average operating cost is input through the mobile terminal, and the number of monitoring inspections is set by applying the store information to Equation 1 below, If it is confirmed that the net profit information exceeds the standard profit more than the number of monitoring checks calculated according to Equation 1 during the predetermined monitoring period, the store operation mode is switched,
[Equation 1]

(In Equation 1, R is the percentage of store information that is the same as the store type entered by the founder among the plurality of store information stored in the database, A is the store size entered by the founder, and is a constant set according to the size ( For example, in the constant range from 1 to 10, the larger the store size, the larger the constant is set), P is the store operation period entered by the founder, and is a constant set according to the period (for example, from 1 to 10) In constants, the longer the store operation period, the larger the constant is set), D is the number of other stores located within a certain distance from the store location entered by the founder, B is the average operating cost entered by the founder, and is set according to the cost. A constant (e.g., a constant from 1 to 10, with a larger constant set as the cost increases), Ba is the initial investment entered by the founder, and a constant is set according to the cost (e.g., a constant from 1 to 10). In a constant, the higher the cost, the larger the constant is set.)
The store operation conversion department,
Calculate the operation score of the auto store mode by applying the operation month of the auto store mode, the rate of change in monthly sales, the standard profit, the number of monthly sales increases compared to the previous month, and the months that fall within the preset peak season period to Equation 2 below, and , Auto store setting and employee management system that switches to offline store mode when the operating score of the auto store mode is below the standard score.
[Equation 2]

(In Equation 2, S is the operation score of the auto store mode, n is the operation month of the auto store mode, M(k) is the increase/decrease rate of monthly sales in the kth operation month of the auto store mode, sp is the standard profit, and sl is the The number of times monthly sales increased compared to the previous month, ss refers to the months that fall within the preset peak season period among the total period n.)
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