WO2023054783A1 - Control method for electronic device for predicting parameter for leisure activity lesson on basis of degree of association between parameters - Google Patents

Abstract

A control method for an electronic device is disclosed. The present control method comprises the steps of: for each lesson provided through at least one seller, acquiring the values of a plurality of parameters associated with at least one among a leisure activity included in the lesson, and a transaction for the lesson; identifying the degree of association between the plurality of parameters on the basis of the values of the plurality of parameters acquired for each provided lesson; acquiring the value of at least one first parameter, among the plurality of parameters, for at least one lesson that is scheduled; and, on the basis of the degree of association between, among the plurality of parameters, a second parameter and the first parameter, predicting the value of the second parameter for the at least one lesson that is scheduled.

Description

Electronic device control method for predicting parameters for leisure activity lessons based on correlation between parameters

The present disclosure relates to an electronic device, and more particularly, to an electronic device that collects and analyzes values of a plurality of parameters for lessons providing leisure activities.

In addition, the present disclosure relates to an electronic device that predicts a parameter of a leisure activity lesson based on an influence of an event.

As the transaction mode of selling lessons about leisure activities through online platforms increases, various online platforms that mediate the sale of leisure activities are emerging.

However, due to the nature of leisure activities, which are mainly based on offline meetings or group meetings at a specific time, each time a lesson for leisure activities is planned, the time or labor of the seller (ex. instructor, coach, expert, etc.) Since there is a characteristic that the cost of employment (employment and labor costs) must exceed a certain level, it is very important to realistically predict the environment (ex. number of participants, sales, etc.) or situation in which lessons are held.

The present disclosure provides a control method of an electronic device that predicts/recommends parameters of future lessons based on a correlation between parameters of lessons provided in the past in a process of planning lessons related to leisure activities.

The present disclosure provides a control method of an electronic device that predicts/recommends parameters of future lessons based on the degree of influence of events that have occurred in the past in the process of planning lessons related to leisure activities.

The objects of the present disclosure are not limited to the above-mentioned objects, and other objects and advantages of the present disclosure not mentioned above can be understood by the following description and will be more clearly understood by the embodiments of the present disclosure. Further, it will be readily apparent that the objects and advantages of the present disclosure may be realized by means of the instrumentalities and combinations indicated in the claims.

A control method of an electronic device according to an embodiment of the present disclosure, for each lesson provided through at least one seller, values of a plurality of parameters related to at least one of a leisure activity included in the lesson and a transaction of the lesson Obtaining, based on the values of the plurality of parameters acquired for each of the provided lessons, identifying a degree of association between the plurality of parameters, with respect to at least one scheduled lesson, at least one of the plurality of parameters obtaining a value of one parameter, and predicting a value of the second parameter for the at least one scheduled lesson based on a degree of association between a second parameter and the first parameter among the plurality of parameters .

The obtaining of the value of the first parameter for the at least one scheduled lesson may include setting a value of at least one third parameter for the scheduled lesson according to a user input, and setting the value of the third parameter and the first parameter for the first lesson. Based on the correlation between the parameters, the value of the first parameter matching the value of the set third parameter may be identified.

The obtaining of the value of the first parameter for the at least one scheduled lesson may include identifying a change history of the value of the first parameter based on the values of the plurality of parameters obtained for each of the provided lessons, According to the identified change history, a value of the first parameter may be predicted for the at least one scheduled lesson.

Obtaining values of a plurality of parameters for each of the provided lessons may include, from a first server providing a first platform for mediating sales of lessons for leisure activities, information about lessons mediated through the first platform. Receive values of the plurality of parameters for a lesson mediated through the second platform from a second server providing a second platform that receives values of a plurality of parameters and mediates sales of lessons for leisure activities. can

Meanwhile, the control method of the electronic device may include setting a target value for the second parameter according to a user input, based on a degree of correlation between the first parameter and the second parameter, The method may include identifying a value of the first parameter that matches a target value, and recommending a value of the identified first parameter.

In this case, when a target value for the second parameter is set according to the user input, the control method of the electronic device includes at least one method in which the second parameter is defined as input data and the first parameter is defined as output data. generating an artificial intelligence model of, training the generated artificial intelligence model based on the values of a plurality of parameters obtained for each of the provided lessons, and using the trained artificial intelligence model for the second parameter. The method may include inputting a set target value and identifying a value of the first parameter that matches the target value of the second parameter.

In the step of identifying the degree of association between the plurality of parameters, the degree of association between the plurality of parameters may be independently identified for each type of leisure activity that is a subject of the lesson.

In addition, identifying the degree of association between the plurality of parameters may include identifying at least one related parameter for one parameter among the plurality of parameters; when a plurality of related parameters for the parameter are identified, the plurality The method may include obtaining an association constant applied to each of the plurality of association parameters in order to calculate a value of the parameter by combining values of each association parameter of .

In this case, the control method of the electronic device may include setting the importance of the parameter according to the number of the related parameters having a degree of association with the parameter equal to or greater than a threshold, and each of the plurality of related parameters according to the importance of the parameter. Identifying an update cycle of an association constant applied to , obtaining a value of the parameter and a value of each of the association parameters for each provided lesson at each update cycle, and obtaining the value of the obtained parameter and the value of the obtained parameter. The method may include updating the degree of association constant based on each value of the association parameter.

On the other hand, the plurality of parameters are the unit price for each buyer of the lesson, the sales of the lesson, the number of platforms used for selling the lesson, the region where the lesson is provided, the day of the week when the lesson is provided, the time zone where the lesson is provided, and the number of participants in the lesson. can include

A control method of an electronic device according to an embodiment of the present disclosure, for each lesson provided through at least one seller, values of a plurality of parameters related to at least one of a leisure activity included in the lesson and a transaction of the lesson Acquiring, based on information about an event that occurred while at least one lesson is provided and the obtained values of the plurality of parameters, identifying the degree of impact of the event on each of the plurality of parameters and predicting a value of at least one parameter among the plurality of parameters for at least one scheduled lesson, based on the schedule information of the event and the degree of influence of the event.

The step of identifying the degree of influence may include identifying a first value of the plurality of parameters for a lesson provided at a time when the event does not occur, and a second value of the plurality of parameters for a lesson provided at a time when the event occurs. A value may be identified, and a degree of influence of the event on each of the plurality of parameters may be identified by comparing the first value and the second value.

In the step of identifying the degree of influence, the degree of influence of each event parameter constituting the event on each of the plurality of parameters may be determined.

In the predicting the value of the at least one parameter, the value of at least one parameter among the plurality of parameters may be predicted based on the value of each event parameter included in the event schedule information.

In the step of predicting the value of the at least one parameter, the degree of influence of the event on at least one parameter among the plurality of parameters is adjusted according to the degree to which the scheduled period of the event matches the provision period of the scheduled lesson. And, according to the adjusted degree of influence, a value of at least one parameter may be predicted for the scheduled lesson.

Meanwhile, the control method of the electronic device may include setting a value of at least one first parameter among the plurality of parameters for a scheduled lesson according to a user input. In this case, in the predicting the value of the at least one parameter, the value of the at least one second parameter not set for the scheduled lesson may be predicted based on the degree of influence of the event.

Here, the control method of the electronic device may include identifying a degree of association between the first parameter and the second parameter based on values of a plurality of parameters acquired for each of the provided lessons. In addition, the step of predicting the value of the at least one parameter may include the value of the set first parameter, a degree of association between the first parameter and the second parameter, schedule information of the event, and the second parameter of the event. Based on the degree of influence on , it is possible to predict the value of the second parameter for the scheduled lesson.

The control method of the electronic device may include registering the first event when a first effect degree of the first event has on each of the plurality of parameters is identified, and receiving schedule information of the registered first event. , Predicting a value of at least one parameter among the plurality of parameters for at least one scheduled lesson based on the schedule information of the first event and the first impact degree of the first event, 2, if schedule information of the event is received, comparing the second event with the registered first event and identifying a second influence degree of the second event on each of the plurality of parameters; The method may include predicting a value of at least one parameter among the plurality of parameters for at least one scheduled lesson based on schedule information and a second influence degree of the second event.

In this case, the step of identifying the second influence degree may include comparing each of the event parameters of the first event with each of the event parameters of the second event, so that each of the event parameters of the second event corresponds to each of the plurality of parameters. It is possible to identify the second degree of influence.

An electronic device according to the present disclosure may collect values of a plurality of parameters for each lesson providing leisure activities, and predict parameters of a future scheduled lesson according to a degree of correlation between the parameters.

In particular, the electronic device according to the present disclosure has an advantage of indirectly predicting an unpredictable parameter over time through another predictable parameter.

In addition, the electronic device according to the present disclosure integrates lessons mediated through various platforms into one big data, analyzes them in real time, and efficiently updates the correlation between parameters to provide predictive information, thereby planning/planning future lessons. It can support the decision-making of the planning seller (: lesson provider).

An electronic device according to the present disclosure may identify an influence of an event on a lesson providing a leisure activity, and predict parameters of a future lesson according to the influence of the event.

The electronic device according to the present disclosure can accurately predict parameters for a lesson by defining various types of events through event parameters and establishing an effect of each event parameter on a lesson.

In addition, the electronic device according to the present disclosure integrates lessons mediated through various platforms into one big data and analyzes them in conjunction with events in real time, thereby making a decision of a seller (: lesson provider) planning/planning future lessons. can support

1 is a block diagram for explaining the configuration of an electronic device according to an embodiment of the present disclosure;

2 is a flowchart for explaining an operation of an electronic device according to an embodiment of the present disclosure;

3 is a diagram for explaining an operation of obtaining transaction data through a plurality of platforms by an electronic device according to an embodiment of the present disclosure;

4 is a flowchart for explaining an operation in which an electronic device identifies related parameters and generates a function according to a combination according to an embodiment of the present disclosure;

5 is a flowchart for explaining an operation in which an electronic device recommends a value of at least one other parameter for a target value of a specific parameter according to an embodiment of the present disclosure;

6 is a flowchart for explaining an operation of predicting a value of a parameter according to an event by an electronic device according to an embodiment of the present disclosure;

7 is a diagram for explaining an operation of predicting parameters of a lesson (eg, mountain climbing) through schedule information of an event (eg, rainy season) by an electronic device according to an embodiment of the present disclosure;

8 is a diagram for explaining an operation of identifying an influence level of an unregistered event by an electronic device according to an embodiment of the present disclosure; and

9 is a block diagram for explaining the configuration of an electronic device according to various embodiments of the present disclosure.

Prior to a detailed description of the present disclosure, the method of describing the present specification and drawings will be described.

First, terms used in the present specification and claims are general terms in consideration of functions in various embodiments of the present disclosure. However, these terms may vary depending on the intention of a technician working in the art, legal or technical interpretation, and the emergence of new technologies. In addition, some terms are arbitrarily selected by the applicant. These terms may be interpreted as the meanings defined in this specification, and if there is no specific term definition, they may be interpreted based on the overall content of this specification and common technical knowledge in the art.

In addition, the same reference numerals or numerals in each drawing attached to this specification indicate parts or components that perform substantially the same function. For convenience of description and understanding, the same reference numerals or symbols are used in different embodiments. That is, even if all components having the same reference numerals are shown in a plurality of drawings, the plurality of drawings do not mean one embodiment.

Also, in the present specification and claims, terms including ordinal numbers such as “first” and “second” may be used to distinguish between elements. These ordinal numbers are used to distinguish the same or similar components from each other, and the meaning of the term should not be construed as being limited due to the use of these ordinal numbers. For example, the order of use or arrangement of elements associated with such ordinal numbers should not be limited by the number. If necessary, each ordinal number may be used interchangeably.

In this specification, singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, the terms "comprise" or "consist of" are intended to designate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other It should be understood that the presence or addition of features, numbers, steps, operations, components, parts, or combinations thereof is not precluded.

In the embodiments of the present disclosure, terms such as “module,” “unit,” and “part” are terms used to refer to components that perform at least one function or operation, and these components are hardware or software. It may be implemented or implemented as a combination of hardware and software. In addition, a plurality of "modules", "units", "parts", etc. are integrated into at least one module or chip, except for cases where each of them needs to be implemented with separate specific hardware, so that at least one processor can be implemented as

Also, in an embodiment of the present disclosure, when a part is said to be connected to another part, this includes not only a direct connection but also an indirect connection through another medium. In addition, the meaning that a certain part includes a certain component means that it may further include other components without excluding other components unless otherwise stated.

1 is a block diagram for explaining the configuration of an electronic device according to an embodiment of the present disclosure.

Referring to FIG. 1 , an electronic device 100 includes a memory 110 and a processor 120 .

The electronic device 100 may be implemented as a server composed of at least one computer. The electronic device 100 may be implemented as a system including a plurality of servers.

In addition, the electronic device 100 may correspond to various terminal devices such as a desktop PC, a notebook PC, a kiosk, a smart phone, and a tablet PC.

The memory 110 is an operating system (OS) for controlling the overall operation of the components of the electronic device 100 and a component for storing at least one instruction or data related to the components of the electronic device 100. .

The memory 110 may include non-volatile memory such as ROM and flash memory, and may include volatile memory such as DRAM. Also, the memory 110 may include a hard disk, a solid state drive (SSD), and the like.

The processor 120 is a component for controlling the electronic device 100 as a whole. Specifically, the processor 120 may perform operations according to various embodiments of the present disclosure by executing at least one instruction stored in the memory 110 while being connected to the memory 110 .

Processor 120 may consist of one or more processors. In this case, the one or more processors may include a general-purpose processor such as a CPU, an AP, or a digital signal processor (DSP), a graphics-only processor such as a GPU, a vision processing unit (VPU), or an artificial intelligence-only processor such as an NPU. An artificial intelligence-only processor may be designed as a hardware structure specialized for training or use of a specific artificial intelligence model.

A control method of an electronic device that predicts/classifies values of various parameters related to a lesson based on the above-described configurations will be described through the following drawings.

2 is a flowchart illustrating an operation of an electronic device according to an embodiment of the present disclosure.

Referring to FIG. 2 , for each lesson provided through at least one seller, the electronic device 100 may obtain values of a plurality of parameters related to at least one of a leisure activity included in the lesson and a transaction of the lesson ( S210).

A lesson refers to a class or service that provides a place, environment, guidelines, goods, education, etc. necessary for carrying out leisure activities. Lessons may be provided for a fee or free of charge through various sellers.

Leisure activities that can be provided through lessons are diverse. Leisure activities include not only sports-related leisure activities such as mountain climbing, cycling, bowling, horseback riding, golf, hiking, skating, skiing, swimming, and fishing, but also various activities such as drawing, reading, ceramics, music appreciation, meditation, camping, games, and board games. It may include, but is not limited to.

Lessons may be sold offline or online.

In order to sell lessons online, at least one online platform that brokers sales of lessons about leisure activities may be utilized. The online platform may be provided through one or more servers, and sales or payment of lessons may be performed by matching seller terminals and buyer terminals through the server.

The plurality of parameters related to the leisure activity/transaction of the lesson include the type of leisure activity, the seller's gender, the seller's age, the seller's region, the seller's career, the seller's official qualification status, the seller's sales period, and the number of lessons provided. Region, duration of lessons, days of the week when lessons are provided, time slots when lessons are provided, frequency/number of lessons, season/month/quarter when lessons are provided, unit price per purchaser of lessons, payment method, participation in lessons It can include various parameters such as the number of people, the number of lessons, the gender of the buyer, the age of the buyer, and the region of the buyer.

In addition, the plurality of parameters include the name of the online platform used for selling lessons, the number of online platforms used for selling lessons, the type of terminal device of the purchaser who purchased the lesson through the online platform, and the OS of the terminal device of the purchaser. (Operating System), etc., may include various parameters related to transactions through online platforms.

In addition, the plurality of parameters may include parameters such as whether or not the purchaser makes repeated purchases (leisure activities of the same type or different types), and the number of times the purchaser makes repeated purchases.

In addition, the plurality of parameters may include parameters such as whether the seller performs advertisement, advertisement means, cost used for advertisement, and advertisement period.

The value of each parameter may be defined differently for each parameter. For example, when the parameter is the number of platforms, the value of the parameter may correspond to 0 or a natural number. For example, when a parameter is a seller's region, the value of the parameter may correspond to various region names.

The electronic device 100 may collect values of a plurality of parameters for each of one or more lessons provided during at least one past period.

For example, through a user input unit of the electronic device 100 or at least one user terminal (ex. seller terminal) connected to the electronic device 100, the electronic device 100 may perform at least one lesson (transaction) performed in the past. A user input for setting the value of the parameter of can be received.

In addition, the electronic device 100 may receive values of a plurality of parameters for a lesson mediated through a corresponding platform from at least one external server operating a platform that mediates sales of lessons.

In this regard, FIG. 3 is a diagram for explaining an operation of obtaining transaction data through a plurality of platforms by an electronic device according to an embodiment of the present disclosure.

Referring to FIG. 3 , the electronic device 100 is provided by a plurality of servers 310, 320, and 330 each operating a separate platform (a first platform, a second platform, and a third platform), mediated through each server. A plurality of parameter values for lessons may be received. As a result, values of a plurality of parameters for each of the lessons provided in the past may be collected.

In this case, the electronic device 100 may determine whether the lessons mediated through each platform are overlapping. For example, the electronic device 100 compares the names of lessons or values of each parameter received from different servers to identify whether information on the same lesson (values of a plurality of parameters) has been duplicatively received. there is. When there are overlapping lessons, the electronic device 100 may delete values of a plurality of parameters of each of the remaining overlapping lessons other than one lesson.

In addition, the electronic device 100 provides each server 310 , 320 , 330 according to information (eg, values of a plurality of parameters of a lesson) received from each server 310 , 320 , 330 . share can be identified.

For example, during a specific period, values of a plurality of parameters for 50 lessons are received from the first server 310 and values of a plurality of parameters for 30 lessons are received from the second server 320; Assume that values of a plurality of parameters for 20 lessons are received from the third server 330 . In this case, the electronic device 100 may identify the occupancy rates of the first to third servers 310, 320, and 330 as 5:3:2, respectively.

In this case, the electronic device 100 may receive information about lessons by communicating with each server according to occupancy rates. For example, the electronic device 100 may set a period (6:10:15) inversely proportional to the occupancy rate for each server and receive information on brokered transactions at each period.

As a specific example, the electronic device 100 receives information about lessons mediated through the first server 310 every 6 days and information about lessons mediated through the second server 320 every 10 days, , Information on lessons mediated through the third server 330 may be received every 15 days. As a result, since the amount of information received at one time is maintained relatively uniformly, it may be helpful to maintain the operation and performance of the electronic device 100 that performs analysis and prediction based on the value of the parameter of the lesson.

Here, since the number of lessons brokered through each server for each period may be flexible, the occupancy rate of each server may be updated according to information on lessons received for each period. As a result, the above-described period set for each server 310, 320, and 330 may also be updated.

Then, again referring to FIG. 2 , based on the values of the plurality of parameters acquired for each lesson, the electronic device 100 may identify a degree of association between the plurality of parameters (S220).

Here, the degree of association is an index indicating the degree to which two or more parameters are related to each other. The degree of association may be used to predict a change in another parameter according to a change in a specific parameter. For example, when the number of participants varies according to the service time zone of the lesson, a degree of association between the service time zone of the lesson and the number of participants may be defined.

The electronic device 100 may independently identify a degree of association between a plurality of parameters for each type of leisure activity that is a subject of a lesson. For example, a degree of correlation between a plurality of parameters is calculated for lessons providing mountain climbing, and a degree of correlation between a plurality of parameters is calculated for lessons providing fishing. analysis can be performed independently.

In addition, the electronic device 100 may identify a degree of association between a plurality of parameters for each period (week/month/quarter/year). For example, the degree of association between a plurality of parameters is calculated for lessons provided during the first quarter, and the degree of association between a plurality of parameters is calculated for lessons provided during the second quarter. and can be performed.

In addition, the electronic device 100 may independently identify a degree of association between a plurality of parameters for each region where a lesson is provided. For example, the electronic device 100 calculates a degree of association between a plurality of parameters for lessons provided in a first region, and separately calculates a degree of association between a plurality of parameters for lessons provided in a second region. can do.

The electronic device 100 may perform linear regression analysis, significance degree analysis (ex. R-squared, F-stastic, p-value), variability analysis, etc. on at least two parameters, but is not limited thereto. For example, the electronic device 100 may obtain a regression coefficient between parameters having a degree of significance equal to or greater than a predetermined value as a correlation constant.

Meanwhile, the degree of correlation between parameters may be defined through a function or a relational expression between three or more parameters. At this time, a correlation constant may be defined and calculated for each parameter within the function/relational expression.

In this regard, FIG. 4 is a flowchart illustrating an operation of generating a function according to a combination by identifying related parameters by an electronic device according to an embodiment of the present disclosure.

Referring to FIG. 4 , the electronic device 100 may identify a parameter associated with a specific parameter (S410). Here, the relevant parameter may refer to a parameter having a significance level of a certain value or more as a result of regression analysis in relation to the above-described specific parameter, but is not limited thereto.

If the number of parameters related to the corresponding parameter among the plurality of parameters is one (S420 - N), the electronic device 100 may obtain one correlation constant such as a regression coefficient (S440).

On the other hand, when the number of parameters related to the corresponding parameter is plural (S420 - Y), the electronic device 100 may generate a function or relational expression for calculating the value of the corresponding parameter by combining the values of each of the plurality of related parameters. Yes (S430). The function or relational expression may include an association constant applied to each of a plurality of association parameters.

For example, each of the correlation constants a1, a2, and a3 constituting the following relational expression may be defined. f (parameter) may indicate the value of the parameter. Alternatively, if the parameter value is not a quantitative numerical value, such as when the parameter is a region or time zone, f (parameter) is a numeric value obtained by converting the parameter value (ex. specific region, specific time zone) according to a preset conversion function. may correspond to

Sales = a1·f (region where lessons are offered) + a2·f (time zones where lessons are offered) + a3·f (number of platforms used for sales).

Meanwhile, in relation to FIG. 4 , the electronic device 100 may set the importance of each parameter according to the number of related parameters.

Specifically, the electronic device 100 may set the importance of parameters according to the number of related parameters. Here, the electronic device 100 may calculate the importance of the parameter higher as the number of related parameters increases.

And, according to the importance of the parameter, the electronic device 100 may identify an update cycle of the correlation constant applied to each of a plurality of related parameters related to the parameter. In this case, the higher the importance, the shorter the update cycle may be.

And, for each update period, the electronic device 100 obtains a parameter value and a value of each related parameter for each lesson provided within the period (corresponding period), and obtains the obtained parameter value and the acquired value of each related parameter. Based on , the relevance constant can be updated.

As a result, the higher the importance (associated with a large number of parameters), the more often the association of the associated parameter is updated, so that the accuracy of future association-based prediction can be guaranteed. In particular, since only the values of the parameters (: parameters, related parameters) that are subject to update (not all parameters) are selectively collected and analyzed every update period, and the values of the remaining parameters may not be shared, efficiency in terms of communication and computation this can be increased. In addition, efficiency can be increased in terms of the amount of communication and calculation in terms of the fact that the update cycle of the correlation constant of the related parameter is relatively long for a parameter having a relatively low importance.

Meanwhile, when a degree of association between a plurality of parameters is identified according to various embodiments described above, the electronic device 100 may obtain a value of at least one first parameter among a plurality of parameters for at least one scheduled lesson. (S230).

As an example, the electronic device 100 may set a value of at least one first parameter for a scheduled lesson according to a user input. The user input may be received through a user input unit of the electronic device 100 or at least one user terminal capable of communicating with the electronic device 100 (eg, a vendor terminal).

Here, the electronic device 100 may create and register at least one scheduled lesson according to a user input, and set at least one first parameter for the scheduled lesson according to the user input. For example, with respect to a scheduled lesson, the electronic device 100 may set values of parameters such as a provision period, region, type of leisure activity, unit price per purchaser, payment method, and platform type according to user input.

As another embodiment, the electronic device 100 may predict the value of the first parameter for the scheduled lesson according to the change history of the first parameter for each lesson provided in the past.

In this case, the electronic device 100 may identify a change history of the value of the first parameter for each lesson provided in the past. And, according to the identified change history, the electronic device 100 may predict the value of the first parameter for the scheduled lesson. For example, in a specific type of leisure activity, the electronic device 100 may identify a change history of a period (length of period) in which lessons were provided in the past, and predict a provision period of scheduled lessons based on the identified change history. there is.

When the value of the first parameter is obtained according to at least one of the above-described embodiments, the electronic device 100 determines the second parameter for the scheduled lesson based on the degree of association between the first parameter and the second parameter (of which the value is obtained). The value of the parameter can be predicted (S240).

For example, when a lesson provision period, region, time zone, etc. are set according to user input, the electronic device 100 utilizes a degree of association between the above-described parameters (provision period, region, time zone) and the 'number of participants' parameter. Thus, the 'number of participants' can be predicted.

As such, the control method of an electronic device according to the present disclosure can predict values of other parameters (ex. number of participants, sales, etc.) by utilizing values of parameters set according to user input or predicted according to change history. There are advantages.

In particular, since it is difficult to predict sales, number of participants, etc. by analyzing the change history of the parameters themselves, and they are not parameters that can be set in advance according to user input, the control method according to the present disclosure indirectly predicts them through the values of other parameters. effect is remarkable.

Meanwhile, even in the process of obtaining the value of the first parameter in step S230 described above, the electronic device 100 may predict the value of the first parameter by utilizing the correlation between the parameters.

For example, the electronic device 100 may set a value of at least one third parameter for the scheduled lesson according to a user input.

In this case, the electronic device 100 may identify a value of the first parameter that matches the value of the set third parameter based on the degree of association between the third parameter and the first parameter.

That is, the electronic device 100 may predict another parameter (eg, second parameter) by utilizing a parameter (first parameter) predicted through another parameter (eg, third parameter).

Meanwhile, in an embodiment, when a target value is set for at least one parameter, the electronic device 100 may recommend other parameter values for achieving the target value of the corresponding parameter.

In this regard, FIG. 5 is a flowchart illustrating an operation of recommending, by an electronic device, a value of at least one other parameter for a target value of a specific parameter according to an embodiment of the present disclosure.

Referring to FIG. 5 , for a scheduled lesson, the electronic device 100 may set a target value for at least one parameter according to a user input (S510). For example, target values for sales of scheduled lessons or the number of participants may be set.

In this case, the electronic device 100 may identify the value of another parameter that matches the target value of the above-described parameter by utilizing the correlation between the parameters (S520).

For example, the electronic device 100 may identify values of various parameters (eg, region where lessons are provided, time zone, type of leisure activity, etc.) for achieving a target value in sales. In this case, the electronic device 100 may identify values of other parameters for achieving a target value by utilizing the correlation between the parameters.

Alternatively, the electronic device 100 may generate and utilize at least one artificial intelligence model using a parameter for which a target value is set as input data.

As a specific example, it is assumed that a target value for a specific parameter (eg, sales, number of participants, repeated purchase, etc.) is set according to user input.

At this time, the electronic device 100 defines a parameter with a target value set as input data and outputs at least one parameter without a target value (ex. payment method, region in which lessons are provided, period in which lessons are provided, etc.) At least one artificial intelligence model defined by data can be created. Here, at least one other parameter (eg, type of leisure activity, number of platforms, etc.) whose value is set according to a user input may also be defined as input data.

The artificial intelligence model may include at least one neural network model, and may correspond to a model for determining a value of each parameter defined as output data.

In this case, the electronic device 100 may train the generated artificial intelligence model based on values of a plurality of parameters obtained for each lesson provided in the past. In the training process, parameter values previously defined as input data may be used as input training data, and parameter values defined as output data may be used as output training data.

In addition, the electronic device 100 may input a target value set prior to the trained artificial intelligence model and identify another parameter value that matches the target value of the input parameter.

As such, whenever a target value is set, the electronic device 100 individually creates and trains artificial intelligence models having different inputs/outputs depending on the parameter for which the target value is set, and other parameters for achieving the target value. value can be identified.

According to the above-described embodiment, when another parameter value for achieving a target value of a specific parameter is identified, the electronic device 100 may provide recommendation information recommending the value of the identified parameter (S530). A user who has been provided with the recommended information in this way can prepare a lesson having parameter conditions optimal for achieving a target value.

Meanwhile, the electronic device 100 according to an embodiment of the present disclosure may predict a parameter value according to an event.

In this regard, FIG. 6 is a flowchart illustrating an operation of predicting a value of a parameter according to an event by an electronic device according to an embodiment of the present disclosure.

Referring to FIG. 6 , for each lesson provided through at least one seller, the electronic device 100 may obtain values of a plurality of parameters related to at least one of a leisure activity included in the lesson and a transaction of the lesson ( S610). This process may correspond to S210 of FIG. 2 described above.

Then, the electronic device 100 may identify the degree of impact of the event on each of the plurality of parameters based on the information about the event and the values of the plurality of parameters (S620).

First, the electronic device 100 may obtain information about an event that occurred during at least one period in the past.

The event may include various events related to leisure activities or transactions of the lesson. For example, the event may be an event related to a weather environment or a natural disaster, such as a cold wave, intense heat, tropical night, rainy season, typhoon, shower, yellow dust/fine dust (above a certain concentration), and an infectious disease. In addition, the event may be an event related to marketing, such as a seller's performance of an advertisement, a media report (for a lesson or leisure activity), and the like. In addition, the event may be an event related to the institutional environment, such as a change in standard working hours, a change in regulations for leisure activities, and a change in the number of allowable gatherings due to a disaster situation (eg, an epidemic).

Information on an event means various pieces of information about an event that has occurred in the past, and may include values of various event parameters constituting the event. For example, the event parameter may correspond to the type of event, the occurrence time of the event, the occurrence (maintenance) period of the event, and the like.

Event parameters may vary for each event. For example, when the event is a rainy season, the event parameters may correspond to rainfall (maximum/minimum/average), rainfall area, temperature (maximum/minimum/average), and the like. As another example, when the event is a seller performing an advertisement, the event parameter may correspond to an advertisement means, an advertisement cost, and the number of advertisement exposures.

The electronic device 100 may obtain information about events that have occurred in the past according to user input. The user input may be received through a user input unit provided in the electronic device 100 or through at least one user terminal capable of communicating with the electronic device 100 (eg, a vendor terminal).

Alternatively, the electronic device 100 may transmit information about past events from at least one external device (eg, a server operating an online platform, a weather forecasting server, a portal/broadcasting system performing weather forecasting, an Internet portal server, etc.) may receive.

In addition, the electronic device 100 may determine the degree of influence of the event on the plurality of parameters by utilizing the obtained information about the event and the values of the plurality of parameters previously obtained for each lesson.

Specifically, the electronic device 100 identifies a first value of a plurality of parameters collected for a lesson that was provided at a time when an event did not occur, and second values of a plurality of parameters for a lesson that was provided at a time when an event occurred. value can be identified. In addition, the electronic device 100 may compare the first value and the second value for each parameter to identify the degree of influence of the event on each of the plurality of parameters.

In addition, the electronic device 100 may determine the degree of influence each event parameter constituting the event has on each of the plurality of parameters. For example, the degree of influence of rainfall according to the rainy season on a specific parameter of the lesson (eg, the number of participants) may be calculated.

The degree of influence may be expressed as an influence constant defining a relationship between event parameters and parameters, and the electronic device 100 may be applied to each event parameter constituting an event and an event parameter within a relational expression defining a relationship between parameters. Influence constants can be determined. The relational expression may be configured so that the value of the parameter of the lesson is derived through a combination of values of a plurality of event parameters.

Meanwhile, the degree of influence of the event may be independently determined according to the type of leisure activity provided through the lesson. For example, even in the same rainy season, the degree of influence of lessons providing outdoor activities (eg, mountain climbing) may be calculated to be greater than the degree of influence of lessons providing indoor activities (eg, reading).

Meanwhile, the degree of impact of an event for each past period may be independently determined. Specifically, the electronic device 100 may determine the degree of influence of the event on each parameter for each of a plurality of past periods (first period, second period, and third period). Here, the first period corresponds to the most recent period, and the third period corresponds to the most recent period.

For example, the degree of influence of advertisement (: event) on sales (: parameter) may be determined for each of the first to third periods. In this case, the electronic device 100 may determine the degree of influence of the advertisement on sales by applying all of the degree of influence determined for each period.

As a specific example, the electronic device 100 may obtain an average value for the degree of influence of each period (first to third periods) or sum up the degree of influence of each period.

Alternatively, the electronic device 100 may add different weights to the determined influence levels for each period. In this case, a greater weight may be assigned to a period closer to the present.

And, referring to FIG. 6 , the electronic device 100 may predict a value of at least one parameter among a plurality of parameters for at least one scheduled lesson based on schedule information of at least one event and the degree of impact of the event. Yes (S630).

Scheduled information about an event means various information about an event to occur in the future, and may include values of various event parameters constituting the event.

The electronic device 100 may obtain scheduled information about an event to occur in the future according to a user input.

Alternatively, the electronic device 100 may receive scheduled information about an event to occur in the future from at least one external device (eg, a server operating an online platform, a weather prediction server, an Internet portal server, etc.).

Scheduled lessons may be created and registered according to user input.

The electronic device 100 may set a value of at least one parameter for a scheduled lesson according to a user input. For example, for a scheduled lesson, values of parameters such as a lesson provision period, region, type of leisure activity, unit price per purchaser, payment method, and platform type may be set according to user input.

As another example, the electronic device 100 may predict a value of a parameter for a scheduled lesson according to a change history of a corresponding parameter for each lesson provided in the past.

In this case, the electronic device 100 may identify a change history of a parameter value for each lesson provided in the past. And, according to the identified change history, the electronic device 100 may predict the value of the parameter for the scheduled lesson. For example, in a specific type of leisure activity, the electronic device 100 may identify a change history of a period (length of period) in which lessons were provided in the past, and predict a provision period of scheduled lessons based on the identified change history. there is.

In addition, the electronic device 100 may predict a value of at least one parameter for a scheduled lesson based on a value of each event parameter included in schedule information of an event (to occur in the future).

In this case, the electronic device 100 may predict the value of at least one parameter by utilizing the degree of influence of each event parameter on each parameter.

In this case, the electronic device 100 may adjust the degree of influence of the event (event parameter) according to the degree to which the scheduled period of the event matches the provision period of the scheduled lesson. Also, the electronic device 100 may predict the value of at least one parameter for the scheduled lesson according to the adjusted influence level.

In this regard, FIG. 7 is a diagram for explaining an operation of predicting parameters of a lesson (eg, mountain climbing) through schedule information of an event (eg, rainy season) by an electronic device according to an embodiment of the present disclosure.

Referring to FIG. 7 , a plurality of event parameters (721, 722, 723, 724, etc.) constituting scheduled information of a rainy season 720, which is an event to occur in the future, and a plurality of parameters (711, 711, 712, 713, 714, 715) are shown.

Specifically, values of event parameters such as a rainy season period 721 , a rainfall region 722 , a temperature 723 , and a rainfall amount 724 may be set for the rainy season 720 .

Among the parameters constituting the mountain climbing 710 corresponding to the lesson, the unit price 711 for each purchaser, the lesson providing area 714, the lesson providing time zone 715, etc. may be set according to user input. The number of platforms 713 may be a parameter value set according to the number of online servers (platforms) that mediate sales of lessons.

Meanwhile, in the case of sales 712 among the parameters constituting mountain climbing 710, it may not be set in advance according to user input.

In this case, the electronic device 100 may predict the value of the sales 712 based on the degree of influence of each event parameter 721 , 722 , 723 , and 724 on the sales 712 .

Specifically, the electronic device 100 may predict the value of the sales 712 by reflecting the degree of influence on the value of each event parameter 721 , 722 , 723 , and 724 . In this case, the electronic device 100 may adjust and reflect the degree of influence according to the length of the period during which the scheduled period of the event and the (scheduled) provision period of the lesson overlap each other. In this case, it goes without saying that the longer the overlapping period, the larger the degree of influence can be adjusted.

In addition, the electronic device 100 may predict the value of the sales 712 by reflecting the values of the other parameters 711 , 713 , 714 , and 715 constituting the lesson together.

Specifically, the electronic device 100 may identify a degree of association between parameters based on values of a plurality of parameters 711, 712, 713, 714, and 715 obtained for each of one or more lessons provided in the past (Fig. Corresponds to step S220 of 2).

For example, the electronic device 100 determines the relationship between at least one parameter of the unit price per buyer 711, the number of platforms 713, the lesson provision area 714, and the lesson provision time period 715 and the sales 712. can be identified.

In this case, the electronic device 100 determines the values of the event parameters 721 , 722 , 723 , and 724 constituting the rainy season 720 and the degree of influence of the event parameters 721 , 722 , 723 , and 724 on the sales 712 . , the values of the parameters 711, 713, 714, 715 set for the mountain climbing 710, and the relationship between the parameters 711, 713, 713, 715 and the sales 712, the scheduled lesson 710 The value of sales 712 for ) can be predicted.

In this way, the parameter of the scheduled lesson can be precisely predicted by reflecting the degree of influence of the event on the parameter as well as the degree of association between the parameters.

Meanwhile, as an example, the electronic device 100 may register at least one event in which the degree of impact (on the lesson) is identified.

Specifically, when the degree of the first influence exerted by the first event on each of the plurality of parameters (related to the lesson) is identified according to the above-described process S620, the electronic device 100 may register the first event.

In this case, the first effect degree of the first event and/or the event parameters constituting the first event on each of a plurality of parameters (related to the lesson) may be stored together. However, in the case of an event parameter whose degree of influence is less than a certain value, information on the event parameter and the degree of influence of the event parameter may not be stored (registered) together with the event.

Then, when the registered information of the first event (information notifying the occurrence of the first event) is received, the electronic device 100 determines the information about the first event and the degree of the first influence of the first event on the lesson. Based on this, it is possible to predict the value of at least one parameter for at least one scheduled lesson.

However, when the schedule information of the unregistered second event is received, there may be a problem in predicting the parameter in that the degree of influence of the second event on the lesson is not identified.

In this regard, FIG. 8 is a diagram for explaining an operation of identifying an influence level of an unregistered event by an electronic device according to an embodiment of the present disclosure.

Referring to FIG. 8 , information 810 on registered events is shown on the premise that events such as a rainy season 811 , a TV commercial 812 , and a law revision 813 are registered. Corresponding information 810 includes the degree of influence of the registered events 811, 812, and 813 on the lesson.

Specifically, information on event parameters (ex. airing time, airing channel, airing frequency, advertising cost) constituting each event (ex. TV advertisement 812), and information on the degree of influence of each event parameter on the lesson Information may be stored.

Here, when schedule information of the TV broadcast 815, which is an unregistered event, is acquired (received), the electronic device 100 matches the TV broadcast 815 among the registered events (811, 812, 813, etc.) Similar events can be identified.

Specifically, the electronic device 100 may compare the TV broadcasting 815 with the pre-registered events 811, 812, and 813.

In this case, the electronic device 100 may compare the event name of the TV broadcast 815 (TV broadcast) with the event name of each event 811, 812, and 813. In addition, the electronic device 100 may compare event parameters constituting the TV broadcasting 815 (ex. broadcasting time, broadcasting channel, broadcasting frequency, etc.) with event parameters constituting the respective events 811, 812, and 813. .

For example, the electronic device 100 may compare the name of each registered event with the name of the TV broadcast 815 to identify a first similarity of each registered event. In addition, the electronic device 100 may identify the second degree of similarity according to the number or ratio of event parameters that match each registered event and the TV broadcasting 815 .

In this case, the electronic device 100 may calculate a similarity in which the first similarity and the second similarity are reflected, and select an event having the greatest similarity among registered events 811 , 812 , and 813 . In this case, the electronic device 100 may select at least one event on the premise that the degree of similarity is greater than or equal to a threshold value.

As a result, the electronic device 100 may identify the TV advertisement 812, which is the most similar event to the TV broadcast 815, among the pre-registered events 811, 812, and 813.

Then, the electronic device 100 determines the degree of influence of the TV broadcasting 815 on the parameter of the lesson according to the degree of influence of the TV advertisement 812 (identified as the most similar) on the parameter of the lesson (pre-registered). can do.

For example, for the same event parameter (ex. airing time, airing channel, airing frequency, etc.) commonly included in events (TV commercials, TV airings), the degree of influence may also be identified as the same.

In addition, the electronic device 100 may determine the degree of influence of the TV broadcasting 815 on the lesson according to the similarity between the calculated events.

As a specific example, the above-described similarity may be applied (eg, multiplied) to the degree of influence of at least one event parameter of the TV advertisement 812 on the lesson. As a result, the degree of influence (B′) of at least one event parameter of the TV broadcasting 815 on the parameter of the lesson may be calculated.

That is, as the similarity to the registered event (ex. TV advertisement 812) increases, the degree of influence of the TV broadcast 815 can be calculated closer to the degree of influence of the registered event (ex. TV advertisement 512). there is.

As such, if the degree of influence of an event (ex. TV broadcast 815) that has not been previously registered is determined, the electronic device 100 may register the corresponding event (ex. TV broadcast 815). That is, the degree of influence of each event parameter constituting the TV broadcasting 815 on at least one lesson may be added to the above-described information 810 .

However, the information 810 on registered events may include only information on events within a certain number, or its capacity may be limited within a certain capacity.

For example, as a result of the process shown in FIG. 5, when an event (ex. TV broadcasting 815) of which the degree of influence is newly determined occurs, the electronic device 100 may identify the number of previously registered events. .

If the number of previously registered events reaches a certain number, the electronic device 100 determines whether an event having a numerically lower degree of impact than a new event (ex. TV broadcasting 815) among the registered events exists. can determine whether If there is, the electronic device 100 may replace the event having the lowest impact with a new event (ex. TV broadcasting 815). On the other hand, if it does not exist, the electronic device 100 may not additionally register a new event (ex. TV broadcasting 815).

Meanwhile, FIG. 9 is a block diagram illustrating a configuration of an electronic device according to various embodiments of the present disclosure.

Referring to FIG. 9 , the electronic device 100 may include a communication unit 130, a user input unit 140, an output unit 150, and the like in addition to a memory 110 and a processor 120.

The communication unit 130 is a component through which the electronic device 100 transmits and receives data with various external devices, and may include at least one circuit for communication.

The communication unit 130 is TCP/IP (Transmission Control Protocol/Internet Protocol), UDP (User Datagram Protocol), HTTP (Hyper Text Transfer Protocol), HTTPS (Secure Hyper Text Transfer Protocol), FTP (File Transfer Protocol), SFTP ( Various types of information may be transmitted and received with one or more external electronic devices using communication protocols such as Secure File Transfer Protocol (MQTT) and Message Queuing Telemetry Transport (MQTT).

To this end, the communication unit 130 may be connected to an external device based on a network implemented through wired communication and/or wireless communication. In this case, the communication unit 130 may be directly connected to an external device, but may also be connected to an external electronic device through one or more external servers (eg, Internet Service Provider (ISP)) providing a network.

The network may be a Personal Area Network (PAN), a Local Area Network (LAN), a Wide Area Network (WAN), etc., depending on the area or size, and an intranet, It may be an extranet or the Internet.

Wireless communication includes LTE (long-term evolution), LTE-A (LTE Advance), 5G (5th generation) mobile communication, CDMA (code division multiple access), WCDMA (wideband CDMA), UMTS (universal mobile telecommunications system), WiBro (Wireless Broadband), GSM (Global System for Mobile Communications), DMA (Time Division Multiple Access), WiFi (Wi-Fi), WiFi Direct, Bluetooth, NFC (near field communication), Zigbee, etc. can include

Wired communication may include at least one of communication methods such as Ethernet, optical network, Universal Serial Bus (USB), and Thunderbolt.

Here, the communication unit 130 may include a network interface or network chip according to the wired/wireless communication method described above. On the other hand, the communication method is not limited to the above-described example, and may include a newly appearing communication method according to the development of technology.

When the electronic device 100 is a server, the electronic device 100 may communicate with various user terminals through the communication unit 130 and interwork with each user terminal through at least one web page or application. Operations according to various embodiments of may be performed.

When the electronic device 100 is a user terminal (eg, a seller terminal) such as a smartphone, the electronic device 100 provides various applications/web pages provided by at least one server connected through the communication unit 130. Operations according to embodiments may be performed.

The electronic device 100 may communicate with one or more external servers related to a platform that mediates sales of lessons through the communication unit 130 . In this case, the electronic device 100 may receive values of a plurality of parameters for each lesson from an external server.

The user input unit 140 is a component for receiving a user command or user information.

When the electronic device 100 is implemented as a user terminal such as a smart phone or a notebook PC, the user input unit 140 may be implemented as a touch sensor, a button, a camera, a microphone, a keyboard, or the like.

When the electronic device 100 is a desktop PC, the user input unit 140 may be implemented as a terminal connected to various input interfaces such as a keyboard or a mouse.

The output unit 150 is a component for outputting various information and providing the information to the user.

When the electronic device 100 is implemented as a user terminal such as a smart phone, the output unit 150 may include a display, a speaker, an earphone/headset terminal, and the like.

For example, the electronic device 100 may visually provide statistical information (eg, minimum value, maximum value, average value, change history, etc. for each parameter) related to values of various parameters through a display.

Also, the electronic device 100 may predict a value of at least one parameter in relation to a scheduled lesson and visually provide the predicted value of the parameter through a display.

In addition, when a target value of a specific parameter is set in relation to a scheduled lesson, the electronic device 100 may visually provide recommendation information recommending a value of at least one other parameter required to achieve the target value through a display. there is.

Meanwhile, the various embodiments described above may be implemented by combining two or more embodiments as long as they do not conflict or contradict each other.

Meanwhile, various embodiments described above may be implemented in a recording medium readable by a computer or a similar device using software, hardware, or a combination thereof.

According to the hardware implementation, the embodiments described in this disclosure are application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), and field programmable gate arrays (FPGAs). ), processors, controllers, micro-controllers, microprocessors, and electrical units for performing other functions.

In some cases, the embodiments described herein may be implemented by a processor itself. According to software implementation, embodiments such as procedures and functions described in this specification may be implemented as separate software modules. Each of the software modules described above may perform one or more functions and operations described herein.

On the other hand, computer instructions or computer programs for performing processing operations in the electronic device 100 according to various embodiments of the present disclosure described above are provided on a non-transitory computer-readable medium can be stored in Computer instructions or computer programs stored in such a non-transitory computer readable medium, when executed by a processor of a specific device, cause the above-described specific device to perform processing operations in the electronic device 100 according to various embodiments.

A non-transitory computer readable medium is a medium that stores data semi-permanently and is readable by a device, not a medium that stores data for a short moment, such as a register, cache, or memory. Specific examples of the non-transitory computer readable media may include CD, DVD, hard disk, Blu-ray disk, USB, memory card, ROM, and the like.

Although the preferred embodiments of the present disclosure have been shown and described above, the present disclosure is not limited to the specific embodiments described above, and is common in the technical field belonging to the present disclosure without departing from the gist of the present disclosure claimed in the claims. Of course, various modifications and implementations are possible by those with knowledge of, and these modifications should not be individually understood from the technical spirit or perspective of the present disclosure.

Claims (10)

1. In the control method of an electronic device,

   obtaining values of a plurality of parameters related to at least one of a leisure activity included in the lesson and a transaction of the lesson, for each lesson provided through at least one seller;

   identifying a degree of association between the plurality of parameters based on values of the plurality of parameters obtained for each of the provided lessons;

   obtaining a value of at least one first parameter among the plurality of parameters for at least one scheduled lesson; and

   and predicting a value of the second parameter for the at least one scheduled lesson based on a degree of association between a second parameter and the first parameter among the plurality of parameters.
2. According to claim 1,

   The step of obtaining the value of the first parameter for the at least one scheduled lesson,

   According to a user input, a value of at least one third parameter for the scheduled lesson is set;

   A method of controlling an electronic device comprising identifying a value of the first parameter that matches a value of the set third parameter based on a degree of association between the third parameter and the first parameter.
3. According to claim 1,

   The step of obtaining the value of the first parameter for the at least one scheduled lesson,

   Identifying a change history of the value of the first parameter based on the values of the plurality of parameters obtained for each of the provided lessons;

   According to the identified change history, predicting a value of the first parameter for the at least one scheduled lesson.
4. According to claim 1,

   Obtaining values of a plurality of parameters for each of the provided lessons,

   Receiving, from a first server providing a first platform for mediating sales of lessons for leisure activities, values of the plurality of parameters for lessons mediated through the first platform;

   A control method of an electronic device comprising receiving, from a second server providing a second platform for mediating sales of lessons about leisure activities, values of the plurality of parameters for lessons mediated through the second platform.
5. According to claim 1,

   The control method of the electronic device,

   setting a target value for the second parameter according to a user input;

   identifying a value of the first parameter that matches the set target value of the second parameter based on a degree of association between the first parameter and the second parameter; and

   A method of controlling an electronic device, including recommending a value of the identified first parameter.
6. According to claim 5,

   The control method of the electronic device,

   generating at least one artificial intelligence model in which the second parameter is defined as input data and the first parameter is defined as output data when a target value for the second parameter is set according to the user input;

   training the generated artificial intelligence model based on values of a plurality of parameters obtained for each of the provided lessons; and

   Inputting a target value set for the second parameter to the trained artificial intelligence model, and identifying a value of the first parameter that matches the target value of the second parameter; including, a control method of an electronic device .
7. According to claim 1,

   Identifying the degree of association between the plurality of parameters,

   identifying at least one associated parameter for one of the plurality of parameters; and

   obtaining a correlation constant applied to each of the plurality of related parameters to calculate a value of the parameter by combining values of each of the plurality of related parameters when a plurality of related parameters are identified; Including, the control method of the electronic device.
8. According to claim 7,

   The control method of the electronic device,

   setting a degree of importance of the parameter according to the number of the associated parameter having a degree of association with the parameter equal to or greater than a threshold value;

   identifying an update cycle of an association constant applied to each of the plurality of association parameters according to the importance of the parameter; and

   For each update period, obtaining a value of the parameter and a value of each of the related parameters for each lesson provided, and updating the degree of association constant based on the obtained value of the parameter and the value of each of the obtained related parameters. A method of controlling an electronic device, comprising the steps of:
9. In the control method of an electronic device,

   obtaining values of a plurality of parameters related to at least one of a leisure activity included in the lesson and a transaction of the lesson, for each lesson provided through at least one seller;

   Identifying a degree of impact of the event on each of the plurality of parameters based on information about an event that occurred while at least one lesson is provided and the obtained values of the plurality of parameters; and

   and predicting a value of at least one parameter among the plurality of parameters for at least one scheduled lesson based on the schedule information of the event and the degree of influence of the event.
10. According to claim 9,

    The step of identifying the degree of influence is,

    identifying a first value of the plurality of parameters for a lesson provided at a time when the event did not occur;

    identify a second value of the plurality of parameters for a lesson provided at the time the event occurred;

    Comparing the first value and the second value, the control method of the electronic device to identify the degree of influence of the event on each of the plurality of parameters.

Images