KR20230019553A - Electronic apparatus and method for providing home appliance repair services, and program stored in computer readable medium performing the same - Google Patents

Abstract

Provided are an electronic apparatus for providing home appliance repair service, a method thereof, and a program stored in a computer-readable recording medium to execute the same. The electronic apparatus for providing home appliance repair service may: receive home appliance information from a plurality of user terminals; generate statistics data regarding a home appliance repair request by using home appliance information; calculate repair estimation time information for each home appliance by using the statistics data; generate recommended dispatch engineer information by using the location information of a dispatch engineer terminal; and provide the calculated repair estimation time information and the recommended dispatch engineer information to the user terminal which is using the corresponding home appliance. Therefore, images may be filmed when home appliance repair service is provided, to be provided to the customer in real time.

Description

Electronic device and method for providing home appliance repair service, and a program stored in a computer readable recording medium to perform the same

The present invention relates to providing a home appliance repair service, and more specifically, to providing a home appliance repair service capable of suggesting inspection before a failure occurs in a home appliance by forming statistical data using home appliance repair records. It relates to an electronic device and method, and a program stored on a computer readable recording medium to perform the same.

In the past, when requesting repairs of home appliances, etc. to a manufacturer, a customer inquires to the service center of the manufacturer by phone, etc., and the service center dispatches an engineer to diagnose and repair the malfunction of the home appliance, or send the service center to the home appliance service center. The product was sent to the service center for diagnosis and repair.

However, this system imposes a heavy burden on customers and takes time to repair, so that it cannot provide sufficient service to customers.

A technical problem to be solved by the present invention is an electronic device and method for providing a home appliance repair service capable of suggesting inspection before a home appliance fails by forming statistical data using home appliance repair records, and , To provide a program stored in a computer readable recording medium to perform the same.

Another technical problem to be solved by the present invention is an electronic device and method for providing a home appliance repair service capable of capturing an image and providing it to consumers in real time when providing a home appliance repair service, and a computer-readable recording medium to perform the same. It is to provide a program stored in.

The technical problem to be solved by the present invention is not limited to the above.

An electronic device for providing a home appliance repair service according to an embodiment of the present invention includes one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, wherein the one or more processors receive home appliance information from a plurality of user terminals, and the home appliance information is stored. Statistical data on home appliance repair requests is formed using product information, repair expected time information for each home appliance is calculated using the statistical data, and recommended business trip driver information is formed using the location information of the travel driver terminal. , The calculated repair expected time information and the recommended business trip driver information may be provided to a user terminal using the corresponding home appliance.

As an embodiment, the one or more processors may calculate the expected repair time information by further considering the user's age and the user's residential area.

As an embodiment, the one or more processors extracts information about the use time of the home appliance using the home appliance information, and uses the extracted information about the use time for a predetermined period before use of the home appliance. Estimated repair time information of the home appliance may be calculated, and the expected repair time information may be transmitted to a user terminal using the corresponding home appliance.

As an embodiment, the one or more processors extract expected repair times of all home appliances possessed by the user, calculate a visit schedule of a business trip driver in consideration of expected repair times included within a preset period, and calculate the visit schedule. It can be transmitted to the user terminal and the travel driver terminal.

As an embodiment, the one or more processors extract expected repair times of home appliances possessed by all users located in a preset regional range, and set a visit schedule of a business trip driver in consideration of expected repair times included within a preset period. The visit schedule may be calculated and transmitted to the user terminal and the travel driver terminal.

As an embodiment, the one or more processors may receive a home appliance repair request from a user terminal, and may receive an image taken while the home appliance is being repaired from a business trip driver terminal and provide the received image to the user terminal.

As an embodiment, the one or more processors may calculate estimated repair time information for each home appliance by further considering an image captured when the home appliance is being repaired.

one or more processors according to one embodiment of the invention; and one or more memories storing instructions for causing the one or more processors to perform calculations when executed by the one or more processors. Receiving home appliance information from a user terminal; forming statistical data on a request for repair of a home appliance using the home appliance information; Calculating an estimated repair time point for each home appliance using the statistical data; forming recommended travel driver information by using location information of the travel driver's terminal; and providing the calculated repair expected time information and the recommended business trip driver information to a user terminal using the corresponding home appliance.

As one embodiment, the step of extracting information about the use time of the home appliance using the home appliance information; Calculating expected repair time information of the home appliance for a predetermined period before use of the home appliance by using the information on the extracted use period; and transmitting the expected repair time information to a user terminal using the corresponding home appliance.

one or more processors according to one embodiment of the invention; And when executed by the one or more processors, a computer program stored in a computer-readable recording medium to be executed by a computer including one or more memories in which instructions for causing the one or more processors to perform operations is stored, from a plurality of user terminals. receiving home appliance information; forming statistical data on a request for repair of a home appliance using the home appliance information; Calculating an estimated repair time point for each home appliance using the statistical data; forming recommended travel driver information by using location information of the travel driver's terminal; and providing the calculated repair expected time information and the recommended business trip driver information to a user terminal using the corresponding home appliance.

An electronic device and method for providing a home appliance repair service and a program stored in a computer readable recording medium to perform the home appliance repair service according to an embodiment of the present invention form statistical data using home appliance repair records to prevent failure of the home appliance. A check may be suggested before this occurs. In this way, it is possible to minimize inconvenience experienced by the user due to a failure of the home appliance, and to prevent a waste of time required to make a repair reservation for the home appliance.

In addition, an electronic device and method for providing a home appliance repair service according to an embodiment of the present invention, and a program stored in a computer readable recording medium to perform the same, take an image when the home appliance repair service is provided and provide the consumer in real time. can provide As a result, reliability can be improved when a business trip driver visits for a home appliance repair service, and an unsavory accident such as a stolen product can be prevented in advance.

1 is an exemplary view showing the configuration of a home appliance repair service providing environment according to an embodiment of the present invention.
2 is an exemplary view showing the configuration of an electronic device according to an embodiment of the present invention.
3 is a diagram for explaining learning of a neural network according to an embodiment of the present invention.
4 and 5 are flowcharts showing procedures of a home appliance repair service providing method according to an embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the technical idea of the present invention is not limited to the embodiments described herein and may be embodied in other forms. Rather, the embodiments introduced herein are provided so that the disclosed content will be thorough and complete, and the spirit of the present invention will be sufficiently conveyed to those skilled in the art.

In this specification, when an element is referred to as being on another element, it means that it may be directly formed on the other element or a third element may be interposed therebetween.

In addition, although terms such as first, second, and third are used to describe various elements in various embodiments of the present specification, these elements should not be limited by these terms. These terms are only used to distinguish one component from another. Therefore, what is referred to as a first element in one embodiment may be referred to as a second element in another embodiment. Each embodiment described and illustrated herein also includes its complementary embodiments. In addition, in this specification, 'and/or' is used to mean including at least one of the elements listed before and after.

In the specification, expressions in the singular number include plural expressions unless the context clearly dictates otherwise. In addition, the terms "comprise" or "having" are intended to designate that the features, numbers, steps, components, or combinations thereof described in the specification exist, but one or more other features, numbers, steps, or components. It should not be construed as excluding the possibility of the presence or addition of elements or combinations thereof.

In addition, in this specification, "connection" is used to mean both indirectly and directly connecting a plurality of components. In addition, "connection" is a concept including physical connection as well as electrical connection.

In addition, in the following description of the present invention, if it is determined that a detailed description of a related known function or configuration may unnecessarily obscure the subject matter of the present invention, the detailed description will be omitted.

1 is an exemplary view showing the configuration of a home appliance repair service providing environment according to an embodiment of the present invention.

As shown in FIG. 1 , a home appliance repair service providing environment 100 may include a plurality of user terminals 110 , an electronic device 120 , and a plurality of travel driver terminals 130 . According to an embodiment, the plurality of user terminals 110, the electronic device 120, and the plurality of travel driver terminals 130 may be connected to communicate with each other through a system bus and/or a network N. For example, the home appliance repair service providing environment 100 may include a plurality of artificial intelligence for performing a machine learning algorithm.

The user terminal 110 may form home appliance information according to a user request. According to an embodiment, the user terminal 110 is a terminal carried by a user of a home appliance and is connected to communicate with the electronic device 120 and a plurality of travel driver terminals 130 to transmit home appliance information to the electronic device 120. and may receive information about a repair expected time point from the electronic device 120 . For example, the home appliance information may include the type, manufacturer, model name, production date, etc. of the home appliance, and by using a camera included in the user terminal 110 to capture a barcode or QR code of the home appliance. can be entered easily. According to an embodiment, the user terminal 110 includes a smartphone, a tablet personal computer (PC), a mobile phone, a video phone, an e-book reader, a desktop PC ( desktop personal computer, laptop personal computer, netbook computer, personal digital assistant (PDA), portable multimedia player (PMP), MP3 player, or wearable device (e.g. electronic glasses and It may include at least one of the same head-mounted-device (HMD), electronic clothing, electronic bracelet, electronic necklace, electronic appcessory, electronic tattoo, smart car, or smart watch, but , but not limited thereto.

The electronic device 120 may use the received home appliance information to form statistical data on a home appliance repair request. According to an embodiment, the electronic device 120 may form statistical data about when a failure occurs according to the type of home appliance, the user's age, the user's residential area, and the like. In addition, the electronic device 120 may form statistical data about when a failure occurs for each component included in the home appliance. That is, the electronic device 120 may provide a user-customized repair service using statistical data according to the user's age or residence area.

The electronic device 120 may calculate expected repair time information for each home appliance using statistical data. According to an embodiment, the electronic device 120 may calculate expected repair time information for each home appliance owned by the user by using the formed statistical data so that the home appliance is repaired by a predetermined period before the statistical data. . For example, if statistical data is formed that a specific home appliance has the most failures in about 1 year or 6 months from the date of purchase, the electronic device 120 starts the home appliance by a preset period (eg, 1 month). In order to proceed with the repair of the home appliance, repair expected time information can be formed by 　 1 year 5 months, and repair expected time information can be formed with the same algorithm for all home appliances owned by the user. According to another embodiment, the electronic device 120 may calculate expected repair time information by further considering the user's age and the user's residential area. For example, when the user is old, the electronic device 120 advances the expected repair time by a preset period of time, and when the user lives in a place such as a beach, calculates the expected repair time ahead by a preset period.

The electronic device 120 may form recommended business trip driver information by using the location information of the travel driver terminal. According to an embodiment, the electronic device 120 periodically receives and stores location information from the plurality of user terminals 110 and the plurality of travel driver terminals 130, and stores the location information closest to the user terminal 110. Business trip drivers using the terminal 130 may be formed as recommended business trip driver information.

The electronic device 120 may provide the calculated repair expected time information and the formed recommended business trip driver information to the user terminal 110 using the corresponding home appliance. According to an embodiment, the electronic device 120 may provide information such as an expected repair date and time, a photo of a business trip driver, a name, and contact information to the user terminal 110 using the corresponding home appliance.

The electronic device 120 extracts information about the use period of the home appliance using the home appliance information, and predicts repair of the home appliance for a preset period before using the home appliance using the extracted information about the use period. Time point information may be calculated, and repair expected point information may be transmitted to a user terminal using the corresponding home appliance. According to an embodiment, the electronic device 120 extracts information about the use time of the home appliance using information about the home appliance, and determines whether the home appliance is used only in a specific season (eg, summer, winter, etc.). You can check. For example, if a specific home appliance is an air conditioner used only in summer, the electronic device 120 may calculate expected repair time information so that the home appliance can be repaired one month ahead of July and August, when the home appliance is used. there is. The electronic device 120 may transmit the calculated estimated repair time information to the user terminal 110 using the corresponding home appliance.

The electronic device 120 extracts the expected repair time of all home appliances owned by the user, calculates the visit schedule of the travel engineer by considering the expected repair time included within a preset period, and transmits the visit schedule to the user terminal and the business trip. It can be transmitted to the article terminal. According to an embodiment, the electronic device 120 extracts expected repair times of all home appliances possessed by the user, and selects expected repair times included within a preset period (eg, 1 month, 2 weeks, etc.). In consideration of this, the travel driver's visit schedule is calculated so that all possible home appliances can be repaired with one visit by the travel driver, so that the user can save the travel driver's travel fee.

The electronic device 120 extracts expected repair times of home appliances possessed by all users located in a preset regional range, calculates a visit schedule of a travel driver by considering repair expected times included within a preset period, and visits The schedule may be transmitted to the user terminal and the travel driver terminal. According to an embodiment, the electronic device 120 extracts expected repair times for home appliances owned by all users residing in a specific village, and includes them within a preset period (eg, 3 weeks, 1 week, etc.) Considering the expected repair time, the travel driver's visit schedule can be calculated so that all possible home appliances can be repaired with one visit, so that users can save the travel driver's travel expenses.

The electronic device 120 may receive a home appliance repair request from the user terminal 110 and may receive an image captured during repair of the home appliance from the travel driver terminal 130 to provide to the user terminal 110 . According to an embodiment, upon receiving a home appliance repair request from the user terminal 110, the travel driver visits a place desired by the user and uses a camera and voice microphone included in the travel driver terminal 130 to check the home appliance in real time. The whole process of opening and closing and repairing can be recorded as a video. The captured video is transmitted to the electronic device 120 through the network N, and the captured video may be provided in real time to the user terminal 110 requesting repair. Therefore, while the user is not at home, it is possible to check in real time how a business trip driver visits an empty house or office and makes repairs, thereby improving the reliability of the business trip driver visit, and preventing unsavory accidents such as theft of goods. can be prevented in advance. In addition, the user can check the repair process in detail using the recorded video so that a transparent and reliable repair service is provided, and even if an accident occurs due to home appliance repair, the responsible person for the cause of the accident can be clearly identified. there is.

The electronic device 120 may calculate expected repair time information for each home appliance by further considering an image captured when the home appliance is being repaired. According to an embodiment, the electronic device 120 analyzes an image captured during repair of a home appliance using a machine learning algorithm such as deep learning, extracts part information on which the repair has been performed, and extracts the extracted part information. Statistical data about the time when the corresponding part fails from the date of purchase is formed using , and information on expected repair time for each home appliance can be calculated using the statistical data.

The travel driver terminal 130 may receive a home appliance repair request from the user terminal 110, and may form an image of the home appliance repair process. According to an embodiment, the business trip driver terminal 130 is a terminal carried by a business trip driver who repairs home appliances, and is connected to communicate with a plurality of user terminals 110 and electronic devices 120 to enable the business trip driver terminal 130 By using the included camera and voice microphone, you can record the entire process of opening and closing and repairing home appliances in real time as a video to form a video about the home appliance repair process. The travel driver terminal 130 may transmit an image of a home appliance repair process to the electronic device 120 . According to an embodiment, the travel driver terminal 130 is a 'smartphone', 'tablet', 'tablet personal computer (PC)', 'mobile phone', 'video phone', 'e-book reader', and 'desktop' PC. (desktop personal computer), 　laptop 　PC (laptop personal computer), 　netbook computer, PDA (personal digital assistant), PMP (portable multimedia player), MP3 　player, or wearable device (e.g. 　electronic glasses) Such as “head-mounted-device (HMD),” “electronic clothing,” “electronic bracelet,” “electronic necklace,” “electronic appcessory,” “electronic tattoo,” “smart car,” or at least one of a smart watch. However, it is not limited to this.

In the present invention, 　Artificial Intelligence (AI) means a technology that imitates human learning ability, 　reasoning ability, 　perceptual ability, etc., and 　implements it with a computer, and 　machine learning, 　symbolic logic, etc. can include Machine learning (ML) is an algorithm technology that classifies or learns the characteristics of input data by itself. Artificial intelligence technology is a machine learning algorithm that analyzes input data, learns the results of the analysis, and can make judgments or predictions based on the results of the learning. In addition, technologies that mimic the functions of the human brain, such as cognition and judgment, using machine learning algorithms, can also be understood as the category of artificial intelligence. For example, technical fields such as 'verbal understanding', 'visual understanding', 'inference/prediction', 'knowledge expression', and 'motion control' may be included.

Machine learning may refer to processing that trains a neural network model using experience of processing data. Through machine learning, computer software could mean improving its own data processing capabilities. The neural network model is built by modeling the correlation between data, and the correlation can be expressed by a plurality of parameters. A neural network model derives a correlation between data by extracting and analyzing features from given data, and optimizing the parameters of a neural network model by repeating this process can be referred to as machine learning. For example, a neural network model can learn a mapping (correlation) between inputs and outputs with respect to data given as an input/output pair. Alternatively, even when only input data is given, the neural network model may learn the relationship by deriving regularities between given data.

An artificial intelligence learning model or neural network model may be designed to implement the structure of a human brain on a computer, and may include a plurality of network nodes that simulate neurons of a human neural network and have weights. A plurality of network nodes may have a connection relationship between them by simulating a synaptic activity of a neuron that transmits and receives a signal through a synapse. In the artificial intelligence learning model, a plurality of network nodes can send and receive data according to convolution and connection relationships while being located in layers of different depths. The artificial intelligence learning model may be, for example, an artificial neural network model, a convolutional neural network model (CNN), and the like. As an embodiment, the artificial intelligence learning model may be machine-learned according to methods such as supervised learning, unsupervised learning, and reinforcement learning. Machine learning algorithms for performing machine learning include 　Decision Tree, 　Bayesian Network, 　Support Vector Machine, 　Artificial Neural Network, 　Ada-boost , 　Perceptron, 　Genetic Programming, 　Clustering　, etc. may be used.

Of these, CNNs are a type of multilayer perceptrons designed to use minimal preprocessing. CNN consists of one or several convolutional layers and general artificial neural network layers on top of them, and additionally utilizes weighting and pooling layers. Thanks to this structure, CNN can make full use of input data with a two-dimensional structure. Compared to other deep learning structures, CNN shows good performance in both video and audio fields. CNNs can also be trained via standard back-propagation. CNNs are easier to train than other feedforward artificial neural network techniques and have the advantage of using fewer parameters.

Convolutional networks are neural networks that contain sets of nodes with bound parameters. The increasing size of available training data and the availability of computational power, combined with algorithmic advances such as piecewise linear unit and dropout training, have greatly improved many computer vision tasks. With huge data sets, such as those available for many tasks today, overfitting is not as important, and increasing the size of the network improves test accuracy. Optimal use of computing resources becomes a limiting factor. To this end, a distributed, scalable implementation of deep neural networks can be used.

2 is an exemplary view showing the configuration of an electronic device according to an embodiment of the present invention.

As shown in FIG. 2, the electronic device 120 may include a receiver 121, a processor 122, a transmitter 123, a system bus 124, and a database 126. According to one embodiment, the receiver 121, the processor 122, the transmitter 123, and the database 126 may be connected to each other to be communicable through the system bus 124, and these components of the electronic device 120 At least one of them may be omitted or another element may be added to the electronic device 120 . In addition, "additionally" or alternatively, "some components may be integrated and implemented," or implemented as singular or plural entities.

The receiving unit 121 may receive home appliance information from the user terminal 110 through the network N in real time or non-real time in the form of a digital packet 125 and transmit it to the processor 122. In addition, the receiving unit 121 receives the location information from the user terminal 110 and the travel driver terminal 130 through the network N in real time or non-real time in the form of a digital packet 125 to the processor 122. can transmit

The processor 122 may form statistical data on a home appliance repair request by using home appliance information received in the form of a digital packet 125 from the user terminal 110 through the receiver 121 . According to an embodiment, the processor 122 may form statistical data at a point in time when a failure is likely to occur for each home appliance and for each part included in the home appliance in the form of a digital packet 125 .

The processor 122 may calculate expected repair time information for each home appliance using statistical data formed in the form of the "digital packet 125". According to an embodiment, the processor 122 uses the statistical data to identify a point in time where a failure is likely to occur for each home appliance or for each part included in the home appliance (eg, 1 year and 6 months from purchase) , 　 information on expected repair time (eg, 1 year = 5 months) may be calculated in the form of a digital packet 125 so as to precede a predetermined period (eg, 1 month) of a point in time when a failure is likely to occur.

The processor 122 may form recommended travel driver information by using location information received in the form of a digital packet 125 from the travel driver terminal 130 through the receiver 121 . According to an embodiment, the processor 122 periodically receives and stores location information from the plurality of user terminals 110 and the plurality of travel driver terminals 130, and stores the location information closest to the user terminal 110 to the business trip driver terminal. A business trip driver using 130 may be formed in the form of a digital packet 125 as recommended business trip driver information.

The processor 122 may transmit repair expected time information and recommended business trip driver information formed in the form of a digital packet 125 to the transmitter 123 through the system bus 124 .

The transmitter 123 may transmit the expected repair time information and the recommended travel driver information received from the processor 122 to the user terminal 110 using the corresponding home appliance in the form of a digital packet 125 .

The database 126 may store various data. The data stored in the database 126 is data obtained, processed, or used by at least one component of the plurality of user terminals 110, the electronic device 120, and the plurality of travel driver terminals 130. , 　 may include software (eg: 　 program). The database 126 may include volatile and/or non-volatile memory. As an embodiment, the database 130 receives and stores home appliance information and location information from a plurality of user terminals 110 through the network N, and stores home appliance repair images and location information at a plurality of travel driver terminals. It can be received from (130) through the network (N) and stored.

3 is a diagram for explaining learning of a neural network according to an embodiment of the present invention.

As shown in FIG. 3, the learning device may train the neural network 128 in order to extract information about the repaired part from an image taken during repair of the home appliance. According to one embodiment, the learning device may be a separate subject different from the electronic device 120, but is not limited thereto.

The neural network 128 includes an input layer 127 into which training samples are input and an output layer 129 which outputs training outputs, and can be learned based on differences between the training outputs and labels. Here, the labels may be defined based on the user's response level corresponding to the video output signal. The neural network 128 is connected as a group of a plurality of nodes, and is defined by weights between the connected nodes and an activation function that activates the nodes.

The learning device may train the neural network 128 using a “Gradient Decent (GD)” technique or a “Stochastic Gradient Descent (SGD)” technique. The learning device may use a loss function designed by outputs and labels of the neural network.

The learning device may calculate a training error using a predefined loss function. The loss function may be predefined with labels, outputs, and parameters as input variables, where the parameters may be set by weights in the neural network 128. For example, the loss function may be designed in the form of “Mean Square Error (MSE)”, the form of “entropy”, etc., and various techniques or methods may be employed in an embodiment in which the loss function is designed.

The learning device may find weights affecting the training error using a backpropagation technique. Here, "weights" are relationships between nodes in the neural network 128. The learning device may use the "SGD" technique using labels and outputs to optimize the weights found through the backpropagation technique. For example, the learning device may update weights of a loss function defined based on labels, outputs, and weights using the SGD technique.

According to an embodiment, the learning device may acquire a training repair image and extract training part information from the training repair image. The learning device may acquire pre-labeled information (first label) for training part information, and the type of part predefined in the training part information (eg, motor, fuse, button, etc.) It is possible to obtain the first 　 labels indicating.

According to an embodiment, the learning device may generate first training feature vectors based on appearance features, pattern features, and color features of training component information. Various methods may be employed to extract features of the training part information.

According to an embodiment, the learning device may obtain training outputs by applying the first training feature vectors to the neural network 128 . The learning device may train the neural network 128 based on the training outputs and the first labels. The learning device may train the neural network 128 by calculating training errors corresponding to the training outputs and optimizing a connection relationship of nodes in the neural network 128 to minimize the training errors. The electronic device 120 may extract repaired part information from an image captured during repair of the home appliance using the neural network 128 that has been learned.

4 and 5 are flowcharts showing procedures of a home appliance repair service providing method according to an embodiment of the present invention. Although process steps, method steps, algorithms, etc. are described in sequential order in the flowcharts of Figures 4 and 5, such processes, methods and algorithms may be configured to operate in any suitable order. In other words, the steps of the processes, methods, and algorithms described in various embodiments of the present invention need not be performed in the order described herein. Additionally, although some steps are described as being performed asynchronously, in other embodiments some of these steps may be performed concurrently. Further, the illustration of a process by depiction in the drawings does not mean that the illustrated process is exclusive of other changes and modifications thereto, and that any of the illustrated process or steps thereof may be one of various embodiments of the present invention. It does not imply that it is essential to one or more, and does not imply that the illustrated process is desirable.

As shown in FIG. 4, in step S410, home appliance information is received. For example, referring to Figures 1 through 3, the electronic device 120 for providing a home appliance repair service may receive home appliance information from a plurality of user terminals 110 . According to an embodiment, the electronic device 120 for providing a home appliance repair service may receive home appliance information formed by the user terminal 110 through the network N. For example, the home appliance product information may include, but is not limited to, the type of the home appliance, the manufacturer, the model name, and the production date.

In step S420, Statistical data is formed. For example, referring to Figures 1 to 3, the electronic device 120 for providing a home appliance repair service may form statistical data for a home appliance repair request using the home appliance information received in step S410. there is. According to an embodiment, the electronic device 120 for providing a home appliance repair service may form statistical data about when a failure occurs depending on the type of home appliance, the user's age, the user's residential area, etc. Not limited to this.

In step S430, expected repair time information is calculated. For example, referring to Figures 1 through 3, the electronic device 120 for providing a home appliance repair service may calculate expected repair time information for each home appliance using the statistical data formed in step S420. According to an embodiment, the electronic device 120 for providing a home appliance repair service uses the statistical data formed in step S420 so that the home appliance is repaired prior to the statistical data by a preset period of time, so that the home appliance owned by the user can be repaired. It is possible to calculate expected repair time information for each product.

In step S440, recommended travel driver information is formed. For example, referring to Figures 1 through 3, the electronic device 120 for providing a home appliance repair service may form recommended business trip driver information by using the location information of the travel driver terminal 130. According to an embodiment, the electronic device 120 for providing a home appliance repair service periodically receives and stores location information from a plurality of user terminals 110 and a plurality of travel driver terminals 130, and stores it in a user terminal ( 110), a business trip driver using the nearest business trip driver terminal 130 may be formed as recommended business trip driver information.

In step S450, information on expected repair time and information on recommended business trip drivers are provided. For example, referring to Figures 1 to 3, the electronic device 120 for providing a home appliance repair service is to send information about expected repair time calculated in Step S430 and recommended travel driver information formed in Step S440 to the corresponding home appliance. It can be provided to the user terminal 110 in use. According to an embodiment, the electronic device 120 for providing a home appliance repair service transmits information such as an expected repair date and time, a photo of a business trip driver, a name, and contact information to a user terminal 110 using a corresponding home appliance. can provide

As shown in FIG. 5, in step S510, information about the usage time of the home appliance is extracted. For example, referring to Figures 1 through 4, the electronic device 120 for providing a home appliance repair service extracts information about the use period of the home appliance by using the home appliance information stored in the database 126. It is possible to determine whether the home appliance is used only in a specific season (eg, summer, winter, etc.).

In step S520, information on expected repair time for a predetermined period is calculated. For example, referring to Figure 1 through Figure 4, the electronic device 120 for providing a home appliance repair service calculates repair expected time information by using the information about the use period of the home appliance extracted in step S510. can According to an embodiment, the 　electronic device 120 for providing a home appliance repair service may be repaired 1 month ahead of 　7-August, when a specific home appliance is an air conditioner used only in summer. It is possible to calculate the estimated repair time information so that it can be repaired.

In step S530, repair expected time information is transmitted. For example, referring to Figures 1 through 4, the electronic device 120 for providing a home appliance repair service transmits the expected repair time information calculated in step S520 to a user who uses the corresponding home appliance through the network N. It can be transmitted to the terminal 110.

Although the method has been described through specific embodiments, it is also possible to implement the method as computer readable code on a computer readable recording medium. A computer-readable recording medium includes all types of recording devices in which data that can be read by a computer system is stored. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage devices. In addition, the computer-readable recording medium is distributed in computer systems connected through a network, so that computer-readable codes can be stored and executed in a distributed manner. In addition, "functional" programs, "codes, and code segments for implementing the embodiments can be easily inferred by programmers in the technical field to which the present invention belongs.

In the above, the present invention has been described in detail using preferred embodiments, but the scope of the present invention is not limited to specific embodiments, and should be interpreted according to the appended claims. In addition, those skilled in the art should understand that many modifications and variations are possible without departing from the scope of the present invention.

100: home appliance repair service provision environment 110: user terminal
120: electronic device 130: travel driver terminal
121: receiver 122: processor
123: transmission unit 124: system bus
125: digital packet 126: database
127: input layer 128: neural network
129: output layer

Claims (10)

In an electronic device for providing a home appliance repair service,
one or more processors; and
one or more memories storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations;
The one or more processors,
Receiving home appliance information from a plurality of user terminals;
Forming statistical data on a request for repair of a home appliance using the home appliance information;
Calculate expected repair time information for each home appliance using the statistical data;
Using the location information of the travel driver's terminal, the recommended travel driver information is formed,
Providing the calculated repair expected time information and the recommended business trip driver information to a user terminal using the corresponding home appliance,
Electronic devices for providing home appliance repair services.
According to claim 1,
The one or more processors,
Calculating the expected repair time information by further considering the user's age and the user's residential area,
Electronic devices for providing home appliance repair services. According to claim 1,
The one or more processors,
Using the home appliance information, extracting information about the use time of the home appliance, and calculating expected repair time information of the home appliance for a preset period before using the home appliance using the extracted information about the use time; , Transmitting the expected repair time information to a user terminal using the corresponding home appliance,
Electronic devices for providing home appliance repair services.
According to claim 1,
The one or more processors,
Extracting the expected repair time of all home appliances owned by the user, calculating the visit schedule of the travel driver in consideration of the expected repair time included within a preset period, and transmitting the visit schedule to the user terminal and the travel driver terminal,
Electronic devices for providing home appliance repair services.
According to claim 1,
The one or more processors,
The expected repair time of home appliances owned by all users located in a preset region is extracted, the visit schedule of the travel engineer is calculated in consideration of the repair expected time included within the preset period, and the visit schedule is calculated between the user terminal and the business trip. transmitted to the article terminal,
Electronic devices for providing home appliance repair services.
According to claim 1,
The one or more processors,
Receiving a home appliance repair request from a user terminal, and receiving an image taken during repair of the home appliance from a business trip driver terminal and providing the video to the user terminal.
Electronic devices for providing home appliance repair services.
According to claim 1,
The one or more processors,
Calculating estimated repair time information for each home appliance by further considering an image captured when the home appliance is repaired.
Electronic devices for providing home appliance repair services.
one or more processors; and
A method of providing a home appliance repair service using an electronic device for providing a home appliance repair service including one or more memories storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations,
Receiving home appliance information from a plurality of user terminals;
forming statistical data on a request for repair of a home appliance using the home appliance information;
Calculating an estimated repair time point for each home appliance using the statistical data;
forming recommended travel driver information by using location information of the travel driver's terminal; and
Providing the calculated repair expected time information and the recommended business trip driver information to a user terminal using the corresponding home appliance,
How to provide home appliance repair services.
According to claim 8,
extracting information about usage time of the home appliance by using the home appliance information;
Calculating expected repair time information of the home appliance for a predetermined period before use of the home appliance by using the information on the extracted use period; and
Further comprising transmitting the repair expected time information to a user terminal using the corresponding home appliance,
How to provide home appliance repair services.
one or more processors; And a computer program stored in a computer-readable recording medium so as to be executable by a computer including one or more memories in which instructions for causing the one or more processors to perform operations when executed by the one or more processors are stored,
Receiving home appliance information from a plurality of user terminals;
forming statistical data on a request for repair of a home appliance using the home appliance information;
Calculating an estimated repair time point for each home appliance using the statistical data;
forming recommended travel driver information by using location information of the travel driver's terminal; and
A computer program stored in a computer readable recording medium to perform the step of providing the calculated repair expected time information and the recommended business trip driver information to a user terminal using the corresponding home appliance.

Images