KR102618762B1 - A method and an electronic device providing separate collection guide of recyclable material - Google Patents

Abstract

One embodiment of the present invention discloses a method in which an electronic device provides guidance on separate collection of recyclables. The method includes visually detecting items subject to separate collection through a camera attached to a monitor of the electronic device, based on at least one of a metal detection sensor attached to the monitor and the camera, the items subject to separate collection. Generating reading information for a product, based on a preset, delivering the reading information to a user through the monitor using at least one of a visual delivery method and an auditory delivery method, and the reading for the target article. It may include storing information in the memory of the electronic device.

Description

Method and electronic device for providing guidance on separate collection of recyclables {A METHOD AND AN ELECTRONIC DEVICE PROVIDING SEPARATE COLLECTION GUIDE OF RECYCLABLE MATERIAL}

The present invention relates to an electronic device that provides guidance on separate collection of recyclables.

When recycling recyclables are separated, there are cases where it is difficult to classify the recycling items. When observing with the human eye, it may be difficult to distinguish whether an item is metal or not, and even if it is plastic, whether it is transparent plastic or not.

Various embodiments of the present invention provide a method and electronic device for providing guidance on separate collection of recyclables.

According to an embodiment of the present invention, a method is provided in which an electronic device provides guidance on separate collection of recyclables. The method includes visually detecting items to be separated and collected through a camera attached to a monitor of the electronic device, based on at least one of a metal detection sensor attached to the monitor and the camera. , generating reading information for the items subject to separate recycling, based on pre-configuration, providing the reading information to the user using at least one of a visual delivery method and an auditory delivery method through the monitor. It may include transmitting and storing the read information about the target article in a memory of the electronic device.

In one embodiment, based on the metal detection sensor detecting that the item to be separated and collected is metal, the read information includes information indicating that the item to be separated and collected is metal, and the metal detection sensor is configured to separate the item. Based on the failure to detect that the item to be collected is metal, the item to be collected is read by the camera, and the camera uses at least one of a transparent detection sensor and a predefined AI (Artificial Intelligence) deep learning image reading technology. Based on this, the items subject to separate collection can be read.

In one embodiment, the camera performs recycling item reading to classify the items subject to separate collection into one of plastic, vinyl, paper, PET bottles, Styrofoam, and glass, based on the predefined AI deep learning image reading technology. can do.

In one embodiment, the camera determines whether the item subject to separate collection corresponds to a transparent PET bottle through the transparent detection sensor, based on the fact that the item subject to separate collection is classified as a PET bottle, and determines whether the item subject to separate collection corresponds to a transparent PET bottle, and If the product is determined to correspond to the transparent PET bottle, it can be determined whether a vinyl label is attached to the product subject to separate collection.

In one embodiment, when it is determined that the vinyl label is attached to the item subject to separate recycling, the read information may include text information or voice information for requesting removal of the vinyl label.

In one embodiment, the reading information includes a reading result of the recycling item of the article subject to separate recycling, and the preset includes the visual delivery method for displaying the reading result of the recycling item on the monitor and the reading result of the recycling item. It may be possible to use all of the above-mentioned auditory transmission methods that transmit the sound through speakers attached to the monitor.

In one embodiment, the reading information includes a reading result of a recycling item of the article subject to separate recycling and the text information or the voice information for a request to remove the plastic label, and when the preset is a first preset, The recycling item reading result is displayed on the monitor, and is transmitted as a voice based on a speaker attached to the monitor, the text for requesting removal of the vinyl label is displayed on the monitor, and the text for requesting removal of the vinyl label is displayed on the monitor. Voice information is transmitted based on the speaker, and if the preset is a second preset, the recycling item reading result is displayed on the monitor, and the text for requesting removal of the vinyl label is displayed on the monitor, If the preset is a third preset, the recycling item reading result may be transmitted as a voice based on a speaker attached to the monitor, and the voice information for the vinyl label removal request may be transmitted based on the speaker. there is.

In one embodiment, based on the metal detection sensor detecting that the item to be separated and collected is metal, the read information includes information indicating that the item to be separated and collected is metal, and the metal detection sensor is configured to separate the item. Based on the failure to detect that the item to be collected is metal, the item to be collected is read by the camera, and the camera uses at least one of a transparent detection sensor and a predefined AI (Artificial Intelligence) deep learning image reading technology. Based on one, the items subject to separate collection are read, and the camera, based on the predefined AI deep learning image reading technology, selects the items subject to separate collection among plastic, vinyl, paper, PET bottles, Styrofoam, and glass. Performs reading of recycling items classified as one, and the camera determines whether the separated collection target item corresponds to a transparent plastic bottle through the transparent detection sensor, based on the fact that the separate collection target item is classified as a plastic bottle; , If the item subject to separate collection is determined to correspond to the transparent PET bottle, it is determined whether a vinyl label is attached to the item subject to separate collection, and it is determined that the vinyl label is attached to the item subject to separate collection. In this case, the reading information includes text information or voice information for requesting removal of the vinyl label, and the reading information includes a reading result of the recycling item of the article subject to separate recycling and the text information for requesting removal of the vinyl label. or includes the audio information, and if the preset is a first preset, the recycling item reading result is displayed on the monitor, transmitted as an audio based on a speaker attached to the monitor, and requests to remove the vinyl label. The text for is displayed on the monitor, the voice information for requesting removal of the vinyl label is transmitted based on the speaker, and if the preset is a second preset, the result of reading the recycling item is displayed on the monitor. displayed, the text for requesting removal of the vinyl label is displayed on the monitor, and if the preset is a third preset, the result of reading the recycling item is transmitted by voice based on a speaker attached to the monitor, The voice information for requesting removal of the vinyl label may be transmitted based on the speaker.

According to one embodiment of the present invention, an electronic device that provides guidance on separate collection of recyclables is provided. The electronic device includes a memory, a monitor, a camera attached to the monitor, a metal detection sensor attached to the monitor, and a processor, where the processor is connected to the camera. ), visually detects items subject to separate collection, generates read information on the items subject to separate collection based on at least one of the metal detection sensor and the camera, and based on pre-configuration In this way, the reading information can be delivered to the user through the monitor using at least one of a visual delivery method and an auditory delivery method, and the reading information about the target article can be stored in the memory.

According to various embodiments of the present invention, it is possible to provide users with reliable discrimination information regarding separate recycling items.

The effects according to the present invention are not limited to the effects described above, and other effects not described will be clearly understood by those skilled in the art from the following description.

Figure 1 shows an example of a communication structure between an electronic device and a user device.
Figure 2 is a flowchart showing the operation of an electronic device according to an embodiment.
Figure 3 is a flowchart showing detailed operations of an electronic device according to an embodiment.
Figure 4 is a diagram showing the structure of an electronic device according to an embodiment.
FIG. 5 is a diagram illustrating an example in which an electronic device performs recycling guidance.
FIG. 6 is a diagram illustrating another example in which an electronic device performs recycling guidance.
Figure 7 is a diagram for explaining examples of electronic devices being placed in a recycling bin.
Figure 8 is a diagram to explain an example of applying AI deep learning image reading technology.
Figure 9 is a block diagram showing the configuration of an electronic device according to an embodiment.
Figure 10 is a block diagram showing the configuration of a processor according to one embodiment.

Since the present invention can make various changes and have various embodiments, specific embodiments will be illustrated in the drawings and described in detail. However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all changes, equivalents, and substitutes included in the spirit and technical scope of the present invention.

Terms such as first, second, etc. may be used to describe various components, but the components should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from another. For example, a first component may be named a second component, and similarly, the second component may also be named a first component without departing from the scope of the present invention. The term “and/or” includes any of a plurality of related stated items or a combination of a plurality of related stated items.

When a component is said to be "connected" or "connected" to another component, it is understood that it may be directly connected to or connected to the other component, but that other components may exist in between. It should be. On the other hand, when it is mentioned that a component is “directly connected” or “directly connected” to another component, it should be understood that there are no other components in between.

The terms used in this application are only used to describe specific embodiments and are not intended to limit the invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, terms such as “comprise” or “have” are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, numbers, steps, operations, components, parts, or combinations thereof.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the technical field to which the present invention pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and unless explicitly defined in the present application, should not be interpreted in an idealized or excessively formal sense. No.

Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the attached drawings. In order to facilitate overall understanding when describing the present invention, the same reference numerals are used for the same components in the drawings, and duplicate descriptions for the same components are omitted.

Figure 1 shows an example of a communication structure between an electronic device and a user device.

In this specification, the electronic device 110 may refer to a device that provides separation guidance for recyclable products. For example, the electronic device 110 may be referred to by various names, such as a recycling guidance device or a recycling guidance electronic device.

In one embodiment, the electronic device 110 may include a memory 111, a monitor 112, a camera 113, a metal detection sensor 114, and a processor 115. The memory 111 can store pre-configuration or control history of the electronic device 110, and the monitor 112 can provide separation guidance for recyclables through a visual method, and the camera (113) can be used to acquire an image of the recycling product, the metal detection sensor 114 can detect whether the recycling product is metal, and the processor 115 can control the overall operation of the electronic device 110. there is. A detailed description of each component constituting the electronic device 110 will be described later in the description of FIGS. 2 to 10 .

In this specification, “user device” includes smartphones, cell phones, smart TVs, smart watches, electronic wristwatches, set-top boxes, tablet PCs, digital cameras, camcorders, laptop computers, desktops, and electronic devices. A variety of devices including book terminals, digital broadcasting terminals, PDAs (Personal Digital Assistants), PMPs (Portable Multimedia Players), navigation, MP3 players, wearable devices, air conditioners, microwave ovens, audio, DVD players, etc. It can be implemented.

In this specification, the memory 111, monitor 112, camera 113, metal detection sensor 114, and processor 115 of the electronic device 110 are mutual signals, messages, digital input, current, data, and control. Information can be sent and received. For example, the camera 113 can transmit image information of recyclable products to the processor 115, and the processor 115 can transmit recycling item information of recyclable products to the monitor 112.

In this specification, the electronic device 110 and the user device 120 may communicate with each other through a communication network. The communication network may be, for example, a wireless communication network or a wired communication network. The wireless communication network may be, for example, a communication network based on Long Term Evolution (LTE) Radio Access Technology (RAT), New Radio (NR) RAT, WiFi, etc.

In one example, the electronic device 110 and the user device 120 may perform device-to-device (D2D) communication with each other and UU communication with a base station (e.g., gNB and/or eNB). It can be done. In time division multiple access (TDMA) and frequency division multiple access (FDMA) systems for performing the D2D communication or the UU communication, accurate time and frequency synchronization may be required. If time and frequency synchronization is not accurate, system performance may deteriorate due to inter-symbol interference (ISI) and inter-carrier interference (ICI). In D2D communication according to one example, for time/frequency synchronization, a synchronization signal may be used in the physical layer, and a master information block (MIB) may be used in the radio link control (RLC) layer.

In the D2D communication process, the electronic device 110 and the user device 120 are synchronized directly to GNSS (global navigation satellite systems), or through a terminal (within network coverage or outside network coverage) directly synchronized to GNSS. Can be indirectly synchronized to GNSS. When GNSS is set as the synchronization source, the electronic device 110 and the user device 120 can calculate the DFN and subframe number using Coordinated Universal Time (UTC) and a (pre)set Direct Frame Number (DFN) offset. there is.

Alternatively, the electronic device 110 and the user device 120 may be synchronized directly to the base station or to another terminal that is time/frequency synchronized to the base station. For example, the base station may be an eNB or gNB. For example, when the electronic device 110 and the user device 120 are within network coverage, the electronic device 110 and the user device 120 receive synchronization information provided by a base station, and directly contact the base station. can be synchronized. Afterwards, the electronic device 110 and the user device 120 may provide synchronization information to another nearby terminal. When the base station timing is set as a synchronization standard, the electronic device 110 and the user device 120 are connected to a cell associated with the frequency (if within cell coverage at the frequency), primary cell, or serving cell for synchronization and downlink measurement. Can follow a cell (if it is outside of cell coverage at that frequency).

A base station (e.g., serving cell) may provide synchronization settings for carriers used for D2D or sidelink (SL) communications. In this case, the electronic device 110 and the user device 120 may follow the synchronization settings received from the base station. If the electronic device 110 and the user device 120 did not detect any cell on the carrier used for the D2D or SL communication and did not receive synchronization settings from the serving cell, the electronic device 110 and User device 120 may follow preset synchronization settings.

Alternatively, the electronic device 110 and the user device 120 may be synchronized to another terminal that fails to obtain synchronization information directly or indirectly from a base station or GNSS. Synchronization source and preference can be set in advance to the terminal. Alternatively, the synchronization source and preference can be set through a control message provided by the base station.

In one embodiment, the electronic device 110 and the user device 120 may communicate with each other through the network 100 (or communication network). For example, when the electronic device 110 transmits data to the network 100, the network 100 may deliver (transmit) the data to the user device 120. Alternatively, when the user device 120 transmits data to the network 100, the network may deliver (transmit) the data to the electronic device 110.

In the following description of FIGS. 2 to 9 , a detailed review will be given of how the electronic device 110 provides guidance on the separation of recyclables.

Figure 2 is a flowchart showing the operation of an electronic device according to an embodiment.

The operations disclosed in the flowchart of FIG. 2 can be performed in combination with various embodiments of the present invention. In one example, the electronic device described in FIG. 2 may correspond to the electronic device shown in FIGS. 1 and 3 to 10 . In one example, the operations disclosed in the flowchart of FIG. 2 may correspond to some of the operations (of the electronic device) disclosed in FIGS. 1 and 3 through 10.

In step S210, the electronic device according to one embodiment may visually detect items subject to separate recycling through a camera attached to a monitor of the electronic device.

In one example, the items currently subject to separate recycling can be visually detected through images or videos captured by the camera.

In step S220, the electronic device according to one embodiment may generate read information about the article to be separated and collected based on at least one of a metal detection sensor attached to the monitor and the camera.

In one example, the metal detection sensor attached to the monitor can detect whether the item to be separated is metal. If it is determined that the item to be collected separately is not metal, the camera can read the item to be collected separately based on at least one of predefined AI deep learning image reading technology and a transparent detection sensor. For example, when the item to be separated and collected is read as plastic through the predefined AI deep learning image reading technology, the transparent detection sensor can determine whether the plastic is transparent plastic.

In step S230, the electronic device according to one embodiment may transmit the read information to the user through the monitor using at least one of a visual transmission method and an auditory transmission method, based on a pre-configuration. there is.

In step S240, the electronic device according to one embodiment may store the read information about the target article in the memory of the electronic device.

In one embodiment, based on the metal detection sensor detecting that the item to be separated and collected is metal, the read information includes information indicating that the item to be separated and collected is metal, and the metal detection sensor is configured to separate the item. Based on the failure to detect that the item to be collected is metal, the item to be collected is read by the camera, and the camera uses at least one of a transparent detection sensor and a predefined AI (Artificial Intelligence) deep learning image reading technology. Based on this, the items subject to separate collection can be read.

In one embodiment, the camera performs recycling item reading to classify the items subject to separate collection into one of plastic, vinyl, paper, PET bottles, Styrofoam, and glass, based on the predefined AI deep learning image reading technology. can do.

In one embodiment, the camera determines whether the item subject to separate collection corresponds to a transparent PET bottle through the transparent detection sensor, based on the fact that the item subject to separate collection is classified as a PET bottle, and determines whether the item subject to separate collection corresponds to a transparent PET bottle, and If the product is determined to correspond to the transparent PET bottle, it can be determined whether a vinyl label is attached to the product subject to separate collection.

In one embodiment, when it is determined that the vinyl label is attached to the item subject to separate recycling, the read information may include text information or voice information for requesting removal of the vinyl label.

In one embodiment, the reading information includes a reading result of the recycling item of the article subject to separate recycling, and the preset includes the visual delivery method for displaying the reading result of the recycling item on the monitor and the reading result of the recycling item. It may be possible to use all of the above-mentioned auditory transmission methods that transmit the sound through speakers attached to the monitor.

In one embodiment, the reading information includes a reading result of a recycling item of the article subject to separate recycling and the text information or the voice information for a request to remove the plastic label, and when the preset is a first preset, The recycling item reading result is displayed on the monitor, and is transmitted as a voice based on a speaker attached to the monitor, the text for requesting removal of the vinyl label is displayed on the monitor, and the text for requesting removal of the vinyl label is displayed on the monitor. Voice information is transmitted based on the speaker, and if the preset is a second preset, the recycling item reading result is displayed on the monitor, and the text for requesting removal of the vinyl label is displayed on the monitor, If the preset is a third preset, the recycling item reading result may be transmitted as a voice based on a speaker attached to the monitor, and the voice information for the vinyl label removal request may be transmitted based on the speaker. there is.

In one embodiment, based on the metal detection sensor detecting that the item to be separated and collected is metal, the read information includes information indicating that the item to be separated and collected is metal, and the metal detection sensor is configured to separate the item. Based on the failure to detect that the item to be collected is metal, the item to be collected is read by the camera, and the camera uses at least one of a transparent detection sensor and a predefined AI (Artificial Intelligence) deep learning image reading technology. Based on one, the items subject to separate collection are read, and the camera, based on the predefined AI deep learning image reading technology, selects the items subject to separate collection among plastic, vinyl, paper, PET bottles, Styrofoam, and glass. Performs reading of recycling items classified as one, and the camera determines whether the separated collection target item corresponds to a transparent plastic bottle through the transparent detection sensor, based on the fact that the separate collection target item is classified as a plastic bottle; , If the item subject to separate collection is determined to correspond to the transparent PET bottle, it is determined whether a vinyl label is attached to the item subject to separate collection, and it is determined that the vinyl label is attached to the item subject to separate collection. In this case, the reading information includes text information or voice information for requesting removal of the vinyl label, and the reading information includes a reading result of the recycling item of the article subject to separate recycling and the text information for requesting removal of the vinyl label. or includes the audio information, and if the preset is a first preset, the recycling item reading result is displayed on the monitor, transmitted as an audio based on a speaker attached to the monitor, and requests to remove the vinyl label. The text for is displayed on the monitor, the voice information for requesting removal of the vinyl label is transmitted based on the speaker, and if the preset is a second preset, the result of reading the recycling item is displayed on the monitor. displayed, the text for requesting removal of the vinyl label is displayed on the monitor, and if the preset is a third preset, the result of reading the recycling item is transmitted by voice based on a speaker attached to the monitor, The voice information for requesting removal of the vinyl label may be transmitted based on the speaker.

Figure 3 is a flowchart showing detailed operations of an electronic device according to an embodiment.

In one embodiment, S310 to S330 may correspond to S220 in FIG. 2.

In step S310, it can be determined whether the metal detection sensor detects that the item to be separated and collected is metal.

When the metal detection sensor detects that the item to be collected separately is metal, in step S320, the electronic device can read the item to be collected separately as metal.

If it is detected that the item to be separated is not metal, in step S330, the camera reads the item to be separated based on at least one of a transparent detection sensor and a predefined AI (Artificial Intelligence) deep learning image reading technology. You can.

Figure 4 is a diagram showing the structure of an electronic device according to an embodiment.

As shown in Figure 4, a camera and a metal detection sensor may be attached to the monitor. The drawing shows that both the camera and the metal detection sensor are attached to the top of the monitor, but the embodiment is not limited to this. For example, the metal detection sensor and camera may be attached to the left front, right front, or lower front of the monitor, or the metal detection sensor may be attached to the upper front and the camera may be attached to the lower front.

In one example, the camera may have a diameter of about 1.5 cm, and the metal detection sensor may have a diameter of about 1.2 cm.

Although a wall-mounted stand is shown in Figure 4, a wall-mounted stand is not necessarily used. The wall-mounted stand will be described later in Figure 6.

FIG. 5 is a diagram illustrating an example in which an electronic device performs recycling guidance.

When the electronic device reads the separate collection items of the separate collection items, it can deliver the reading result to the user through at least one of a visual delivery method and an auditory delivery method. Referring to Figure 5, after the electronic device reads that the recycling item is a plastic bottle, it can display the text “It is a plastic bottle” on the monitor as a visual delivery method and play a voice saying “It is a plastic bottle” as an auditory delivery method. there is.

Meanwhile, the embodiment is not limited to this, and the method by which the electronic device delivers the reading result to the user may vary. The electronic device can display the text “This is a plastic bottle” on a monitor using only a visual delivery method, or it can play a voice saying “It’s a plastic bottle” using only an auditory delivery method. It can also display an image (this is a plastic bottle) instead of text using a visual delivery method. For example, a method of displaying a plastic bottle image may be used.

FIG. 6 is a diagram illustrating another example in which an electronic device performs recycling guidance.

The camera of the electronic device can read the recycling items of items subject to separate recycling. If the recycling item is read as a plastic bottle, and as a result of detection with a transparent detection sensor, it is read as a transparent plastic bottle, the electronic device can determine whether a vinyl label is attached to the transparent plastic bottle based on the camera image/video.

If a plastic label is attached to a transparent PET bottle, the text “Please remove the label” can be displayed on the monitor. In addition, a voice saying “Please remove the label” can be played through the monitor, or a video of removing the label from a transparent plastic bottle can be displayed.

Figure 7 is a diagram for explaining examples of electronic devices being placed in a recycling bin.

If the recycling bin has a wall, electronic devices (monitors, metal detection sensors, cameras, etc.) can be attached to the wall. Alternatively, if there is no wall, electronic devices (monitors, metal detection sensors, cameras, etc.) can be attached to a waste sorting frame or waste sorting stand, etc. through a wall-mounted holder.

Figure 8 is a diagram to explain an example of applying AI deep learning image reading technology.

AI deep learning image reading technology may be based, for example, on the rule-based artificial intelligence of FIG. 8. After reading the eggplant image in the drawing, rule-based artificial intelligence generates appearance simulation information such as 'long', 'purple', and 'slightly curved', and then synthesizes the appearance simulation information to determine that the image is an 'eggplant'. It can be derived.

Alternatively, AI deep learning image reading technology may be based, for example, on the deep learning of FIG. 8. After reading the eggplant image in the drawing, a preliminary reading result of 'branch' is derived, and appearance simulation information such as 'long', 'purple', and 'slightly curved' is generated, and then the appearance simulation information and the preliminary reading You can check whether the results match.

AI deep learning image reading technology will be further explained in the description of FIGS. 9 and 10.

Figure 9 is a block diagram showing the configuration of an electronic device according to an embodiment.

As shown in FIG. 9, the electronic device 900 according to one embodiment may include a memory 910, a monitor 920, a camera 930, a metal detection sensor 940, and a processor 950. . However, in some cases, not all of the components shown in FIG. 9 may be essential components of the electronic device 900, and the electronic device 900 may be comprised of more or fewer components than those shown in FIG. 9. It can be implemented. For example, the electronic device 900 may further include a transceiver.

The memory 910 and the processor 950 of the electronic device 900 according to one embodiment may each be implemented as separate chips, or at least two or more components may be implemented through one chip.

The memory 910 may store data received from the processor 950 or pre-configurations input in advance. In one embodiment, the database may store at least one program. In one example, the at least one program may be used when the processor 950 executes a learning model.

The monitor 920 can display recycling item information derived as a result of reading recycling products. For example, the monitor 920 may display that the recycled product is metal or display that the recycled product is transparent plastic so that the user can refer to it when recycling.

The camera 930 is attached to the monitor 920 and can visually detect items subject to separate recycling.

The metal detection sensor 940 is attached to the monitor 920 and can detect whether the item to be separated is metal.

The processor 950 according to one embodiment may control the overall operation of the electronic device 900. In one example, the processor 950 may generally control the electronic device 900 by executing programs stored in the memory 910 of the electronic device 900. In one example, the processor 950 may perform some of the operations of the electronic device 900 described above in FIGS. 1 to 8 by executing programs stored in the memory 910 of the electronic device 900.

The processor 950 according to one embodiment can visually detect items subject to separate recycling through the camera.

The processor 950 according to one embodiment may generate read information about the items to be separated and collected based on at least one of the metal detection sensor and the camera.

The processor 950 according to one embodiment may deliver the read information to the user through the monitor using at least one of a visual delivery method and an auditory delivery method, based on pre-configuration.

The processor 950 according to one embodiment may store the read information about the target article in the memory.

In one example, the processor 950 may execute an artificial intelligence (AI) learning model based on at least one program stored in the memory 910. Below, in FIG. 10, an example of the configuration of the processor 950 for executing the AI learning model will be described.

Figure 10 is a block diagram showing the configuration of a processor according to one embodiment.

As shown in FIG. 10, the processor 950 according to one embodiment includes a data acquisition unit 952, a learning data selection unit 954, a learning model execution unit 956, and a learning result providing unit 958. can do. However, in some cases, not all of the components shown in FIG. 10 may be essential components of the processor 950, and the processor 950 may be implemented by more or fewer components than those shown in FIG. 10. You can.

Those skilled in the art will know that the configuration of the processor 950 shown in FIG. 10 is a module of the processor 950 that can be used when the electronic device 900 performs machine learning based on an AI learning model. This is only an example, and the electronic device 900 does not necessarily perform machine learning, so some or all of the modules of the processor 950 shown in FIG. 10 may not be included in the processor 950. You will easily understand that it is possible.

The data acquisition unit 952 according to one embodiment may acquire data necessary to determine the user's intention, provide relevant information, and recommend an alternative operation. Alternatively, the data acquisition unit 952 may acquire data necessary for learning to determine the user's intention, provide related information, and recommend alternative actions.

In one example, the data acquisition unit 952 may acquire at least one of, for example, a user's voice, image information, and predetermined context information. In one example, the data acquisition unit 952 may convert the acquired data into a pre-defined format.

The learning data selection unit 954 according to one embodiment may select data necessary for learning from the data acquired by the data acquisition unit 952. For example, the learning data selection unit 954 selects the data necessary for learning from the data acquired by the data acquisition unit 952 according to predefined criteria for determining the user's intention, providing related information, and recommending alternative actions. You can select . Alternatively, the learning data selection unit 954 may select data according to predefined criteria based on learning by the learning model execution unit 956.

The learning model execution unit 956 according to one embodiment may learn criteria for how to determine the user's intention, how to determine related information, and how to recommend an alternative action based on the learning data. Additionally, the learning model executing unit 956 may learn standards for what learning data should be used to determine the user's intention, determine related information, and recommend alternative actions.

Additionally, the learning model execution unit 956 can train an AI learning model used to determine user intention, determine related information, and recommend alternative actions using learning data. In this case, the AI learning model may be a pre-built model. For example, an AI learning model may be a pre-built model that receives basic learning data (e.g., sample data, etc.) as input.

An AI learning model can be built considering the application field of the learning model, the purpose of learning, or the computer performance of the device. The AI learning model may be, for example, a model based on a neural network. For example, models such as Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Bidirectional Recurrent Deep Neural Network (BRDNN) may be used as AI learning models, but embodiments are not limited thereto.

If there are a plurality of pre-built AI learning models, the learning model execution unit 956 according to one embodiment determines an AI learning model that has a high correlation between the input learning data and the basic learning data as the AI learning model to be learned. You can. In this case, the basic learning data may be pre-classified by data type, and the AI learning model may be pre-built by data type. For example, the basic training data is pre-classified by various criteria such as the region where the training data was created, the time the training data was created, the size of the training data, the genre of the training data, the creator of the training data, the type of object in the training data, etc. It may be.

The learning model execution unit 956 according to one embodiment may train an AI learning model using, for example, a learning algorithm including error back-propagation or gradient descent. You can.

The learning model execution unit 956 according to one embodiment may train an AI learning model through, for example, supervised learning using learning data as input. In addition, the learning model execution unit 956 determines the user's intention, provides related information, and learns the type of data necessary to recommend an alternative operation without any guidance, for example, to determine the user's intention; Unsupervised learning can be performed to discover criteria for providing relevant information and recommending alternative actions. Through the unsupervised learning, the learning model execution unit 956 can train an AI learning model. In addition, the learning model execution unit 956, for example, determines the user's intention according to learning, provides relevant information, and uses feedback on whether the result of recommending an alternative action is correct through reinforcement learning. AI learning models can be trained.

Additionally, when the AI learning model is learned, the learning model execution unit 956 may store the learned AI learning model. In this case, the learning model execution unit 956 may store the learned AI learning model in the memory 910 of the electronic device 900. Alternatively, the learning model execution unit 956 may store the learned AI learning model in the memory or database of a server or device connected to the electronic device 900 through a wired or wireless network.

The learning result providing unit 958 according to one embodiment may derive or obtain (machine) learning results based on the AI learning model learned in the learning model executing unit 956. The learning result provider 958 may provide the (machine) learning result to the memory 910 or the transceiver.

In one embodiment, the AI learning model is the predefined AI deep learning image reading technology described above in FIGS. 2 and 8, and the learning result provider 958 uses a camera based on the AI deep learning image reading technology. Recyclable items can be read by analyzing images/videos of items subject to separate collection obtained through .

Some embodiments may also be implemented in the form of a recording medium containing instructions executable by a computer, such as program modules executed by a computer. Computer-readable media can be any available media that can be accessed by a computer and includes both volatile and non-volatile media, removable and non-removable media. Additionally, computer-readable media may include both computer storage media and communication media. Computer storage media includes both volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Communication media typically includes computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, or other transmission mechanism, and includes any information delivery medium.

So far, the present invention has been examined focusing on its preferred embodiments. A person skilled in the art to which the present invention pertains will understand that the present invention may be implemented in a modified form without departing from the essential characteristics of the present invention. Accordingly, the scope of the present invention is not limited to the above-described embodiments, but should be construed to include various embodiments within the scope equivalent to the content described in the patent claims.

Claims (5)

In a method for an electronic device to provide separation guidance for recyclables,
Visually detecting items to be separated through a camera attached to a monitor of the electronic device;
Generating read information about the items to be separated and collected based on the camera and a metal detection sensor attached to the monitor;
Based on a pre-configuration, delivering the read information to a user through the monitor using at least one of a visual delivery method and an auditory delivery method; and
storing the read information about the target article in a memory of the electronic device; Including,
Based on the metal detection sensor detecting that the item to be collected separately is metal, the read information includes information indicating that the item to be collected separately is metal,
Based on the fact that the metal detection sensor does not detect that the item to be collected separately is metal, the item to be collected separately is read by the camera,
The camera:
Reading the items subject to separate recycling based on at least one of a transparent detection sensor and a predefined AI (Artificial Intelligence) deep learning image reading technology,
Based on the predefined AI deep learning image reading technology, perform recycling item reading to classify the items subject to separate collection into one of plastic, vinyl, paper, PET bottles, Styrofoam, and glass,
The camera determines whether the item subject to separate collection corresponds to a transparent PET bottle through the transparent detection sensor, based on the fact that the item subject to separate collection is classified as a plastic bottle,
If the item subject to separate collection is determined to correspond to the transparent PET bottle, determine whether a vinyl label is attached to the item subject to separate collection,
When it is determined that the vinyl label is attached to the item subject to separate collection, the reading information includes text information or voice information for requesting removal of the vinyl label,
The reading information includes a reading result of the recycling item of the article subject to separate collection,
The preset uses both the visual delivery method of displaying the recycling item reading result on the monitor and the auditory delivery method of delivering the recycling item reading result through a speaker attached to the monitor,
The reading information includes a reading result of the recycling item of the article subject to separate recycling and the text information or the voice information for requesting removal of the vinyl label,
If the preset is a first preset, the recycling item reading result is displayed on the monitor, transmitted by voice based on a speaker attached to the monitor, and the text for requesting removal of the vinyl label is displayed on the monitor. displayed, and the voice information for requesting removal of the vinyl label is transmitted based on the speaker,
If the preset is a second preset, the recycling item reading result is displayed on the monitor, and the text for requesting removal of the vinyl label is displayed on the monitor,
When the preset is a third preset, the recycling item reading result is transmitted by voice based on the speaker attached to the monitor, and the voice information for the vinyl label removal request is transmitted based on the speaker. Characteristically,
The AI deep learning image reading technology is implemented through a pre-built AI learning model based on sample data,
The AI learning model includes any one of DNN (Deep Neural Network), RNN (Recurrent Neural Network), and BRDNN (Bidirectional Recurrent Deep Neural Network),
The AI learning model is selected from a plurality of learning models built for each data type, and the data type includes the region where the learning data was created, the time when the learning data was created, the size of the learning data, the genre of the learning data, and the creator of the learning data. , A method that considers at least one of the types of objects in the learning data. delete delete delete delete

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