KR102289256B1 - Method, apparatus and system for providing health functional food recommendation service based on self-diagnosis - Google Patents

Abstract

Disclosed are a method of providing a health-functional food recommendation service based on self-diagnosis, an apparatus thereof, and a system thereof. According to one embodiment, the method of providing a health-functional food recommendation service based on self-diagnosis includes the following steps of: transmitting a self-diagnosis request to a user terminal; receiving self-diagnosis information in accordance with the self-diagnosis request from the user terminal; forming health-functional food information by days of the week, which is suitable for the user, as first health-functional food recommendation information using the self-diagnosis information; transmitting the first health-functional food recommendation information to the user terminal; receiving feedback information about the first health-functional food recommendation information from the user terminal; forming second health-functional food recommendation information modified from the first health-functional food recommendation information by reflecting the feedback information; transmitting the second health-functional food recommendation information to the user terminal; acquiring approval on the second health-functional food recommendation information from the user terminal; performing a control operation so as to prepare health-functional food by days of the week in accordance with the second health-functional food recommendation information by transmitting the second health-functional food recommendation information to a health-functional food production server; receiving a health-functional food preparation completion signal from the health-functional food production server; acquiring address information of the user by transmitting the preparation completion signal to the user terminal; and requesting the delivery of the completely prepared health-functional food by days of the week to the address of the user by transmitting the address information of the user to a delivery server. Therefore, the present invention is capable of improving convenience for the user to take the health-functional food.

Description

Method, device and system for providing health functional food recommendation service based on self-diagnosis

The following embodiments relate to a service providing method, apparatus, and system for performing self-diagnosis using eating habits and the like and recommending health functional food based on the self-diagnosis.

As a background technology related to embodiments, Korean Patent Laid-Open Publication No. KR 10-2020-0134783 A discloses a compensation method for recommended purchase of health functional food and a health functional food recommendation reward system using the same. Specifically, the preceding literature states that when a plurality of members select and purchase a health functional food recommended through the individually customized health functional food recommendation step as a personal recommended product, each member selects a personal recommended product as soon as possible. Initiate a compensation method for recommended purchase of health functional food in which a higher ranking is given and a relatively more accumulated compensation amount is allocated, but the ranking is independently assigned to each health functional food, and a health functional food recommendation reward system using the same do.

Through this, the prior literature is given a reward ranking independently for each health functional food.

In addition, Korean Patent Publication No. KR 10-2186267 B1 discloses a system and method for providing personalized health functional food information. Specifically, the prior literature discloses a mobile-based personalized health functional food information providing system and method that can scientifically recommend the most suitable health functional food for each individual through individual health status and health-related genetic analysis.

Through this, the prior literature further gives a weight ratio of health functional food recommendation to the self-phenomenon value according to the health abnormality of the client according to the health-related self-judgment data and the self-phenomenon, but the health abnormality information possessed by the genetic information data of the client. In addition, by recommending health functional food through the obtained percentage, more scientific and reliable health functional food can be recommended, and the recommended health functional food item is provided by personal smartphone or user PC.

However, prior literatures provide health functional food information suitable for each day of the week to the user using self-diagnosis information including at least one of the user's urine analysis information, the user's blood analysis information, the user's eating habits analysis information, and the user's life pattern analysis information. 1 We do not disclose the method of forming health functional food recommendation information.

Korean Patent Publication No. KR 10-2020-0134783 A Republic of Korea Patent Publication KR 10-2186267 B1

Embodiments intend to provide health functional food information suitable for each day of the week to the user by using self-diagnosis information.

The embodiments are intended to provide a method for calculating the body age index and using it to recommend a health functional food.

The embodiments are intended to provide a method for calculating the panic disorder and depression index and using it for health functional food recommendation.

In the self-diagnosis-based health functional food recommendation service providing method according to an embodiment of the present invention, a self-examination request to a user terminal- The self-examination request includes a user's urine test, a user's blood test, a user's eating habit test, and a user's life. sending - comprising at least one of the pattern checks; Self-diagnosis information according to the self-examination request from the user terminal- The self-diagnosis information includes at least one of a user's urine analysis information, a user's blood analysis information, a user's eating habits analysis information, and a user's life pattern analysis information - receiving; forming health functional food information suitable for each day of the week as first health functional food recommendation information by using the self-diagnosis information; transmitting the first health functional food recommendation information to the user terminal; receiving feedback information on the first health functional food recommendation information from the user terminal; forming the first health functional food recommendation information into second health functional food recommendation information by reflecting the feedback information; transmitting the second health functional food recommendation information to the user terminal; obtaining approval for the second health functional food recommendation information from the user terminal; transmitting the second health functional food recommendation information to a health functional food manufacturing server and controlling to prepare health functional food for each day according to the second health functional food recommendation information; receiving a health functional food preparation completion signal from the health functional food manufacturing server; transmitting the ready completion signal to the user terminal to obtain address information of the user; and sending the address information of the user to the delivery server to request that the health functional food for each day of the week be prepared and delivered to the address of the user.

In one embodiment, the method comprising: storing recommended health functional food information corresponding to the body age as a first mapping table; calculating a user's first body age index by using the user's urine analysis information; calculating a user's second body age index by using the user's blood analysis information; calculating a user's third body age index by using the user's eating habits analysis information; calculating a user's fourth body age index by using the user's life pattern analysis information; assigning weights to each of the first to fourth body age indices; calculating an average of values obtained by multiplying the first to fourth body age indices by weights assigned to each of the first to fourth body age indices; forming the calculated average as a final body age index; extracting recommended health functional food information from the first mapping table using the final body age index; and providing the extracted health functional food information to the user terminal.

In one embodiment, the method comprising: storing recommended health functional food information corresponding to the degree of panic disorder and depression as a second mapping table; calculating a panic disorder index by using the user's life pattern analysis information; calculating a depression index by using the user's life pattern analysis information; calculating a weight for each of the panic disorder index and the depression index by using the user's eating habits analysis information; calculating a final panic disorder index by multiplying the calculated weight by the panic disorder index; multiplying the calculated weight by the depression index to calculate a final depression index; extracting recommended health functional food information from the second mapping table using the final panic disorder index and the final depression index; and providing the extracted health functional food information to the user terminal.

The self-diagnosis-based health functional food recommendation service providing apparatus according to an embodiment of the present invention requests a self-examination to a user terminal- The self-examination request includes a user's urine test, a user's blood test, a user's eating habit test, and a user's life. Transmitting - including at least one item of the pattern test, and self-diagnosis information according to the self-examination request from the user terminal - The self-diagnosis information includes the user's urine analysis information, the user's blood analysis information, and the user's eating habits. Receives - including at least one of analysis information and user's life pattern analysis information; Sending health functional food recommendation information to the user terminal, receiving feedback information on the first health functional food recommendation information from the user terminal, and modifying the first health functional food recommendation information by reflecting the feedback information forming second health functional food recommendation information, transmitting the second health functional food recommendation information to the user terminal, obtaining approval for the second health functional food recommendation information from the user terminal, and the second health Functional food recommendation information is transmitted to the health functional food manufacturing server to control to prepare health functional food for each day according to the second health functional food recommendation information, and a health functional food preparation completion signal is received from the health functional food manufacturing server, By transmitting the ready signal to the user terminal to obtain the user's address information, and by transmitting the user's address information to the delivery server, it is possible to request that the health functional food for each day of the week be delivered to the user's address.

In an embodiment, when it is determined that the amount of the first health functional food recommendation information is smaller than a first set value, determining whether there is an active rapid processor forming the rapid recommendation information among a plurality of processors; controlling the operation of the plurality of processors to be stopped when it is determined that the operation rapid processor does not exist; when it is determined that there is the operation rapid processor, determining whether the formation amount of the first health functional food recommendation information is smaller than a second set value set through a state of health (SOH) of the server; When it is determined that the formation amount of the first health functional food recommendation information is greater than the second set value, power is supplied to the running rapid processor, so that the formation of the first health functional food recommendation information through the running quick processor continues controlling; When the formation of the first health functional food recommendation information through the rapid operation processor is continued, and when the formation of the first health functional food recommendation information through the rapid operation processor is completed, the operation of the rapid operation processor is stopped controlling; If it is determined that the formation amount of the first health functional food recommendation information is smaller than the second set value, even if the formation of the first health functional food recommendation information through the operation rapid processor is not completed, the operation of the operation rapid processor is not completed. controlling to stop; Receiving a first command from a host controller that multi-manages a plurality of processors arranged for each region when the frequency of the grid is higher than a reference value, and setting the first health functional food recommendation information formation mode according to the first command ; receiving a second command from the host controller when the frequency of the grid is lower than a reference value, and setting a mode to stop the formation of the first health functional food recommendation information according to the second command; When the frequency of the power distribution network connected to the plurality of processors is detected and it is confirmed that the frequency of the distribution network is high outside the reference range, the first health functional food recommendation information is set as the forming mode, and the frequency of the distribution network is setting the first health functional food recommendation information formation stop mode when it is confirmed that it is lower than the reference range; When the plurality of processors and the power distribution network are connected, it is controlled so that power is not supplied from the plurality of processors to the grid, so that the power supplied from the plurality of processors is preferentially stored in the plurality of processors or consumed on-site. Controlling to be used as; and when a third command is received from the host controller, controlling power to be supplied from the plurality of processors to the grid according to the third command.

The apparatus according to an embodiment may be controlled by a computer program stored in the medium to execute the method of any one of the above-described methods in combination with hardware.

Embodiments may provide health functional food recommendation information suitable for each user, and deliver the manufactured health functional food to the user, thereby improving the convenience of taking the health functional food for the user. In addition, a user's body age index can be calculated using various indices, and the body age index can be used to recommend a health functional food, so that a health functional food can be recommended to a user more precisely.

1 is a diagram for explaining a self-diagnosis-based health functional food recommendation service providing system according to an embodiment.
2 is a diagram for explaining a server according to an embodiment.
3 is a flowchart of a method for providing a self-diagnosis-based health functional food recommendation service according to an embodiment.
4 is an exemplary diagram of a configuration of an apparatus according to an embodiment.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, since various changes may be made to the embodiments, the scope of the patent application is not limited or limited by these embodiments. It should be understood that all modifications, equivalents and substitutes for the embodiments are included in the scope of the rights.

Specific structural or functional descriptions of the embodiments are disclosed for purposes of illustration only, and may be changed and implemented in various forms. Accordingly, the embodiments are not limited to a specific disclosure form, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical spirit.

Although terms such as first or second may be used to describe various elements, these terms should be interpreted only for the purpose of distinguishing one element from another. For example, a first component may be termed a second component, and similarly, a second component may also be termed a first component.

When a component is referred to as being “connected to” another component, it may be directly connected or connected to the other component, but it should be understood that another component may exist in between.

The terms used in the examples are used for description purposes only, and should not be construed as limiting. The singular expression includes the plural expression unless the context clearly dictates otherwise. In the present specification, terms such as “comprise” or “have” are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one or more other features It should be understood that this does not preclude the existence or addition of 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 one of ordinary skill in the art to which the embodiment belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or excessively formal meaning unless explicitly defined in the present application. does not

In addition, in the description with reference to the accompanying drawings, the same components are given the same reference numerals regardless of the reference numerals, and the overlapping description thereof will be omitted. In the description of the embodiment, if it is determined that a detailed description of a related known technology may unnecessarily obscure the gist of the embodiment, the detailed description thereof will be omitted.

The embodiments may be implemented in various types of products, such as personal computers, laptop computers, tablet computers, smart phones, televisions, smart home appliances, intelligent cars, kiosks, wearable devices, and the like.

An artificial intelligence (AI) system is a computer system that implements human-level intelligence, and unlike the existing rule-based smart system, the machine learns and makes decisions on its own. The more the AI system is used, the higher the recognition rate and the more accurate understanding of user preferences, and the existing rule-based smart systems are gradually being replaced by deep learning-based AI systems.

Artificial intelligence technology consists of machine learning and element technologies using machine learning. Machine learning is an algorithm technology that categorizes/learns characteristics of input data by itself, and element technology uses machine learning algorithms such as deep learning to simulate functions such as cognition and judgment of the human brain. It consists of technical fields such as understanding, reasoning/prediction, knowledge expression, and motion control.

The various fields where artificial intelligence technology is applied are as follows. Linguistic understanding is a technology for recognizing and applying/processing human language/text, and includes natural language processing, machine translation, dialogue system, question and answer, and speech recognition/synthesis. Visual understanding is a technology for recognizing and processing objects like human vision, and includes object recognition, object tracking, image search, human recognition, scene understanding, spatial understanding, image improvement, and the like. Inferential prediction is a technique for logically reasoning and predicting by judging information, and includes knowledge/probability-based reasoning, optimization prediction, preference-based planning, recommendation, and the like. Knowledge expression is a technology that automatically processes human experience information into knowledge data, and includes knowledge construction (data generation/classification) and knowledge management (data utilization). Motion control is a technology for controlling autonomous driving of a vehicle and movement of a robot, and includes motion control (navigation, collision, driving), manipulation control (action control), and the like.

In general, in order to apply the machine learning algorithm to real life, learning is performed in the Trial and Error method due to the characteristics of the basic methodology of machine learning. In particular, deep learning requires hundreds of thousands of iterations. It is impossible to execute this in an actual physical external environment, so instead, the actual physical external environment is implemented on a computer and learning is performed through simulation.

1 is a diagram for explaining a self-diagnosis-based health functional food recommendation service providing system according to an embodiment.

1, the self-diagnosis-based health functional food recommendation service providing system 100 includes a plurality of user terminals 110-1, 110-2, ..., 110-n, a server 120, a database ( 130 ), a manufacturing server 140 , a delivery server 150 , and a network N may be included. According to an embodiment, although the database 130 is illustrated as being configured separately from the server 120 , the present invention is not limited thereto, and the database 130 may be provided in the server 120 . For example, the server 120 may include a plurality of artificial intelligence for performing a machine learning algorithm.

The server 120 may transmit a self-examination request to any one of the plurality of user terminals 110-1, 110-2, ..., 110-n (eg, 110-1). According to an embodiment, the self-examination request may include at least one of a user's urine test, a user's blood test, a user's eating habit test, and a user's lifestyle test, but the self-test request item is not limited thereto. does not

The user terminal 110 - 1 that has received the self-examination request may receive an input according to the self-examination from the user to form self-diagnosis information. According to an embodiment, the self-diagnosis information may include at least one of the user's urine analysis information, the user's blood analysis information, the user's eating habits analysis information, and the user's life pattern analysis information. not limited For example, the user terminal 110-1 receives urine color information from the user according to the self-examination request received from the server 120 to form urine analysis information, and receives a blood sample from the user to receive the blood analysis information. form, receiving an answer to the eating habits questionnaire from the user to form eating habits analysis information, and receiving an answer to the life pattern questionnaire from the user to form life pattern analysis information.

The server 120 may use the self-diagnosis information received from the user terminal 110-1 to form health functional food information for each day suitable for the user as the first health functional food recommendation information. According to an embodiment, the server 120 forms insufficient nutrient information for the user by using the urine analysis information and the blood analysis information, and forms the first health functional food recommendation information to recommend a health functional food according to the nutrient information. can do. According to another embodiment, the server 120 forms the nutrient information required for the user by using the eating habit analysis information and the life pattern analysis information, and provides the first health functional food recommendation information to recommend the health functional food according to the nutrient information. can also be formed.

The server 120 transmits the formed first health functional food recommendation information to the user terminal 110-1 through the network N, and the first health function from the user terminal 110-1 through the network N It is possible to receive feedback information on the food recommendation information. According to an embodiment, the feedback information may include information such as item change, day change, order change, dose change, etc. of health functional food recommended for each day of the week, but the items of feedback information are not limited thereto.

The server 120 may modify the first health functional food recommendation information by reflecting the feedback information received from the user terminal 110 - 1 , and form it as the second health functional food recommendation information.

The server 120 transmits the formed second health functional food recommendation information to the user terminal 110-1 through the network N, and the second health function from the user terminal 110-1 through the network N Approval for food recommendation information can be obtained.

The server 120 transmits the approved second health functional food recommendation information to the health functional food manufacturing server 140 and can control to prepare a health functional food for each day according to the second health functional food recommendation information, and health by day of the week When the preparation of the functional food is completed, a preparation completion signal may be received from the manufacturing server 140 .

The server 120 transmits the ready completion signal received from the manufacturing server 140 to the user terminal 110-1 to inquire about the user's address information, and obtains the user's address information from the user terminal 110-1 can do.

The server 120 may transmit the user's address information to the delivery server 150 to request that the health functional food prepared for each day be delivered to the user's address.

In addition, the server 120 maps the recommended health functional food information corresponding to the body age and stores it in the database 130 as a first mapping table, and uses the user's urine analysis information included in the self-diagnosis information. calculates the first body age index of the user, calculates the user's second body age index by using the user's blood analysis information, calculates the user's third body age index by using the user's eating habits analysis information, and A fourth body age index of the user is calculated using the life pattern analysis information, weights are given to each of the first to fourth body age indexes, and the first to fourth body age indexes and the first to fourth body age indexes are assigned. Calculate the average of the values multiplied by the weight assigned to each, form the calculated average as the final body age index, extract recommended health functional food information from the first mapping table using the final body age index, and extract the health The nutraceutical information may be provided to any one user terminal (eg, 110-2) of a plurality of user terminals 110-1, 110-2, ..., 110-n. For example, when the user's age is less than the first threshold value (eg, 30 years old), the server 120 sets the weights of the first and second body age indices to a value smaller than 0.5, and the third and a weight of the fourth body age index may be set to a value greater than 0.5. In addition, the server 120, when the user's age is greater than the second threshold value (eg, 50 years old), sets the weights of the first and second body age indices to values greater than 0.5, and the third and third 4 The weight of the body age index can be set to a value less than 0.5.

In addition, the server 120 stores the recommended health functional food information corresponding to the degree of panic disorder and depression as a second mapping table, and calculates the panic disorder index by using the user's life pattern analysis information included in the self-diagnosis information. The depression index is calculated using the user's life pattern analysis information, the weight for each of the panic disorder index and the depression index is calculated using the user's eating habits analysis information included in the self-diagnosis information, and calculated in the panic disorder index The final panic disorder index is calculated by multiplying the calculated weight, the final depression index is calculated by multiplying the depression index by the calculated weight, and the recommended health functional food information is extracted from the second mapping table using the final panic disorder index and the final depression index And, it is possible to provide the extracted health functional food information to any one user terminal (eg, 110-n) of the plurality of user terminals (110-1, 110-2, ..., 110-n).

Network (N), a plurality of user terminals (110-1, 110-2, ..., 110-n), server 120, database 130, manufacturing server 140, delivery server 150, etc. Alternatively, wired communication may be performed. For example, the network includes long-term evolution (LTE), LTE Advanced (LTE-A), code division multiple access (CDMA), wideband CDMA (WCDMA), Wireless BroadBand (WiBro), wireless fidelity (WiFi), Bluetooth ( Bluetooth), near field communication (NFC), GPS (Global Positioning System), or GNSS (global navigation satellite system) may be configured to perform wireless communication. For example, the network may perform wired communication according to a method such as universal serial bus (USB), high definition multimedia interface (HDMI), recommended standard 232 (RS-232), or plain old telephone service (POTS). .

The database 130 may store various data. Data stored in the database 130, a plurality of user terminals (110-1, 110-2, ..., 110-n), the server 120, the database 130, the manufacturing server 140, the delivery server 150 ) as data obtained, processed, or used by at least one component of ), and may include software (eg: a program). Database 130 may include volatile and/or non-volatile memory. In one embodiment, the database 130, the self-examination request formed in the server 120, the self-diagnosis information formed in the plurality of user terminals (110-1, 110-2, ..., 110-n), first and first 2 Health functional food recommendation information, user address information, etc. can be stored.

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

Machine learning may refer to the process of training a neural network model using experience of processing data. With machine learning, computer software could mean improving its own data processing capabilities. The neural network model is constructed by modeling the correlation between data, and the correlation may be expressed by a plurality of parameters. A neural network model extracts and analyzes features from given data to derive correlations between data, and repeating this process to optimize parameters of a neural network model can be called machine learning. For example, a neural network model may learn a mapping (correlation) between an input and an output with respect to data given as an input/output pair. Alternatively, the neural network model may learn the relationship by deriving regularity between the given data even when only input data is given.

The artificial intelligence learning model or neural network model may be designed to implement a human brain structure 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 with each other by simulating a synaptic activity of a neuron through which a neuron sends and receives a signal through a synapse. In the AI learning model, a plurality of network nodes can exchange data according to a convolutional connection relationship while being located in layers of different depths. The AI learning model may be, for example, an artificial neural network model (Artificial Neural Network), a convolutional neural network model (CNN), or the like. As an embodiment, the AI learning model may be machine-learned according to a method such as supervised learning, unsupervised learning, reinforcement learning, or the like. Machine learning algorithms for performing machine learning include decision trees, Bayesian networks, support vector machines, artificial neural networks, and Ada-boost. , Perceptron, Genetic Programming, Clustering, etc. may be used.

Among them, 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 it, and additionally utilizes weights and pooling layers. Thanks to this structure, CNN can fully utilize the input data of the two-dimensional structure. Compared with other deep learning structures, CNN shows good performance in both video and audio fields. CNNs can also be trained through standard back-passing. CNNs are easier to train than other feed-forward 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 advances in algorithms such as piecewise linear units and dropout training, have greatly improved many computer vision tasks. For huge data sets, such as those available for many tasks today, overfitting is not 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 may be used.

2 is a diagram for explaining a server according to an embodiment.

As shown in FIG. 2 , server 120 may include one or more processors 122 , one or more memories 124 and/or transceivers 126 . As an embodiment, at least one of these components of the server 120 may be omitted, or another component may be added to the server 120 . Additionally (additionally) or alternatively (alternatively), some of the elements may be implemented as an integrated or singular or plural entity. At least some of the internal and external components of the server 120 are connected to each other through a bus, general purpose input/output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI), etc. Data and/or signals may be exchanged.

The one or more processors 122 may control at least one component of the server 120 connected to the processor 122 by driving software (eg, an instruction, a program, etc.). In addition, the processor 122 may perform various operations, processing, data generation, processing, etc. related to the present invention. In addition, the processor 122 may load data or the like from, or store to, the one or more memories 124 .

As described above, the one or more processors 122, through the network (N), a plurality of user terminals (110-1, 110-2, ..., 110-n) any one of the user terminals (eg, 110) -1) to send a self-inspection request. According to an embodiment, the self-examination request may be stored in the memory 124 in the form of a digital packet, fetched from the memory 124 by the processor 122 and transmitted to the user terminal 110 - 1 .

The one or more processors 122 digitally transmit self-diagnosis information from any one user terminal 110-1 among a plurality of user terminals 110-1, 110-2, ..., 110-n through the transceiver 126. It can be received in real-time or non-real-time in the form of packets. According to an embodiment, the self-diagnosis information may be formed in response to a self-examination request transmitted to the user terminal 110 - 1 .

The one or more processors 122 use the self-diagnosis information received from the user terminal 110-1 through the transceiver 126 to provide health functional food information for each day suitable for the user, first health functional food recommendation information in the form of a digital packet. can be formed with According to an embodiment, the one or more processors 122 digitally form insufficient nutrient information for the user by using urine analysis information and blood analysis information among the received self-diagnosis information and recommend health functional food according to the nutrient information. It is possible to form the first health functional food recommendation information in the form of a packet. According to another embodiment, the one or more processors 122 are configured to form nutrient information necessary for the user by using eating habits analysis information and life pattern analysis information among the received self-diagnosis information, and to recommend health functional foods according to the nutrient information. The first health functional food recommendation information in the form of a digital packet may be formed.

The one or more processors 122 transmit the first health functional food recommendation information in the form of digital packets to the user terminal 110-1 through the transceiver 126, and transmit the network N from the user terminal 110-1. Through this, it is possible to receive feedback information in the form of a digital packet for the first health functional food recommendation information. According to an embodiment, the feedback information may include information such as item change, day change, order change, dose change, etc. of health functional food recommended for each day of the week, but the items of feedback information are not limited thereto.

The one or more processors 122 reflect the feedback information in the form of a digital packet received from the user terminal 110-1 through the transceiver 126 to modify the first health functional food recommendation information, and convert it into a digital packet form. 2 It can be formed from recommended information on health functional food.

The one or more processors 122 may transmit the second health functional food recommendation information in the form of digital packets to the user terminal 110 - 1 through the transceiver 126 .

The one or more processors 122 obtain approval for the second health functional food recommendation information in the form of a digital packet received from the user terminal 110-1, and transmit the second health functional food recommendation information to the health functional food manufacturing server 140 ), and can control to prepare health functional food for each day according to the second health functional food recommendation information, and when the preparation of health functional food for each day is completed, digital packet form from the manufacturing server 140 through the transceiver 126 can receive the ready completion signal of

The one or more processors 122 transmit the ready completion signal in the form of a digital packet received from the manufacturing server 140 through the transceiver 126 to the user terminal 110-1 through the transceiver 126 to provide address information of the user. , and may obtain address information of the user in the form of a digital packet from the user terminal 110 - 1 through the transceiver 126 .

The one or more processors 122 may transmit the address information of the user in the form of digital packets to the delivery server 150 through the transceiver 126 to request that the health functional food prepared for each day be delivered to the user's address.

The one or more processors 122, if it is determined that the amount of the first health functional food recommendation information is smaller than the first set value (eg, 50%), the one or more processors 122 that form the quick recommendation information It can be determined whether there is a running rapid processor.

The one or more processors 122 may control the operation of the one or more processors 122 to be stopped when it is determined that there is no active rapid processor.

One or more processors 122, if it is determined that there is a running rapid processor, the health of the server 120 (SOH: State of Health) is set through the second set value (eg, 30%) than the first health It may be determined whether the formation amount of the functional food recommendation information is smaller.

When it is determined that the formation amount of the first health functional food recommendation information is greater than the second set value, the one or more processors 122 are supplied with power to the operation rapid processor, and of the first health functional food recommendation information through the operation rapid processor It is possible to control the amount of formation to continue.

One or more processors 122, when the formation of the first health functional food recommendation information through the rapid operation processor continues, when the formation of the first health functional food recommendation information through the rapid operation processor is completed, the operation of the rapid processor It can be controlled to stop working.

When it is determined that the amount of the first health functional food recommendation information formed is smaller than the second set value, the one or more processors 122 are configured to operate the rapid processor even if the formation of the first health functional food recommendation information is not completed through the operation rapid processor. can be controlled to stop the operation.

The one or more processors 122 receive a first command from an upper controller that multi-manages a plurality of processors arranged for each region when the frequency of the grid is higher than a reference value (eg, 100 MHz), and according to the first command It can be set as a mode for forming the first health functional food recommendation information.

The one or more processors 122 may receive the second command from the host controller when the frequency of the grid is lower than the reference value, and set the mode to stop the formation of the first health functional food recommendation information according to the second command.

The one or more processors 122 detect the frequency of the power distribution network connected to the one or more processors 122, and when it is confirmed that the frequency of the power distribution network is high out of the reference range, set to the formation mode of the first health functional food recommendation information And, if it is confirmed that the frequency of the power distribution network is low outside the reference range, the first health functional food recommendation information may be set to a stop mode.

When the one or more processors 122 and the power distribution network are connected, the one or more processors 122 control so that power is not supplied to the grid from the one or more processors 122, Power may be controlled to be preferentially stored in one or more processors 122 or used for in-house consumption.

When a third command is received from the upper controller, the one or more processors 122 may control power to be supplied from the one or more processors 122 to the grid according to the third command.

The one or more memories 124 may store various data. Data stored in the memory 124 is data obtained, processed, or used by at least one component of the server 120 , and may include software (eg, instructions, programs, etc.). Memory 124 may include volatile and/or non-volatile memory. In the present invention, commands or programs are software stored in the memory 124 , and various functions are applied so that the operating system, applications and/or applications for controlling the resources of the server 120 can utilize the resources of the server 120 . It may include middleware provided to .

The one or more memories 124 may include the above-described self-examination request, self-diagnosis information received from a plurality of user terminals 110-1, 110-2, ..., 110-n through the network N, feedback information, and the Address information, a preparation completion signal received from the manufacturing server 140 through the network N, etc. may be stored, and the first and second health functional food recommendation information formed by the one or more processors 122 may be stored. In addition, the one or more memories 124 may store instructions that, when executed by the one or more processors 122 , cause the one or more processors 122 to perform an operation.

In one embodiment, the server 120 may further include a transceiver 126 . Transceiver 126, a plurality of user terminals (110-1, 110-2, ..., 110-n), server 120, database 130, manufacturing server 140, delivery server 150 and / or It can perform wireless or wired communication between other devices. For example, the transceiver 126 may include enhanced Mobile Broadband (eMBB), Ultra Reliable Low-Latency Communications (URLLC), Massive Machine Type Communications (MMTC), long-term evolution (LTE), LTE Advance (LTE-A), UMTS (Universal Mobile Telecommunications System), GSM (Global System for Mobile communications), CDMA (code division multiple access), WCDMA (wideband CDMA), WiBro (Wireless Broadband), WiFi (wireless fidelity), Bluetooth (Bluetooth), NFC ( Near field communication), a global positioning system (GPS), or a global navigation satellite system (GNSS) may perform wireless communication. For example, the transceiver 126 may perform wired communication according to a method such as universal serial bus (USB), high definition multimedia interface (HDMI), recommended standard232 (RS-232), or plain old telephone service (POTS). there is.

In one embodiment, one or more processors 122 control the transceiver 126 to control a plurality of user terminals 110-1, 110-2, ..., 110-n, server 120, database 130, manufacturing Information may be obtained from the server 140 and the delivery server 150 . Information obtained from the plurality of user terminals 110-1, 110-2, ..., 110-n, the server 120, the database 130, the manufacturing server 140, and the delivery server 150 is stored in one or more memories 124 may be stored.

As an embodiment, the server 120 may be a device of various types. For example, the server 120 may be a portable communication device, a computer device, or a device according to a combination of one or more of the above devices. The server 120 of the present invention is not limited to the above-described devices.

Various embodiments of the server 120 according to the present invention may be combined with each other. Each embodiment may be combined according to the number of cases, and the embodiment of the server 120 made in combination also falls within the scope of the present invention. In addition, the above-described internal/external components of the server 120 according to the present invention may be added, changed, replaced, or deleted according to embodiments. In addition, the above-described internal/external components of the server 120 may be implemented as hardware components.

3 is a flowchart of a method for providing a self-diagnosis-based health functional food recommendation service according to an embodiment.

Although process steps, method steps, algorithms, etc. are described in a sequential order in the flowchart of FIG. 3 , 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 invention need not be performed in the order described herein. Also, although some steps are described as being performed asynchronously, in other embodiments some of these steps may be performed concurrently. Further, the exemplification of a process by description in the drawings does not imply that the exemplified process excludes other changes and modifications thereto, and that the illustrated process or any of its steps is not one of the various embodiments of the present invention. It is not meant to be essential to one or more, nor does it imply that the illustrated process is preferred.

As shown in FIG. 3 , in step S310 , a self-examination request is transmitted to the user terminal. For example, referring to FIGS. 1 and 2 , the server 120 may include any one of the plurality of user terminals 110-1, 110-2, ..., 110-n (eg, 110 -1) to send a self-inspection request.

In step S320, self-diagnosis information is received. For example, referring to FIGS. 1 and 2 , the server 120 may receive self-diagnosis information formed in the user terminal 110 - 1 .

In step S330, the first health functional food recommendation information is formed. For example, referring to FIGS. 1 and 2 , the server 120 may use the self-diagnosis information received in step S320 to form health functional food information for each day suitable for the user as the first health functional food recommendation information. there is.

In step S340, the first health functional food recommendation information is transmitted. For example, referring to FIGS. 1 and 2 , the server 120 may transmit the first health functional food recommendation information formed in step S330 to the user terminal 110 - 1 through the network N .

In step S350, feedback information is received. For example, referring to FIGS. 1 and 2 , the server 120 recommends the first health functional food from the user terminal 110-1 through the network N in response to the first health functional food recommendation information. You can receive feedback information about the information.

In step S360, the second health functional food recommendation information is formed. For example, referring to FIGS. 1 and 2 , the server 120 reflects the feedback information received from the user terminal 110 - 1 through the network N to correct the first health functional food recommendation information and , this may be formed as the second health functional food recommendation information.

In step S370, the second health functional food recommendation information is transmitted. For example, referring to FIGS. 1 and 2 , the server 120 may transmit the second health functional food recommendation information formed in step S360 to the user terminal 110 - 1 through the network N.

In step S380, approval for the second health functional food recommendation information is obtained. For example, referring to FIGS. 1 and 2 , the server 120 may obtain approval for the second health functional food recommendation information in response to the second health functional food recommendation information transmitted in step S370. .

In step (S390), health functional food for each day of the week is prepared. For example, referring to FIGS. 1 and 2 , the server 120 transmits the approved second health functional food recommendation information to the manufacturing server 140 through the network N, and the second health functional food It is possible to control to prepare health functional food for each day of the week according to the recommended information.

In step S400, a preparation completion signal is received. For example, referring to FIGS. 1 and 2 , the server 120 may receive a preparation completion signal from the manufacturing server 140 when the preparation of the health functional food for each day of the week is completed according to the second health functional food recommendation information. there is.

In step S410, address information is obtained. For example, referring to FIGS. 1 and 2 , the server 120 transmits the ready completion signal received from the manufacturing server 140 to the user terminal 110 - 1 through the network N to address the user's address. It is possible to inquire for information and obtain address information of the user from the user terminal 110 - 1 through the network N .

In step S420, delivery is requested with address information. For example, referring to FIGS. 1 and 2 , the server 120 transmits the user's address information to the delivery server 150 through the network N, and prepares health functional foods for each day of the week to the user's address. You can request delivery.

4 is an exemplary diagram of a configuration of an apparatus according to an embodiment.

The device 401 according to one embodiment includes a processor 402 and a memory 403 . The device 401 according to an embodiment may be the above-described server or terminal. The processor may include at least one of the devices described above with reference to FIGS. 1 to 3 , or may perform at least one method described above with reference to FIGS. 1 to 3 . The memory 403 may store information related to the above-described method or may store a program in which the above-described method is implemented. The memory 403 may be a volatile memory or a non-volatile memory.

The processor 402 may execute a program and control the device 401 . The code of the program executed by the processor 402 may be stored in the memory 403 . The device 401 may be connected to an external device (eg, a personal computer or a network) through an input/output device (not shown) and exchange data.

The embodiments described above may be implemented by a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the apparatus, methods and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA). array), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, although one processing device is sometimes described as being used, one of ordinary skill in the art will recognize that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that can include For example, the processing device may include a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as parallel processors.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - includes magneto-optical media, and hardware devices specially configured to store and carry out program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

The software may comprise a computer program, code, instructions, or a combination of one or more thereof, which configures a processing device to operate as desired or is independently or collectively processed You can command the device. The software and/or data may be any kind of machine, component, physical device, virtual equipment, computer storage medium or device, to be interpreted by or to provide instructions or data to the processing device. , or may be permanently or temporarily embody in a transmitted signal wave. The software may be distributed over networked computer systems, and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

As described above, although the embodiments have been described with reference to the limited drawings, those skilled in the art may apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order than the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (3)

As a method of providing health functional food recommendation service based on self-diagnosis,
Transmitting, by one or more processors, a self-examination request to the user terminal - the self-examination request includes at least one of a user's urine test, a user's blood test, a user's eating habits test, and a user's lifestyle test to do;
Self-diagnosis information according to the self-examination request from the user terminal by the one or more processors - The self-diagnosis information includes the user's urine analysis information, the user's blood analysis information, the user's eating habits analysis information, and the user's life pattern analysis receiving at least one of the information;
forming, by the one or more processors, health functional food information for each day suitable for a user as first health functional food recommendation information using the self-diagnosis information;
transmitting, by the one or more processors, the first health functional food recommendation information to the user terminal;
receiving, by the one or more processors, feedback information on the first health functional food recommendation information from the user terminal;
forming, by the one or more processors, the first health functional food recommendation information as modified second health functional food recommendation information by reflecting the feedback information;
transmitting, by the one or more processors, the second health functional food recommendation information to the user terminal;
obtaining, by the one or more processors, approval for the second health functional food recommendation information from the user terminal;
transmitting, by the one or more processors, the second health functional food recommendation information to a health functional food manufacturing server, and controlling to prepare health functional food for each day according to the second health functional food recommendation information;
receiving, by the one or more processors, a health functional food preparation completion signal from the health functional food manufacturing server;
obtaining, by the one or more processors, the user's address information by transmitting the ready completion signal to the user terminal;
sending, by the one or more processors, the address information of the user to a delivery server, and requesting that the health functional food for each day of the week be delivered to the address of the user;
determining, by the one or more processors, if the amount of the first health functional food recommendation information is smaller than a first set value, determining whether there is a running rapid processor forming the rapid recommendation information among a plurality of processors;
controlling, by the one or more processors, to stop the operation of the plurality of processors when it is determined that there is no operation rapid processor;
When it is determined by the one or more processors that there is the operation rapid processor, the amount of the first health functional food recommendation information is smaller than the second set value set through the state of health (SOH) of the server. determining whether or not;
When it is determined by the one or more processors that the formation amount of the first health functional food recommendation information is greater than the second set value, power is supplied to the operation rapid processor, and the first health functional food through the operation rapid processor controlling the formation of recommendation information to continue;
If, by the one or more processors, the formation of the first health functional food recommendation information through the rapid operation processor is continued, when the formation of the first health functional food recommendation information through the rapid operation processor is completed, the operation controlling the operation of the rapid processor to be stopped; and
If it is determined by the one or more processors that the formation amount of the first health functional food recommendation information is smaller than the second set value, even if the formation of the first health functional food recommendation information through the operation rapid processor is not completed, controlling the operation of the operating rapid processor to be stopped
containing,
A method of providing health functional food recommendation service based on self-diagnosis. According to claim 1,
storing, by the one or more processors, information on recommended health functional foods corresponding to the body age as a first mapping table;
calculating, by the one or more processors, a first body age index of the user by using the user's urine analysis information;
calculating, by the one or more processors, a second body age index of the user using the user's blood analysis information;
calculating, by the one or more processors, a third body age index of the user using the eating habits analysis information of the user;
calculating, by the one or more processors, a fourth body age index of the user using the life pattern analysis information of the user;
assigning, by the one or more processors, a weight to each of the first to fourth body age indices;
calculating, by the one or more processors, an average of values obtained by multiplying the first to fourth body age indices and weights assigned to each of the first to fourth body age indices;
forming, by the one or more processors, the calculated average as a final body age index;
extracting, by the one or more processors, recommended health functional food information from the first mapping table using the final body age index; and
Further comprising the step of providing the extracted health functional food information to the user terminal by the one or more processors,
A method of providing health functional food recommendation service based on self-diagnosis. According to claim 1,
Storing, by the one or more processors, information on recommended health functional foods corresponding to the degree of panic disorder and depression as a second mapping table;
calculating, by the one or more processors, a panic disorder index using the life pattern analysis information of the user;
calculating, by the one or more processors, a depression index using the life pattern analysis information of the user;
calculating, by the one or more processors, a weight for each of the panic disorder index and the depression index by using the user's eating habits analysis information;
calculating, by the one or more processors, a final panic disorder index by multiplying the calculated weight by the panic disorder index;
calculating, by the one or more processors, the final depression index by multiplying the depression index by the calculated weight;
extracting, by the one or more processors, recommended health functional food information from the second mapping table using the final panic disorder index and the final depression index; and
Further comprising the step of providing the extracted health functional food information to the user terminal by the one or more processors,
A method of providing health functional food recommendation service based on self-diagnosis.

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