KR102619865B1 - Method, apparatus and system for providing sterilizer maintenance and management platform service based on data analysis - Google Patents

Abstract

A data analysis-based sterilizer maintenance and management platform service providing method, device, and system are disclosed. A method of providing a data analysis-based sterilizer maintenance and management platform service according to an embodiment of the present invention includes receiving sterilizer-related data from a first user terminal and using the sterilizer-disinfector-related data to provide a sterilizer-disinfector maintenance and management platform service. 1 A manager is assigned, a manager visit schedule is calculated using data related to the sterilizer, a sterilizer management request including the manager visit schedule is sent to the first manager terminal used by the first manager, and a sterilizer management request including the manager visit schedule is sent from the first manager terminal. Maintenance data is received, sterilizer inspection data is generated using the maintenance data, and the sterilizer inspection data is transmitted to the first user terminal and the public institution server.

Description

Method, device and system for providing data analysis-based sterilizer maintenance and management platform service {METHOD, APPARATUS AND SYSTEM FOR PROVIDING STERILIZER MAINTENANCE AND MANAGEMENT PLATFORM SERVICE BASED ON DATA ANALYSIS}

The following embodiments relate to a method, device, and system for providing data analysis-based sterilizer sterilizer maintenance and management platform services.

As background technology related to the embodiments, Republic of Korea Patent Publication KR 10-2399805 B1 discloses a person-to-person quarantine sterilizer in which the spray height is adjusted according to the height of the subject and the water tank is separated all together for easy maintenance. Specifically, the prior literature provides customized spraying according to the subject's height, and the water tank can be maintained hygienically. The spray height is adjusted according to the subject's height with a built-in IoT function, and the water tank is separated all together for easy maintenance. Initiate person-to-person quarantine sterilizer.

Through this, the prior literature has the effect of being able to spray in an appropriate disinfection range according to the height of the subject by embedding a sensor that recognizes the motion of an object and providing a plurality of spray nozzles.

Additionally, Republic of Korea Patent Publication KR 10-1951159 B1 discloses a sterilizing device. Specifically, the prior literature includes a disinfection main body that accommodates at least one medium containing any one of mushroom raw materials, seeds, or mushroom strains, and includes a clamper in which a first zigzag pattern portion is formed; An opening/closing door portion that is provided to be openable and closed on one side of the disinfection body and includes a pressure plate on which a second zigzag pattern portion is provided at a position corresponding to the first zigzag pattern portion; And a pressure inclined portion provided on the clamper and the pressurizing plate, respectively, to pressurize the opening/closing door portion toward the disinfection body by its own weight when the opening/closing door portion moves up/down with respect to the disinfection body. Start configuration.

Through this, the prior literature provides a rectangular disinfection body, which can increase the badge capacity of the device and can be sealed by its own weight through the pressurized inclined portion, so that wear and tear of the packing member is prevented compared to the past, providing a sealing effect. can be improved, and maintenance is easy with relatively simple equipment.

However, prior literature receives data related to the sterilizer from the first user terminal, assigns a first manager for maintenance and management of the sterilizer using the data related to the sterilizer, and schedules the manager's visit using the data related to the sterilizer. Calculate, transmit a sterilizer/disinfector management request including a manager visit schedule to the first manager terminal used by the first manager, receive maintenance data from the first manager terminal, and use the maintenance data to obtain sterilizer/disinfector inspection data. and does not disclose a method, device, or system for providing a data analysis-based sterilizer maintenance and management platform service that transmits sterilizer inspection data to the first user terminal and public institution server.

Republic of Korea Patent Publication KR 10-2399805 B1 Republic of Korea Patent Publication KR 10-1951159 B1

Embodiments seek to provide a method of generating sterilizer inspection data and sharing it with users and public institutions.

Embodiments seek to provide a method for generating sterilizer inspection data.

Embodiments seek to provide a method for calculating a manager visit schedule.

A method of providing a data analysis-based sterilizer maintenance and management platform service according to an embodiment of the present invention includes the steps of receiving sterilizer-related data from a first user terminal; Assigning a first manager for maintenance and management of the sterilizer/disinfector using the data related to the sterilizer/disinfector; Calculating a manager visit schedule using the sterilizer-related data; Transmitting a sterilizer/disinfector management request including the manager visit schedule to a first manager terminal used by the first manager; Receiving maintenance data from the first manager terminal; Generating sterilizer inspection data using the maintenance data; And it may include transmitting the sterilizer inspection data to the first user terminal and a public institution server.

In one embodiment, generating the sterilizer inspection data includes acquiring training maintenance data; extracting training inspection record items from the training maintenance data; Obtaining first labels representing predefined sterilizer inspection data for the inspection record items; applying the training check record items to a pre-trained first neural network to generate training outputs corresponding to the training check record items; Based on the training outputs and the first labels, training the first neural network; extracting inspection record items from the maintenance data using the first neural network; And it may include the step of grouping the inspection record items to generate the sterilizer inspection data.

In one embodiment, calculating the manager visit schedule may include calculating a first luminous intensity index of a first lamp included in the sterilizer/disinfector using data related to the sterilizer/disinfector; calculating an initial luminous intensity index of the first lamp; calculating a luminous intensity reduction rate by dividing the first luminous intensity index by the initial luminous intensity index; calculating the manager's visit schedule within one week from the current time when the light intensity reduction rate is less than a first threshold; calculating a first time required for the luminance decrease rate to decrease to the first threshold when the luminance decrease rate is greater than the first threshold; and calculating the manager visit schedule by adding the first time to the current time.

In one embodiment, the calculating the first time may include calculating a first luminous intensity index difference between the initial luminous intensity index and the first luminous intensity index; calculating a first viewpoint difference between a first viewpoint at which the initial luminous intensity index is measured and a second viewpoint at which the first luminous intensity index is calculated; calculating a luminance index reduction rate per unit time by dividing the first luminance index difference by the first viewpoint difference; calculating a second luminous intensity index by multiplying the initial luminous index by the first threshold; calculating a second luminous intensity index difference between the first luminous intensity index and the second luminous intensity index; and calculating the first time by dividing the second luminance index difference by the luminance index reduction rate per unit time.

In one embodiment, calculating a first harmful bacteria index before the first manager visits and maintains the sterilizer/disinfector using the sterilizer/disinfector inspection data;

calculating a second harmful bacteria index after the first manager visits and maintains the sterilizer/disinfector using the sterilizer inspection data; calculating a second time difference between the previous maintenance visit and the current maintenance visit using the sterilizer inspection data; calculating an increase rate of harmful bacteria per unit time by dividing the first harmful bacteria index difference between the first harmful bacteria index and the second harmful bacteria index by the second time point difference; calculating a second harmful bacteria index difference between a second threshold and the second harmful bacteria index; calculating a second time by dividing the second harmful bacteria index difference by the harmful bacteria increase rate per unit time; calculating a next manager visit schedule by adding the second time to the current visit maintenance time; transmitting the next manager visit schedule to the first user terminal and the first manager terminal; And it may further include receiving an approval request for the next manager visit schedule from the first user terminal and the first manager terminal.

The device according to one embodiment may be combined with hardware and controlled by a computer program stored in a medium to execute any one of the above-described methods.

Embodiments can generate sterilizer inspection data and share it with users and public institutions to efficiently manage hygiene, safety and health data and contribute to the improvement of public health. In addition, embodiments can economically perform management and maintenance of the sterilizer by automatically calculating a manager visit schedule using sterilizer inspection data.

Figure 1 is a diagram showing a data analysis-based sterilization/disinfector maintenance and management platform service provision system according to an embodiment.
Figure 2 is a diagram for explaining a server according to an embodiment.
Figure 3 is a diagram for explaining learning of a neural network according to an embodiment.
Figure 4 is a flowchart of a method for providing data analysis-based sterilization/disinfector maintenance and management platform services according to an embodiment.
Figure 5 is an exemplary diagram of the configuration of a device according to an embodiment.

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

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

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

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

The terms used in the examples are for descriptive purposes only and should not be construed as limiting. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, 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 generally understood by a person of ordinary skill in the technical field to which the embodiments belong. 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 ideal or excessively formal sense. No.

In addition, when describing with reference to the accompanying drawings, identical components will be assigned the same reference numerals regardless of the reference numerals, and overlapping descriptions thereof will be omitted. In describing the embodiments, if it is determined that detailed descriptions of related known technologies may unnecessarily obscure the gist of the embodiments, the detailed descriptions are omitted.

Embodiments may be implemented in various types of products such as personal computers, laptop computers, tablet computers, smart phones, televisions, smart home appliances, intelligent vehicles, kiosks, and wearable devices.

Figure 1 is a diagram showing a data analysis-based sterilization/disinfector maintenance and management platform service provision system according to an embodiment.

As shown in FIG. 1, the data analysis-based sterilizer maintenance and management platform service providing system 100 includes a first user terminal 110-1, a first manager terminal 110-2, a server 120, It may include a database 130 and a public institution server 140. According to one embodiment, the database 130 is shown as being configured separately from the server 120, but the present invention is not limited thereto, and the database 130 may be provided within the server 120. For example, the server 120 may include multiple artificial neural networks for performing machine learning algorithms. According to one embodiment, the first user terminal 110-1, the first administrator terminal 110-2, the server 120, the database 130, and the public institution server 140 are connected to each other through the network N. Can be connected to enable communication.

The server 120 may receive sterilizer-related data from the first user terminal. According to one embodiment, the server 120 may periodically or aperiodically receive sterilizer-related data from the first user terminal 110-1 of the first user using the sterilizer/disinfector through the network (N). there is. For example, data related to a sterilizer may include a first lamp image taken of a first lamp included in the sterilizer.

The server 120 may assign a first manager to maintain and manage the sterilizer/disinfector using data related to the sterilizer/disinfector. According to one embodiment, the server 120 uses the location information of the manager terminal 110-2 included in the sterilizer-related data to select the manager located closest to the first user's sterilizer to maintain and maintain the sterilizer. Can be assigned as the first manager for management.

The server 120 may calculate the manager visit schedule using data related to the sterilizer. According to one embodiment, the server 120 calculates the luminous intensity index of the first lamp using the first lamp image included in the sterilizer-related data, and calculates the manager visit schedule using the luminous intensity index of the first lamp. You can. The specific method of calculating the manager's visit schedule will be described later.

The server 120 may transmit a sterilizer management request including a manager visit schedule to the first manager terminal used by the first manager. According to one embodiment, the server 120 may transmit a sterilizer management request including the manager visit schedule, location, etc. to the first manager terminal 110-2 through the network N.

The server 120 may receive maintenance data from the first manager terminal. According to one embodiment, the server 120 stores maintenance data before and after the first manager visits the first user and performs maintenance of the first user's sterilizer/disinfector at the first manager terminal 110-2. ) can be received through the network (N).

The server 120 may generate sterilizer inspection data using maintenance data. According to one embodiment, the server 120 may generate sterilizer inspection data from maintenance data using the first neural network.

The server 120 may transmit sterilizer inspection data to the first user terminal and the public institution server. According to one embodiment, the server 120 may transmit sterilizer inspection data to the first user terminal 110-1 and the public institution server 140 through the network (N).

For example, the public institution server 140 may include, but is not limited to, a server used by a public institution that supervises the management and supervision of sterilizers installed in various locations.

In addition, when generating sterilizer inspection data, the server 120 acquires training maintenance data, extracts training inspection record items from the training maintenance data, and collects predefined sterilizer inspection data for the inspection record items. obtain first labels representing, apply the training check record items to a pre-trained first neural network, generate training outputs corresponding to the training check record items, and based on the training outputs and the first labels, A first neural network can be trained, inspection record items can be extracted from maintenance data using the first neural network, and inspection record items can be grouped to generate sterilizer inspection data.

Additionally, when calculating the manager visit schedule, the server 120 may calculate the first luminous intensity index of the first lamp included in the sterilizer/disinfector using data related to the sterilizer/disinfector. According to one embodiment, the server 120 may calculate the first luminous intensity index of the first lamp using the first lamp image included in the sterilizer-related data.

The server 120 may calculate the initial luminance index of the first lamp. According to one embodiment, the manager replaces the first lamp of the sterilizer and operates the first lamp and then photographs the first lamp to create an initial installation image, and the server 120 stores the first lamp in the database 130. The initial luminous intensity indication of the first lamp can be calculated using the saved initial installation image.

The server 120 may calculate the luminance reduction rate by dividing the first luminance index by the initial luminance index. According to one embodiment, the server 120 may calculate the luminance reduction rate using Equation 1. For example, if the first luminance index is 20 candela (cd) and the initial luminance index is 100 candela, the server 120 may calculate the luminance reduction rate as 20%.

[Equation 1]

If the luminance reduction rate is less than the first threshold, the server 120 may calculate the manager's visit schedule within one week from the current time. According to one embodiment, the server 120 schedules an administrator visit when the light intensity reduction rate (e.g., 20%) is less than the first threshold (e.g., 30%) because management maintenance of the sterilizer is urgent. At present, it can be calculated within one week.

When the luminance reduction rate is greater than the first threshold, the server 120 may calculate a first time required for the luminance reduction rate to decrease to the first threshold. According to one embodiment, when the luminance reduction rate (e.g., 50%) is greater than the first threshold (e.g., 30%), the server 120 determines the time required for the luminance reduction rate to decrease to the first threshold. 1 hour can be calculated.

The server 120 may calculate the manager visit schedule by adding the first time to the current time. According to one embodiment, the server 120 may calculate the manager visit schedule by adding the first time to the current time.

Additionally, when calculating the first time, the server 120 may calculate the first luminance index difference between the initial luminance index and the first luminance index. According to one embodiment, the server 120 may calculate the first luminance index difference using Equation 2. For example, if the initial luminance index is 100 candela and the first luminance index is 50 candela, the server 120 may calculate the first luminance index difference as 50 candela.

[Equation 2]

The server 120 may calculate a first viewpoint difference between a first viewpoint at which the initial luminance index is measured and a second viewpoint at which the first luminance index is calculated. According to one embodiment, the server 120 may calculate the first viewpoint difference using Equation 3. For example, if the first time point at which the first luminous intensity index was measured is January 1, 2023, and the second time point at which the first luminous intensity index was calculated is April 10, 2023, the server 120 determines the first time point difference. It can be calculated as 100 days.

[Equation 3]

The server 120 may calculate the luminance index reduction rate per unit time by dividing the first luminance index difference by the first viewpoint difference. According to one embodiment, the server 120 may calculate the luminance index reduction rate per unit time using Equation 4. For example, if the first luminance index difference is 50 candela and the first viewpoint difference is 100 days, the server 120 may calculate the luminance index reduction rate per unit time as 0.5 cd/day.

[Equation 4]

The server 120 may calculate the second luminance index by multiplying the initial luminance index by the first threshold. According to one embodiment, the server 120 may calculate the second luminance index using Equation 5. For example, if the initial luminance index is 100 candela and the first threshold is 30%, the server 120 may calculate the second luminance index as 30 candela.

[Equation 5]

The server 120 may calculate the second luminance index difference between the first luminance index and the second luminance index. According to one embodiment, the server 120 may calculate the second luminance index difference using Equation 6. For example, if the first luminance index is 50 candela and the second luminance index is 30 candela, the server 120 may calculate the second luminance index difference as 20 candela.

[Equation 6]

The server 120 may calculate the first time by dividing the second luminance index difference by the luminance index reduction rate per unit time. According to one embodiment, the server 120 may calculate the first time using Equation 7. For example, the server 120 may calculate the first time as 40 days when the second luminance index difference is 20 candela and the luminance index reduction rate per unit time is 0.5 cd/day.

[Equation 7]

Additionally, the server 120 may use the sterilizer inspection data to calculate the first harmful bacteria index before the first manager visits and maintains the sterilizer. According to one embodiment, the first manager measures the first harmful bacteria index of the sterilizer/disinfector before visiting and maintaining the assigned sterilizer/disinfector using the first manager terminal 110-2, and uses the first manager terminal 110-2 to measure the first harmful bacteria index of the sterilizer/disinfector. -2) The first harmful bacteria index may be included in the sterilizer inspection data and transmitted to the server 120.

The server 120 may use the sterilizer inspection data to calculate the second harmful bacteria index after the first manager visits and maintains the sterilizer. According to one embodiment, the first manager measures the second harmful bacteria index of the sterilizer/disinfector after visiting maintenance of the assigned sterilizer/disinfector using the first manager terminal 110-2, and uses the first manager terminal 110-2 to measure the second harmful bacteria index of the sterilizer/disinfector. -2) The sterilizer inspection data may be transmitted to the server 120 including the second harmful bacteria index.

The server 120 may calculate a second time difference between the previous maintenance visit and the current maintenance visit using the sterilizer inspection data. According to one embodiment, the server 120 may calculate the second viewpoint difference using Equation 8. For example, if the previous maintenance visit was on January 1, 2023 and the current maintenance visit was on April 10, 2023, the server 120 may calculate the second time difference as 100 days.

[Equation 8]

The server 120 may calculate the increase rate of harmful bacteria per unit time by dividing the first harmful bacteria index difference between the first harmful bacteria index and the second harmful bacteria index by the second time point difference. According to one embodiment, the server 120 may calculate the growth rate of harmful bacteria per unit time using Equation 9. For example, if the first harmful bacteria index is 100, the second harmful bacteria index is 10, and the second time point difference is 100 days, the server 120 may calculate the harmful bacteria increase rate per unit time as 0.9/day.

[Equation 9]

The server 120 may calculate the difference between the second threshold and the second harmful bacteria index. According to one embodiment, the server 120 may calculate the second harmful bacteria index difference using Equation 10. For example, if the second threshold is 50 and the second harmful bacteria index is 10, the server 120 may calculate the second harmful bacteria index difference as 40.

[Equation 10]

The server 120 may calculate the second time by dividing the second harmful bacteria index difference by the harmful bacteria increase rate per unit time. According to one embodiment, the server 120 may calculate the second time using Equation 11. For example, if the second harmful bacteria index difference is 40 and the increase rate of harmful bacteria per unit time is 0.9/day, the server 120 may calculate the second time as 44 days.

[Equation 11]

The server 120 may calculate the next manager visit schedule by adding a second time to the current visit maintenance time. According to one embodiment, the server 120 may calculate the next manager visit schedule as May 23, 2023 if the current visit maintenance point is April 10, 2023, and the second time is 44 days. According to another embodiment, if the calculated next meeting manager visit schedule corresponds to a public holiday, the server 120 may set the next meeting manager visit schedule before or after the public holiday.

The server 120 may transmit the next manager visit schedule to the first user terminal and the first manager terminal. According to one embodiment, the server 120 may transmit the calculated next manager visit schedule to the first user terminal 110-1 and the first manager terminal 110-2 through the network N, respectively.

The server 120 may receive an approval request for the next manager visit schedule from the first user terminal and the first manager terminal. According to one embodiment, the server 120 may receive a request for approval for the next manager visit schedule from the first user terminal 110-1 and the first manager terminal 110-2 through the network N. .

The database 130 can store various data. Data stored in the database 130 is acquired by at least one component of the first user terminal 110-1, the first administrator terminal 110-2, the server 120, and the public institution server 140. Data that is processed, processed, or used, and may include software (e.g., a program). Database 130 may include volatile and/or non-volatile memory. As an embodiment, the database 130 includes sterilizer-related data received from the first user terminal 110-1, maintenance data received from the first manager terminal, and sterilizer inspection data generated by the server 120. etc. can be saved.

The network (N) performs wireless or wired communication between the first user terminal 110-1, the first administrator terminal 110-2, the server 120, the database 130, the public institution server 140, etc. You can do it. For example, the network (N) may be LTE (long-term evolution), LTE-A (LTE Advanced), CDMA (code division multiple access), WCDMA (wideband CDMA), WiBro (Wireless BroadBand), WiFi (wireless fidelity) , wireless communication can be performed using methods such as Bluetooth, NFC (near field communication), GPS (Global Positioning System), or GNSS (global navigation satellite system). For example, the network (N) is configured to perform wired communication according to methods such as universal serial bus (USB), high definition multimedia interface (HDMI), recommended standard 232 (RS-232), or plain old telephone service (POTS). You may.

In the present invention, artificial intelligence (AI) refers to technology that imitates human learning ability, reasoning ability, perception ability, etc. and implements this with a computer, and includes concepts such as machine learning and symbolic logic. may include. Machine Learning (ML) is an algorithmic technology that classifies or learns the characteristics of input data on its own. Artificial intelligence technology is a machine learning algorithm that analyzes input data, learns the results of the analysis, and makes judgments or predictions based on the results of the learning. Additionally, technologies that mimic the functions of the human brain, such as cognition and judgment, using machine learning algorithms can also be understood as the category of artificial intelligence. For example, technical fields such as verbal understanding, visual understanding, reasoning/prediction, knowledge representation, and motion control may be included.

Machine learning can refer to the process of training a neural network model using experience processing data. Machine learning can mean that computer software improves its own data processing capabilities. A neural network model is built by modeling the correlation between data, and the correlation can be expressed by a plurality of parameters. A neural network model extracts and analyzes features from given data to derive correlations between data. Repeating this process to optimize the parameters of the neural network model can be called machine learning. For example, a neural network model can learn the mapping (correlation) between input and output for data given as input-output pairs. Alternatively, a neural network model may learn the relationships by deriving regularities between given data even when only input data is given.

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

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

Convolutional networks are neural networks that contain sets of nodes with bound parameters. The increasing size of available training data and the availability of computational power, combined with algorithmic advances such as piecewise linear unit and dropout training, have led to significant improvements in many computer vision tasks. For extremely large data sets, such as those available for many tasks today, overfitting is not critical, and increasing the size of the network improves test accuracy. Optimal use of computing resources becomes a limiting factor. For this purpose, distributed, scalable implementations of deep neural networks can be used.

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

As shown in Figure 2, server 120 may include one or more processors 122, one or more memory 124, and/or transceiver 126. In one embodiment, at least one of these components of server 120 may be omitted, or other components may be added to server 120. Additionally or alternatively, some components may be integrated and implemented, or may be implemented as a single or plural entity. At least some of the components inside and outside 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), Data and/or signals can be sent and received.

One or more processors 122 may run software (eg, commands, programs, etc.) to control at least one component of the server 120 connected to the processor 122. Additionally, the processor 122 can perform various operations related to the present invention, such as calculation, processing, data generation, and processing. Additionally, the processor 122 may load data, etc. from one or more memories 124 or store them in one or more memories 124 .

One or more processors 122 may receive sterilizer-related data from the first user terminal. According to one embodiment, the processor 122 may periodically or aperiodically receive sterilizer-related data from the first user terminal 110-1 of the first user using the sterilizer/disinfector through the transceiver 126. there is. For example, data related to a sterilizer may include a first lamp image taken of a first lamp included in the sterilizer.

One or more processors 122 may assign a first manager for maintenance and management of the sterilizer/disinfector using data related to the sterilizer/disinfector. According to one embodiment, the processor 122 uses the location information of the manager terminal 110-2 included in the sterilizer-related data to select the manager located closest to the first user's sterilizer to maintain and maintain the sterilizer. Can be assigned as the first manager for management.

One or more processors 122 may calculate a manager visit schedule using sterilizer-related data. According to one embodiment, the processor 122 calculates the luminous intensity index of the first lamp using the first lamp image included in the sterilizer-related data, and calculates the manager visit schedule using the luminous intensity index of the first lamp. You can.

One or more processors 122 may transmit a sterilizer/disinfector management request including a manager visit schedule to the first manager terminal used by the first manager. According to one embodiment, the processor 122 may transmit a sterilizer management request including the manager visit schedule, location, etc. to the first manager terminal 110-2 through the transceiver 126.

One or more processors 122 may receive maintenance data from the first manager terminal. According to one embodiment, the processor 122 stores maintenance data before and after the first manager visits the first user and performs maintenance of the first user's sterilizer/disinfector at the first manager terminal 110-2. ) can be received through the transceiver 126.

One or more processors 122 may generate sterilizer inspection data using maintenance data. According to one embodiment, the processor 122 may generate sterilizer inspection data from maintenance data using a first neural network.

One or more processors 122 may transmit sterilizer inspection data to the first user terminal and the public institution server. According to one embodiment, the processor 122 may transmit sterilizer inspection data to the first user terminal 110-1 and the public institution server 140 through the transceiver 126.

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

One or more memories 124 store sterilizer-related data received from the above-described first user terminal 110-1, maintenance data received from the first manager terminal, sterilizer inspection data generated by the server 120, etc. You can save it. Additionally, one or more memories 124 may store instructions that, when executed by one or more processors 122, cause one or more processors 122 to perform operations.

As an example, the server 120 may further include a transceiver 126. The transceiver 126 is a wireless device between the first user terminal 110-1, the first administrator terminal 110-2, the server 120, the database 130, the public institution server 140, and/or other devices. Alternatively, wired communication can be performed. For example, the transceiver 126 may support 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, NFC ( Wireless communication can be performed using methods such as near field communication, GPS (Global Positioning System), or GNSS (global navigation satellite system). 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.

As an embodiment, one or more processors 122 control the transceiver 126 to control the first user terminal 110-1, the first administrator terminal 110-2, the database 130, and the public institution server 140. Information can be obtained from. Information obtained from the first user terminal 110-1, the first manager terminal 110-2, the database 130, and the public institution server 140 may be stored in one or more memories 124.

As an example, the server 120 may be a device of various types. For example, server 120 may be a portable communication device, a computer device, or a combination of one or more of the foregoing 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 can be combined depending on the number of cases, and the embodiment of the server 120 created by combining the embodiments also falls within the scope of the present invention. Additionally, the internal/external components of the server 120 according to the present invention described above may be added, changed, replaced, or deleted depending on the embodiment. Additionally, the internal/external components of the aforementioned PLC may be implemented as hardware components.

Figure 3 is a diagram for explaining learning of a neural network according to an embodiment.

As shown in FIG. 3, the learning device can learn the neural network 123 to generate sterilizer inspection data using a machine learning algorithm. According to one embodiment, the learning device may be a separate entity from the server 120, but is not limited thereto.

The neural network 123 includes an input layer 121 through which training samples are input and an output layer 125 through which training outputs are output, and can be learned based on the difference between training outputs and labels. Here, the first labels may be defined based on sterilizer inspection data predefined for inspection record items. The neural network 123 is connected to a group of a plurality of nodes, and is defined by weights between connected nodes and an activation function that activates the nodes.

The learning device may learn the neural network 123 using a gradient descent (GD) technique or a stochastic gradient descent (SGD) technique. The learning device can use a loss function designed by the outputs and labels of the neural network.

The learning device can calculate the training error using a predefined loss function. The loss function may be predefined with a label, output, and parameter as input variables, where the parameter may be set by weights in the neural network 123. For example, the loss function may be designed in the form of MSE (Mean Square Error), entropy, etc., and various techniques or methods may be employed in embodiments in which the loss function is designed.

The learning device can use the backpropagation technique to find weights that affect the training error. Here, the weights are relationships between nodes in the neural network 123. The learning device can use the SGD technique using labels and outputs to optimize the weights found through the backpropagation technique. For example, the learning device can update the weights of the loss function defined based on the labels, outputs, and weights using the SGD technique.

According to one embodiment, the learning device extracts training inspection record items from training maintenance data, acquires first labels representing predefined sterilizer inspection data for the inspection record items, and pre-records the training inspection record items. By applying it to the learned first neural network, training outputs corresponding to training check record items can be generated, and the first neural network can be learned based on the training outputs and the first labels.

According to one embodiment, the learning device may generate training feature vectors based on shape features, location features, and pattern features of the training maintenance data. Various methods can be employed to extract features.

According to one embodiment, the learning device may obtain training outputs by applying training feature vectors to the neural network 123. The learning device may learn the sterilizer inspection data generation algorithm of the neural network 123 based on the training outputs and the first labels. The server 120 may generate sterilizer inspection data from maintenance data using the first neural network on which learning has been completed.

Figure 4 is a flowchart of a method for providing data analysis-based sterilization/disinfector maintenance and management platform services according to an embodiment.

Although the process steps, method steps, algorithms, etc. are described in the flow chart of FIG. 4 in a sequential order, 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 do not need to be performed in the order described herein. Additionally, although some steps are described as being performed asynchronously, in other embodiments, some such steps may be performed concurrently. Additionally, the illustration of a process by depiction in the drawings does not mean that the illustrated process excludes other variations and modifications thereto, and that any of the illustrated process or steps thereof may be incorporated into any of the various embodiments of the invention. It does not imply that more than one is required, nor does it imply that the illustrated process is preferred.

As shown in Figure 4, in step S410, data related to the sterilizer is received. For example, referring to FIGS. 1 to 3, the server 120 of the data analysis-based sterilizer maintenance and management platform service provision system 100 provides information about the first user using the sterilizer through the network N. 1 Data related to the sterilizer may be received periodically or aperiodically from the user terminal 110-1. For example, data related to a sterilizer may include a first lamp image taken of a first lamp included in the sterilizer.

In step S420, a first manager is assigned. For example, referring to FIGS. 1 to 3, the server 120 of the data analysis-based sterilizer maintenance and management platform service provision system 100 stores the administrator terminal 110 containing the sterilizer-related data received in step S410. Using the location information in -2), the manager located closest to the first user's sterilizer can be assigned as the first manager for maintenance and management of the sterilizer.

In step S430, a manager visit schedule is calculated. For example, referring to FIGS. 1 to 3, the server 120 of the data analysis-based sterilizer maintenance and management platform service provision system 100 displays a first lamp image including the sterilizer-related data received in step S410. The luminous intensity index of the first lamp can be calculated using , and the manager visit schedule can be calculated using the luminous intensity index of the first lamp.

In step S440, a sterilizer management request is transmitted. For example, referring to FIGS. 1 to 3, the server 120 of the data analysis-based sterilizer maintenance and management platform service provision system 100 provides information to the first manager terminal 110-2 through the network (N). You can send a sterilizer management request that includes manager visit schedule, location, etc.

In step S450, maintenance data is received. For example, referring to FIGS. 1 to 3, the server 120 of the data analysis-based sterilizer maintenance and management platform service provision system 100 allows the first manager to visit the first user to install the sterilizer and sterilizer of the first user. Maintenance data before and after performing on-site maintenance can be received from the first manager terminal 110-2 through the network N.

In step S460, sterilizer inspection data is generated. For example, referring to FIGS. 1 to 3, the server 120 of the data analysis-based sterilizer maintenance and management platform service provision system 100 receives maintenance data from the maintenance data received in step S460 using a first neural network. Sterilizer inspection data can be generated.

In step S470, sterilizer inspection data is transmitted. For example, referring to FIGS. 1 to 3, the server 120 of the data analysis-based sterilizer maintenance and management platform service provision system 100 receives the sterilizer inspection data generated in step S460 through the network (N). It can be transmitted to the first user terminal 110-1 and the public institution server 140.

Figure 5 is an exemplary diagram of the configuration of a device according to an embodiment.

Device 501 according to one embodiment includes a processor 502 and memory 503. The device 501 according to one embodiment may be the server or terminal described above. The processor may include at least one device described above with reference to FIGS. 1 to 4 or may perform at least one method described with reference to FIGS. 1 to 4 . The memory 503 may store information related to the above-described method or store a program implementing the above-described method. Memory 503 may be volatile memory or non-volatile memory.

The processor 502 can execute programs and control the device 501. The code of the program executed by the processor 502 may be stored in the memory 503. The device 501 is connected to an external device (eg, a personal computer or a network) through an input/output device (not shown) and can exchange data.

The embodiments described above may be implemented with hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, 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, and a field programmable gate (FPGA). It may be implemented using one or more general-purpose or special-purpose computers, such as an array, programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. A processing device may execute an operating system (OS) and one or more software applications that run on the operating system. Additionally, a processing device may access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, a single processing device may be described as being used; however, those skilled in the art will understand that a processing device includes multiple processing elements and/or multiple types of processing elements. It can be seen that it may include. For example, a processing device may include a plurality of processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are possible.

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 on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., singly or in combination. 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 computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -Includes optical media (magneto-optical media) and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc. Examples of program instructions include machine language code, such as that produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. 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.

Software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing unit to operate as desired, or may be processed independently or collectively. You can command the device. Software and/or data may be used on any type of machine, component, physical device, virtual equipment, computer storage medium or device to be interpreted by or to provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed over networked computer systems and thus stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

Although the embodiments have been described with limited drawings as described above, those skilled in the art can 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 components of the described system, structure, device, circuit, etc. are combined or combined in a different form than the described method, or other components are used. Alternatively, appropriate results may be achieved even if substituted or substituted by an equivalent.

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

Claims (3)

As a method of providing a data analysis-based sterilizer maintenance and management platform service by a server of a data analysis-based sterilizer maintenance and management platform service provision system,
Receiving, by the server, data related to a sterilizer from a first user terminal;
assigning, by the server, a first manager for maintenance and management of the sterilizer/disinfector using data related to the sterilizer/disinfector;
calculating, by the server, a manager visit schedule using data related to the sterilizer;
transmitting, by the server, a sterilizer/disinfector management request including the manager visit schedule to a first manager terminal used by the first manager;
Receiving maintenance data from the first manager terminal by the server;
generating, by the server, sterilizer inspection data using the maintenance data; and
Transmitting, by the server, the sterilizer inspection data to the first user terminal and a public institution server,
The step of calculating the manager visit schedule is,
calculating, by the server, a first luminous intensity index of a first lamp included in the sterilizer/disinfector using data related to the sterilizer/disinfector;
calculating, by the server, an initial luminance index of the first lamp;
calculating, by the server, a luminous intensity reduction rate by dividing the first luminous intensity index by the initial luminous intensity index;
calculating, by the server, a schedule for the manager's visit within one week from the current time when the luminance reduction rate is less than a first threshold;
calculating, by the server, a first time required for the luminance decrease rate to decrease to the first threshold when the luminance decrease rate is greater than the first threshold; and
Comprising, by the server, calculating the manager visit schedule by adding the first time to the current time,
The step of calculating the first time is,
Calculating, by the server, a first luminance index difference using Equation 1 below;
[Equation 1]

Calculating a first viewpoint difference by the server using Equation 2 below;
[Equation 2]

Calculating, by the server, a luminance index reduction rate per unit time using Equation 3 below;
[Equation 3]

Calculating, by the server, a second luminance index using Equation 4 below;
[Equation 4]

Calculating, by the server, a second luminance index difference using Equation 5 below; and
[Equation 5]

Comprising the step of calculating the first time by the server using Equation 6 below,
[Equation 6]

Method of providing data analysis-based sterilizer maintenance and management platform service. According to paragraph 1,
The step of generating the sterilizer inspection data is,
obtaining, by the server, training maintenance data;
extracting, by the server, training inspection record items from the training maintenance data;
Obtaining, by the server, first labels representing predefined sterilizer inspection data for the inspection record items;
applying, by the server, the training check record items to a pre-trained first neural network to generate training outputs corresponding to the training check record items;
training, by the server, the first neural network based on the training outputs and the first labels;
extracting, by the server, an inspection record item from the maintenance data using the first neural network; and
Comprising: generating, by the server, the sterilizer inspection data by grouping the inspection record items,
Method of providing data analysis-based sterilizer maintenance and management platform service. delete