KR20220075565A - Apparatus for managing sterilizers in hospitals - Google Patents

Abstract

A server for managing a sterilizer in a hospital according to the present invention includes: a communication unit for receiving sterilization-related information of each sterilizer in the hospital from a terminal provided in the hospital; a memory for storing the received sterilization-related information of each sterilizer; and a processor for generating sterilization scheduling information by applying the sterilization-related information of each sterilizer to a predetermined learned machine learning model, wherein the sterilization scheduling information is sterilization scheduling information of a sterilizer in a hospital based on a priority, the priority The ranking is the sterilization urgency ranking of the sterilization target medical device in the hospital or the sterilization target space in the hospital based on the number of sterilization and sterilization processing time performed for a predetermined period for the sterilization target medical device in the hospital or the sterilization target space in the hospital has been decided by

Description

Apparatus for managing sterilizers in hospitals {APPARATUS FOR MANAGING STERILIZERS IN HOSPITALS}

The present invention relates to an apparatus for managing a sterilizer in a hospital, and more particularly, to an apparatus for managing a sterilizer in a hospital based on artificial intelligence and IOT.

Medical institutions perform invasive treatment in the course of diagnosis, surgery, and treatment of patients, and since the medical instruments or surgical instruments used in this case come into contact with the sterile tissue or mucous membrane of the patient, appropriate disinfection and sterilization are essential. Sterilization refers to the complete destruction of all kinds of microorganisms and spores. As a method used in hospitals, steam sterilization, E.O. (Ethylene Oxide) There are gas sterilization method, dry heat sterilization method, hydrogen peroxide gas plasma sterilization method, and peracetic acid sterilization method.

Which method is appropriate for disinfection and sterilization of medical devices depends on the intended purpose of the medical device. Critical items must be sterilized prior to use, semicritical items require high-level disinfection, and noncritical items require low-level disinfection.

Cleaning should be performed before disinfection or sterilization, and the guidelines for disinfection and sterilization should be strictly followed. In the case of improper disinfection and sterilization of medical devices, pathogenic microorganisms remaining on the devices during invasive treatment may invade the patient's tissues or mucous membranes and cause infection.

Recording and managing matters related to sterilization in hospitals is stipulated by law, and so far, in hospitals, people have created a log manually, causing a lot of inconvenience and inaccurate management. In addition, as the methods for managing the sterilizer became more complicated as the sterilization methods were varied, there were many aspects that made it difficult to manage the sterilizer.

In order to solve this problem, the present invention intends to propose a device for managing an artificial intelligence and IOT-based hospital sterilizer.

Korean Patent Registration No. 10-1577020

The technical problem to be achieved in the present invention is to provide a server for managing a sterilizer in a hospital.

Another technical problem to be achieved in the present invention is to provide a terminal for managing a sterilizer in a hospital.

The technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those of ordinary skill in the art to which the present invention belongs from the description below. will be able

In order to achieve the above technical problem, a server for managing a sterilizer in a hospital according to the present invention includes: a communication unit for receiving sterilization-related information of each sterilizer in the hospital from a terminal provided in the hospital; a memory for storing the received sterilization-related information of each sterilizer; and a processor for generating sterilization scheduling information by applying the sterilization-related information of each sterilizer to a predetermined learned machine learning model, wherein the sterilization scheduling information is sterilization scheduling information of a sterilizer in a hospital based on a priority, the priority The ranking is the sterilization urgency ranking of the sterilization target medical device in the hospital or the sterilization target space in the hospital based on the number of sterilization and sterilization processing time performed for a predetermined period for the sterilization target medical device in the hospital or the sterilization target space in the hospital has been decided by The server may further include a communication unit for transmitting the generated sterilization scheduling information to the terminal. The communication unit is capable of wireless communication based on the Internet of Things (IOT).

In order to achieve the above other technical task, a terminal for managing a sterilizer in a hospital includes: a communication unit for transmitting sterilization-related information of each sterilizer in the hospital to a server, and receiving sterilization scheduling information from the server; A processor for determining a sterilizer to perform a sterilization operation based on the received sterilization scheduling information, wherein the sterilization scheduling information is sterilization scheduling information of a sterilizer in a hospital based on a priority through a predetermined machine learning model, The priority is the sterilization of the sterilization target medical device in the hospital or the sterilization target space in the hospital based on the number of sterilization and sterilization processing time performed for a predetermined period for the sterilization target medical device in the hospital or the sterilization target space in the hospital It may be determined by urgency ranking.

The communication unit may further receive the sterilization-related information including the sterilization operation result from each sterilizer in the hospital.

The processor analyzes the sterilization-related information received by the sterilization, and the terminal may further include a memory for storing the analyzed sterilization-related information.

By recording and storing sterilization-related information according to an embodiment of the present invention, efficient management of the sterilizer is possible, and the user's inconvenience is significantly improved.

According to an embodiment of the present invention, by analyzing sterilization record information on the server based on artificial intelligence and optimal scheduling based on priority, the sterilizer in the hospital can efficiently sterilize the target medical device/target space.

The effects obtainable in the present invention are not limited to the above-mentioned effects, and other effects not mentioned may be clearly understood by those of ordinary skill in the art to which the present invention belongs from the following description. will be.

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included as a part of the detailed description for better understanding of the present invention, provide embodiments of the present invention, and together with the detailed description, explain the technical spirit of the present invention.
1 is a diagram illustrating a system for managing sterilization of medical devices in a hospital according to the present invention.
2 is a diagram illustrating a block diagram for explaining the function of the sterilizer 100 according to the present invention.
3 is a diagram illustrating a block diagram for explaining the function of the terminal 200 according to the present invention.
4 is a diagram illustrating a block diagram for explaining the function of the server 300 according to the present invention.
5 is a diagram illustrating a layer structure of an artificial neural network.
6 is a diagram illustrating an example of a deep neural network.

Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings. DETAILED DESCRIPTION The detailed description set forth below in conjunction with the appended drawings is intended to describe exemplary embodiments of the present invention and is not intended to represent the only embodiments in which the present invention may be practiced. The following detailed description includes specific details in order to provide a thorough understanding of the present invention. However, it will be apparent to one skilled in the art that the present invention may be practiced without these specific details.

In some cases, well-known structures and devices may be omitted or shown in block diagram form focusing on core functions of each structure and device in order to avoid obscuring the concept of the present invention. In addition, the same reference numerals are used to describe the same components throughout the present specification.

1 is a diagram illustrating a system for managing sterilization of medical devices in a hospital according to the present invention.

Referring to FIG. 1 , a system for managing sterilization of medical devices in a hospital may include a sterilizer 100 , a terminal 200 , and a server 300 .

Each of the sterilizer 100, the terminal 200, and the server 300 may be connected wirelessly based on the Internet of Things (IoT) or may be connected by a cable or the like in a wired manner. Wireless communication technologies used in IoT include WiFi, BLE, Zigbee, Zwave, LTE, 5G, LTE-M, NB-IoT, and the like.

The sterilizer 100 is a medical tool for sterilization, and may communicate with the terminal 200 provided in the hospital through wireless communication or wired communication such as IoT. The sterilizer 100 performs a function of sterilizing medical devices in a hospital. 1, since there are many medical devices requiring sterilization and obligatory sterilization in the hospital, a plurality of sterilizers 100 may be provided, as an example, in each operating room, each intensive care unit, and each ward. Since there are used medical devices, the sterilizer 100 may be provided for each space.

The terminal 200 and each sterilizer 100 can be connected to each other by wireless or wired communication.

The sterilizer 100 is an identifier (ID) of the sterilizer 100 that performed sterilization, information on the medical device to be sterilized, the sterilization progress status (eg, sterilization rate) of the corresponding medical device, sterilization (processing) pressure, sterilization ( Sterilization-related information including at least one of temperature, sterilization processing time (process information as information of the operating state of the sterilizer 100), etc. may be transmitted to the terminal 200 through wireless communication or wired communication. Here, the medical device information is management information in the form of text, image, or video, and may include a medical device identifier, a medical device purchase date, an expiration date, and information on a medical device status.

The terminal 200 may transmit the sterilization-related information received from the sterilizer 100 to the server 300 interlocked in the system to record the sterilization-related information and sterilization-related information in the server.

The terminal 200 may receive the sterilization-related information from the sterilizer 100 through wireless communication or wired communication. In addition, when the sterilization of the sterilizer 100 is completed, the terminal 200 may additionally receive and store sterilization completion information from the sterilizer 100 .

The server 300 may receive sterilization-related information transmitted by each sterilizer 100 from each terminal 200 . The server 300 records and stores sterilization information based on the sterilization-related information received from each terminal 200, analyzes it, and generates optimal sterilization scheduling information based on artificial intelligence for medical devices in the hospital. This enables optimal sterilization service in the hospital.

2 is a diagram illustrating a block diagram for explaining the function of the sterilizer 100 according to the present invention.

Referring to FIG. 2 , the sterilizer 100 may include a main body 110 , a communication unit 120 , and a memory 130 including components necessary to perform sterilization. The sterilizer 100 according to the present invention may be of a type fixed in a predetermined place, but may also be of a type capable of moving in the form of an intelligent robot. Accordingly, the main body 110 of the sterilizer 100 includes a sterilization processing unit 112 for performing sterilization and a display unit 114 for displaying the sterilization processing result, history, and the like to the user. When the sterilizer 100 is a movable intelligent robot sterilizer, it may optionally further include a transport unit (wheel, etc.).

The communication unit 120 may receive matters related to the sterilization operation (eg, an instruction to perform the sterilization operation, etc.) from the terminal 200 . Instructions for performing sterilization may include matters regarding start of sterilization, cessation of sterilization, time for sterilization, pressure for sterilization, temperature for sterilization, and movement to another space for sterilization. The memory 130 may store a result of sterilization, received instructions, and the like.

The sterilizer 100 according to the present invention may be, for example, an ultraviolet (ultraviolet, UV) lamp sterilizer, a UV-C LED sterilizer, a plasma (air purifying) sterilizer, a microbial spraying (air purifying) sterilizer, etc. It may be a sterilizer in the form of anticorrosion.

And, the display unit 114 of the sterilizer 100 displays the sterilization operation result so that the operator can see it, the operator can check the sterilization operation result through the display unit 114, and on the display unit 114, the previous The history of sterilization operation results, etc. can also be checked. In this way, the operator manually recorded and managed the sterilization record (sterilization date and time, sterilization time, etc.) in the log based on the items displayed on the display unit 114, but after the sterilizer 100 performs the sterilization operation, the sterilization operation By transmitting the sterilization-related information to the terminal 200 for the result, it is possible to reduce the difficulty of creating and managing the sterilization record of the operator. Here, matters included in the transmitted sterilization-related information have been described in the description related to FIG. 1 .

3 is a diagram illustrating a block diagram for explaining the function of the terminal 200 according to the present invention.

The terminal 200 may include a processor 210 , a communication unit 220 , and a memory 230 . The terminal 200 according to the present invention is a terminal capable of transmitting data provided in a hospital, and may take various forms such as, for example, various desktop computers and notebook computers, as well as smart phones, tablet PCs, and phablets. As described above, the terminal 200 may communicate with the sterilizer 100 and the server 300 wirelessly or by wire, respectively, and may be provided in a department managing sterilization records in a hospital.

The processor 210 may perform various arithmetic processing such as performing arithmetic processing on the sterilization-related information received from the sterilizer 100 , and may control the operation of the communication unit 220 and the memory 230 .

The communication unit 220 may receive sterilization-related information from the sterilizer 100 . Sterilization-related information includes the identifier (ID) of the sterilizer 100 that performed sterilization, information on the medical device to be sterilized, the sterilization status of the medical device (sterilization rate, number of sterilization within a predetermined period, etc.), sterilization pressure, sterilization process At least one of temperature, sterilization time (process information as information of the operating state of the sterilizer 100), etc. may be included. The communication unit 220 may receive sterilization-related information from the sterilizer 100 through wireless communication or wire.

The processor 210 may control to store and process the sterilization-related information received from the sterilizer 100 and transmit it to the server 300 . The communication unit 220 transmits sterilization operation-related information including sterilization performance of the sterilizer 100, sterilization execution stop, sterilization processing time, sterilization processing pressure, sterilization processing temperature, and the like under the control of the processor 210 . The sterilizer 100 performs sterilization based on the sterilization operation-related information received from the communication unit 220 and controls the sterilization operation.

4 is a diagram illustrating a block diagram for explaining the function of the server 300 according to the present invention.

Referring to FIG. 4 , the server 300 may include a processor 310 , a communication unit 320 , and a memory 330 .

The server 300 may be in the form of a cloud, a remote storage device, a local storage device, etc. existing on the Internet network, and to receive the sterilization-related information, store and manage the sterilization-related information in the memory 330 (database). can In this case, the information stored in the database may be encrypted to increase security.

In general, hospitals manage sterilization records by manually recording diaries for sterilization records, but in the present invention, the communication unit 320 of the server 300 receives the sterilization record from the terminal 200 Receive sterilization-related information, including The processor 220 of the server 300 records and stores the sterilization record of the hospital sterilizer received in the memory 330, and analyzes the sterilization-related information based on artificial intelligence.

The processor 310 of the server 300 may analyze the sterilization record based on artificial intelligence (AI) and perform optimal sterilization scheduling for each division or space in the hospital using the sterilizer or for each target medical device. Hereinafter, we will briefly look at artificial intelligence.

Explain artificial intelligence (AI), machine learning, and deep learning. The easiest way to understand the relationship between these three concepts is to imagine three concentric circles. Artificial intelligence is the biggest circle, followed by machine learning, and deep learning, which is driving the current artificial intelligence boom, is the smallest circle.

The concept of artificial intelligence first appeared in 1956 at a Dartmouth conference held by Professor John McCarthy at Dartmouth College, USA, and has been growing explosively in recent years. Especially since 2015, it has been accelerated by the introduction of GPUs that provide fast and powerful parallel processing performance. The exponentially increasing storage capacity and the advent of the big data era in which data in all areas such as images, texts, and mapping data overflowed also had a major impact on this growth.

Artificial Intelligence - Human Intelligence into Machines

In 1956, the dream of the pioneers of artificial intelligence at the time was to finally build a complex computer with characteristics similar to human intelligence. Artificial intelligence that thinks like a human while possessing human senses and thinking is called 'General AI', but the artificial intelligence that can be created at the current level of technological advancement is based on the concept of 'Narrow AI'. Included. Narrow AI is characterized by being able to perform certain tasks with more than human capabilities, such as image classification services in social media or facial recognition functions.

Machine Learning (Machine Learning) - A Specific Approach to Implementing Artificial Intelligence

Machine learning is responsible for automatically filtering out spam in your mailbox. On the other hand, machine learning basically uses algorithms to analyze data, learns through analysis, and makes judgments or predictions based on what has been learned. Therefore, ultimately, the goal is to learn how to perform tasks by “learning” the computer itself through a large amount of data and algorithms, rather than directly coding specific guidelines for decision-making standards into the software. Machine learning comes from the concepts proposed by early artificial intelligence researchers, and algorithmic methods include decision tree learning, inductive logic programming, clustering, reinforcement learning, and Bayesian networks. However, none of these achieved the ultimate goal of general AI, and it is true that even narrow AI was often difficult to achieve with early machine learning approaches.

Currently, machine learning is making great achievements in fields such as computer vision, but it has encountered a limitation in that a certain amount of coding work is involved in the overall process of implementing artificial intelligence even without specific guidelines. For example, when recognizing an image of a stop sign based on a machine learning system, developers can programmatically identify the start and end of an object, a boundary detection filter, identify a face of an object, shape detection, and characters such as 'S-T-O-P' A classifier, etc. that recognizes In this way, machine learning works by recognizing images from ‘coded’ classifiers and ‘learning’ stop signs through an algorithm.

The image recognition rate of machine learning realizes sufficient performance for commercialization, but the image recognition rate may drop in certain situations where signs are difficult to see due to fog or trees. The reason computer vision and image recognition did not reach the level of humans until recently is because of such a recognition rate problem and frequent errors.

Deep Learning - the technology that enables full machine learning

It was the biological properties of the human brain, particularly the neuronal connections, that inspired another algorithm, artificial neural networks, created by early machine learning researchers. However, unlike the brain, where any neurons in physically close proximity can be interconnected, artificial neural networks have consistent layer connections and direction of data propagation.

For example, if an image is cut into numerous tiles and fed to the first layer of a neural network, the neurons repeat the process of passing data to the next layer until the final output is generated in the last layer. Each neuron is then assigned a weight that represents the accuracy of the input based on the operation it is performing, and then the weights are summed to determine the final output. In the case of a stop sign, characteristics of the image, such as its octagonal shape, red color, marker text, size, and whether it is moving, are chopped up and ‘examined’ in neurons, and the neural network’s job is to identify whether it is a stop sign. Here, a ‘probability vector’ that predicts the result according to weight based on sufficient data is used.

Deep learning is a form of artificial intelligence developed from artificial neural networks. It learns data using an information input/output layer similar to neurons in the brain. However, since even basic neural networks require a huge amount of computation, the commercialization of deep learning faced difficulties from the beginning. Nevertheless, the researchers continued their research and succeeded in parallelizing an algorithm that proves the concept of deep learning based on a supercomputer. And the advent of GPUs optimized for parallel computation dramatically accelerated the computational speed of neural networks and brought about the emergence of true deep learning-based artificial intelligence.

Neural networks are likely to give a lot of incorrect answers in the ‘learning’ process. Going back to the stop sign example, it may need to train hundreds, thousands, maybe even millions of images to adjust the weights of neuronal inputs precisely enough to always give an answer regardless of weather conditions, day or night changes. It is only at this level of accuracy that the neural network can be considered to have properly learned the stop sign. In 2012, Google and Stanford University professor Andrew NG implemented a ‘Deep Neural Network’ consisting of more than 1 billion neural networks with 16,000 computers. Through this, after analyzing 10 million images from YouTube, we succeeded in having the computer classify pictures of people and cats. The computer learns the process of recognizing and judging the shape and appearance of the cat on its own.

The image recognition capabilities of systems trained with deep learning are already ahead of humans. Other areas of deep learning include the ability to identify cancer cells in blood and tumors in MRI scans. Google's AlphaGo learned the basics of Go and strengthened its neural network in the process of repeatedly playing against an AI like himself. With the advent of deep learning, the practicality of machine learning has been strengthened, and the realm of artificial intelligence has expanded. Deep learning subdivides a task in any way supportable by a computer system. Deep learning-based technologies such as driverless cars, better preventive medicine, and more accurate movie recommendations are already being used in our daily lives or are about to be put to practical use. Deep learning is being evaluated as the present and future of artificial intelligence with the potential to realize general AI that appeared in science fiction.

Hereinafter, we will look at deep learning in more detail.

Deep learning is a kind of artificial neural network (ANN) using the theory of human neural network. It is a set of machine learning models or algorithms that refer to deep neural networks (DNNs) that have more than one hidden layer (hereinafter referred to as the middle layer). Simply put, Deep Learning is an artificial neural network with deep layers.

It is estimated that the human brain consists of 25 billion nerve cells. The brain consists of nerve cells, and each nerve cell (neuron) refers to one nerve cell constituting the neural network. Neurons contain one cell body (cell body) and one axon (Axon or nurite), which is a projection of the cell body, and usually several dendrites (dendrite or protoplasmic process). Information exchange between these neurons is transmitted through junctions between neurons called synapses. It is very simple when one nerve cell is taken apart, but when these nerve cells are put together, it can have human intelligence. The dendrite is the part that receives signals from other neurons (Input), and the axon is the part that extends very long from the cell body and transmits signals to other neurons (Output). There is a connection called synapse that connects the axon and the dendrites that transmit signals between neurons. The signal is not transmitted unconditionally, but only when the signal strength exceeds a certain value (threshold). will do That is, each synapse has a different connection strength and determines whether or not to transmit a signal.

Artificial neural network (ANN), a field of artificial intelligence, is a mathematical model modeled by imitation of the brain structure (neural network) of biology (usually human). That is, the artificial neural network is implemented by mimicking the information processing and transmission process of these biological neurons. As implemented similarly to the way the human brain solves problems, the neural network has excellent parallelism because each neuron operates independently. Also, since information is distributed in many connection lines, even if a problem occurs in some neurons, it does not affect the whole, so it is resistant to a certain level of error and has the ability to learn about a given environment.

Deep neural networks can be seen as descendants of artificial neural networks, and they are the latest version of artificial neural networks that have achieved success in areas where numerous artificial intelligence technologies have failed in the past by going beyond existing limitations. Looking at the modeling of artificial neural networks by mimicking biological neural networks, in terms of processing units, biological neurons are nodes, and connections are synapses as weights. It was modeled as shown in Table 1.

biological neural network artificial neural network cell body node dendrite input Axon output synapse weight

5 is a diagram illustrating a layer structure of an artificial neural network.

Just as many human biological neurons are connected to each other and do meaningful work, in the case of an artificial neural network, individual neurons are connected to each other through synapses, so that a plurality of layers are connected to each other, so the connection strength between each layer is increased. It can be updated by weight. As such, it is used in fields for learning and cognition with its multi-layered structure and connection strength.

Each node is connected by weighted links, and the entire model learns by repeatedly adjusting the weights. The weight expresses the importance of each node as a basic means for long-term memory. Simply put, an artificial neural network trains the entire model by initializing these weights and adjusting them by updating them with the data set to be trained. When a new input value comes in after training is completed, an appropriate output value is inferred. The learning principle of artificial neural networks can be seen as the process of forming intelligence from the generalization of experiences, and it is done in a bottom-up manner. In Fig. 1, the case where there are two or more intermediate layers (that is, 5 to 10) is called a deep neural network, and the learning and inference model made through such a deep neural network can be referred to as deep learning. have.

An artificial neural network can play a certain role even if it has one middle layer (commonly referred to as a hidden layer, 'hidden layer') except for inputs and outputs, but as the complexity of the problem increases, the number of nodes or the number of layers increases. number should be increased. Among them, it is effective to take a multi-layered model by increasing the number of layers, but the range of application is limited due to the limitation that efficient learning is impossible and the amount of computation to learn the network is large.

However, by overcoming the existing limitations as above, the artificial neural network can have a deep structure. As a result, a complex and expressive model can be built, and groundbreaking results are being announced in various fields such as voice recognition, face recognition, object recognition, and character recognition.

6 is a diagram illustrating an example of a deep neural network.

A deep neural network (DNN) is an artificial neural network (ANN) composed of several hidden layers between an input layer and an output layer. A machine learning (Machine Learning) model or algorithm that refers to a Deep Neural Network (DNN) having one or more hidden layers between an input layer and an output layer. is a set of The neural network connections are made from the input layer to the hidden layer and from the hidden layer to the output layer.

Deep neural networks can model complex non-linear relationships like general artificial neural networks. For example, in a deep neural network structure for an object identification model, each object may be expressed as a hierarchical configuration of basic elements of an image. In this case, the additional layers may aggregate the characteristics of the gradually gathered lower layers. This feature of deep neural networks enables modeling of complex data with fewer units (units, nodes) compared to similarly performed artificial neural networks.

Previous deep neural networks have usually been designed as forward neural networks, but recent studies have successfully applied deep learning structures to Recurrent Neural Networks (RNNs). As an example, there is a case of applying a deep neural network structure to the field of language modeling. In the case of Convolutional Neural Network (CNN), it has been well applied in the field of computer vision, and each successful application case is well documented. More recently, convolutional neural networks have been applied to the field of acoustic modeling for Automatic Speech Recognition (ASR), and are being evaluated as being more successfully applied than existing models. Deep neural networks can be trained with standard error backpropagation algorithms. In this case, the weights may be updated through stochastic gradient descent using the following equation.

The processor 310 of the server 300 analyzes the sterilization-related records for various sterilizers with artificial intelligence, and derives an optimized scheduling to perform the sterilization service for each department/space/target medical device using the sterilization service in the hospital. can In the memory 330 of the server 300, sterilization records related to the sterilization of the sterilizer 100 in the hospital are accumulated and stored for a predetermined time/number of times. The processor 310 may derive the optimal scheduling to perform the sterilization service through a machine learning model (algorithm) that has learned the sterilization records in the hospital.

Here, the optimal scheduling may include sterilization scheduling according to priority. When the processor 310 receives sterilization-related information from the terminal 200 into the machine learning model, scheduling information according to the priority so that sterilization can be preferentially performed in the medical device that is most urgently sterilized and the space in the hospital where sterilization is most urgent. to create Scheduling information according to this priority may be determined based on the sterilization target medical device, the sterilization number of the sterilization target space, and the sterilization processing time.

The server 300 can efficiently manage sterilization records in the hospital by analyzing sterilization-related information based on artificial intelligence to derive the optimal scheduling of each hospital sterilization service. The server 300 increases the efficiency of hospital work by performing the role of creating and managing the sterilization record log by the existing user instead. The server 300 receives the information and stores and manages the information in a database, thereby increasing the safety and convenience of medical record management by data distribution.

The processor 310 of the server 300 may control the communication unit 320 to transmit the generated sterilization scheduling information to the terminal 200 . The generated sterilization scheduling information may include information on the sterilizer 100 to start sterilization according to the priority, the sterilization target medical device, and information on the space in the sterilization target hospital.

The processor 210 of the terminal 200 may transmit sterilization-related instructions to each sterilizer 100 according to the sterilization priority of the communication unit 220 based on the sterilization scheduling information received from the server 300 .

The sterilizer 100 receives the sterilization operation instruction based on the priority-based scheduling information from the terminal 200, and performs the sterilization operation.

The embodiments described above are those in which elements and features of the present invention are combined in a predetermined form. Each component or feature should be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form that is not combined with other components or features. It is also possible to configure embodiments of the present invention by combining some elements and/or features. The order of operations described in the embodiments of the present invention may be changed. Some features or features of one embodiment may be included in another embodiment, or may be replaced with corresponding features or features of another embodiment. It is apparent that claims that are not explicitly cited in the claims can be combined to form an embodiment or included as a new claim by amendment after filing.

The processors 210 and 310 may also be referred to as controllers, microcontrollers, microprocessors, microcomputers, or the like. Meanwhile, the processors 210 and 310 may be implemented by hardware, firmware, software, or a combination thereof. When implementing the embodiment of the present invention using hardware, ASICs (application specific integrated circuits) or DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices) configured to perform the present invention , field programmable gate arrays (FPGAs), etc. may be provided in the processors 210 and 310 .

It is apparent to those skilled in the art that the present invention may be embodied in other specific forms without departing from the essential characteristics of the present invention. Accordingly, the above detailed description should not be construed as restrictive in all respects but as exemplary. The scope of the present invention should be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the present invention are included in the scope of the present invention.

Claims (6)

a communication unit for receiving sterilization-related information of each sterilizer in the hospital from a terminal provided in the hospital;
a memory for storing the received sterilization-related information of each sterilizer; and
A processor for generating sterilization scheduling information by applying the sterilization-related information of each sterilizer to a predetermined learned machine learning model,
The sterilization scheduling information is sterilization scheduling information of a sterilizer in a hospital based on priority,
The priority is the sterilization of the sterilization target medical device in the hospital or the sterilization target space in the hospital based on the number of sterilization and sterilization processing time performed for a predetermined period for the sterilization target medical device in the hospital or the sterilization target space in the hospital A server for managing the sterilizer in the hospital, which is determined by the urgency priority. The method of claim 1,
A server for managing a sterilizer in a hospital, further comprising a communication unit for transmitting the generated sterilization scheduling information to the terminal. The method of claim 1,
The communication unit is a server for managing the Internet of Things (IOT)-based wireless communication is possible, in-hospital sterilizer. The sterilization-related information of each sterilizer in the hospital is transmitted to the server,
a communication unit for receiving sterilization scheduling information from the server;
A processor for determining a sterilizer to perform a sterilization operation based on the received sterilization scheduling information,
The sterilization scheduling information is sterilization scheduling information of a sterilizer in a hospital based on priority through a predetermined learned machine learning model,
The priority is the sterilization of the sterilization target medical device in the hospital or the sterilization target space in the hospital based on the number of sterilization and sterilization processing time performed for a predetermined period for the sterilization target medical device in the hospital or the sterilization target space in the hospital A terminal for managing the sterilizer in the hospital, which is determined by the urgency priority. 5. The method of claim 4,
The communication unit further receives the sterilization-related information including the sterilization operation results from each sterilizer in the hospital, terminal for managing the sterilizer in the hospital. 6. The method of claim 5,
The processor analyzes the sterilization-related information received by the sterilization,
A terminal for managing a sterilizer in a hospital, further comprising a memory for storing the analyzed sterilization-related information.

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