Method and system for operating infection-control air-conditioning according to concentration of airborne infectious agents

Abstract

A method for operating infection-control air-conditioning according to a concentration of airborne infectious agents according to the present disclosure may comprise the steps of: collecting air quality data of a target space in which one or more infection-control air-conditioning devices are disposed; calculating prediction data associated with a concentration of airborne infectious agents in the target space on the basis of the air quality data; and controlling an operating condition of the infection-control air-conditioning devices on the basis of the prediction data.

Description

Method and System for Coordinated Disease Control Based on Airborne Infectious Source Concentration

[0001] The present disclosure relates to a method and system for operating a disease control air conditioning system based on the concentration of airborne infectious agents. Specifically, it relates to a method for operating a disease control air conditioning system based on the concentration of airborne infectious agents using air quality data, and an information processing system for the same.

[0002] In multi-use facilities such as hospitals and nursing homes, there is a possibility that infectious agents containing biological substances, such as viruses, bacteria, and fungi, exist suspended in the air. If these agents enter the body through the respiratory system or come into contact with the body, they can cause disease; furthermore, some are highly contagious, potentially leading to widespread pathogen infections or fatalities within a short period.

[0003] To address these issues, effective indoor air quality management is required to eliminate airborne infectious agents. Furthermore, if an indoor space becomes contaminated, it is necessary to rapidly control the affected area and carry out disinfection. When conducting disinfection in the target space, using a disinfection ventilation system to remove airborne infectious agents is an effective method.

[0004] However, if it is scientifically verified which air quality data influences the concentration of airborne infectious agents, it can be effectively utilized to reduce such concentrations. Therefore, it is necessary to identify the factors in air quality data associated with infectious agent concentrations and to develop practical methods for eliminating infectious agents based on these identified factors.

[0005] The information described above disclosed in the background technology of this invention is intended only to enhance understanding of the background of the present invention and may therefore include information that does not constitute prior art.

[0006] The present disclosure provides a method and system for preemptively predicting the concentration of airborne infectious agents in a target space and performing disease control coordination based on the predicted data to solve the above-mentioned problems.

[0007] In addition, the present disclosure provides a method and system for determining a factor highly correlated with the concentration of airborne infectious sources based on an artificial neural network, and for performing disease control coordination to reduce or eliminate airborne infectious sources by monitoring the determined factor.

[0008] However, the technical problems that the present invention aims to solve are not limited to those described above, and other unmentioned problems can be clearly understood by those skilled in the art from the description of the invention below.

[0009] The present disclosure may be implemented in various ways, including a method, an apparatus (system), or a computer program stored on a readable storage medium.

[0010] According to one embodiment of the present disclosure, a method for operating a disinfection air conditioning system based on airborne infectious agent concentration, performed by at least one processor, comprises the steps of: collecting air quality data of a target space in which one or more disinfection air conditioning devices are installed; calculating prediction data associated with the airborne infectious agent concentration of the target space based on the air quality data; and controlling the operating conditions of the disinfection air conditioning devices based on the prediction data.

[0011] According to one embodiment of the present disclosure, air quality data includes carbon dioxide concentration.

[0012] According to one embodiment of the present disclosure, the step of calculating prediction data associated with the concentration of airborne infectious agents in a target space based on air quality data comprises: inputting pre-collected air quality data and airborne infectious agent concentration data of the target space over a predetermined period into an artificial neural network module; calculating a correlation between a plurality of factors of the air quality data and the concentration of airborne infectious agents by the artificial neural network module; identifying one or more factors that have the highest correlation with the concentration of airborne infectious agents based on the correlation by the artificial neural network module; and calculating prediction data associated with the concentration of airborne infectious agents in the target space based on the identified one or more factors by the artificial neural network module.

[0013] According to one embodiment of the present disclosure, pre-collected airborne infectious agent concentration data is obtained by a detection device installed separately from the disinfection air conditioning device.

[0014] According to one embodiment of the present disclosure, the concentration of one or more identified factors can be measured in a disinfection air conditioning device.

[0015] According to one embodiment of the present disclosure, the artificial neural network module includes a Long Short-Term Model (LSTM) module.

[0016] According to one embodiment of the present disclosure, the step of controlling the operating conditions of a disinfection air conditioning device based on prediction data includes the step of controlling the disinfection air conditioning device to a first mode when the concentration of airborne infectious agents based on the prediction data exceeds a preset threshold, and the first mode may be a mode in which the disinfection air conditioning device is operated to have a predetermined intake volume or more.

[0017] According to one embodiment of the present disclosure, the step of controlling the operating conditions of a disease control air conditioning device based on prediction data includes the step of sending a warning message to a manager device associated with the disease control air conditioning device when the concentration of airborne infectious agents based on the prediction data exceeds a preset threshold.

[0018] According to one embodiment of the present disclosure, the step of controlling the operating conditions of a disinfection air conditioning device based on prediction data includes the step of controlling the disinfection air conditioning device to a second mode when the concentration of airborne infectious agents based on the prediction data is less than a preset threshold, and the second mode may have a smaller intake volume of the disinfection air conditioning device compared to the first mode.

[0019] According to one embodiment of the present disclosure, the step of controlling the operating conditions of a disinfection air conditioning device based on prediction data involves, when a predetermined time has elapsed after the disinfection air conditioning device is controlled in a first mode, switching the first mode to a second mode in which the intake volume of the disinfection air conditioning device is lower than that of the first mode.

[0020] According to one embodiment of the present disclosure, the step of controlling the operating conditions of a disinfection air conditioning device based on prediction data further controls the operating conditions of the disinfection air conditioning device based on prediction data and spatial data of a target space.

[0021] According to one embodiment of the present disclosure, spatial data of a target space includes data on the average number of occupants per time period of the target space.

[0022] According to one embodiment of the present disclosure, the step of controlling the operating conditions of a disinfection air conditioning device based on prediction data includes the step of switching the disinfection air conditioning device to an energy-saving mode when the average number of occupants is less than a threshold value, based on data regarding the average number of occupants per time period.

[0023] According to one embodiment of the present disclosure, an artificial neural network module further performs the steps of: comparing predicted data calculated in association with the concentration of airborne infectious agents in a target space with the actual concentration of airborne infectious agents in a target space; and performing reinforcement learning in a direction that reduces the error in the result of comparing the predicted data and the actual concentration of airborne infectious agents.

[0024] According to one embodiment of the present disclosure, a disinfection air conditioning device comprises at least one disinfection air conditioning device installed in a target space and a control module for controlling at least one disinfection air conditioning device, wherein the control module comprises a memory and a processor, and the processor executes a computer program stored in the memory to collect air quality data of a target space in which at least one disinfection air conditioning device is installed, calculates prediction data associated with the concentration of airborne infectious agents in the target space based on the air quality data, and operates to control the operating conditions of the disinfection air conditioning device based on the prediction data.

[0025] According to some embodiments of the present disclosure, a method for performing disease control based on predicted data of the predicted concentration of airborne infectious agents in a target space is provided, thereby protecting the health of users utilizing the target space and effectively preventing infectious diseases.

[0026] According to some embodiments of the present disclosure, a method for performing disease control based on a predicted value of a factor highly correlated with a determined airborne infectious agent concentration is provided, thereby enabling efficient disease control operation.

[0027] However, the effects obtainable through the present invention are not limited to those described above, and other unmentioned technical effects will be clearly understood by those skilled in the art from the description of the invention below.

[0028] The following drawings attached to this specification illustrate preferred embodiments of the present invention and serve to further enhance understanding of the technical concept of the present invention together with the detailed description of the invention provided below; therefore, the present invention should not be interpreted as being limited only to the matters described in such drawings.

[0029] FIG. 1 is a schematic diagram illustrating a method for operating a disease control air conditioning system according to one embodiment of the present disclosure.

[0030] FIG. 2 is a schematic diagram showing a configuration in which an information processing system is connected to communicate with a plurality of user terminals to perform disease control coordination according to the concentration of airborne infectious agents in accordance with one embodiment of the present disclosure.

[0031] FIG. 3 is a block diagram showing the internal configuration of a user terminal and an information processing system according to one embodiment of the present disclosure.

[0032] FIG. 4 is a diagram showing the location of an air quality measurement sensor in a target space according to one embodiment of the present disclosure.

[0033] FIG. 5 is a diagram illustrating a method for operating a disease control air conditioning system based on an artificial neural network concentration according to an embodiment of the present disclosure.

[0034] FIG. 6 is a drawing showing the operating mode of a disease prevention air conditioning device according to one embodiment of the present disclosure.

[0035] FIG. 7 is a diagram illustrating a reinforcement learning process for a prediction model using actual measurement data according to one embodiment of the present disclosure.

[0036] FIG. 8 is a diagram illustrating a prediction learning process for carbon dioxide concentration according to one embodiment of the present disclosure.

[0037] FIG. 9 is a graph showing a carbon dioxide concentration predicted using a measured carbon dioxide concentration according to one embodiment of the present disclosure.

[0038] FIG. 10 is a diagram showing the correlation between carbon dioxide concentration and the number of occupants according to one embodiment of the present disclosure.

[0039] FIG. 11 is a diagram showing the correlation between carbon dioxide concentration and airborne infectious agent concentration according to one embodiment of the present disclosure.

[0040] FIG. 12 is a diagram showing the correlation between the concentration of airborne infectious agents and various factors according to one embodiment of the present disclosure.

[0041] FIG. 13 is a flowchart illustrating a method for operating a disease control air conditioning system according to the concentration of airborne infectious agents according to one embodiment of the present disclosure.

[0042] FIG. 14 is a flowchart illustrating a method for calculating prediction data associated with airborne infectious agent concentration according to one embodiment of the present disclosure.

[0043] Embodiments of the present disclosure will be described with reference to the accompanying drawings described below, wherein similar reference numerals indicate similar elements, but are not limited thereto.

[0044] Hereinafter, specific details for implementing the present disclosure will be described in detail with reference to the attached drawings. However, in the following description, specific descriptions regarding widely known functions or configurations will be omitted if there is a risk that the gist of the present disclosure may be unnecessarily obscured.

[0045] In the attached drawings, identical or corresponding components are assigned the same reference numerals. Additionally, in the description of the following embodiments, the description of identical or corresponding components may be omitted. However, even if a description of a component is omitted, it is not intended that such component is not included in any embodiment.

[0046] The advantages and features of the disclosed embodiments and the methods for achieving them will become clear by referring to the embodiments described below in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below but may be implemented in various different forms, and the embodiments provided are merely to make the present disclosure complete and to fully inform those skilled in the art of the scope of the invention.

[0047] The terms used in this specification will be briefly explained, and the disclosed embodiments will be described in detail. The terms used in this specification have been selected to be as generally used as possible, taking into account their functions in this disclosure; however, these terms may vary depending on the intent of those skilled in the art, case law, the emergence of new technologies, etc. Additionally, in specific cases, terms may be arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the relevant description of the invention. Therefore, the terms used in this disclosure should be defined not merely by their names, but based on their meanings and the content throughout this disclosure.

[0048] In this specification, singular expressions include plural expressions unless the context clearly specifies them as singular. Additionally, plural expressions include singular expressions unless the context clearly specifies them as plural. Throughout the specification, when a part is described as including a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0049] Additionally, the terms 'module' or 'part' as used in the specification refer to software or hardware components, and the 'module' or 'part' performs certain roles. However, the meaning of 'module' or 'part' is not limited to software or hardware. The 'module' or 'part' may be configured to reside in an addressable storage medium or configured to run on one or more processors. Thus, as an example, the 'module' or 'part' may include components such as software components, object-oriented software components, class components, and task components, and at least one of processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, or variables. The components and the functions provided within the 'module' or 'part' may be combined into a smaller number of components and 'modules' or 'parts', or further separated into additional components and 'modules' or 'parts'.

[0050] According to one embodiment of the present disclosure, a ‘module’ or ‘part’ may be implemented as a processor and memory. The term ‘processor’ should be broadly interpreted to include a general-purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, etc. In some environments, the term ‘processor’ may refer to an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc. The term ‘processor’ may also refer to a combination of processing devices, such as, for example, a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors combined with a DSP core, or any other combination of such configurations. Additionally, the term ‘memory’ should be broadly interpreted to include any electronic component capable of storing electronic information. 'Memory' may refer to various types of processor-readable media, such as Random Access Memory (RAM), Read-Only Memory (ROM), Non-Volatile Random Access Memory (NVRAM), Programmable Read-Only Memory (PROM), Erasable-Programmable Read-Only Memory (EPROM), Electrically Erasable PROM (EEPROM), Flash Memory, Magnetic or Optical Data Storage Devices, and Registers. If a processor can read information from memory and write information to memory, the memory is said to be in an electronic communication state with the processor. Memory integrated into the processor is in an electronic communication state with the processor.

[0051] In the present disclosure, the 'system' may include at least one of a server device and a cloud device, but is not limited thereto. For example, the system may be composed of one or more server devices. As another example, the system may be composed of one or more cloud devices. As yet another example, the system may be configured and operated with both a server device and a cloud device.

[0052] In the present disclosure, 'display' may refer to any display device associated with a computing device, for example, any display device capable of displaying any information / data controlled by or provided by the computing device.

[0053] In the present disclosure, 'each of a plurality of A' or 'each of a plurality of A' may refer to each of all components included in a plurality of A, or each of some components included in a plurality of A.

[0054] FIG. 1 is a schematic diagram for explaining a method of operating a disease control air conditioning system (1) according to one embodiment of the present disclosure.

[0055] Referring to FIG. 1, the disease control air conditioning system (1) can measure or predict the level of airborne infectious agents, such as bioaerosols, in target spaces such as hospitals, nursing hospitals, nursing homes, schools, public transportation, and airports. The disease control air conditioning system (1) can perform the necessary processes for disease control air conditioning operations based on the measured or predicted values. Here, bioaerosols include fine particles containing biological materials such as microorganisms, cells, and organic matter floating in the air, and can comprehensively include pathogens such as viruses, bacteria, fungi, protozoa, and prions.

[0056] The disinfection air conditioning system (1) can collect air quality data (10) of a target space. The air quality data (10) may include a plurality of factors. The plurality of factors may include at least one of temperature, humidity, carbon dioxide concentration, carbon monoxide concentration, fine dust (PM10), ultrafine dust (PM2.5), TVOC (total volatile organic compounds), or the number of occupants of the target space, but the present disclosure is not limited thereto.

[0057] Additionally, the quarantine air conditioning system (1) can collect airborne infectious source concentrations (30), and the airborne infectious source concentrations (30) may include concentration data associated with airborne infectious sources that can be collected in the target space. For example, the airborne infectious source concentrations (30) may be obtained by installing a detection device (e.g., a detection device including a reagent for detecting infectious sources) in the target space for a predetermined period of time and analyzing the detection results. However, the method of obtaining the airborne infectious source concentrations (30) is not limited.

[0058] The disinfection air conditioning system (1) may include at least one disinfection air conditioning device installed in the target space and a control module for controlling the disinfection air conditioning device. The capacity (performance) of the disinfection air conditioning device may be determined by how many cubic meters (CMM, cubic meter per minute) of air it can process. The larger the capacity of the disinfection air conditioning device, the more air can be circulated quickly, so the efficiency of air purification and ventilation required for disinfection can be increased, but the cost may be relatively higher.

[0059] The disease control air conditioning system (1) can collect air quality data (10) of a target space in which one or more disease control air conditioning devices are installed, and can collect airborne infectious agent concentrations (30).

[0060] The disease control air conditioning system (1) can calculate the correlation between each factor of the collected air quality data and the concentration of airborne infectious agents (20). The disease control air conditioning system (1) can calculate the correlation between each factor of the collected air quality data and the concentration of airborne infectious agents using an artificial neural network module, but the present disclosure is not limited thereto.

[0061] The disease control air conditioning system (1) can identify one or more factors that have the highest correlation with the concentration of airborne infectious agents (30) (40). Based on the identified one or more factors, the disease control air conditioning system (1) can produce prediction data associated with the concentration of airborne infectious agents in the target space (50).

[0062] In one embodiment, when the quarantine air conditioning system (1) identifies carbon dioxide concentration as the factor with the highest correlation to the concentration of airborne infectious agents, it can calculate prediction data related to the concentration of airborne infectious agents in the target space based on the concentration of carbon dioxide. However, this is merely an example, and other factors other than the concentration of carbon dioxide may be used.

[0063] The disease control air conditioning system (1) can control the operating conditions of the disease control air conditioning device based on calculated prediction data (60). For example, the disease control air conditioning system (1) can predict the concentration of airborne infectious agents in the target space according to the carbon dioxide concentration, and control the operating conditions of the disease control air conditioning device based on the predicted concentration of airborne infectious agents.

[0064] In this way, the disinfection air conditioning system (1) can efficiently operate the disinfection air conditioning device by controlling the operating conditions of the disinfection air conditioning device using only the measurement value of a simple sensor (e.g., an indoor air quality sensor).

[0065] Furthermore, the disinfection air conditioning system (1) can predict the measurement value of the sensor without installing a separate sensor and use the predicted value as the sensing value of the target space, and accordingly, the cost of installing the sensor can be reduced.

[0066] In one embodiment, the disease control air conditioning system (1) may perform a process associated with at least one of data analysis, learning, or prediction for performing various predictions. The disease control air conditioning system (1) may perform a disease control process by utilizing the analysis, learning, or prediction process. The disease control process may include processes such as controlling a disease control air conditioning device in a target space, providing an alarm requiring disease control, or providing disease prediction information, but the present disclosure is not limited thereto. The specific operation and implementation method of the disease control air conditioning system (1) will be described in detail below.

[0067] FIG. 2 is a schematic diagram showing a configuration in which an information processing system (230) is connected to communicate with a plurality of user terminals to perform disease control coordination according to the concentration of airborne infectious agents according to one embodiment of the present disclosure.

[0068] Referring to FIG. 2, a plurality of user terminals (210_1, 210_2, 210_3) can be connected to an information processing system (230) (e.g., an integrated quarantine system) that performs quarantine coordination management according to the predicted concentration of airborne infectious agents through a network (220).

[0069] In one embodiment, the information processing system (230) may include a computer-executable program (e.g., a downloadable application) for determining a factor of air quality data that is highly correlated with the concentration of airborne infectious agents and calculating prediction data associated with the concentration of airborne infectious agents based on the determined factor, and one or more server devices and / or databases capable of storing, providing, and executing data, or one or more distributed computing devices and / or distributed databases based on cloud computing services.

[0070] Information related to disease prevention provided by the information processing system (230) may be provided to the user through an image generation application web browser or web browser extension program installed on each of the multiple user terminals (210_1, 210_2, 210_3). For example, the information processing system (230) may provide information corresponding to a service related to disease prevention received from the user terminals (210_1, 210_2, 210_3) through the application, etc., or perform corresponding processing.

[0071] Multiple user terminals (210_1, 210_2, 210_3) can communicate with an information processing system (230) through a network (220). The network (220) can be configured to enable communication between the multiple user terminals (210_1, 210_2, 210_3) and the information processing system (230). Depending on the installation environment, the network (220) may be configured as a wired network such as Ethernet, Power Line Communication, telephone line communication device and RS-serial communication, a mobile communication network, a Wireless LAN (WLAN), Wi-Fi, Bluetooth and ZigBee, or a combination thereof. The communication method is not limited and may include not only communication methods utilizing communication networks that the network (220) may include (e.g., mobile communication network, wired internet, wireless internet, broadcasting network, satellite network, etc.) but also short-range wireless communication between user terminals (210_1, 210_2, 210_3).

[0072] In FIG. 2, a mobile phone terminal (210_1), a tablet terminal (210_2), and a PC terminal (210_3) are illustrated as examples of user terminals, but are not limited thereto. The user terminals (210_1, 210_2, 210_3) may be any computing device capable of wired and / or wireless communication and capable of installing and running GUI service applications or web browsers, or GUI service applications or web browsers, etc. For example, the user terminal may include a terminal possessed by an administrator, user, etc. located in the target space, an AI speaker, a smartphone, a mobile phone, a navigation device, a computer, a laptop, a digital broadcasting terminal, a PDA (Personal Digital Assistants), a PMP (Portable Multimedia Player), a tablet PC, a game console, a wearable device, an IoT (Internet of Things) device, a VR (Virtual Reality) device, an AR (Augmented Reality) device, a set-top box, etc. Additionally, FIG. 2 illustrates three user terminals (210_1, 210_2, 210_3) communicating with an information processing system (230) through a network (220), but is not limited thereto, and may be configured so that a different number of user terminals communicate with an information processing system (230) through a network (220).

[0073] In FIG. 2, a configuration in which user terminals (210_1, 210_2, 210_3) receive information related to disease control by communicating with an information processing system (230) is illustrated as an example, but is not limited thereto. For example, user terminals (210_1, 210_2, 210_3) may not communicate with the information processing system (230), but may directly collect information related to disease control, perform an analysis or prediction process based on the collected information, and then use this to perform a process related to disease control.

[0074] FIG. 3 is a block diagram showing the internal configuration of a user terminal (210) and an information processing system (230) according to one embodiment of the present disclosure.

[0075] Referring to FIG. 3, the user terminal (210) may refer to any computing device capable of running applications, web browsers, etc., and capable of wired / wireless communication, and may include, for example, the mobile phone terminal (210_1), tablet terminal (210_2), PC terminal (210_3) of FIG. 2. As illustrated, the user terminal (210) may include memory (312), a processor (314), a communication module (316), and an input / output interface (318). Similarly, the information processing system (230) may include memory (332), a processor (334), a communication module (336), and an input / output interface (338). As illustrated in FIG. 3, the user terminal (210) and the information processing system (230) may be configured to communicate information and / or data through a network (220) using their respective communication modules (316, 336). Additionally, the input / output device (320) may be configured to input information and / or data to the user terminal (210) or output information and / or data generated from the user terminal (210) through the input / output interface (318).

[0076] The memory (312, 332) may include any non-transient computer-readable recording medium. According to one embodiment, the memory (312, 332) may include a permanent mass storage device such as ROM (read-only memory), a disk drive, a solid-state drive (SSD), or flash memory. As another example, a permanent mass storage device such as ROM, an SSD, flash memory, or a disk drive may be included in the user terminal (210) or information processing system (230) as a separate permanent storage device distinct from the memory. Additionally, an operating system and at least one program code may be stored in the memory (312, 332).

[0077] These software components may be loaded from a computer-readable recording medium separate from memory (312, 332). This separate computer-readable recording medium may include a recording medium that can be directly connected to the user terminal (210) and the information processing system (230), for example, a computer-readable recording medium such as a floppy drive, disk, tape, DVD / CD-ROM drive, or memory card. As another example, the software components may be loaded into memory (312, 332) via a communication module (316, 336) rather than a computer-readable recording medium. For example, at least one program may be loaded into memory (312, 332) based on a computer program installed by files provided through a network (220) by developers or a file distribution system that distributes installation files for the application.

[0078] The processor (314, 334) may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input / output operations. Instructions may be provided to the processor (314, 334) by memory (312, 332) or a communication module (316, 336). For example, the processor (314, 334) may be configured to execute instructions received according to program code stored in a recording device such as memory (312, 332).

[0079] The communication module (316, 336) may provide a configuration or function for the user terminal (210) and the information processing system (230) to communicate with each other via the network (220), and may provide a configuration or function for the user terminal (210) and / or the information processing system (230) to communicate with another user terminal or another system (e.g., a separate cloud system). For example, a request or data generated by the processor (314) of the user terminal (210) according to program code stored in a recording device such as memory (312) may be transmitted to the information processing system (230) via the network (220) under the control of the communication module (316). Conversely, a control signal or command provided under the control of the processor (334) of the information processing system (230) may be received by the user terminal (210) through the communication module (316) of the user terminal (210) via the communication module (336) and the network (220).

[0080] The input / output interface (318) may be a means for interfacing with an input / output device (320). As an example, the input device may include a device such as a camera including an audio sensor and / or an image sensor, a keyboard, a microphone, or a mouse, and the output device may include a device such as a display, a speaker, or a haptic feedback device. As another example, the input / output interface (318) may be a means for interfacing with a device in which the configuration or function for performing input and output is integrated into one, such as a touchscreen. For example, when the processor (314) of the user terminal (210) processes instructions of a computer program loaded in memory (312), a service screen configured using information and / or data provided by an information processing system (230) or another user terminal may be displayed on a display through the input / output interface (318). In FIG. 3, the input / output device (320) is depicted as not being included in the user terminal (210), but is not limited thereto and may be configured as a single device with the user terminal (210). Additionally, the input / output interface (338) of the information processing system (230) may be a means for interfacing with a device for input or output (not shown, such as a display module or device) that is connected to the information processing system (230) or that the information processing system (230) may include. In FIG. 3, the input / output interface (318, 338) is shown as an element configured separately from the processor (314, 334), but is not limited thereto, and the input / output interface (318, 338) may be configured to be included in the processor (314, 334).

[0081] The user terminal (210) and the information processing system (230) may include more components than those of FIG. 3. However, it is not necessary to clearly illustrate most of the prior art components. In one embodiment, the user terminal (210) may be implemented to include at least some of the input / output devices (320) described above. Additionally, the user terminal (210) may further include other components such as a transceiver, a GPS (Global Positioning System) module, a camera, various sensors, a database, etc.

[0082] While a program for training an artificial neural network model, an image generation application, etc. is running, the processor (314) can receive text, images, video, voice and / or actions, etc. that are input or selected through an input device such as a touch screen, keyboard, audio sensor and / or image sensor, camera, microphone, etc. connected to an input / output interface (318), and can store the received text, images, video, voice and / or actions, etc. in memory (312) or provide them to an information processing system (230) through a communication module (316) and a network (220).

[0083] The processor (314) of the user terminal (210) may be configured to manage, process, and / or store information and / or data received from an input / output device (320), another user terminal, an information processing system (230), and / or multiple external systems. The information and / or data processed by the processor (314) may be provided to the information processing system (230) through a communication module (316) and a network (220). The processor (314) of the user terminal (210) may transmit information and / or data to the input / output device (320) through an input / output interface (318) to output it. For example, the processor (314) may output or display the received information and / or data on the screen of the user terminal (210).

[0084] The processor (334) of the information processing system (230) may be configured to manage, process, and / or store information and / or data received from a plurality of user terminals (210) and / or a plurality of external systems. Information and / or data processed by the processor (334) may be provided to the user terminal (210) through a communication module (336) and a network (220). Additionally, if the information processing system (230) includes an input / output device (not shown), information, data, images, etc., may be output through the input / output device. Various GUI-based content related to epidemic prevention air conditioning shown below may be displayed on the input / output device through the input / output interface (318, 338) of the user terminal (210) or the information processing system (230). Processing described in various embodiments in the drawings below may be performed by the processor (314, 334).

[0085] FIG. 4 is a drawing showing the location of an air quality measurement sensor in a target space (400) according to one embodiment of the present disclosure.

[0086] A plurality of air quality measuring sensors (412, 414, 416, 418, 420) may be placed in the target space (400). The air quality measuring sensors may be indoor air quality (IAQ) sensors and may measure values ​​(concentration, amount) associated with at least one of temperature, humidity (RH), carbon monoxide, carbon dioxide, TVOC, or fine dust with a diameter of 10 μm or less.

[0087] In one embodiment, the processor may place an air quality measurement sensor based on at least one of the concentration of airborne infectious agents collected over a predetermined period, the concentration of air quality data highly correlated with the concentration of airborne infectious agents, or spatial information of the target space. The spatial information of the target space may include information associated with at least one of the following: area information of the target space, space usage information, information on the number of people that can be accommodated, information on areas where multiple people can be placed (chair placement area, reception area, etc.), information on the placement of ventilation openings, or location information where a disinfection air conditioning device is placed.

[0088] In one embodiment, the processor may not install an air quality measurement sensor and may predict the value of the air quality measurement sensor and use the predicted value. To this end, the processor may predict the sensing value of the IAQ sensor based on the measurement values ​​of the IAQ sensor of the target space collected over a predetermined period. A neural network-based algorithm may be used for the prediction.

[0089] FIG. 5 is a diagram illustrating a method for operating a disease control air conditioning system based on an artificial neural network concentration according to an embodiment of the present disclosure.

[0090] Referring to FIG. 5, the processor can collect air quality data and airborne infectious agent concentration data for a predetermined period of time that are pre-collected in a target space where one or more disinfection air conditioning devices are installed.

[0091] In this case, pre-collected airborne infectious agent concentration data can be obtained by a detection device installed separately from the disinfection air conditioning system. Accordingly, since there is no need to install the detection device directly within the disinfection air conditioning system and it is sufficient to collect airborne infectious agent concentrations through a separate detection device only for a predetermined period (such as weeks or months), this can be effective for cost savings. Once the airborne infectious agent concentration data is collected, the concentration can subsequently be predicted based on carbon dioxide levels. In other words, the detection device may be installed continuously in the target space or temporarily for a predetermined period.

[0092] The processor may perform data preprocessing (510) before inputting the collected data into the artificial neural network module. Here, the data preprocessing (510) may include data normalization and standardization, and since the collected air quality data and airborne infectious agent concentrations change over time, the data may be time-sequenced by dividing it into a sliding window method within a predetermined time range.

[0093] The processor can input time-series air quality data and data associated with airborne infectious agent concentrations into an artificial neural network module. Here, the artificial neural network module may include a Long Short-Term Model (LSTM) module suitable for processing time-series data.

[0094] The processor can create and train an LSTM-based artificial neural network module (520). The processor can add multiple LSTM layers to the input layer to learn the pattern of time-series data, and can configure a dense layer to predict the concentration of airborne infectious agents and an output layer to output the concentration of airborne infectious agents. Input gates, forget gates, and output gates are placed in the LSTM layers so that time-series data can be processed effectively. In addition, the processor can train an LSTM-based artificial neural network module by predicting the concentration of airborne infectious agents based on training and test data and comparing the actual value with the predicted value.

[0095] The processor can evaluate the contribution of parameters to the air quality data (530). Here, parameters are elements included in the air quality data and may be factors that influence the concentration of airborne infectious agents. Based on a trained neural network module, the processor can analyze how one or more parameters of the air quality data influence the concentration of airborne infectious agents. The processor can perform an importance analysis for each parameter or further identify the correlations between parameters through cross-correlation analysis.

[0096] In one embodiment, the processor may evaluate the importance of each factor (e.g., carbon dioxide concentration, number of occupants, temperature, humidity, TVOC, fine dust, carbon monoxide concentration, etc.) using a permutation importance technique or a SHAP (SHapley Additive exPlanations) technique. Additionally, the processor may perform cross-correlation analysis by analyzing the correlation between two time series data based on time lag.

[0097] The processor can identify one or more factors (e.g., key factors) that have the highest correlation with the concentration of airborne infectious agents based on correlations, by means of an artificial neural network module. The concentrations of the identified one or more factors can be measured in the disinfection air conditioning unit. In one embodiment, the artificial neural network module can identify the factor that has the highest correlation with the concentration of airborne infectious agents as carbon dioxide concentration.

[0098] The processor can produce predicted data associated with the concentration of airborne infectious agents in the target space based on key factors (540).

[0099] Ultimately, the processor can control the operating conditions of the disinfection air conditioning system based on predicted data (550). Accordingly, even if the concentration of airborne infectious agents in the target space is not directly measured, it can be predicted based on the numerical values ​​of key factors (e.g., carbon dioxide concentration or number of occupants). Additionally, the operating conditions of the disinfection air conditioning system can be controlled based on this. The operating conditions may include conditions related to the degree of suction volume, wind speed, use of plasma-related functions, ultraviolet irradiation functions, etc., but the present disclosure is not limited thereto.

[0100] FIG. 6 is a drawing showing an operating mode (1400) of a disinfection air conditioning device according to one embodiment of the present disclosure.

[0101] Referring to FIG. 6, the operating mode (1400) may include at least one of a strong wind mode (first mode, 1410), a normal mode (second mode, 1420), or a saving mode (third mode, 1430). The first mode (1410) to the third mode (1430) may be determined according to the intake volume of the disinfection air conditioning device. However, although not illustrated, the operating mode may include a mode for removing specific airborne infectious sources.

[0102] The processor can control the disinfection air conditioning device to a first mode (1410) when the concentration of airborne infectious agents based on the predicted data exceeds a preset threshold. The first mode (1410) may be a mode in which the disinfection air conditioning device is operated to have a predetermined intake volume or more, and may be a mode in which more than 80% of the power is used depending on the capacity of the disinfection air conditioning device.

[0103] The processor can send a warning message to a manager device associated with the quarantine air conditioning system if the concentration of airborne infectious agents based on predicted data exceeds a preset threshold. For example, the warning message can be output through a speaker placed in the target space or displayed on the manager device.

[0104] The processor can control the disinfection air conditioning device to a second mode when the concentration of airborne infectious agents based on the predicted data is below a preset threshold. The second mode (1420) is a mode in which the intake volume of the disinfection air conditioning device is lower than that of the first mode (1410), and may be a mode that uses 50% to 80% of the power depending on the capacity of the disinfection air conditioning device.

[0105] After controlling the disinfection air conditioning device in the first mode (1410), if a predetermined time has elapsed, the processor may switch to controlling it in the second mode (1420), in which the intake volume of the disinfection air conditioning device is lower than that of the first mode (1410). The processor may determine a predetermined time for switching modes based on air conditioning efficiency information for the target space. In this way, by limiting the time during which the disinfection air conditioning device operates in the first mode (1410) to a predetermined time, disinfection air conditioning can be performed while ensuring the energy efficiency of the disinfection air conditioning device.

[0106] The processor can control the operating conditions of the disinfection air conditioning device based further on predictive data associated with the concentration of airborne infectious agents in the target space and spatial data of the target space. Here, the spatial data of the target space may include data on the average number of occupants per time period in the target space.

[0107] Based on data regarding the average number of occupants per time period, the processor can control the disinfection air conditioning unit by switching it to an energy-saving mode when the average number of occupants is below a threshold.

[0108] In one embodiment, when a plurality of disinfection air conditioning units are placed in a target space, the processor can manage the area where disinfection air conditioning by the plurality of disinfection air conditioning units is effective by mapping it to the disinfection air conditioning units. The processor can map a large-capacity disinfection air conditioning unit to an area where occupants are densely concentrated. When operating the first to third modes, the processor can distinguish the area where the disinfection air conditioning unit is mapped and perform disinfection air conditioning.

[0109] In one embodiment, the processor can set the operating time of the first to third modes. For example, the processor can set the operating time of the first mode to a first time, the operating time of the second mode to a second time, and the operating time of the third mode to a third time. The processor can adjust the first to third times according to the predicted concentration of airborne infectious agents.

[0110] FIG. 7 is a diagram illustrating a reinforcement learning process for a prediction model using actual measurement data according to one embodiment of the present disclosure.

[0111] Referring to Fig. 7, the prediction model can be implemented based on an artificial neural network and can perform a reinforcement learning process. Reinforcement learning (RL) is a domain of machine learning (ML) and corresponds to a learning method in which an agent defined within a given environment recognizes its current state and selects an action or sequence of actions that maximizes the reward among selectable actions.

[0112] An agent refers to a subject that observes the state and selects an action, and the environment is the entity that changes its state and provides rewards through interaction when the agent takes action; the state refers to information representing the current situation of the environment, the action refers to what the agent does in the current state, and the reward corresponds to information expressing the merits or demerits of the action. Here, the reward must be well matched with the single or multiple objectives to be improved, and the state must accurately represent the environment.

[0113] In the reinforcement learning process, the prediction model becomes the agent, the data provider becomes the environment, the action of the prediction model comparing predicted data and actual data related to airborne infectious agent concentrations becomes the action, and the data collected by the data provider becomes the state (S t It can be recognized as ). In this case, the concentration prediction model, which is the agent, uses the data collected by the data provider as a state (State, S t Recognized as ) selectable action, that is, the comparison operation of the concentration prediction model is rewarded (R t Reinforcement learning can be performed to generate such that ) can be maximized.

[0114] More specifically, the prediction model, acting as the agent, is the entity that observes the implemented state (i.e., collected data gathered by the data provider from resources) and selects an action (i.e., compares predicted data with actual data). The reward system can be designed to provide positive feedback regarding the data provider's collected data as a result of the action. For instance, a relatively large reward is given if the difference between the predicted data and the actual measured data is within a first setpoint; a small reward is given if the difference exceeds the first setpoint but is within a second setpoint; and no reward is given if the difference exceeds the second setpoint. Furthermore, reinforcement learning can be performed on the agent as the agent repeats the process of comparing actions—specifically, the concentration prediction model—in a direction that maximizes the reward. Consequently, the concentration prediction model, acting as the agent, becomes capable of continuously performing optimized data classification.

[0115] In one embodiment, the artificial neural network module of the prediction model may be designed not only to undergo a pre-training process based on sample data, but also to continuously perform the operation of comparing the prediction data and the actual measurement data through such reinforcement learning even after pre-training.

[0116] In summary, the processor acquires actual measurement data regarding the concentration of airborne infectious agents in the target space, and after an agent generates prediction data regarding the concentration of airborne infectious agents in the target space, it performs reinforcement learning by comparing the prediction data with the acquired actual measurement data to minimize the error, and can update the calculated prediction data using the reinforcement-learned prediction data to improve the accuracy of the concentration prediction model.

[0117] Meanwhile, the processor can also predict the concentration of airborne infectious agents using predicted carbon dioxide data without directly measuring the carbon dioxide concentration.

[0118] FIG. 8 is a diagram showing a prediction learning process (900) for carbon dioxide concentration according to one embodiment of the present disclosure, and FIG. 9 is a diagram showing a graph (800) for predicting carbon dioxide concentration using a measured carbon dioxide concentration according to one embodiment of the present disclosure.

[0119] Referring to FIG. 8, the processor can predict the carbon dioxide concentration using a carbon dioxide concentration prediction model (e.g., a deep learning-based concentration prediction model). The concentration prediction model can be generated based on an artificial neural network and can collect carbon dioxide concentrations (indicated by dotted lines) for a predetermined period (e.g., from November 14 to December 5, about one month).

[0120] The concentration prediction model can predict the carbon dioxide concentration (indicated by the bold solid line) based on the carbon dioxide concentration (indicated by the solid line) measured during the training period (e.g., after December 12).

[0121] Referring to Fig. 9, the measured carbon dioxide concentration can be measured in units of PPM (parts per million). It can be confirmed that there is a linear correspondence between the measured carbon dioxide concentration and the predicted carbon dioxide concentration.

[0122] The processor can predict data related to the concentration of airborne infectious agents that are correlated with the concentration of carbon dioxide by utilizing predicted data on the concentration of carbon dioxide, without directly installing sensors.

[0123] FIG. 10 is a diagram showing the correlation between carbon dioxide concentration and the number of occupants according to one embodiment of the present disclosure, and FIG. 11 is a diagram showing the correlation between carbon dioxide concentration and the concentration of airborne infectious agents according to one embodiment of the present disclosure.

[0124] Referring to FIG. 10, the graph (1000) shows that the number of occupants increases as the carbon dioxide concentration increases, and it can be confirmed that there is a linear correlation.

[0125] In one embodiment, the processor can control the operating conditions of the disinfection air conditioning system based on the number of occupants without using the measured or predicted carbon dioxide concentration.

[0126] Referring to Fig. 11, the graph (1100) shows that there is a correlation between the concentration of carbon dioxide and the concentration of airborne infectious agents.

[0127] In one embodiment, the processor can confirm that there is a correlation between the concentration of airborne infectious agents and the concentration of carbon dioxide, and a correlation between the concentration of carbon dioxide and the number of occupants. Accordingly, the processor can predict the concentration of airborne infectious agents based on the number of occupants.

[0128] In one embodiment, the processor can measure the number of occupants for a predetermined period and then predict the number of occupants after the measurement period. The processor can predict the concentration of airborne infectious agents based on the predicted number of occupants and control the operating conditions of the disinfection air conditioning system based on the predicted concentration of airborne infectious agents.

[0129] FIG. 12 is a diagram (1200) showing the correlation between the concentration of an airborne infectious agent (AB) and various factors according to one embodiment of the present disclosure.

[0130] Referring to FIG. 12, it can be seen that the concentration of specific airborne bacteria (AB) has a high correlation with the carbon dioxide concentration (1210) and the number of occupants (1220). Specifically, it can be seen that the carbon dioxide concentration has a correlation of 0.87 with the number of occupants. This can be analyzed as being due to the fact that the amount of carbon dioxide emitted increases as the number of occupants increases.

[0131] FIG. 13 is a flowchart (1500) for explaining a method of operating a quarantine air conditioning system according to the concentration of airborne infectious agents according to one embodiment of the present disclosure.

[0132] Referring to FIG. 13, in step S1510, the processor may collect air quality data of a target space in which one or more disinfection air conditioning devices are installed. The air quality data may include carbon dioxide concentration.

[0133] In step S1520, the processor can calculate prediction data associated with the concentration of airborne infectious agents in the target space based on air quality data.

[0134] In step S1530, the processor can control the operating conditions of the epidemic prevention air conditioning device based on predicted data.

[0135] FIG. 14 is a flowchart (1600) illustrating a method for calculating prediction data associated with airborne infectious agent concentration according to one embodiment of the present disclosure (corresponding to step S1520 of FIG. 13).

[0136] Referring to FIG. 14, in step S1610, the processor can input air quality data and airborne infectious agent concentration data of the target space collected in advance for a predetermined period into an artificial neural network module.

[0137] Here, pre-collected airborne infectious agent concentration data can be obtained by a detection device installed separately from the quarantine air conditioning system.

[0138] In step S1620, the processor can calculate the correlation between multiple factors of air quality data and the concentration of airborne infectious agents through an artificial neural network module.

[0139] Subsequently, the processor, through an artificial neural network module, can identify one or more factors that have the highest correlation with the concentration of airborne infectious agents based on correlations. The concentrations of the identified one or more factors can be measured by the disease control air conditioning unit.

[0140] In step S1630, the processor can generate prediction data associated with the concentration of airborne infectious agents in the target space based on one or more factors identified by the artificial neural network module.

[0141] The sequence diagrams of FIGS. 13 and 14 and the description above are merely examples and the scope of the present disclosure is not limited thereto. For example, at least one step may be added, changed, or deleted, or the order of each step may be changed.

[0142] The method described above may be provided as a computer program stored on a computer-readable recording medium for execution on a computer. The medium may continuously store a program executable by a computer, or temporarily store it for execution or download. Additionally, the medium may be various recording or storage means in the form of a single or multiple hardware components combined, and may not be limited to a medium directly connected to a computer system but may exist distributed over a network. Examples of media may include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and media configured to store program instructions, including ROM, RAM, and flash memory. Furthermore, other examples of media may include recording or storage media managed by app stores that distribute applications or sites and servers that supply or distribute various other software.

[0143] The methods, operations, or techniques of the present disclosure may be implemented by various means. For example, these techniques may be implemented in hardware, firmware, software, or a combination thereof. Those skilled in the art will understand that the various exemplary logical blocks, modules, circuits, and algorithmic steps described in connection with the disclosure herein may be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate such interchangeability between hardware and software, various exemplary components, blocks, modules, circuits, and steps have been generally described above in terms of their functional aspects. Whether such functions are implemented in hardware or in software depends on the design requirements imposed on the specific application and the overall system. Those skilled in the art may implement the functions described in various ways for each specific application, but such implementations should not be construed as departing from the scope of the present disclosure.

[0144] In a hardware implementation, the processing units used to perform the techniques may be implemented in one or more ASICs, DSPs, GPUs, digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic units designed to perform the functions described in this disclosure, computers, or a combination thereof.

[0145] Accordingly, the various exemplary logic blocks, modules, and circuits described in connection with the present disclosure may be implemented or performed by any combination of general-purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or those designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, for example, a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors coupled with a DSP core, or any other combination of configurations.

[0146] In firmware and / or software implementations, techniques may be implemented as instructions stored on a computer-readable medium such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, compact disc (CD), magnetic or optical data storage devices, etc. The instructions may be executable by one or more processors, and the processor(s) may be enabled to perform specific aspects of the functions described in this disclosure.

[0147] When implemented in software, techniques may be stored on a computer-readable medium as one or more instructions or code, or transmitted through a computer-readable medium. Computer-readable media include both computer storage media and communication media, including any medium that facilitates the transmission of a computer program from one place to another. Storage media may be any available medium that can be accessed by a computer. As a non-limiting example, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to transfer or store desired program code in the form of instructions or data structures and can be accessed by a computer. Additionally, any connection is appropriately made to the computer-readable medium.

[0148] For example, if software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair cable, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, coaxial cable, fiber optic cable, twisted pair cable, digital subscriber line, or wireless technologies such as infrared, radio, and microwave are included within the definition of a medium. As used herein, disk and disc include CD, laser disc, optical disc, DVD (digital versatile disc), floppy disk, and Blu-ray disc, wherein disks usually play data magnetically, whereas discs play data optically using a laser. The above combinations should also be included within the scope of computer-readable media.

[0149] The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other known form of storage medium. An exemplary storage medium may be connected to a processor so that the processor can read information from the storage medium or write information to the storage medium. Alternatively, the storage medium may be integrated into the processor. The processor and the storage medium may exist within an ASIC. The ASIC may exist within a user terminal. Alternatively, the processor and the storage medium may exist as separate components within the user terminal.

[0150] Although the embodiments described above have been described as utilizing aspects of the subject matter disclosed herein in one or more standalone computer systems, the present disclosure is not limited thereto and may be implemented in conjunction with any computing environment, such as a network or a distributed computing environment. Furthermore, aspects of the subject matter in the present disclosure may be implemented in a plurality of processing chips or devices, and storage may be similarly affected across a plurality of devices. Such devices may include PCs, network servers, and portable devices.

[0151] Although the present disclosure has been described in relation to some embodiments, various modifications and changes may be made without departing from the scope of the present disclosure as understood by a person skilled in the art to which the invention of the present disclosure pertains. Furthermore, such modifications and changes should be considered to fall within the scope of the claims appended to this specification.

Claims

1. A method for operating a disease control air-dampening system according to the concentration of airborne infectious agents, performed by at least one processor, A step of collecting air quality data of a target space in which one or more disinfection air conditioning devices are installed; A step of calculating predicted data associated with the concentration of airborne infectious agents in the target space based on the above air quality data; and A step of controlling the operating conditions of the above-mentioned disinfection air conditioning device based on the above-mentioned prediction data; A method for coordinating disease control operations including 2. In Paragraph 1, The above air quality data includes carbon dioxide concentration, Method for coordinating disease control operations.

3. In Paragraph 1, The step of calculating prediction data associated with the concentration of airborne infectious agents in the target space based on the above air quality data is: A step of inputting the air quality data and airborne infectious agent concentration data of the target space collected in advance for a predetermined period into an artificial neural network module; A step of calculating the correlation between a plurality of factors of the air quality data and the concentration of the airborne infectious agent by the artificial neural network module; A step of identifying one or more factors that have the highest correlation with the concentration of the floating infectious agent based on the correlation by the artificial neural network module above; and A step of calculating prediction data associated with the concentration of airborne infectious agents in the target space based on one or more identified factors by the artificial neural network module. A method for coordinating disease control operations including 4. In Paragraph 3, The above-mentioned prior-collected airborne infectious agent concentration data is obtained by a detection device installed separately from the above-mentioned quarantine air conditioning device, Method for coordinating disease control operations.

5. In Paragraph 3, A method for operating a disease control air conditioning system in which the concentration of one or more of the identified factors is measurable in the disease control air conditioning system.

6. In Paragraph 3, A method for operating a disease control air conditioning system, wherein the above artificial neural network module includes an LSTM (Long Short-Term Model) module.

7. In Paragraph 1, The step of controlling the operating conditions of the above-mentioned disease control air conditioning device based on the above-mentioned prediction data is: If the concentration of airborne infectious agents based on the above prediction data exceeds a preset threshold, the method includes the step of controlling the above-mentioned disinfection air conditioning device in a first mode. The above first mode is a mode in which the above-mentioned disinfection air conditioning device is operated to have a predetermined intake volume or more, Method for coordinating disease control operations.

8. In Paragraph 1, The step of controlling the operating conditions of the above-mentioned disease control air conditioning device based on the above-mentioned prediction data is: A step comprising sending a warning message to a manager device associated with the quarantine air conditioning device when the concentration of airborne infectious agents based on the above prediction data exceeds a preset threshold. Method for coordinating disease control operations.

9. In Paragraph 7, The step of controlling the operating conditions of the above-mentioned disease control air conditioning device based on the above-mentioned prediction data is: If the concentration of airborne infectious agents based on the above prediction data is below a preset threshold, the method includes the step of controlling the disinfection air conditioning device to a second mode. The above second mode has a lower intake volume of the disinfection air conditioning device compared to the above first mode, Method for coordinating disease control operations.

10. In Paragraph 7, The step of controlling the operating conditions of the above-mentioned disease control air conditioning device based on the above-mentioned prediction data is: When a predetermined time has elapsed after the above-mentioned disinfection air conditioning device is controlled in a first mode, the first mode is switched to a second mode in which the intake volume of the disinfection air conditioning device is lower than that of the first mode, thereby controlling it. Method for coordinating disease control operations.

11. In Paragraph 1, The step of controlling the operating conditions of the above-mentioned disease control air conditioning device based on the above-mentioned prediction data is: Controlling the operating conditions of the disinfection air conditioning device based further on the above prediction data and the spatial data of the above target space, Method for coordinating disease control operations.

12. In Paragraph 11, A method for operating a quarantine air conditioning system, wherein spatial data of the above-mentioned target space includes data on the average number of occupants per time period of the above-mentioned target space.

13. In Paragraph 12, The step of controlling the operating conditions of the above-mentioned disease control air conditioning device based on the above-mentioned prediction data is: A method for operating a disinfection air conditioning system, comprising the step of switching the disinfection air conditioning system to an energy-saving mode and controlling it when the average number of occupants is below a threshold value, based on data regarding the average number of occupants per time period.

14. In Paragraph 3, The above artificial neural network module is, A step of comparing the predicted data calculated in association with the concentration of airborne infectious sources in the target space with the actual concentration of airborne infectious sources in the target space; and A step of performing reinforcement learning in a direction that reduces the error in the comparison result between the above-mentioned prediction data and the above-mentioned actual airborne infectious source concentration. A method for coordinating disease control operations to further perform.

15. At least one disinfection air conditioning device installed in the target space; A control module for controlling at least one of the above-mentioned quarantine air conditioning devices; Includes, The above control module includes memory and a processor, and the processor executes a computer program stored in the memory, Collect air quality data of the target space in which at least one of the above-mentioned disinfection air conditioning devices is installed, and Based on the above air quality data, predictive data associated with the concentration of airborne infectious agents in the above target space is calculated, and Operating to control the operating conditions of the above-mentioned disease control air conditioning device based on the above-mentioned prediction data, Disease control cooperation system.

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