Method and system for Anti-epidemic air conditioning management

Abstract

According to the present disclosure, a method for anti-epidemic air conditioning management based on airborne infectious agent concentration may comprise the steps of: collecting spatial information of a target space in which one or more anti-epidemic air conditioning devices are disposed, air quality data, and infectious agent concentration data for a predetermined period acquired from the target space; acquiring epidemic infectious disease data; calculating prediction data associated with airborne infectious agent concentration on the basis of at least one of the spatial information, the air quality data, and the infectious agent concentration data; and controlling the anti-epidemic air conditioning devices on the basis of the epidemic infectious disease data and the prediction data.

Description

Disease Control and Prevention Coordination Management Methods and Systems

[0001] The present disclosure relates to a method and system for managing disease control air conditioning, and specifically, to a method for managing disease control air conditioning for the prevention or control of infectious diseases in a target space and an information processing system for the same.

[0002] In multi-use facilities such as hospitals, nursing hospitals, nursing homes, schools, public transportation, and airports, there is a possibility that infectious agents, such as bioaerosols containing biological substances like viruses, bacteria, and fungi, exist suspended in the air. If these substances 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, and rapid disinfection is necessary to quickly control the affected area if the indoor space becomes contaminated, thereby preventing further transmission incidents caused by the agents.

[0004] 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.

[0005] 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.

[0006] In addition, the present disclosure provides a method and system for performing disease control coordination of a target space based on epidemic infectious disease data collected from a disease control agency.

[0007] 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.

[0008] 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.

[0009] According to one embodiment of the present disclosure, a method for managing an airborne infectious agent concentration based on airborne infectious agent concentration, performed by at least one processor, comprises the steps of: collecting spatial information of a target space in which one or more airborne infectious agent devices are installed, air quality data, and infectious agent concentration data obtained in the target space over a predetermined period; obtaining epidemic infectious disease data; calculating prediction data associated with airborne infectious agent concentration based on at least one of the spatial information, air quality data, and infectious agent concentration data; and controlling the airborne infectious agent devices based on the epidemic infectious disease data and the prediction data.

[0010] According to one embodiment of the present disclosure, after the calculation step, the method further includes the step of determining an infection risk level for a target space based on epidemic infectious disease data and the calculated prediction data.

[0011] According to one embodiment of the present disclosure, the step of controlling a disease control air conditioning device includes the step of controlling the disease control air conditioning device based on a determined infection risk class.

[0012] According to one embodiment of the present disclosure, the step of controlling a disease control air conditioning device includes setting operating conditions of the disease control air conditioning device based on a determined infection risk class.

[0013] According to one embodiment of the present disclosure, the operating conditions include at least one of the wind speed of the disinfection air conditioning device, the use of plasma, or the plasma operating intensity according to the epidemic infectious pathogen to be disinfected.

[0014] According to one embodiment of the present disclosure, after the step of determining an infection risk level, the method further includes the step of providing at least one of an alarm requiring disease control, disease prediction information based on the concentration of an infectious agent, or action guidelines information to a terminal associated with disease control according to the determined infection risk level.

[0015] According to one embodiment of the present disclosure, spatial information of a target space includes information associated with at least one of area information of the target space, space use information, information on the number of people that can be accommodated, or location information where a disinfection air conditioning device is installed, and air quality data of the target space includes data associated with at least one of temperature, humidity, carbon dioxide, fine dust, oxygen concentration, or TVOC (total volatile organic compounds).

[0016] According to one embodiment of the present disclosure, epidemic infectious disease data includes epidemic infectious disease trend data, and the epidemic infectious disease trend data is updated at a preset period through communication with an external server.

[0017] According to one embodiment of the present disclosure, the method further comprises the steps of: acquiring actual measurement data regarding the concentration of airborne infectious agents in a target space; reinforcing learning by comparing the predicted data of an agent regarding the concentration of airborne infectious agents in the target space with the acquired actual measurement data; and updating the predicted data calculated from the reinforced learning predicted data.

[0018] According to one embodiment of the present disclosure, the step of determining an infection risk grade includes determining a first risk level associated with epidemic infectious disease data, determining a second risk level associated with prediction data, and determining an infection risk grade for a target space based on at least one of the first risk level and the second risk level.

[0019] According to one embodiment of the present disclosure, the method further comprises the step of displaying at least one of collected information and data, calculated prediction data, determined infection risk level, information associated with an alarm requiring disease prevention, disease prediction information based on infectious agent concentration, or action guideline information through a display device.

[0020] According to one embodiment of the present disclosure, the displaying step includes the step of displaying concentration prediction data for an epidemic infectious disease based on the particle size of a bioaerosol through a display device.

[0021] A computer program stored on a computer-readable recording medium may be provided to execute a method according to one embodiment of the present disclosure on a computer.

[0022] According to one embodiment of the present disclosure, an information processing system comprises a memory and a processor connected to the memory and configured to execute at least one computer-readable program included in the memory, wherein the at least one program includes instructions for collecting spatial information of a target space in which one or more disinfection air conditioning devices are installed, air quality data, and infectious agent concentration data obtained in the target space for a predetermined period, obtaining epidemic infectious disease data, calculating prediction data associated with airborne infectious agent concentration based on at least one of the spatial information, air quality data, and infectious agent concentration data, and controlling the disinfection air conditioning devices based on the epidemic infectious disease data and prediction data.

[0023] According to one embodiment of the present disclosure, at least one program includes instructions for determining an infection risk level for a target space based on epidemic infectious disease data and the calculated prediction data, after calculating prediction data.

[0024] According to one embodiment of the present disclosure, at least one program includes instructions for controlling a disease control air conditioning device based on a determined infection risk class.

[0025] According to one embodiment of the present disclosure, at least one program includes instructions for providing at least one of a disease prevention need alarm, disease prediction information based on the concentration of an infectious agent, or action guideline information to a terminal associated with disease prevention, according to a determined infection risk level.

[0026] According to one embodiment of the present disclosure, epidemic infectious disease data includes epidemic infectious disease trend data, and the epidemic infectious disease trend data is updated at a preset period through communication with an external server.

[0027] According to one embodiment of the present disclosure, at least one program includes instructions for acquiring actual measurement data regarding the concentration of airborne infectious agents in a target space, reinforcing learning by comparing the agent's prediction data regarding the concentration of airborne infectious agents in the target space with the acquired actual measurement data, and updating the prediction data calculated from the reinforcement-learned prediction data.

[0028] According to one embodiment of the present disclosure, at least one program includes instructions for determining a first risk level associated with epidemic infectious disease data, determining a second risk level associated with prediction data, and determining an infection risk class for a target space based on at least one of the first risk level and the second risk level.

[0029] According to some embodiments of the present disclosure, a method for performing disease control coordination based on predicted data of airborne infectious agent concentrations in a target space and data on epidemic infectious diseases is provided, thereby protecting the health of users utilizing the target space and effectively preventing infectious diseases.

[0030] 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.

[0031] 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.

[0032] FIG. 1 is a schematic diagram illustrating an integrated disease control system according to one embodiment of the present disclosure.

[0033] 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 airborne infectious agent concentration control according to one embodiment of the present disclosure.

[0034] 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.

[0035] FIG. 4 is a drawing showing a user interface for notifying infectious disease risks in a target space according to one embodiment of the present disclosure.

[0036] FIG. 5 exemplarily illustrates infectious agent concentration data collected in a target space according to one embodiment of the present disclosure.

[0037] FIG. 6 is a diagram showing infectious disease data by specific period provided by a disease control agency according to one embodiment of the present disclosure.

[0038] FIG. 7 is a diagram illustrating a reinforcement learning process for a concentration prediction model using actual measurement data according to one embodiment of the present disclosure.

[0039] FIG. 8 is a diagram illustrating the process of determining the infection level of a target space according to a risk level according to one embodiment of the present disclosure.

[0040] FIG. 9 is a drawing showing state information associated with an air conditioning device of a target space according to one embodiment of the present disclosure.

[0041] FIGS. 10 to 12 are drawings comparing air quality data before and after operation of a disease prevention air conditioning device according to one embodiment of the present disclosure.

[0042] FIG. 13 is a sequence diagram illustrating a method for managing disease control air conditioning according to the concentration of airborne infectious agents according to one embodiment of the present disclosure.

[0043] FIG. 14 is a sequence diagram illustrating a method for determining an infection risk class for a target space according to one embodiment of the present disclosure.

[0044] 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.

[0045] 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 well-known functions or configurations will be omitted if there is a risk that the gist of the present disclosure may be unnecessarily obscured.

[0046] 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.

[0047] 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.

[0048] 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 selected at the applicant's discretion, 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.

[0049] 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.

[0050] 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'.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] FIG. 1 is a schematic diagram illustrating an integrated disease prevention system (1000) according to one embodiment of the present disclosure.

[0056] Referring to FIG. 1, the integrated disease control system (1000) can measure or predict the levels of airborne infectious agents, such as bioaerosols, in target spaces such as hospitals, nursing hospitals, nursing homes, schools, public transportation, and airports. The integrated disease control system (1000) can perform necessary processes related to disease control based on the measured or predicted levels. 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.

[0057] The integrated quarantine system (1000) can collect data (10) related to the target space, and the collected data (10) may include at least one of air quality data (12), infectious agent concentration data (14), or spatial information (16).

[0058] The air quality data (12) of the target space may include data associated with at least one of temperature, humidity, carbon dioxide, fine dust, oxygen concentration, or TVOC (total volatile organic compounds), but the present disclosure is not limited thereto. The infectious agent concentration data (14) may include concentration data associated with airborne infectious agents collectible in the target space. The spatial information (16) of the target space may include information associated with at least one of the area information, space usage information, capacity information, or location information where a disinfection air conditioning device is installed, but the present disclosure is not limited thereto. The air quality data (12) and / or infectious agent concentration data (14) may be combined with the spatial information (16) to measure or predict infectious agent concentration data in a specific space or location.

[0059] Additionally, the integrated disease control system (1000) can collect epidemic infectious disease data (20). The epidemic infectious disease data (20) may be data collected through national or local government agencies and may include epidemic infectious disease trend data (22). However, the method of collecting epidemic infectious disease data (20) is not limited. The epidemic infectious disease trend data (22) may include data on infectious diseases collected in a time series or data that visually indicates the trend of infectious diseases. Such epidemic infectious disease trend data (22) may be used to predict whether an infectious source corresponding to a specific epidemic infectious disease exists in a target space and the likelihood of its occurrence.

[0060] The integrated quarantine system (1000) can receive collected data (10, 20) (S1, S2) and perform processing (30) related to quarantine. Specifically, the integrated quarantine system (1000) can perform a process associated with at least one of data analysis (32), learning (34) or prediction (36) for performing various predictions.

[0061] The integrated quarantine system (1000) can perform a quarantine process (40) by utilizing an analysis (32), learning (34), or prediction (36) process (S3). The quarantine process (40) may include processes such as controlling a quarantine air conditioning device (42) of a target space, providing a quarantine need alarm (44), or providing disease prediction information (46), but the present disclosure is not limited thereto. The specific operation and implementation method of the integrated quarantine system (1000) will be described in detail below.

[0062] 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 quarantine airborne infectious agent concentration control according to one embodiment of the present disclosure.

[0063] 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 concentration of airborne infectious agents through a network (220).

[0064] In one embodiment, the information processing system (230) may include a computer-executable program (e.g., a downloadable application) for calculating prediction data associated with airborne infectious agent concentration based on spatial information of a collected target space, air quality data, and infectious agent concentration data, 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.

[0065] 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.

[0066] 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 devices 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).

[0067] 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).

[0068] 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.

[0069] 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.

[0070] 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).

[0071] 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).

[0072] 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.

[0073] 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).

[0074] 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) via the communication module (316) of the user terminal (210) through the communication module (336) and the network (220).

[0075] 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).

[0076] 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.

[0077] 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).

[0078] 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).

[0079] 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. In the case of GUI-based content shown below, it 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).

[0080] FIG. 4 is a drawing showing a user interface (400) for notifying of infectious disease risk in a target space according to one embodiment of the present disclosure.

[0081] Referring to FIG. 4, the user interface (400) can visually provide the risk of infectious diseases in the target space based on a digital twin. The user interface (400) can provide a virtual model (e.g., a virtual 3D model) corresponding to the target space based on a digital twin. However, it is not limited to this, and actual measurement data or prediction data of the target space may be visually represented in a way other than a virtual model.

[0082] The user interface (400) may provide an area (430) for selecting a target space to be displayed, and the target space (432) may be selected by user input. The user interface (400) may display space information (410), operating status information (420) of a disinfection air conditioning device located in the target space, air quality data (425) of the target space, a display information selection section (440), information on detected infectious agents (450), concentration data of airborne infectious agents (460), and a target space display area (470) separately for the selected target space (432, CC Nursing Home). In the example of FIG. 4, all of these data were displayed simultaneously, but some of these data may be displayed selectively.

[0083] The spatial information (410) may display at least one of the area information, space usage information, capacity information, average number of visitors information, or congestion information of the target space, but the present disclosure is not limited thereto.

[0084] Information on the operating status of the disinfection air conditioning device of the target space (420) may include at least one of the number of disinfection air conditioning devices of the target space, information on the operating status of each disinfection air conditioning device, airflow information, information related to plasma usage, and information related to normal operation.

[0085] The air quality data (425) of the target space may include data associated with at least one of temperature, humidity, carbon dioxide, fine dust, ultrafine dust, oxygen concentration, or TVOC (total volatile organic compounds), but the present disclosure is not limited thereto.

[0086] The display information selection unit (440) can receive a user selection regarding whether to display the installation locations of a sensor for detecting airborne bacteria, an air quality data acquisition device, a disinfection air conditioning device (air conditioner), etc., in the target space display area (470). For example, if the user wishes to display the disinfection air conditioning device and the air quality data acquisition device installed in the target space in the target space display area (470), the user may select a selection box for the disinfection air conditioning device and the air quality data acquisition device. In the example of FIG. 4, an example is illustrated in which the air quality data acquisition device (474A, 474B, 474C) and the disinfection air conditioning device (472A, 472B, 472C) are respectively displayed in the target space display area (470) when the user selects a selection box for the disinfection air conditioning device and the air quality data acquisition device. Additionally, although not illustrated, a simulation of the diffusion of airborne bacteria particles by size by building air conditioning may also be displayed in the target display area (470).

[0087] The detected source of infection information (450) may include source of infection information detected in the target space. Additionally, optionally, the current status of an epidemic infectious disease may be displayed based on epidemic infectious disease trend data, or an infectious disease with potential for occurrence may be predicted and displayed.

[0088] The concentration data (460) of airborne infectious sources may include at least one of actual airborne infectious source concentration data and predicted airborne infectious source concentration data of the target space. For example, the processor may collect and display the concentration data (460) of airborne infectious sources in a time series. Additionally, the processor may generate predicted airborne infectious source concentration data through deep learning and / or reinforcement learning based on the actual concentration data of airborne infectious sources collected in a time series. Meanwhile, the concentration data (460) of airborne infectious sources may display concentration data for a specific airborne infectious source or concentration data for the total airborne infectious sources within the target space.

[0089] FIG. 5 illustrates, in an exemplary manner, infectious agent concentration data collected in a target space according to one embodiment of the present disclosure. FIG. 6 is a diagram showing infectious disease data by specific period provided by a disease control agency according to one embodiment of the present disclosure.

[0090] Referring to FIG. 5, the processor may collect infectious agent concentration data (500) for a predetermined period obtained in the target space. The predetermined period may be set to one of a preset time, day, week, or month, but the present disclosure is not limited thereto.

[0091] The processor may select an infectious agent to be monitored based on the concentration of the infectious agent measured in the target space. In one embodiment, the processor may select an infectious agent (512, 514, 516, 518, 528) that is higher than a preset concentration among the measured infectious agent concentrations. At this time, the processor may select and exclude an infectious agent (516) with a relatively low risk, but the present disclosure is not limited thereto.

[0092] Referring to FIG. 6, the processor can receive time-series epidemic infectious disease data from a disease control agency. For example, the processor can receive epidemic infectious disease data corresponding to each recent week (weeks 32, 33, 34, and 35) from the disease control agency.

[0093] In one embodiment, when selecting an infectious agent to be monitored based on the concentration of the infectious agent measured in the target space, the processor may select an infectious agent associated with epidemic infectious disease data in which the variation at the time of selection exceeds a preset range. For example, the processor may select the infectious agent (518 in FIG. 5) and target monitor it after recognizing that the variation of 35 weeks of Staphylococcus aureus (522) for one infectious agent (518 in FIG. 5) exceeds a preset range (e.g., 10) (13), but the present disclosure is not limited thereto.

[0094] A processor executing a disease control air conditioning management method based on airborne infectious source concentration according to one embodiment of the present disclosure can calculate prediction data associated with airborne infectious source concentration based on at least one of spatial information regarding a target space, air quality data, and infectious source concentration data. Additionally, epidemic infectious disease data may be additionally considered when calculating such prediction data.

[0095] When the processor calculates prediction data, it may use a concentration prediction model, which is an artificial neural network algorithm. The concentration prediction model may be a deep learning model trained to receive spatial information, air quality data, and infectious agent concentration data as input, analyze the data, and calculate prediction data associated with airborne infectious agent concentrations.

[0096] Furthermore, the processor can improve the accuracy of the generated concentration prediction model by reinforcing the concentration prediction model. This will be explained below.

[0097] FIG. 7 is a diagram illustrating a reinforcement learning process for a concentration prediction model using actual measurement data according to one embodiment of the present disclosure.

[0098] Referring to Fig. 7, the concentration 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.

[0099] 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.

[0100] In the reinforcement learning process, the concentration prediction model becomes the agent, the data provider becomes the environment, the action of the concentration prediction model comparing predicted data and actual data related to airborne infectious source 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.

[0101] More specifically, the concentration 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 on 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 it exceeds the second setpoint. Furthermore, reinforcement learning can be performed on the agent as the agent repeats the process of comparing actions—that is, the concentration prediction model's comparison—in a direction that maximizes the reward. Consequently, the concentration prediction model, acting as the agent, becomes capable of continuously performing optimized data classification.

[0102] In one embodiment, the artificial neural network module of the concentration 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 predicted data and the actual measured data through such reinforcement learning even after pre-training.

[0103] 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.

[0104] FIG. 8 is a drawing for explaining the process (530) of determining the infection level of a target space according to a risk level according to one embodiment of the present disclosure.

[0105] Referring to FIG. 8, the processor can acquire epidemic infectious disease data. The epidemic infectious disease data includes epidemic infectious disease trend data, and the epidemic infectious disease trend data can be updated at a preset interval (e.g., weekly, monthly, etc.) through communication with an external server. The external server may correspond to a disease control agency, but the present disclosure is not limited thereto.

[0106] The processor can determine the epidemic disease risk level (532) based on collected epidemic disease data. The processor can determine the epidemic disease risk level based on the speed of transmission of the disease, the scale of damage, and the possibility of response. For example, Level 1 (low risk) may be a level corresponding to a situation where the disease occurs locally and is controllable, and Level 2 (moderate risk) may be a level corresponding to a situation where multiple infected people occur in a limited area and are controllable. Additionally, Level 3 (high risk) may be a level corresponding to a situation where the disease spreads to multiple regions and there is a risk of large-scale infection (above a preset threshold), and Level 4 (very high risk) may be a level corresponding to a situation where the disease progresses extensively nationally or internationally and is difficult to control, but the present disclosure is not limited thereto.

[0107] In addition, the processor can generate predictive data associated with the concentration of airborne infectious agents in the target space, and the predictive data is the concentration of total airborne infectious agents (CFU / m³). 3Depending on the, it may be classified into Grade 1 (low risk, e.g., 200 or less), Grade 2 (moderate risk, 400 or less), Grade 3 (high risk, 800 or less), Grade 4 (very high risk, e.g., over 800), etc., but the present disclosure is not limited thereto. In one embodiment, the numerical value for determining the grade may differ depending on the virus, bacteria, fungi, etc.

[0108] The processor can determine a first risk level (532, first risk level) associated with epidemic infectious disease data and a second risk level (534, second risk level) associated with prediction data.

[0109] The processor can determine the infection risk level for the target space based on at least one of the first risk level (532) and the second risk level (534) (536).

[0110] In one embodiment, the processor can control the epidemic prevention air conditioning device based on at least one of epidemic infectious disease data and prediction data, and can control the epidemic prevention air conditioning device based on a determined infection risk level. Specifically, the processor can set the operating conditions of the epidemic prevention air conditioning device based on the determined infection risk level.

[0111] As a non-limiting example, the operating conditions of the epidemic prevention air conditioning device may include at least one of the wind speed of the epidemic prevention air conditioning device, the use of plasma, or the plasma operating intensity according to the epidemic infectious pathogens to be prevented.

[0112] FIG. 9 is a drawing showing state information (600) associated with an air conditioning device of a target space according to one embodiment of the present disclosure.

[0113] Referring to FIG. 9, when a disinfection air conditioning device (610) of a target space (AR) is selected in a user interface representing a target space (AR), the processor may display status information associated with the disinfection air conditioning device (610). The processor may display information associated with at least one of the power status, automatic operation status, wind wave information, airborne bacteria range information, plasma usage status, and normal operation status of the disinfection air conditioning device (610) (620).

[0114] In addition, the processor may provide at least one of a disease prevention need alarm, disease prediction information based on the concentration of the infectious agent, or action guideline information to a terminal associated with disease prevention, depending on the determined infection risk level.

[0115] In addition, the processor can display at least one of collected information and data, calculated prediction data, determined infection risk level, information related to the alarm for disease prevention, disease prediction information based on the concentration of the infectious agent, or action guideline information through a display device.

[0116] In addition, the processor can display concentration prediction data for epidemic infectious diseases through a display device based on the particle size of airborne infectious sources, for example, bioaerosols.

[0117] FIGS. 10 to 12 are drawings comparing air quality data before and after operation of an air conditioning device according to one embodiment of the present disclosure.

[0118] The processor can control the disinfection air conditioning device based on epidemic infectious disease data and concentration prediction data. In one embodiment, the processor can additionally operate the disinfection air conditioning device when the epidemic infectious disease risk level is level 2 or higher and the prediction data risk level is level 2 or higher. The processor can operate the disinfection air conditioning device at a preset time even if the infection level of the target space is at its lowest, and can additionally operate the disinfection air conditioning device when the infection level is high.

[0119] Referring to FIG. 10, it can be seen that when the disinfection air conditioning device is operated (740) compared to before (720) the disinfection air conditioning device is operated, the carbon dioxide measurement data (714, 724) decreases by approximately 200 CFU, and in the case of fine dust or ultrafine dust (pm10, pm25, pm1, etc.) (728, 730, 732), it is measured by being suspended by the air conditioning, but the value is not significant. Furthermore, it can be seen that the temperature (716, 726) and humidity (712, 722) are stabilized by the operation of the disinfection air conditioning device.

[0120] Referring to FIG. 11, it can be seen that the total number of airborne bacteria is significantly reduced after the disinfection air conditioning device is activated (820) compared to before the disinfection air conditioning device is activated (810). It can also be seen that the concentration of carbon dioxide is reduced.

[0121] Referring to Fig. 12, it can be seen that the total number of airborne bacteria decreased significantly after operation (920, 940) compared to before operation (910, 930) and that carbon dioxide also decreased significantly.

[0122] Referring to Figures 10 to 12, it can be seen that when the disinfection air conditioning system is operated, the amount of airborne infectious agents is significantly reduced, and the measured amount of carbon dioxide is also reduced.

[0123] FIG. 13 is a sequence diagram (S1000) showing a method for managing disease control air conditioning according to the concentration of airborne infectious agents according to one embodiment of the present disclosure.

[0124] Referring to FIG. 13, first, in step S1010, the processor can collect spatial information of a target space in which one or more disinfection air conditioning devices are installed, air quality data, and infectious agent concentration data obtained in the target space for a predetermined period.

[0125] In step S1020, the processor can acquire epidemic infectious disease data. In one embodiment, step S1020 may be executed before or simultaneously with step S1010.

[0126] In step S1030, the processor can calculate prediction data associated with airborne infectious agent concentration based on at least one of spatial information, air quality data, and infectious agent concentration data.

[0127] In addition, the processor can determine the infection risk level for the target space based on epidemic infectious disease data and calculated prediction data.

[0128] In step S1040, the processor can control the epidemic prevention air conditioning device based on epidemic infectious disease data and prediction data.

[0129] The processor can set operating conditions for the disinfection air conditioning device based on a determined infection risk class. The operating conditions here may include at least one of the wind speed of the disinfection air conditioning device, whether plasma is used, or the plasma operating intensity according to the epidemic infectious pathogens to be disinfected.

[0130] After determining the infection risk level, the processor may provide at least one of an alarm requiring disease control, disease prediction information based on the concentration of the infectious agent, or action guidelines information to a terminal associated with disease control, depending on the determined infection risk level.

[0131] The processor acquires actual measurement data regarding the concentration of airborne infectious agents in the target space, performs reinforcement learning by comparing the agent's prediction data regarding the concentration of airborne infectious agents in the target space with the acquired actual measurement data, and can update the prediction data calculated from the reinforcement-learned prediction data.

[0132] The processor can display at least one of collected information and data, calculated prediction data, determined infection risk level, information related to disease prevention alarms, disease prediction information based on infectious agent concentration, or action guideline information through a display device.

[0133] The processor can display concentration prediction data for epidemic infectious diseases based on the particle size of bioaerosols through a display device.

[0134] FIG. 14 is a sequence diagram (S1100) illustrating a method for determining an infection grade for a target space according to one embodiment of the present disclosure.

[0135] Referring to FIG. 14, first, in step S1110, the processor can determine a first risk level associated with epidemic infectious disease data. In step S1120, the processor can determine a second risk level associated with prediction data. In step S1130, the processor can determine an infection risk grade for a target space based on at least one of the first risk level and the second risk level.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 may cause the processor(s) to perform specific aspects of the functions described in this disclosure.

[0142] When implemented in software, the 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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

In a method for managing disease control airborne infectious agent concentrations, performed by at least one processor, A step of collecting spatial information of a target space in which one or more disease control air conditioning devices are installed, air quality data, and infectious agent concentration data for a predetermined period obtained in said target space; Step to acquire epidemic infectious disease data; A step of calculating predicted data associated with airborne infectious agent concentration based on at least one of the above spatial information, the above air quality data, and the above infectious agent concentration data; and A step of controlling the disease control air conditioning device based on the above epidemic infectious disease data and the above prediction data. A disease control cooperation management method including In paragraph 1, After the above calculation step, A step of determining the infection risk level for the target space based on the above epidemic infectious disease data and the above calculated prediction data. A disease control cooperation management method that further includes In paragraph 2, The step of controlling the above-mentioned disease control air conditioning device is, A step of controlling the above-determined disease control air conditioning device based on the above-determined infection risk class A disease control cooperation management method including In paragraph 3, The step of controlling the above-mentioned disease control air conditioning device is, Step of setting the operating conditions of the above-determined disease control air conditioning device based on the above-determined infection risk class A disease control cooperation management method including In paragraph 4, The above operating conditions are, A method for managing disease control air conditioning, comprising at least one of the wind speed of the disease control air conditioning device, the use of plasma, or the plasma operation intensity according to the epidemic infectious pathogens to be controlled. In paragraph 2, After the step of determining the infection risk level mentioned above, A step of providing at least one of a disease control need alarm, disease prediction information based on the concentration of the infectious agent, or action guideline information to a terminal associated with disease control according to the infection risk class determined above. A disease control cooperation management method that further includes In paragraph 1, The spatial information of the above target space is, It includes information associated with at least one of the area information, space usage information, capacity information, or location information where the disinfection air conditioning device is installed of the above-mentioned target space, and The air quality data of the above-mentioned target space is, A method for managing disease control air conditioning, comprising data associated with at least one of temperature, humidity, carbon dioxide, fine dust, oxygen concentration, or TVOC (total volatile organic compounds). In paragraph 1, The above epidemic infectious disease data includes epidemic infectious disease trend data, A disease control cooperation management method in which the above-mentioned epidemic infectious disease trend data is updated at a preset interval through communication with an external server. In paragraph 1, The above method is, A step of obtaining actual measurement data regarding the concentration of airborne infectious agents in the above-mentioned target space; A step of reinforcement learning by comparing the agent's predicted data regarding the concentration of airborne infectious agents in the target space with the acquired actual measurement data; and A step of updating the calculated prediction data with the above reinforcement-learned prediction data. A disease control cooperation management method that further includes In paragraph 2, The step of determining the infection risk level above is, A step of determining a first risk level associated with the above epidemic infectious disease data; A step of determining a second risk level associated with the above-mentioned prediction data; and A step of determining an infection risk grade for the target space based on at least one of the first risk level and the second risk level. A disease control cooperation management method including In paragraph 2, The above method is, A method for managing disease control and coordination, further comprising the step of displaying at least one of the collected information and data, the calculated prediction data, the determined infection risk grade, information related to the disease control need alarm, disease prediction information or action guideline information based on the infectious agent concentration, through a display device. In Paragraph 11, The above-mentioned displaying step is, A method for managing disease control air conditioning, comprising the step of displaying concentration prediction data for the epidemic infectious disease based on the particle size of the bioaerosol through the display device. A computer-readable, non-transient recording medium having instructions for executing a method according to any one of paragraphs 1 through 12 on a computer. In information processing systems, Memory; and A processor connected to the memory and configured to execute at least one computer-readable program contained in the memory. Includes, The above at least one program is, Collecting spatial information of a target space in which one or more disinfection air conditioning devices are installed, air quality data, and infectious agent concentration data obtained over a predetermined period in said target space, and Acquire data on epidemic infectious diseases, Predicted data associated with airborne infectious agent concentration is calculated based on at least one of the above spatial information, the above air quality data, and the above infectious agent concentration data, and Commands for controlling the disease control air conditioning device based on the above epidemic infectious disease data and the above prediction data, Information processing system. In Paragraph 14, The above at least one program is, Instructions for determining an infection risk level for the target space based on the epidemic infectious disease data and the calculated prediction data, after calculating the prediction data. Information processing system. In paragraph 15, The above at least one program is, Commands for controlling the above-determined quarantine air conditioning device based on the above-determined infection risk class, Information processing system. In paragraph 15, The above at least one program is, Commands for providing at least one of a disease prevention need alarm, disease prediction information based on the concentration of the infectious agent, or action guideline information to a terminal associated with disease prevention according to the above-determined infection risk grade, Information processing system. In Paragraph 14, The above epidemic infectious disease data includes epidemic infectious disease trend data, The above epidemic infectious disease trend data is updated at a preset interval through communication with an external server, Information processing system. In Paragraph 14, The above at least one program is, Acquire actual measurement data regarding the concentration of airborne infectious agents in the above-mentioned target space, and Reinforcement learning is performed by comparing the agent's predicted data regarding the concentration of airborne infectious agents in the above target space with the above acquired actual measurement data, and Instructions for updating the calculated prediction data with the above reinforcement-learned prediction data, Information processing system. In paragraph 15, The above at least one program is, Determining the first risk level associated with the above epidemic infectious disease data, and Determining a second risk level associated with the above prediction data, Instructions for determining an infection risk class for the target space based on at least one of the first risk level and the second risk level, Information processing system.

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