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KR-102962080-B1 - AI-based Air conditioner outdoor unit fire prevention subsystem

KR102962080B1KR 102962080 B1KR102962080 B1KR 102962080B1KR-102962080-B1

Abstract

The present invention relates to an artificial intelligence-based air conditioner outdoor unit fire prevention subsystem. In particular, the entire process of real-time monitoring of the outdoor unit's status through a multi-sensor module, supplying self-power to the sensors through an energy harvesting module, and preventing fire risks through warnings and automatic control by detecting abnormal signs through an artificial intelligence algorithm analysis module of collected data through a data processing and analysis module is provided to the user in real-time via a communication module, enabling remote management of the outdoor unit's status through a mobile app. The present invention comprises a multi-sensor module (1) equipped with sensors capable of detecting temperature, current, voltage, dust, and gas conditions around the compressor and electrical components of an air conditioner, an energy harvesting module (4) attached near the outdoor unit fan (3) that supplies power to the sensors and communication module through wind power generation using high-temperature wind emitted from the outdoor unit (2) and power generation using a Seebeck element utilizing the temperature difference, an edge computing module (5) that analyzes and processes collected information data in real time to determine the possibility of fire, a communication module (6) that transmits the collected and analyzed data to a cloud server or a user's smartphone to monitor the status of the outdoor unit (2) in real time, and a warning and control module (7) that sends a warning notification to a user's smartphone (8) when danger is detected and controls the operation of the outdoor unit (2).

Inventors

  • 서오권
  • 서경환

Assignees

  • 강남대학교 산학협력단

Dates

Publication Date
20260511
Application Date
20241114

Claims (3)

  1. A multi-sensor module (1) that detects each condition in real time by mounting various sensors capable of detecting temperature, current, voltage, dust, and gas conditions around the compressor and electrical components of an air conditioner installed on the outside of a building, and An energy harvesting module (4) that generates electricity using a Seebeck element, which is a power generation system that converts heat into electricity from a temperature difference, and is attached near the outdoor unit fan (3) and uses high-temperature wind emitted from the outdoor unit (2) to generate wind power, and supplies power to sensors and communication modules, and An edge computing module (5) that analyzes and processes collected information data in real time and controls the operation of the outdoor unit (2) to automatically stop when a possibility of fire is detected in the outdoor unit (2), and A communication module (6) that monitors the status of the outdoor unit (2) in real time by transmitting and storing collected and analyzed data to a cloud server or transmitting it to a user's smartphone, and It is configured to include a warning and control module (7) that sends a warning notification to the user's smartphone (8) when danger is detected and controls the operation of the outdoor unit (2). The above energy harvesting module (4) An AI-based air conditioner outdoor unit fire prevention subsystem characterized by being composed of a wind power generation system, a thermoelectric power generation system, and a power management system, configured to generate electricity using the photoelectric effect, piezoelectric effect, induction phenomenon, and thermoelectric effect.
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Description

AI-based Air conditioner outdoor unit fire prevention subsystem The present invention relates to an AI-based fire prevention subsystem for an air conditioner's outdoor unit. Specifically, the entire process of monitoring the status of the outdoor unit in real time through a multi-sensor module, supplying self-power to the sensors through an energy harvesting module, and preventing fire risks by providing warnings and automatic control upon detection of abnormal signs through a data processing and analysis module that analyzes collected data using an AI algorithm is provided to the user in real time via a communication module, and enables remote management of the outdoor unit's status through a mobile app. Generally, in the case of apartments or buildings, an air conditioner (e.g., an air conditioner) is installed indoors, and an outdoor unit connected to this air conditioner via a control line is installed separately on the outside, such as on a veranda, and an outdoor unit switch is installed on the wall to control the AC power supply to the outdoor unit. Previously, fires occasionally occurred in outdoor units due to equipment aging, dust, or poor contact, and also due to circuit component failures and abnormalities in input current caused by heat generation. Recently, as fire accidents originating from air conditioner outdoor units have become more frequent, the need for fire prevention has emerged. While internal defects, electrical issues, and environmental factors (accumulation of dust or foreign substances) are analyzed as causes of fire, there is a lack of systems to monitor these factors in real time and predict fires. Therefore, conventional fire prevention devices rely on simple temperature detection or face difficulties in real-time monitoring, resulting in insufficient preemptive response to fires. Furthermore, while methods such as manually cleaning the air conditioner or entrusting it to a service center exist, there is a drawback in that no means are presented to automatically detect and prevent the risk of air conditioner fire. FIG. 1 is a schematic overall configuration diagram of the outdoor unit fire prevention subsystem of the artificial intelligence-based air conditioner according to the present invention. FIG. 2 is an exemplary illustration of FIG. 1. FIG. 3 is an operation flowchart of the data processing and analysis module of the present invention. Figure 4 is an example of Figure 3. Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings. FIGS. 1 and 2 are schematic overall configuration diagrams and exemplary diagrams of the fire prevention subsystem for an outdoor unit of an artificial intelligence-based air conditioner according to the present invention, and FIGS. 3 and 4 are flowcharts and exemplary diagrams of the data processing and analysis module according to the present invention. As shown in the figures, the system comprises a multi-sensor module (1) that detects the temperature, current, voltage, dust, and gas conditions in real time by mounting various sensors around the compressor and electrical components of an air conditioner installed outside an apartment, etc., and an energy harvesting module (4) that supplies power to the sensor and communication module by wind power generation using high-temperature wind emitted from the outdoor unit (2) and power generation using a Seebeck element using a temperature difference, an edge computing module (5) that determines the possibility of fire by analyzing collected information data in real time, a communication module (6) that monitors the status of the outdoor unit (2) in real time by transmitting collected and analyzed data to a cloud server or a user's smartphone, and a warning and control module (7) that sends a warning notification to a user's smartphone (8) when danger is detected and controls the operation of the outdoor unit (2). However, the above energy harvesting module (4) has a self-generating power function like solar power generation to operate devices installed in remote mountain villages where it is difficult to supply electricity or small devices used in daily life for several years without battery replacement, and individual devices can collect energy generated from natural energy sources such as sunlight, vibration, heat, and wind and convert it into useful electrical energy for use. However, solar power generation, piezoelectric power generation, electromagnetic power generation, and thermoelectric power generation are used to obtain energy. Electricity is produced using the photoelectric effect, piezoelectric effect, induction phenomenon, and thermoelectric effect. The photoelectric effect is a phenomenon in which materials such as metals emit electrons when they absorb electromagnetic waves with wavelengths shorter than their inherent specific wavelengths. Electrons within a material emit electrons when they absorb photon energy exceeding t