Advanced AI-Powered Universal Control Board with Enhanced Connectivity for HVAC and Refrigeration Systems

Abstract

The present invention is an advanced, artificial intelligence (AI)-powered universal control board with enhanced connectivity for heating ventilation and air conditioning systems (HVAC) herein referred to as the ‘AIPUCB.’ The AIPUCB includes a control board, a plurality of sensors, an edge-based, cloud network and a software application with AI algorithms. When installed inside a conditioned space (such as an air conditioner, heater, walk-in freezer cooling system etc.) the sensors send data to the control board which in turn transmits data to the cloud network wirelessly. AI algorithms on the cloud network analyze the data and make predictions that are used to automatically adjust HVAC conditions in real time. The object of the AIPUCB is to leverage proactive prediction methods and take corrective action measures before problems can arise within heating and cooling systems. These systems can also include other HVAC such as furnaces and even computers.

What is claimed is: 
     
         1 . A system for enhancing performance and efficiency of Heating, Ventilation, and Air Conditioning (HVAC) systems, comprising:
 a. A controller incorporating advanced Artificial Intelligence (AI) processing, dual connectivity modules, precise motor control, and a comprehensive sensor array for optimizing system performance and energy efficiency across various HVAC environments; and   b. A set of sensors including, but not limited to, ambient temperature sensor, RPM sensor, ambient humidity sensor, in-line high pressure sensor, in-line low pressure sensor, in-line high temperature sensor, in-line low temperature sensor, current sensor, voltage sensor, door status sensor, internal temperature sensor, main board current sensor, rear door sensor, and vibration sensors, gas flow meters, air flow meters, carbon monoxide detectors and VOC detectors provide real-time data for system analysis and predictive maintenance.   
     
     
         2 . The system of  claim 1 , wherein said controller further comprises:
 a. A STM32 main processor for managing complex tasks and system operations;   b. Dual ESP32 Modules for mesh network connectivity, enhancing communication range and reliability between multiple devices;   c. An ESP32 for Wi-Fi Access Point mode, web server management, and Bluetooth connectivity;   d. An ethernet port for providing a stable and reliable wired network connection;   e. A real-time clock for accurate timekeeping;   f. An EEPROM and a backup battery for continuous operation during power outages;   g. An ADE9789 IC for managing power consumption and precision in AC load control.   h. A 24V DC and AC power supply for system operation;   i. A PWM for DC or AC motor control implemented through the STM32 processor for precise speed and torque control;   j. A variable frequency drive for controlling DC or AC motor speed and torque;   k. Relays for operation and management of various external devices;   l. Over-the-air updates for automatic wireless firmware updates;   m. Wi-Fi and Bluetooth connectivity, LoRaWAN module, and a mesh network module for enhanced communication capabilities;   n. An OLED display, push buttons, LED indicators, and an audio buzzer for user interface;   o. RS485 communications port and a programming port for interfacing with external systems and programming functionalities; and   p. a software interface to manage multiple HVACs within a single building or within buildings at multiple locations and include mapping of internal, groupings, and multiple facility HVAC locations along with their maintenance records, and log books. Said software having sensor parameters that can be automatically controlled by the AI or manually over-ridden by a user that include but are not limited to: temperature setpoints; run time; coolant line pressures on high and low sides; compressor pressure; door status; pressure switches; emergency shutoff; defrost timer settings; machine tonnage; line set weight; refrigerant type; WiFi; LoRa wan and Mesh settings; and others that may be applicable to HVAC systems.   
     
     
         3 . A method for integrating the AIPUCB system into existing HVAC units, comprising the steps of:
 a. providing the system of  claim 1 ;   b. Installing pressure sensors on both the high and low sides of the compressor and measuring pressure levels within the coolant lines for predictive maintenance and system optimization;   c. Installing temperature sensors and monitoring temperature variations within the compressor, coolant lines, and condenser in real-time for optimal heating and cooling cycles and detecting potential system failures;   d. Installing electrical current sensors and measuring electrical current and voltage flowing through system components in real-time for identifying irregularities in operation and enabling proactive maintenance;   e. Installing door sensors for detecting the status of doors within the system and predicting energy use patterns;   f. Installing vibration sensors for measuring vibrations and movements within the system to predict failures and enable proactive maintenance;   g. Installing air flow sensors for measuring flow rates at intake and exhausts to predict obstructions or blockages in ducts or filters;   h. Installing humidity sensors for measuring humidity levels and adjusting humidification levels to prevent mold growth and maintain occupant comfort; and   i. Installing geolocation sensors for determining the geographical location of the HVAC system and predicting energy usage and comfort levels in response to changing weather patterns.