Systems and methods for automated, artificial intelligence based monitoring of, and/or alerting for, digital out of home display units

A method for automated, artificial intelligence (“AI”) based monitoring of, and alerting for, display units for digital out of home applications, said method comprising:

• receiving data from display units at a monitoring subsystem, said data including sensor data and operational status data, each of the display units being installed at a respective geographic location and including a structural subassembly, a display subassembly connected to the structural subassembly, sensors for measuring conditions at the respective display unit, airflow pathways, fans for cooling the display units located along the airflow pathways, and electronics for operating the display units, including controlling the display subassembly, controlling the fans, and receiving data from the sensors, and the monitoring subsystem being in electronic communication with the display units and comprising one or more databases and one or more computing devices;
• tagging the received data, including with a respective unique display unit identifier corresponding to the respective one of the display units from which the received data originated;
• analyzing the received, tagged data using an AI model trained using historical data from same or different ones of the display units to identify if the received, tagged data indicates that any of the display units are experiencing any of:
• abnormal conditions and maintenance needs within a predetermined time period, wherein the historical data includes the sensor data and the operational status data from the same or different ones of the display units tagged as one of normal and abnormal;
• and
• generating an alert and electronic service request for each of the abnormal conditions and maintenance needs identified.

A system for automated, artificial intelligence (“AI”) based monitoring of, and alerting for, display units for digital out of home applications, said system comprising:

• the display units, each installed at a respective geographic location and including a structural subassembly, a display subassembly connected to the structural subassembly and comprising a cover, a liquid crystal layer behind the cover, and a backlight behind the liquid crystal layer, sensors for measuring conditions at the respective display unit, airflow pathways, fans for cooling the display units located along the airflow pathways, and a controller for operating the display units, said controller configured to control at least certain operations of the display subassembly, including illumination levels of the backlight, and at least certain operations of the fans, including operating speed levels, and receive data from the sensors, including temperature readings from temperature sensors, humidity readings from a humidity sensor, location information from a location sensor, pressure readings from pressure sensors, ambient light levels from an ambient light sensor, and dew point spread (“DPS”) readings derived from the humidity readings and the temperature readings from at least one of the temperature sensors;
• and
• a monitoring subsystem in electronic communication with the display units, the monitoring subsystem comprising one or more non-transitory electronic storage devices comprising software instructions, which when executed, configure one or more processors of the monitoring subsystem to:
• establish a repository of historical data including sensor data from the sensors and operational status data from the controllers for the display units, each portion of said repository of said historical data being tagged as one of normal and abnormal operating conditions;
• establish normal operating parameters for the display units;
• receive further data from the display units, including the sensor data and the operational status data, sampled at a first rate;
• analyze the received, tagged further data to determine if one or more of the normal operating parameters is violated, and if so:
• begin sampling the further data from the respective display unit at a second rate, which is higher than the first rate;
• identify a selection of the historical data of the repository having at least one of the sensor data and the operational status data within a predetermined margin of the sensor data and the operational status data, respectively, of the received, tagged further data determined to be violating the one or more of the normal operating parameters;
• retrieve the selection of the historical data from the repository of the historical data;
• utilize the selection of the historical data to train an AI model;
• analyze the further data indicated as violating the normal operating parameters, including the further data sampled at the second rate, using the AI model to identify if the received data indicates that any of the display units are likely experiencing abnormal conditions, including based on the tags applied to the selection of the historical data;
• generate an alert and electronic service request for each of the abnormal conditions identified;
• tag the received data indicated as violating the normal operating parameters with the abnormal tag;
• and
• add the received, tagged data to the repository of the historical data;
• where any of:
• the analysis of the received data determines that none of the normal operating parameters is violated, and the analysis of the received data using the AI model indicates that no abnormal conditions are detected:
• tag the received data with the normal tag;
• and
• add the received, tagged data to the repository of the historical data.