Patent ID: 11859767
Assignee: CENTRI GROUP INC.
Field: Digital communication (Electrical engineering)
Classification: CPC F  G  H  Y | IPC F  G  H

Claim 0:
1. A method for monitoring an amount of a commodity in a remote storage container via a system, said method comprising:
(i) measuring via a sensor the amount of the commodity in the remote storage container and outputting an analog or digital signal, wherein if the signal outputted by the sensor is an analog signal, the method further comprises converting the analog signal to a digital signal;
(ii) packaging the digital signal into a data file;
(iii) publishing via a wireless connection the data file to a message query telemetry transport (MQTT) broker for access by a user;
(iv) receiving confirmation that the MQTT broker received the data file;
(v) accessing by the user information from the data file; and
(vi) repeating steps (i) to (v) after a predetermined time;
wherein the system further comprises a solar cell;
wherein the method further includes storing as a timeseries energy generation data including an amount of energy generated by the solar cell over each of a series of time periods, each time period being an amount of time that is between a current step (iii) and a step (iii) immediately preceding the current step (iii), and storing as a timeseries energy consumption data including an amount of energy usage of the system over each of the series of said time periods;
wherein after receiving confirmation that the MQTT broker received the data file, the method further comprises putting the system into a sleep mode for the predetermined time prior to (vi) repeating steps (i) to (v);
wherein the method cycles between steps (i) to (v) and the sleep mode based on a determined schedule, the determined schedule is determined by a machine learning algorithm based on a calculation of an average energy generated by the solar cell using the timeseries stored in the energy generation data and a calculation of an average energy usage using the timeseries stored in the energy consumption data, and the method further comprises determining an optimum determined schedule by the machine learning algorithm.