Edge computing architecture for pressurized water data

A water usage tracking system (100) for tracking local water usage data to generate a global water usage model, the water usage tracking system (100) comprising:

• a. a plurality of sensor groups, each sensor group comprising a plurality of sensors (900);
• wherein each sensor group is operatively coupled to a plumbing system;
• wherein, for each sensor group, each sensor of the plurality of sensors (900) is disposed at different locations along the plumbing system and is capable of collecting raw data from the plumbing system at a fixed time interval and transmitting the raw data to a local computing system;
• b. a plurality of local computing systems, wherein each local computing system is communicatively coupled to a sensor group, each local computing system comprising:
• i. a cleaning module (200) comprising instructions for:
• A. receiving the raw data from the sensor group;
• and
• B. cleaning the raw data, wherein cleaning the raw data comprises removing garbage values comprising data values exceeding a maximum threshold and falling below a minimum threshold, and adjusting for missing values that were not correctly collected at the fixed time interval;
• ii. a preprocessing module (300) comprising instructions for:
• A. converting the cleaned raw data into a plurality of features and a set of anomaly data;
• and
• iii. a decision making module (400) comprising instructions for:
• A. building a local water usage model based on the plurality of features, the local water usage model comprising a machine learning model;
• B. predicting, based on the set of anomaly data and the local water usage model, a plurality of potential anomalies;
• C. adding the plurality of potential anomalies to the set of anomaly data;
• D. alerting a user of each anomaly of the set of anomaly data;
• and
• E. transmitting the local water usage model to an edge computing system (800);
• c. the edge computing system (800) communicatively coupled to the plurality of local computing systems and a cloud computing system to allow for federated learning, the edge computing system (800) comprising:
• i. a plurality of edge servers for receiving the local water usage model and the set of anomaly data from each local computing system of the plurality of local computing systems and receiving a global water usage model and a set of global anomaly data from the cloud computing system;
• wherein the global water usage model and the set of global anomaly data are used to retrain the decision making module of each local computing system;
• and
• ii. a compilation module comprising instructions for:
• I. compiling the local water usage models and the sets of anomaly data from the plurality of edge servers into compiled local data to send to the cloud computing system;
• II. sending the compiled local data to the cloud computing system;
• III. compiling the global water usage model and the set of global anomaly data from the cloud computing system into compiled global data to send to the plurality of edge servers;
• and
• IV. sending the compiled global data to the plurality of local computing systems;
• and
• d. the cloud computing system communicatively coupled to the edge computing system (800) comprising:
• i. a training module (600) comprising instructions for:
• I. accepting the local water usage models from the edge computing system (800),
• II. aggregating the local water usage models into a plurality of parameters,
• III. constructing the global water usage model based on the plurality of parameters, and
• IV. transmitting the global water usage model to a central server (700) and a feedback module (500);
• and
• ii. the feedback module (500) comprising instructions for:
• A. receiving the sets of anomaly data from the edge computing system (800);
• B. compiling the sets of anomaly data into the set of global anomaly data;
• C. sending the set of global anomaly data to the edge computing system (800);
• D. analyzing water usage behavior based on the global water usage model;
• and
• E. transmitting the set of global anomaly data and the water usage behavior to a plurality of external output sources;
• and
• iii. the central server (700) for passing the global water usage model and the set of global anomaly data to the edge computing system (800) for retraining the plurality of decision making modules (400) of the plurality of local computing systems.