Multi-access edge computing for remote locations

1 . A system comprising:

a plurality of sensor devices that are configured to collect construction site sensor data and transmit the sensor data to a local computing system;

the local computing system that is configured to combine the sensor data with user feedback data and transmit the combined data to an edge computing system;

the edge computing system that is configured to process the combined data and transmit the combined data to a cloud computing system; and

the cloud computing system that is configured to process the transmitted data from the edge computing system,

wherein the edge computing system and the cloud computing system are configured to execute a site risk prediction application, using machine learning models, and predict a hazard within a construction site based on the collected construction site sensor data and generate an output signal to equipment operating at the construction site,

wherein the cloud computing system is configured to provide instructions to the edge computing system to adjust its machine learning model based on the user feedback data.

2 . The system of claim 1 , wherein the output signal causes the equipment to discontinue operation.

3 . The system of claim 1 , wherein the output signal comprises a safety alert.

4 . The system of claim 1 , wherein the cloud computing system is configured to integrate the sensor data and user feedback data with internet-enabled data, wherein the integrated data is analyzed to generate the control signal.

5 . The system of claim 1 , wherein the sensor data comprises motion data, proximity data, location data, thermal data, air quality data, humidity data, or Radio Frequency Identification (RFID) data.

6 . The system of claim 1 , wherein the user feedback data comprises video captured from a wearable camera.

7 . A method comprising:

obtaining, by an edge computing device, a plurality of sensing data within a remote location in combination with user feedback data from the remote location;

analyzing, by the edge computing device using a machine learning model, at least the plurality of sensing data and user feedback data and generating a control signal for adjusting performance of equipment operating at the remote location;

transmitting, by the edge computing device, the control signal to the equipment operating at the remote location;

transmitting, by the edge computing device, the plurality of sensing data and the user feedback data to a cloud computing system;

receiving, by the edge computing device from the cloud computing system, instructions to the edge computing system to adjust its machine learning model based on the user feedback data transmitted from the edge computing device; and

adjusting, by the edge computing device, the machine learning model based on the instructions from the cloud computing system.

8 . The method of claim 7 , wherein the sensing data comprises motion data, proximity data, location data, or thermal data.

9 . The method of claim 7 , further comprising integrating the sensing data and user feedback data with internet-enabled data, wherein the integrated data is analyzed to generate the control signal.

10 . The method of claim 9 , wherein the internet-enabled data comprises weather data.

11 . The method of claim 7 , wherein the remote location comprises a construction site.

12 . The method of claim 7 , wherein the user feedback data comprises video captured from a wearable camera.

13 . A non-transitory computer-readable medium having instructions stored therein, wherein the instructions, when executed by a processor, cause the processor to:

obtain a plurality of sensing data within a remote location in combination with user feedback data from the remote location;

analyze, using a machine learning model, at least the plurality of sensing data and user feedback data and generating a control signal for adjusting performance of equipment operating at the remote location;

transmit the control signal to the equipment operating at the remote location;

transmit the plurality of sensing data and the user feedback data to a cloud computing system;

receive, from the cloud computing system, instructions to a edge computing system to adjust its machine learning model based on the user feedback data transmitted from the edge computing system; and

adjust the machine learning model based on the instructions from the cloud computing system.

14 . The non-transitory computer-readable medium of claim 13 , wherein the sensing data comprises motion data, proximity data, location data, or thermal data.

15 . The non-transitory computer-readable medium of claim 13 , wherein the instructions further cause the processor to integrate the sensing data and user feedback data with internet-enabled data, wherein the integrated data is analyzed to generate the control signal.

16 . The non-transitory computer-readable medium claim 15 , wherein the internet-enabled data comprises weather data.

17 . The non-transitory computer-readable medium of claim 13 , wherein the remote location comprises a construction site.

18 . The non-transitory computer-readable medium of claim 13 , wherein the user feedback data comprises video captured from a wearable camera.