Systems and methods for location fencing within a controlled environment
Methods and systems for providing location fencing within a controlled environment are disclosed herein. A location fencing server determines a location of a first inmate based on a first beacon device, and determines a location of a second inmate based on a second beacon device. Further, the location fencing server determines a proximity status based on the location of the first inmate and the location of the second inmate. Additionally, the location fencing server determines that the first inmate and the second inmate are in violation of a proximity policy based on the proximity status. In some embodiments, the location fencing server sends a notification to an employee device based on the violation of a proximity policy.
1. A method, comprising:
assigning a first inmate to a first device;
assigning a second inmate to a second device;
receiving first location information from a first location beacon, the first location information indicating that the first location beacon has detected a first presence of the first device within a first predetermined proximity of the first location beacon;
determining a first inmate location based on the first location information;
receiving second location information from a second location beacon, the second location information indicating that the second location beacon has detected a second presence of the second device within a second predetermined proximity of the second location beacon;
determining a second inmate location based on the second location information;
determining a proximity status corresponding to the first inmate and the second inmate based on a first predictive path of the first inmate and the second inmate location, wherein the first predictive path is determined using a machine learning model trained to determine the first predictive path based on a daily schedule of the first inmate;
determining a predicted violation of a proximity policy based on the proximity status; and
transmitting a notification to an employee device based on the predicted violation of the proximity policy.
2. The method of claim 1 , further comprising:
generating a graphical user interface (GUI) with a graphical representation of the first predictive path;
transmitting the GUI to the employee device for display.
3. The method of claim 1 , further comprising:
transmitting the notification to the first device or the second device warning of the predicted violation of the proximity policy.
4. The method of claim 3 , wherein:
the notification includes instructions for avoiding the predicted violation of the proximity policy.
5. The method of claim 1 , wherein:
the machine learning model is further trained to determine the first predictive path based on historical daily location information of the first inmate.
6. The method of claim 1 , wherein:
the machine learning model is further trained to determine the first predictive path based on historical daily location information of other inmates.
7. The method of claim 1 , wherein:
the machine learning model is further trained to determine the first predictive path based on date information.
8. A non-transitory computer readable medium having instructions stored thereon that when executed by one or more processors of a computing system, cause the computing system to perform operations comprising:
assigning a first inmate to a first device;
assigning a second inmate to a second device;
receiving first location information from a first location beacon, the first location information indicating that the first location beacon has detected a first presence of the first device within a first predetermined proximity of the first location beacon;
determining a first inmate location based on the first location information;
receiving second location information from a second location beacon, the second location information indicating that the second location beacon has detected a second presence of the second device within a second predetermined proximity of the second location beacon;
determining a second inmate location based on the second location information;
determining a proximity status corresponding to the first inmate and the second inmate based on a first predictive path of the first inmate and the second inmate location, wherein the first predictive path is determined using a machine learning model trained to determine the first predictive path based on a daily schedule of the first inmate;
determining a predicted violation of a proximity policy based on the proximity status; and
transmitting a notification to an employee device based on the predicted violation of the proximity policy.
9. The non-transitory computer readable medium of claim 8 , wherein the operations further comprise:
generating a graphical user interface (GUI) with a graphical representation of the first predictive path;
transmitting the GUI to the employee device for display.
10. The non-transitory computer readable medium of claim 8 , wherein the operations further comprise:
transmitting the notification to the first device or the second device warning of the predicted violation of the proximity policy.
11. The non-transitory computer readable medium of claim 10 , wherein:
the notification includes instructions for avoiding the predicted violation of the proximity policy.
12. The non-transitory computer readable medium of claim 8 , wherein:
the machine learning model is further trained to determine the first predictive path based on historical daily location information of the first inmate.
13. The non-transitory computer readable medium of claim 8 , wherein:
the machine learning model is further trained to determine the first predictive path based on historical daily location information of other inmates.
14. The non-transitory computer readable medium of claim 8 , wherein:
the machine learning model is further trained to determine the first predictive path based on date information.
15. A computing system comprising:
a memory storing instructions; and
one or more processors, coupled to the memory, configured to process the stored instructions to:
assign a first inmate to a first device;
assign a second inmate to a second device;
receive first location information from a first location beacon, the first location information indicating that the first location beacon has detected a first presence of the first device within a first predetermined proximity of the first location beacon;
determine a first inmate location based on the first location information;
receive second location information from a second location beacon, the second location information indicating that the second location beacon has detected a second presence of the second device within a second predetermined proximity of the second location beacon;
determine a second inmate location based on the second location information;
determine a proximity status corresponding to the first inmate and the second inmate based on a first predictive path of the first inmate and the second inmate location, wherein the first predictive path is determined using a machine learning model trained to determine the first predictive path based on a daily schedule of the first inmate;
determine a predicted violation of a proximity policy based on the proximity status; and
generate a notification for transmission to an employee device based on the predicted violation of the proximity policy.
16. The computing system of claim 15 , wherein the one or more processors are further configured to:
generate a graphical user interface (GUI) with a graphical representation of the first predictive path for transmission to the employee device for display.
17. The computing system of claim 15 , wherein the one or more processors are further configured to:
generate the notification for further transmission to the first device or the second device, and to include a warning of the predicted violation of the proximity policy.
18. The computing system of claim 17 , wherein the one or more processors are further configured to:
generate the notification to further include instructions for avoiding the predicted violation of the proximity policy.
19. The computing system of claim 15 , wherein:
the machine learning model is further trained to determine the first predictive path based on historical daily location information of the first inmate.
20. The computing system of claim 15 , wherein:
the machine learning model is further trained to determine the first predictive path based on historical daily location information of other inmates; or
the machine learning model is further trained to determine the first predictive path based on date information.