Monitoring moveable entities in a predetermined area
A method and system that enables a computer to determine a cause of occupancy of a predetermined area using real-time location information. The real-time location information is processed to determine occupancy information of each of a plurality of zones of the predetermined area. The (combined) occupancy information is processed using a machine-learning model to predict an occupancy cause of the predetermined zones of the predetermined area.
1. A computer-implemented method of monitoring moveable entities within a predetermined area, the method comprising:
obtaining a stream of real-time location, RTL, information identifying current locations of movable entities within the predetermined area;
processing the stream of real-time location information to generate occupancy information of each of a plurality of predetermined zones of the predetermined area; and
processing the occupancy information using a machine-learning model to thereby determine one or more occupancy causes, each occupancy cause indicating the occurrence of a predetermined event within one or more predetermined zones, wherein the machine-learning model defines a relationship between occupancy information and occupancy causes,
wherein the occupancy information comprises a plurality of sequences of occupancy data entries and the step of processing the occupancy information comprises individually processing each sequence of occupancy data using the machine-learning model to thereby determine one or more occupancy causes, and
wherein the plurality of sequences forms a series of sequences, and each of the series of sequences is associated with a period of time that overlaps a period of time associated with an immediately preceding sequence in the series of sequences.
2. The computer-implemented method of claim 1 , wherein: the occupancy information indicates a number of moveable entities in each of the plurality of predetermined zones of the predetermined area; and
the machine-learning model defines a relationship between a number of moveable entities within one predetermined zone relative to other predetermined zones and occupancy causes.
3. The computer-implemented method of claim 1 , further comprising determining one or more roles of each at least one movable entity, and wherein:
the occupancy information indicates the one or more roles of each movable entity in each of the plurality of predetermined zones of the predetermined area; and
the machine-learning model defines a relationship between the one or more roles of each moveable entity in each predetermined zone and occupancy causes.
4. The computer-implemented method of claim 1 , wherein:
the step of processing the stream of real-time location information comprises generating, as the occupancy information, a sequence among the plurality of sequences of occupancy data entries, each occupancy data entry indicating a number of moveable entities within each of the plurality of predetermined zones of the predetermined area at a single point in time; and
each occupancy data entry is associated with a single point in time later than a previous occupancy data in the sequence, so that the overall sequence indicates a number of moveable entities within each of the plurality of predetermined zones over a period of time.
5. The computer-implemented method of claim 4 , wherein the machine-learning model defines a relationship between the sequence and one or more occupancy causes.
6. The computer-implemented method of claim 1 , further comprising determining one or more roles of each at least one movable entity, wherein the step of generating each sequence of occupancy data entries comprises generating a sequence of occupancy data entries wherein each occupancy data entry indicates a number of moveable entities of each role within each of the plurality of predetermined zones of the predetermined area at a single point in time.
7. The computer-implemented method of claim 1 , wherein determining the one or more occupancy causes comprises determining whether a predetermined event has occurred during a time during which the stream of real-time location, RTL, information is provided.
8. A tangible computer readable storage medium having a computer program stored therein, the computer program comprising code which executes the method of claim 1 when said computer program is run on a computer.
9. A system for monitoring moveable entities within a predetermined area, the system comprising:
a stream receiving unit adapted to obtain a stream of real-time location, RTL, information identifying current locations of movable entities within the predetermined area;
a processing unit adapted to process the stream of real-time location information to generate occupancy information of each of a plurality of predetermined zones of the predetermined area; and
an occupancy cause identifying unit adapted to process the occupancy information using a machine-learning model to thereby determine one or more occupancy causes, each occupancy cause indicating the occurrence of a predetermined event within one or more predetermined zones, wherein the machine-learning model defines a relationship between occupancy information and occupancy causes,
wherein the occupancy information comprises a plurality of sequences of occupancy data entries and the step of processing the occupancy information comprises individually processing each sequence of occupancy data using the machine-learning model to thereby determine one or more occupancy causes, and
wherein the plurality of sequences forms a series of sequence, and each of the series of sequences is associated with a period of time that overlaps a period of time associated with an immediately preceding sequence in the series of sequences.
10. The system of claim 9 , further comprising a role determining unit adapted to determine one or more roles of each at least one movable entity, wherein:
the occupancy information indicates the one or more roles of each movable entity in each of the plurality of predetermined zones of the predetermined area; and
the machine-learning model defines a relationship between the one or more roles of each moveable entity in each predetermined zone and occupancy causes.
11. The system of claim 9 , wherein the processing unit is adapted to processing the stream of real-time location information to generate a sequence among the plurality of sequences of occupancy data entries, each occupancy data entry indicating a number of moveable entities within each of the plurality of predetermined zones of the predetermined area at a single point in time.
12. The system of claim 11 , wherein:
the processing unit is adapted to generate the plurality of sequences of occupancy data entries; and
the occupancy cause identifying unit is adapted to individually process each sequence of occupancy data in the plurality of sequences using a machine-learning model to thereby determine one or more occupancy causes.