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1. (WO2018227117) PRÉDICTION DE FAUSSES ALARMES DANS DES SYSTÈMES DE SÉCURITÉ BASÉS SUR DES CAPTEURS
Note: Texte fondé sur des processus automatiques de reconnaissance optique de caractères. Seule la version PDF a une valeur juridique

WHAT IS CLAIMED IS:

1. A computer program product tangibly stored on a computer readable hardware storage device, the computer program product for detecting changes in operational characteristics of a group of sensor devices, the computer program product comprising instructions to cause a processor to:

collect sensor information from plural sensor devices deployed in a system, with the collected sensor information including sensor data and sensor device metadata;

continually analyze the collected sensor information to detect changes in the operational characteristics of a sensor device in the group of sensor devices; upon detection of a change in the operational characteristics of the sensor,

access a database that stores maintenance organization contact information; generate based on the detected changes and the access to the database a request for maintenance on the sensor device; and

send the request to the maintenance organization contact.

2. The computer program product of claim 1 wherein instructions to continually analyze, further comprises instructions to:

execute one or more unsupervised learning models that continually analyze the collected sensor information to detect the changes in the operational characteristics of the sensor;

produce sequences of sensor state transitions;

detect during the continual analysis of the sensor data that the detect changes in the operational characteristics corresponds to one or more of the sequences of sensor state transitions that corresponds to the sensor device is in a drift sequence, by correlating the determined drift state sequence to a stored determined condition to determine whether a potential false alarm could occur by the sensor being in the drift sequence;

convert states corresponding to sensor device values into a semantic representation of the state; and

assign a label to the semantic representation of the state.

3. The computer program product of claim 1 wherein the sensor data is collected continuously.

4. The computer program product of claim 1 wherein for the produced states the instructions

determine from state transition metrics that are stored in a state transition matrix, occurrence of a drift condition for one or more of the sensor devices;

determine from the sensor device metadata, age of the one or more sensor devices in the drift condition; and

access a database by sensor device type to retrieve metadata on sensor devices of the type of the one or more sensor devices in the drift condition, and having an age approximate to the determined age of the sensor device.

5. The computer program product of claim 1 wherein for the produced states the instructions

generate a second set of alerts based on the determined drift condition and the age of the sensor device, a request for maintenance on the one or more of the sensor devices in the determined drift condition, and for those sensor devices having the approximate age.

6. A system comprises:

a gateway to couple the plural sensors to a network;

a server computer comprising processor and memory, the server computer coupled to the network;

a storage device storing a computer program product for detecting conditions at a premises, the computer program product comprising instructions to cause the server to:

collect sensor information from plural sensor devices, with the collected sensor information including sensor data and sensor device metadata;

execute one or more unsupervised learning models to continually analyze the collected sensor information to produce operational states of sensor information;

produce sequences of sensor state transitions;

detect during the continual analysis of sensor data that one or more of the sequences of sensor state transitions corresponds to one of the sensor device being in a drift sequence, by correlating the determined drift state sequence to a stored determined condition to determine whether a potential false alarm could occur by the sensor being in the drift sequence;

access a database that stores maintenance organization contact information; and

generate an alert based on the determined condition and the access to the database with the alert being a request for maintenance on the one of the sensor devices.

7. The system of claim 6 wherein the computer program product, further comprises instructions to:

convert states corresponding to sensor device values into a semantic representation of the state; and

assign a label to the semantic representation of the state.

8. The system of claim 6 wherein the system monitors a premises and is installed at the premises and further comprises:

plural sensor devices installed at the premises.

9. The system of claim 6 wherein the system monitors a premises and is remote from the premises.

10. The system of claim 6 wherein the sensor data is collected continuously.

11. The system of claim 6 wherein for the produced states the instructions: determine from state transition metrics that are stored in a state transition matrix, occurrence of a drift condition for one or more of the sensor devices;

determine from the sensor device metadata, age of the one or more sensor devices in the drift condition;

access a database by sensor device type to retrieve metadata on sensor devices of the type of the one or more sensor devices in the drift condition, and having an age approximate to the determined age of the sensor device.

12. The system of claim 6 wherein for the produced states the instructions generate a second set of alerts based on the determined drift condition and the age of the sensor device, a request for maintenance on the one or more of the sensor devices in the determined drift condition, and for those sensor devices having the approximate age.

13. A computer implemented method comprises:

collecting sensor information from plural sensor devices deployed in a system, with the collected sensor information including sensor data and sensor device metadata;

executing one or more unsupervised learning models to continually analyze the collected sensor information to produce operational states of sensor information; producing sequences of sensor state transitions;

detecting during the continual analysis of sensor data that one or more of the sequences of sensor state transitions corresponds to one of the sensor device being in a drift sequence, by correlating the determined drift state sequence to a stored determined condition to determine whether a potential false alarm could occur by the sensor being in the drift sequence;

determining from the sensor device metadata, age of the one or more sensor devices in the drift condition;

accessing a database by sensor device type to retrieve metadata on sensor devices of the type of the one or more sensor devices in the drift condition, and having an age approximate to the determined age of the sensor device;

accessing a database that stores maintenance organization contact information; and

generating an alert based on the determined condition and the access to the database with the alert being a request for maintenance on the one of the sensor devices.