초록 ▼

Embodiments relate to a container that can be installed at a remote location, detect a disaster event, and automatically deploy a UAV. In response to detection of the disaster event, such a container may be configured to: (i) determine whether or not one or more weather conditions affecting operatio

Embodiments relate to a container that can be installed at a remote location, detect a disaster event, and automatically deploy a UAV. In response to detection of the disaster event, such a container may be configured to: (i) determine whether or not one or more weather conditions affecting operation of an unmanned aerial vehicle (UAV) are conducive to deployment of the UAV to fly to the first geographic area, (ii) if the one or more conditions are conducive to deployment of the UAV, then deploy the UAV to fly to the first geographic area, and (iii) if the one or more conditions are not conducive to deployment of the UAV, then monitor the second data until it is determined that the one or more conditions are conducive to deployment of the UAV, and then deploy the UAV to fly to the first geographic area.

대표청구항 ▼

1. A computer-implemented method comprising: receiving, by a control system comprising a processor, first data corresponding to a first geographic area;based at least in part on analysis of the first data, detecting, by the processor an occurrence of a disaster event at the first geographic area; an

1. A computer-implemented method comprising: receiving, by a control system comprising a processor, first data corresponding to a first geographic area;based at least in part on analysis of the first data, detecting, by the processor an occurrence of a disaster event at the first geographic area; andin response to detection of the disaster event, the processor: determining, based on second data, whether or not one or more weather conditions affecting operation of an unmanned aerial vehicle (UAV) are conducive to deployment of the UAV to fly to the first geographic area;when the one or more conditions are conducive to deployment of the UAV, then deploying the UAV to fly to the first geographic area; andwhen the one or more conditions are not conducive to deployment of the UAV, then monitoring the second data until it is determined that the one or more conditions are conducive to deployment of the UAV, and then deploying the UAV to fly to the first geographic area,wherein the UAV is deployed automatically via a computer-automated deployment system. 2. The method of claim 1, wherein the first data comprises image data of at least a portion of the first geographic area, and wherein detecting the disaster event comprises: analyzing the image data for one or more indications of the disaster event; anddetecting, in the image data, at least one of the one or more indications of the disaster event. 3. The method of claim 1, wherein the first data comprises audio data corresponding to at least a portion of the first geographic area, and wherein detecting the disaster event comprises: analyzing the audio data for one or more indications of the disaster event; anddetecting, in the audio data, at least one of the one or more indications of the disaster event. 4. The method of claim 1, wherein detecting the disaster event comprises detecting an abnormal data pattern in the first data. 5. The method of claim 4, wherein the first data comprises data indicating ground-level light intensity in the first geographic area, and wherein detecting the abnormal data pattern comprises detecting an abnormal change in ground-level light intensity. 6. The method of claim 4, wherein the first data comprises data indicating levels of particulate matter in the air in first geographic area, and wherein detecting the abnormal data pattern comprises data indicating particulate matter in the first geographic area, detecting an abnormal change in the level of particulate matter in the air. 7. The method of claim 4, wherein the first data comprises data indicating use of one or more data networks in the first geographic area, and wherein detecting the abnormal data pattern comprises detecting an abnormal level of network usage in the first geographic area. 8. The method of claim 4, wherein the first data comprises data indicating social network activity originating from the first geographic area, and wherein detecting the abnormal data pattern comprises detecting an abnormal level of social network activity originating from the first geographic area. 9. The method of claim 1, wherein determining whether or not the one or more weather conditions affecting operation of the UAV are conducive to deployment of the UAV comprises: determining whether or not wind speed in the first geographic area is above a threshold. 10. The method of claim 1, wherein determining whether or not the one or more weather conditions affecting operation of the UAV are conducive to deployment of the UAV comprises: determining whether or not precipitation in the first geographic area is conducive to deployment of the UAV. 11. A computer-implemented method comprising: receiving, at a control system for an unmanned-aerial-vehicle (UAV) deployment system, sensor data corresponding to a first geographic area, wherein the sensor data comprises first wind data, and wherein the control system comprises a processor;based at least in part on the sensor data, detecting, by the processor, a disaster event at the first geographic area, wherein the disaster event is of a type associated with high wind speed; andin response to detecting the disaster event, the processor: (a) receiving second wind data corresponding to at least one potential flight path to the first geographic area;(b) determining, based at least in part on the second wind data, whether or not weather conditions associated with the at least one potential flight path are conducive to deployment of the UAV to fly to the first geographic area;(c) when the weather conditions are conducive to deployment of the UAV, then deploying the UAV to fly to the first geographic area; and(d) when the weather conditions are not conducive to deployment of the UAV, then monitoring the second wind data until it is determined that the one or more wind parameters are conducive to deployment of the UAV to fly to the first geographic area, and then deploying the UAV to fly to the first geographic area,wherein the UAV is deployed automatically via a computer-automated deployment system. 12. The method of claim 11, wherein the disaster event comprises one of a hurricane, a tornado, a tropical storm, and a tsunami. 13. The method of claim 11, wherein the first wind data comprises information regarding wind speed in the first geographic area, and wherein detecting the disaster event at the first geographic area comprises: comparing the wind speed in the first geographic area to a threshold; anddetermining that the wind speed in the first geographic area is above the threshold. 14. The method of claim 11, wherein the sensor data further comprises data indicating atmospheric pressure in the first geographic area, and wherein detecting the disaster event further comprises: detecting at least a threshold change in the atmospheric pressure in the first geographic area. 15. The method of claim 11, wherein monitoring the second wind data until it is determined that the one or more wind parameters are conducive to deployment of the UAV to fly to the first geographic area comprises waiting until the wind speed in the geographic area is below a second threshold. 16. The method of claim 11, wherein determining, based at least in part on the second wind data, whether or not weather conditions associated with the at least one potential flight path are conducive to deployment of the UAV comprises: using wind data for one or more locations on the at least one potential flight path to determine whether or not weather conditions associated with the at least one potential flight path are conducive to deployment of the UAV, wherein at least one of the one or more locations is a location other than a deployment location. 17. An apparatus comprising: an unmanned aerial vehicle;a container configured to at least partially enclose the unmanned aerial vehicle; anda control system comprising a processor, wherein the control system is operable to: receive sensor data corresponding to a first geographic area;based at least in part on the sensor data, detect an occurrence of a disaster event at the first geographic area, wherein the disaster event is of a type associated with high wind speed; andin response to detection of the disaster event: (a) receive wind data indicating one or more wind parameters corresponding to a potential flight-path area to the first geographic area;(b) determine whether or not the one or more wind parameters are conducive to deployment of the UAV to fly to the first geographic area;(c) when the one or more wind parameters are conducive to deployment of the UAV, then automatically deploy the UAV to fly to the first geographic area; and(d) when the one or more wind parameters are not conducive to deployment of the UAV, then monitor the wind data until it is determined that the one or more wind parameters are conducive to deployment of the UAV to fly to the first geographic area, and then automatically deploy the UAV to fly to the first geographic area. 18. The apparatus of claim 17, wherein the disaster event comprises one or more of a forest fire, a hurricane, a tornado, a tropical storm, and a tsunami. 19. The apparatus of claim 17, wherein the first wind data comprises information regarding wind speed in the first geographic area, and wherein detection of the disaster event at the first geographic area comprises: a comparison of the wind speed in the first geographic area to a threshold; anda determination that the wind speed in the first geographic area is above the threshold. 20. The apparatus of claim 17, wherein the apparatus further comprises one or more anemometers, and wherein the sensor data comprises data comprises data from the one or more anemometers. 21. The apparatus of claim 17, wherein the apparatus further comprises a barometer, and wherein the sensor data comprises data from the barometer.