The possibility of a work machine 40 being affected by a disaster in a second designated area including an existence position of the work machine 40 is predicted based on an amount of rainfall in a first designated area. A hazard map representing a result of the prediction of the possibility of the work machine 40 being affected by a disaster in the second designated area is outputted to a remote output interface 220 in a remote operation apparatus 20 (a client) (or a management output interface 620 in a management client 60). Accordingly, a user can take measures to reduce the possibility of the work machine being affected by a disaster, for example, to communicate with the persons involved in order to move the work machine 40 from a current position.

The possibility of a work machine 40 being affected by a disaster in a second designated area including an existence position of the work machine 40 is predicted based on an amount of rainfall in a first designated area. A hazard map representing a result of the prediction of the possibility of the work machine 40 being affected by a disaster in the second designated area is outputted to a remote output interface 220 in a remote operation apparatus 20 (a client) (or a management output interface 620 in a management client 60). Accordingly, a user can take measures to reduce the possibility of the work machine being affected by a disaster, for example, to communicate with the persons involved in order to move the work machine 40 from a current position.

An example wear detection system receives first image data related to at least one ground engaging tool (GET) of a work machine from one or more sensors at a first time instance in a dig-dump cycle of the work machine. The wear detection system processes the first image data to determine a first wear measurement and first wear level for the at least one GET. The wear detection system determines whether the first wear level is indicative of a GET replacement condition. The wear detection system generates an alert when the first wear level is indicative of the GET replacement condition. The wear detection system receives second image data related to the at least one GET a second time instance different from the first time instance when the first wear level is not indicative of the GET replacement condition and determines a second wear measurement and second wear level for the at least one GET. The wear detection system generates an alert indicative of the first wear level and the second wear level based on determining that the first wear level and the second wear level are indicative of the GET replacement condition.

An example wear detection system receives first image data related to at least one ground engaging tool (GET) of a work machine from one or more sensors at a first time instance in a dig-dump cycle of the work machine. The wear detection system processes the first image data to determine a first wear measurement and first wear level for the at least one GET. The wear detection system determines whether the first wear level is indicative of a GET replacement condition. The wear detection system generates an alert when the first wear level is indicative of the GET replacement condition. The wear detection system receives second image data related to the at least one GET a second time instance different from the first time instance when the first wear level is not indicative of the GET replacement condition and determines a second wear measurement and second wear level for the at least one GET. The wear detection system generates an alert indicative of the first wear level and the second wear level based on determining that the first wear level and the second wear level are indicative of the GET replacement condition.

Embodiments for managing real-time alerts using machine learning are disclosed. For example, a method includes receiving real-time data for one or more parameters of a device for which an alert is to be generated, from one or more sources associated with the device, and selecting a first machine learning model from a plurality of machine learning models based on the received real-time data. The method further includes determining at least one anomaly in the device based on the selected first machine learning model and predicting an impact of the determined at least one anomaly based on a second machine learning model of the plurality of machine learning models. Furthermore, the method includes generating the alert for the device in real-time based on the predicted impact of the determined at least one anomaly and receiving feedback on the generated alert in real-time.

Embodiments for managing real-time alerts using machine learning are disclosed. For example, a method includes receiving real-time data for one or more parameters of a device for which an alert is to be generated, from one or more sources associated with the device, and selecting a first machine learning model from a plurality of machine learning models based on the received real-time data. The method further includes determining at least one anomaly in the device based on the selected first machine learning model and predicting an impact of the determined at least one anomaly based on a second machine learning model of the plurality of machine learning models. Furthermore, the method includes generating the alert for the device in real-time based on the predicted impact of the determined at least one anomaly and receiving feedback on the generated alert in real-time.

Methods and systems including computer programs encoded on a computer storage medium, for receiving, for a multi-tenant dwelling unit (MDU), a map of the MDU, where the map includes locations corresponding to multiple sensors at the MDU and defines multiple sub-areas of the MDU, receiving sensor data from one or more sensors of the plurality of sensors, where the sensor data is indicative of a fire event at the MDU, determining, from the sensor data, one or more sub-areas of the multiple sub-areas included in the fire event, generating, based on the sensor data, a targeted fire event response for the one or more sub-areas of the multiple sub-areas of the MDU, and providing, to the one or more sub-areas of the multiple sub-areas, the targeted fire event response.

A safety system for residential and commercial use includes a plurality of sensor modules that may be distributed about the environment, and that are in communication with a remote server environment and with other devices over a wireless communication network (e.g., cellular, Wi-Fi). Each sensor module includes a plurality of sensors that are capable of measuring or detecting characteristics of the environment such as smoke, small particulate, large particulate, chemicals, gasses, temperature, humidity, pressure, geolocation, and other characteristics. Analysis of sensor data is performed locally on the sensor module, as well as remotely on a server, in order to fuse and consider multiple sensor data points to identify emergency and non-emergency scenarios. By fusing and analyzing sensor data emergencies can be detected more quickly, and false alarms can be filtered out and avoided.

A method and an apparatus for early warning of climbing behaviors, an electronic device, and a storage medium are disclosed. The method includes: acquiring video image data including a monitored target and at least one object (11); acquiring behavior information of the at least one object when it is determined that the at least one object enters a target area corresponding to the monitored target (12); marking video frames in which the at least one object is included when it is determined that the behavior information indicates that the at least one object climbs the monitored target (13). By marking the video frames in the video image data, the behavior of the object climbing the monitored target can be found in time, and the management efficiency can be improved.

A method and an apparatus for early warning of climbing behaviors, an electronic device, and a storage medium are disclosed. The method includes: acquiring video image data including a monitored target and at least one object (11); acquiring behavior information of the at least one object when it is determined that the at least one object enters a target area corresponding to the monitored target (12); marking video frames in which the at least one object is included when it is determined that the behavior information indicates that the at least one object climbs the monitored target (13). By marking the video frames in the video image data, the behavior of the object climbing the monitored target can be found in time, and the management efficiency can be improved.