A lightning strike alarm system using a Bipolar Conventional Air Terminal comprises a sensor electrically connected to a rod element electrified by a ground charge and an electrification plate-cone or an electrification plate-tube electrified by a thunderstorm cloud, and configured to measure electric energy induced in the electrification plate-cone or the electrification plate-tube; and a controller configured to determine that the thunderstorm cloud has approached when the electrical energy is greater than a reference energy; and an alarm device configured to output an alarm signal, based on a result of determining that the thunderstorm cloud has approached, wherein the sensor comprises: a luminescence unit, including a voltage stabilizer preventing induction of a high voltage due to the thunderstorm cloud, configured to emit light by the electric energy; and a light receiver configured to receive the light, convert the light to electric energy, and transfer the electric energy to the controller.

A server includes: a registration unit that registers identification information for identifying a detection target in a storage unit; a detection target determination unit that determines whether the detection target is shown in an image captured on or around a road, based on the identification information; an abnormal behavior determination unit that determines whether the detection target exhibits an abnormal behavior that is different from a normal behavior of the detection target, when the detection target is shown in the image; and an alert transmission unit that transmits an alert when the detection target exhibits the abnormal behavior.

Systems and methods for operating a security system. The methods comprise: monitoring an area for a presence of an individual or vehicle using wireless signals of a first type that are received by a first radio of the security system; operating the first radio as a wireless sensor for generating sensor data; determining whether the individual or vehicle is exhibiting an unusual behavior using the sensor data and pre-programmed or machine learned patterns of unusual behavior; changing an operating frequency of the first radio when a determination is made that the individual or vehicle is exhibiting unusual behavior; and communicating a wireless signal of a second type from the first radio when the individual or vehicle is exhibiting unusual behavior, where the second type is different than the first type.

A method for monitoring and securing a facility without employing a manned control center, according to which a server acquires surveillance data from each of a plurality of distributed surveillance devices deployed within or adjacent to a given security sensitive facility. Then the server receives an intrusion alert from a guard, or from one or more of the plurality of surveillance devices to initiate a triggering event, if the acquired surveillance data is indicative of activity that has been detected at an indicated location. A first guard is alerted and presented with a full situational awareness image to assess the severity of the detected activity and the triggering event is terminated if found by the first guard to be of a low severity, or an interception operation initiated by self-dispatching or dispatching one or more additional guards to a close by location that holds tactical advantage relative to the indicated location if the triggering event was found by the first guard to be of a high severity.

A method for monitoring and securing a facility without employing a manned control center, according to which a server acquires surveillance data from each of a plurality of distributed surveillance devices deployed within or adjacent to a given security sensitive facility. Then the server receives an intrusion alert from a guard, or from one or more of the plurality of surveillance devices to initiate a triggering event, if the acquired surveillance data is indicative of activity that has been detected at an indicated location. A first guard is alerted and presented with a full situational awareness image to assess the severity of the detected activity and the triggering event is terminated if found by the first guard to be of a low severity, or an interception operation initiated by self-dispatching or dispatching one or more additional guards to a close by location that holds tactical advantage relative to the indicated location if the triggering event was found by the first guard to be of a high severity.

The disclosure provides a method for predicting gas leakage based on indoor safety and a smart gas Internet of Things system. The method includes obtaining a gas data of a first area from at least one gas detection device; obtaining the gas data of the first area from a smart gas data center, predicting a gas concentration of a second area; judging whether the gas leakage occurs and determining a diffusion range of the gas leakage, and determining an emergency treatment plan; sending gas leakage information to the smart gas data center, and sending the gas leakage information to a smart gas user platform; and generating a remote control instruction based on the emergency treatment plan and sending the remote control instruction to the smart gas data center, and sending the remote control instruction to an indoor gas device object platform to execute control.

A technique is introduced for utilizing data associated with a monitored premises to determine a likelihood of a crime, or other activity, occurring at the premises. In an example embodiment, premises data is received from one or more sources including sensor devices located at the premises and other data sources including third-party databases. The premises data is processed using a machine learning model, such as an artificial neural network, to generate a risk score that is indicative of the likelihood of a crime occurring at the premises in real-time or in the future. The introduced technique for risk evaluation can be implemented in conjunction with a premises security system, for example, to route alarms generated by monitoring devices located at the premises.

The present disclosure provides a self-adaption faster-than-real-time method for estimating a working condition start-up state of a unit. Firstly, an event recording sequence, an analog measurement point ID, an analog measurement point first-level alarm value and a history scatter point distribution record are read from a time sequence event record table, an analog measurement point table, an alarm threshold table and a historical scatter record table. A measured value slope of the analog measurement point is calculated according to an event recording relative time. Then, an abnormal state estimated value is calculated based on the history scatter point distribution record, the analog measurement point first-level alarm value and the measured value slope of the analog measurement point. Finally, a pre-warning is sent to remind a watchman to perform a preventive operation when the abnormal state estimated value is above the threshold.

A method includes executing, on a processor of a computing device, instructions that cause the computing device to perform operations. The operations include executing, on a processor of a computing device, instructions that cause the computing device to perform operations. The operations include receiving site features associated with a communication site and receiving event features associated with a potential outage-causing event. A classifying engine is employed to generate an impact metric indicating an effect on the communication site from the potential outage-causing event based on the site features and the event features.

A method includes executing, on a processor of a computing device, instructions that cause the computing device to perform operations. The operations include executing, on a processor of a computing device, instructions that cause the computing device to perform operations. The operations include receiving site features associated with a communication site and receiving event features associated with a potential outage-causing event. A classifying engine is employed to generate an impact metric indicating an effect on the communication site from the potential outage-causing event based on the site features and the event features.