Generating event analysis

Abstract

This document describes devices, systems, and methods for generating event analytics. In some examples, one or more embodiments include a memory and a processor for executing instructions stored in the memory to receive information about events in an area of ​​the facility from an event detection system in the facility, receive video streams from cameras in the area of ​​the facility, generate event analytics for the area of ​​the facility based on the information received from the event detection system and the video streams, and send the event analytics to a mobile device located remotely from the computing device.

Description

Technical Field

[0001] This disclosure relates to devices, systems, and methods for generating event analysis. Background Technology

[0002] Facilities such as commercial facilities, office buildings, hospitals, and campuses (e.g., including buildings and outdoor spaces) may have event detection systems that can be triggered during events such as emergencies, like fires, to warn occupants to evacuate. Such event detection systems may include alarm systems with control panels and multiple event devices (e.g., sensors, sounders, pull-out fire alarm boxes, etc.) located throughout the facility (e.g., on different floors and / or in different rooms), which can act when an event (e.g., a hazardous event, a malfunction event, etc.) occurs in the facility. In the example of the event, multiple event devices may notify occupants of the event via alarms and / or other mechanisms. Attached Figure Description

[0003] Figure 1 This is an example of a system for generating event analysis according to one or more embodiments of this disclosure.

[0004] Figure 2 Examples of facilities with emergency events according to one or more embodiments of this disclosure are illustrated.

[0005] Figure 3 Examples of emergency event analysis according to one or more embodiments of this disclosure are illustrated.

[0006] Figure 4 This is an example of a computing device for emergency event analysis according to one or more embodiments of this disclosure. Detailed Implementation

[0007] This document describes devices, systems, and methods for generating event analytics. In some examples, one or more embodiments include a memory and a processor for executing instructions stored in the memory to receive information about events in an area of ​​the facility from an event detection system in the facility, receive video streams from cameras in the area of ​​the facility, generate event analytics for the area of ​​the facility based on the information received from the event detection system and the video streams, and send the event analytics to a mobile device located remotely from the computing device.

[0008] Facilities may utilize event detection systems to alert occupants of the facility to emergencies, such as fires. An event detection system can be a system of devices that operate to collect information about the facility and provide the collected information for analysis. Such event detection systems may also take action based on the collected information, such as providing auditory and / or visual warnings in an emergency. For example, an event detection system may utilize event devices to alert occupants to emergencies occurring in a space, such as fires. As used herein, the term "event device" refers to a device capable of receiving event-related inputs and / or generating event-related outputs. Such event devices may be part of an event detection system within a space / the entire facility and may include devices such as fire equipment, including fire sensors, smoke detectors, heat detectors, carbon monoxide (CO) detectors, or combinations thereof; fire control panels; air quality sensors; interfaces; manual alarm points (MCPs); pull-out fire alarm boxes; input / output modules; aspirating devices; fire doors; and / or auditory / visual devices (e.g., speakers, emitters, flashers, buzzers, microphones, cameras, video displays, video screens, etc.), relay output modules, and other types of event devices. Such event devices may also include self-testing capabilities.

[0009] First responders may face challenges when entering facilities with active events. For example, during a fire, first responders may have limited visibility due to smoke and / or flames. Camera systems utilizing the facility, such as closed-circuit television (CCTV) systems, may also fail to provide information to these first responders, as cameras in CCTV systems may also be obstructed by smoke and / or flames.

[0010] In some cases, an emergency may additionally and / or alternatively cause obstruction of routes through the facility. For example, debris from the event may cause a blockage that could act as an obstacle for first responders to access the facility. These blockages may restrict the movement of first responders and may be dangerous to them. Furthermore, restricted movement caused by debris within the facility may delay or prevent first responders from accessing areas of the facility where other occupants may be trapped, injured, and / or incapacitated. Such obstacles may be invisible to CCTV systems within the facility due to smoke and / or flames.

[0011] The limited visibility and lack of real-time situational awareness caused by flames and / or smoke can increase the personal safety risks to first responders. For example, without such information, first responders may find it difficult to assess the severity, heat distribution, and progression of a fire or other emergency. Additionally, it can make locating victims, identifying entry and / or escape routes more difficult, and delay response efforts.

[0012] According to this disclosure, generating event analysis allows for the identification of objects of interest within a facility and contextual information about an ongoing emergency, which can help first responders respond effectively to the emergency. Event analysis can include identified objects of interest, such as obstructions to entrance / exit routes, the presence of a fire in the area, temperature information, and / or whether anyone (such as a resident of the facility) is located in the area. Event analysis can utilize the thermal imaging capabilities of CCTV cameras to identify objects of interest, allowing for object identification even in the presence of smoke or flames that would otherwise obscure normal CCTV cameras. Therefore, compared to previous methods, generating event analysis according to this disclosure allows first responders to more easily assess emergencies, making it easier to locate victims and / or identify entrance / exit routes, while reducing personal risk to first responders.

[0013] In the following detailed description, reference is made to the accompanying drawings, which form part of the detailed description. The drawings illustrate, by way of example, how one or more embodiments of this disclosure can be practiced.

[0014] These embodiments are described in sufficient detail to enable one or more embodiments of this disclosure to be practiced by a person skilled in the art. It should be understood that other embodiments may be utilized and process, electrical and / or structural changes may be made without departing from the scope of this disclosure.

[0015] It should be understood that elements shown in the various embodiments herein may be added, interchanged, combined, and / or eliminated to provide several additional embodiments of this disclosure. The scale and relative dimensions of the elements provided in the accompanying drawings are intended to illustrate embodiments of this disclosure and should not be construed as limiting.

[0016] The figures in this document follow the following numbering convention: one or more first digits correspond to the figure number, while the remaining digits identify elements or parts in the figure. Similar elements or parts between different figures can be identified by using similar digits. For example, 102 could refer to... Figure 1 The component "02" in the text, and similar components in Figure 4 The middle part can be represented by 402.

[0017] As used in this article, “one,” “a,” or “several” can refer to one or more such things, while “multiple” can refer to more than one such thing. For example, “several parts” can refer to one or more parts, while “multiple parts” can refer to more than one part.

[0018] Figure 1This is an example of a system 100 for generating event analysis according to one or more embodiments of the present disclosure. System 100 may include a computing device 102, an event detection system 104, a gateway 110, a camera 112, and a mobile device 114. Event detection system 104 may include a control panel 106 and an event device 108.

[0019] As described above, system 100 may be included in a facility, the space of the facility, etc. System 100 may include a device / a set of devices for detecting events and / or processing and / or analyzing the detected events to determine whether to generate an alarm for the occupants of the facility.

[0020] For example, system 100 may include event device 108. Event device 108 may be a device that detects events and transmits the detected events for processing and / or analysis. As described above, event device 108 may be, for example, a camera, motion sensor, fire equipment including fire sensors, smoke detectors, heat detectors, carbon monoxide (CO) detectors, or combinations thereof; a fire control panel; an air quality sensor; an interface; a manual alarm point (MCP); a pull-out fire alarm box; an input / output module; an aspirating device; a fire door; and / or auditory / visual devices (e.g., speakers, sound generators, flashers, buzzers, microphones, cameras, video displays, video screens, etc.), relay output modules, and other types of event devices. Additionally, event device 108 may also include self-testing capabilities.

[0021] although Figure 1 A single event device 108 is illustrated, but the implementation is not limited thereto. For example, system 100 may include multiple event devices 108.

[0022] System 100 may also include a control panel 106 as part of event detection system 104. Control panel 106 can be used to control event devices 108 included in system 100.

[0023] Control panel 106 can be connected to event device 108, send multiple commands to event device 108, and / or provide power to event device 108. Control panel 106 can apply voltage to the event device loop to power the event devices in the loop. Such power supply can allow event device 108 to perform actions such as communication between event device 108 and control panel 106, self-test procedures and / or providing audible and / or visual warnings during events, and other actions.

[0024] Control panel 106 can also be connected to computing device 102 via gateway 110. Gateway 110 can be a device that provides a communication link between control panel 106 and other devices such as computing device 102 (e.g., a building system gateway). For example, gateway 110 can enable data (e.g., system device data, activation signals, etc.) to be sent from control panel 106 to computing device 102 and vice versa. Communication between control panel 106 and computing device 102 is further described herein.

[0025] As described above, system 100 can be used to generate event analysis. Event analysis can be used to identify objects of interest within system 100 and can include contextual information. In emergency or maintenance modes, event analysis can assist users in resolving emergency events or maintenance activities, as further described herein.

[0026] To generate event analysis, computing device 102 can receive information about events in an area of ​​the facility. Computing device 102 can be located, for example, remotely from the facility and can be included as part of a cloud computing environment. Event detection system 104 can send information about events in the facility to computing device 102 via gateway 110. Additionally, camera 112 can send information about events to computing device 102. Such information is further described herein.

[0027] In an example where the information relates to an emergency, computing device 102 may receive information from control panel 106, including alarm information about an emergency in an area of ​​the facility, when event detection system 104 is in emergency mode. For example, event device 108 may be a smoke detector that detects smoke in an area (e.g., a conference room). The smoke may be a result of an emergency (e.g., a fire).

[0028] In such an example, system 100 may include camera 112. Camera 112 may be part of a CCTV system included in the facility. Camera 112 may include a field of view of an area of ​​the facility and may capture information in the field of view and send that information as a video stream to computing device 102. Thus, computing device 102 can receive a video stream of an area of ​​the facility with events from camera 112.

[0029] For example, camera 112 can operate in normal operating mode. In normal operating mode, camera 112 can capture information by recording wavelengths in the visible light spectrum. Camera 112 can then send the captured information (such as in a video stream format) to computing device 102.

[0030] Additionally, camera 112 can operate in thermal imaging mode. In thermal imaging mode, camera 112 can capture information by detecting and visualizing the thermal energy emitted in the invisible light spectrum by objects within its field of view. Camera 112 can switch between normal operation mode and thermal imaging mode based on whether its field of view is obstructed, such as in combination. Figure 2 Further description.

[0031] The computing device 102 can generate an event analysis for a specific area of ​​the facility based on information received from the event detection system 104 and video streams from the camera 112. The event analysis can notify users (such as first responders) of an ongoing event, allowing them to easily assess the event. For example, the event analysis can allow users to help them respond safely to emergencies and / or allow them to respond effectively to maintenance events, as further described herein.

[0032] As mentioned above, in some examples, the event may be an emergency. Event analysis can be emergency event analysis. Emergency event analysis can include objects of interest in the region of the emergency and / or contextual information about the emergency, as further described herein.

[0033] As described above, computing device 102 can receive alarm information for detected emergency events. The alarm information may include, for example, the location in the facility and the type of event device 108 that detected the event, sensor information about the detected event (e.g., detected hot water level compared to a threshold alarm level), the time of detection, and other examples of alarm information.

[0034] The computing device 102 can utilize alarm information and video streams to generate emergency event analysis. For example, the computing device 102 can use an artificial intelligence (AI) model to identify objects of interest in the area of ​​the facility where an emergency event was detected and determine contextual information about the emergency event. The AI ​​model can be a classification model and can receive alarm information and video streams as input using a predetermined training dataset, analyze the alarm information and video streams, and generate objects of interest in the area and contextual information as output from the AI ​​model.

[0035] In some examples, the AI ​​model can be a generative AI model configured to process inputs to generate outputs. For instance, the AI ​​model could be a machine learning model that uses alarm information and video streams as inputs to generate event analysis.

[0036] AI models can be, for example, artificial neural networks (ANNs). An artificial neural network (ANN) is a network that processes information by modeling a network of neurons. This is how a network of neurons can be modeled to process information. For example, an ANN can include a multi-neuron topology, which can be referred to as an artificial neuron or unit. An ANN operation refers to the operation of using units to process input to perform a given task.

[0037] ANN operations can involve applying various machine learning algorithms to process the input. For example, an ANN can perform machine learning tasks by performing a weighted combination of inputs (from network inputs or previous layers) at each unit to generate an output. Probabilistic weight associations can be provided by the multiple units that make up the ANN. Units, along with weights, biases, embeddings, and / or activation functions, can be used to generate the ANN's output based on the inputs to the ANN. The units of an ANN can be grouped to form layers of the ANN. An ANN can implement or represent an algorithm consisting of a series of connected layers that process signals based on the outputs from other connected layers in the series.

[0038] Although the AI ​​model is described above as an ANN, the implementation is not limited to this. For example, the AI ​​model can be any other type of machine learning model.

[0039] To enable users to utilize event analysis, computing device 102 can generate event analysis in a user-readable format. For example, event analysis may include images of areas containing identified objects of interest, video streams from camera 112 including the areas of interest, and / or contextual information to supplement the image / video streams. Objects of interest in the area may include whether there are occupants (e.g., people, animals, etc.) in the area of ​​an emergency, whether there are obstructions at the area's entrance / exit routes, and / or whether there is a fire in the area, as further described herein.

[0040] The computing device 102 can use AI models to determine whether there are occupants in the area of ​​an emergency. For example, the computing device can determine whether there are people in the meeting room where a fire is detected, the status of people in the area (e.g., whether they are moving, trapped, injured, and / or otherwise incapacitated), the body temperature of people (e.g., via thermal imaging operating mode of camera 112), and other types of objects of interest associated with occupants.

[0041] Additionally, objects of interest may include whether there is obstruction on the entrance / exit routes to the area. For example, obstruction could be an object that blocks the path of a person attempting to cross the entrance and / or exit routes to the area where the event has occurred, where the entrance and / or exit routes to the area would not otherwise obstruct a person's path in the absence of such an object. Obstruction could be, for example, debris obstructing the entrance / exit routes caused by the event and / or objects that obstructed the entrance / exit routes prior to the emergency. For example, in the event of a fire, ceiling panels or other structural debris may have fallen, obstructing a corridor leading to a meeting room where the fire has been detected.

[0042] In addition, the object of interest may include whether camera 112 captures the presence of a fire. For example, camera 112 may capture a fire that leads to an emergency within its field of view (e.g., whether in normal operating mode and / or thermal imaging operating mode).

[0043] Furthermore, the object of interest may include temperature information of the area. For example, when camera 112 is operating in thermal imaging mode, camera 112 may provide video stream information to computing device 102, thereby allowing computing device 102 to determine temperature information in the area, such as hot spots, cold spots, and temperature sensor data.

[0044] Emergency analysis can also include contextual information about the emergency. This contextual information can provide insights into the situation surrounding the emergency.

[0045] For example, contextual information may include area information about the facilities. Area information may include information describing the location of the area in the facility where an emergency occurred. For example, area information may include the name of the area in the facility where the emergency was detected (e.g., meeting room, second floor).

[0046] Additionally, the contextual information may include the location information of camera 112. For example, the location of camera 112 may be a corridor adjacent to the conference room where the emergency was detected.

[0047] In addition, contextual information may include the status of the identified object of interest. For example, contextual information may include information about residents in the area where the emergency was detected (e.g., the resident's status, body temperature, severity of any obstruction, type of object causing the obstruction, etc.).

[0048] Once event analysis is generated, computing device 102 can send the event analysis to mobile device 114. As used herein, mobile devices can include devices that a user carries and / or wears (or can be carried and / or worn by a user). For example, mobile devices can be telephones (e.g., smartphones), tablet computers, personal digital assistants (PDAs), smart glasses and / or wrist-worn devices (e.g., smartwatches) and other types of mobile devices.

[0049] For example, computing device 102 can send emergency event analysis to mobile device 114, which is located remotely from computing device 102. Mobile device 114 can be, for example, a device used by a first responder. Mobile device 114 can allow the first responder to view the emergency event analysis, including identified objects of interest and contextual information, so as to allow the first responder to easily assess the emergency, locate potential trapped residents / victims, identify entry / exit routes, and reduce the personal risk to the first responder when responding to the emergency.

[0050] As described above, in some examples, the information received by computing device 102 may be information about congestion of entrance and / or exit routes for an area of ​​the facility when the event detection system is in maintenance mode. For example, when event device 108 has not yet detected any event, computing device 102 may receive information about congestion of entrance / exit routes (e.g., video stream from camera 112).

[0051] The computing device 102 can generate a maintenance event analysis for congestion based on the video stream from the camera 112. For example, the computing device 102 can use an AI model to analyze the video stream and determine the presence, type, and / or location of congestion included in the maintenance event analysis. The computing device 102 can also send the maintenance event analysis to a mobile device 114. For example, in some examples, the mobile device 114 could be a mobile device belonging to a facility's construction engineer, facility maintenance personnel, etc. Users can use the maintenance event analysis to locate and remove congestion.

[0052] Figure 2 An example of a facility 215 with an emergency event according to one or more embodiments of this disclosure is illustrated. Facility 215 may include various areas, including staircases, corridors, meeting rooms, multi-purpose rooms, and offices 1-4. Facility 215 may also include event devices 208-1, 208-2, 208-3, 208-4, 208-5, and 208-6, and cameras 212-1 and 212-2. Although not described in detail... Figure 2As illustrated, but event devices 208-1, 208-2, 208-3, 208-4, 208-5 and 208-6 and cameras 212-1 and 212-2 can be connected to computing devices (e.g., remote computing devices) and can send information to computing devices, enabling computing devices to generate event analysis, as further described herein.

[0053] like Figure 2 As illustrated, event device 208-1 can detect fire 216 (e.g., an emergency). For example, event device 208-1 can be a smoke detector that detects smoke generated by fire 216. Event device 208-1 can generate alarm information accordingly and transmit it via control panel and gateway (e.g., Figure 2 (Not illustrated) The alarm information is sent to the computing device, and the computing device can receive the alarm information.

[0054] like Figure 2 As illustrated, camera 212-1 can be located in the area where fire 216 is detected. For example, the area can be part of facility 215, including a meeting room, office 1, a portion of a corridor, office 2, and office 3. Camera 212-1 can have a field of view that may include a portion of the meeting room in which event device 208-1 detects fire 216.

[0055] Camera 212-1 can determine whether the field of view of the area captured by camera 212-1 is obstructed. If the field of view of camera 212-1 is unobstructed, camera 212-1 can send a video stream in a default format from camera 212-1 to a computing device in normal operating mode. As described above, in normal operating mode, camera 212-1 can capture information by recording wavelengths in the visible light spectrum.

[0056] However, in some examples, smoke generated by fire 216 may obscure the field of view of camera 212-1. In response to the obstruction of the field of view of camera 212-1, the camera can change its operating mode from normal operating mode to thermal imaging operating mode. In thermal imaging operating mode, camera 212-1 can capture information by detecting and visualizing the thermal energy emitted by objects in the invisible light spectrum (e.g., infrared) by objects in the field of view of camera 212-1. Therefore, in response to the obstruction of the field of view, camera 212-1 can transmit a video stream in thermal imaging format to a computing device.

[0057] To receive a video stream, the computing device can identify camera 212-1 from among multiple cameras 212-1, 212-2 in the facility. For example, using an AI model, the computing device can determine that camera 212-1 is located in the area where event device 208-1 detected fire 216. The computing device can also send a request to camera 212-1 to receive the video stream, and camera 212-1 can respond to the request from the computing device by sending the video stream from the camera to the computing device in a specific format (e.g., normal and / or thermal imaging).

[0058] As previously described above, the computing device can receive alarm information and video streams regarding emergency events from camera 212-1. Therefore, the computing device can generate an emergency event analysis for the area based on the alarm information from the control panel and the video stream from the camera. The emergency event analysis can include identified objects of interest and contextual information about the emergency event, such as... Figure 3 Further description.

[0059] Figure 3 Examples of emergency event analysis 322 according to one or more embodiments of this disclosure are illustrated. Emergency event analysis 322 may include an object of interest 324 and context information 328, as further described herein.

[0060] Emergency event analysis 322 may include images of an area containing an object of interest 324. For example, the event could be a fire detected by a smoke detector included in a conference room. Emergency event analysis may include images of the conference room provided by cameras included in an area of ​​the facility that includes a conference room.

[0061] In some examples, emergency event analysis may include a video stream from a camera that includes an area of ​​interest 324. For example, the camera may provide a video stream in emergency event analysis that includes recorded and / or live video from the area of ​​the camera.

[0062] As previously described above, the images and / or video streams of the area may include the field of view 326 of the facility's area captured by a camera. Figure 3 As illustrated, field of view 326 can be captured by the camera in the camera's thermal imaging mode. For example, field of view 326 can show the object of interest in thermal imaging format. If the camera's field of view 326 is otherwise blurred / obstructed (e.g., blurred / obstructed by smoke from a fire), then the thermal imaging format of field of view 326 may be particularly useful to the user.

[0063] like Figure 3As illustrated, object of interest 324 may include resident 320, resident 320's location in the area, resident 320's state, resident 320's body temperature, and temperature information in the area. For example, resident 320 may appear to be mobile but is trapped in an office adjacent to the meeting room where the fire was detected, and hotspots that may be fire spots may be included in the field of view. Additionally, resident 320's body temperature may be provided, allowing the first responder to prepare for a specific number of injuries (e.g., burns) that resident 320 may suffer.

[0064] In addition, although the object of interest 324 is included in the emergency analysis in a visual form, the emergency analysis may also include contextual information about the emergency. The contextual information may include textual information that further supplements the object of interest 324.

[0065] For example, context information 328 may include area information about the area of ​​the facility. For example, field of view 326 is associated with office 2 in area 3 of the facility. In addition, context information 328 may include the status of the identified object of interest 324. For example, occupant 320 is trapped in office 2 due to a blocked corridor, a fire in the meeting room, etc.

[0066] Therefore, according to this disclosure, generating event analysis can allow the use of event analysis to identify objects of interest within a facility and contextual information about an ongoing emergency, which can help first responders respond effectively to the emergency. Event analysis can include identified objects of interest, such as obstructions to entrance / exit routes, the presence of a fire in the area, temperature information, and / or whether anyone (such as a resident of the facility) is located in the area, as well as contextual information to further supplement the identified objects of interest. Compared to previous methods, this allows first responders to more easily assess the emergency, making it easier to locate victims and / or identify entrance / exit routes, while reducing personal risk to the first responder.

[0067] Figure 4 This is an example of a computing device 402 for generating event analysis according to one or more embodiments of this disclosure. Figure 4 As illustrated, computing device 402 may include memory 442 and processor 440 for generating event analysis according to this disclosure.

[0068] Memory 442 can be any type of storage medium that can be accessed by processor 440 to execute various examples of this disclosure. For example, memory 442 can be a non-transitory computer-readable medium on which computer-readable instructions (e.g., executable instructions / computer program instructions) are stored, which can be executed by processor 440 to generate event analysis according to this disclosure.

[0069] Memory 442 may be volatile or non-volatile memory. Memory 442 may also be removable (e.g., portable) memory or non-removable (e.g., internal) memory. For example, memory 442 may be random access memory (RAM) (e.g., dynamic random access memory (DRAM) and / or phase-change random access memory (PCRAM)), read-only memory (ROM) (e.g., electrically erasable programmable read-only memory (EEPROM) and / or optical disc read-only memory (CD-ROM)), flash memory, laser disc, digital versatile disc (DVD) or other optical storage devices and / or magnetic media (such as cassette tape, magnetic tape, or disk) and other types of memory.

[0070] Furthermore, although memory 442 is illustrated as being located within computing device 402, embodiments of this disclosure are not limited thereto. For example, memory 442 may also be located within another computing resource (e.g., enabling computer-readable instructions to be downloaded via the Internet or another wired or wireless connection).

[0071] Processor 440 may be a central processing unit (CPU), a semiconductor-based microprocessor, and / or other hardware device suitable for retrieving and executing machine-readable instructions stored in memory 442.

[0072] Although specific embodiments have been illustrated and described herein, those skilled in the art will understand that any arrangement calculated to achieve the same technology may replace the specific embodiments shown. This disclosure is intended to cover any and all modifications or variations of the various embodiments of this disclosure.

[0073] It should be understood that the above description is given by way of illustration and not limitation. Combinations of the above embodiments, as well as other embodiments not specifically described herein, will be apparent to those skilled in the art upon reading the above description.

[0074] The scope of the various embodiments of this disclosure includes any other application using the structures and methods described above. Therefore, the scope of the various embodiments of this disclosure should be determined with reference to the appended claims and the full scope of their equivalents.

[0075] In the above detailed description, for the purpose of simplifying this disclosure, various features are combined in the exemplary embodiments illustrated in the accompanying drawings. The approach of this disclosure should not be construed as reflecting an intention to require more features than expressly recited in each claim.

[0076] Instead, as reflected in the following claims, the subject matter of the invention lies in fewer than all the features of a single disclosed embodiment. Therefore, the claims below are incorporated into the detailed description, wherein each claim exists independently as a separate embodiment.

Claims

1. A computing device (102, 402), the computing device comprising: Memory (442); and Processor (440), the processor being configured to execute executable instructions stored in the memory (442) to: Receive information about events in the area of ​​the facility from the event detection system (104) in the facility; Video streams are received from cameras (112, 212) in the area of ​​the facility; Based on the information received from the event detection system (104) and the video stream, an event analysis is generated for the area of ​​the facility; as well as The event analysis is sent to a mobile device (114) located away from the computing device (102, 402).

2. The computing device (102, 402) of claim 1, wherein the received information includes alarm information from the control panel (106) of the event detection system (104) for an emergency event in the area of ​​the facility when the event detection system (104) is in emergency mode.

3. The computing device (102, 402) of claim 2, wherein generating the event analysis includes generating an emergency event analysis for the area within the facility based on the alarm information.

4. The computing device (102, 402) of claim 3, wherein the processor (440) is configured to generate the emergency event analysis by identifying objects of interest in the region based on the video stream via an artificial intelligence (AI) model.

5. The computing device (102, 402) according to claim 4, wherein the object of interest includes at least one of the following: People in the area; Blockage of the entrance and / or exit routes to the area; and There is a fire in the area.

6. The computing device (102, 402) of claim 3, wherein the processor (440) is configured to send the emergency event analysis to the mobile device (114).

7. The computing device (102, 402) of claim 1, wherein the received information includes information about blockages of entrance and / or exit routes for the area of ​​the facility when the event detection system (104) is in maintenance mode.

8. The computing device (102, 402) of claim 7, wherein generating the event analysis includes generating maintenance event analysis for the congestion based on the video stream from the camera (112, 212).

9. The computing device (102, 402) of claim 8, wherein the processor (440) is configured to send the maintenance event analysis to the mobile device (114).

10. The computing device (102, 402) of claim 1, wherein the event analysis includes an image of the region containing the object of interest.

Images