Description:FIELD OF THE INVENTION

The present disclosure pertains to fire safety systems and methods, specifically focusing on utilizing Internet of Things (IoT) technology for centralized monitoring, response, and management of fire incidents. It also encompasses the maintenance and management of fire control instrumentations within buildings or sites. This invention addresses the need for advanced systems capable of integrating various fire detection devices, such as smoke detectors, heat detectors, and manual pull stations, into a centralized framework. By leveraging IoT technology, the invention facilitates real-time monitoring of fire hazards, timely response to emergencies, and efficient management of fire control systems. Additionally, it emphasizes the importance of remote access and diagnostics for system maintenance, ensuring optimal performance and safety standards are maintained over time.

BACKGROUND OF THE INVENTION

In the current era, ensuring fire safety in both commercial and residential buildings is a critical concern globally, driven by stringent mandates from governmental authorities. Traditional fire control systems typically operate as independent units, often consisting of a single control panel overseeing multiple fire detection components. When a fire is detected, these systems alert building owners or designated personnel, who then initiate emergency response protocols.

However, existing fire control systems face several limitations. They lack real-time monitoring capabilities and rely on outdated communication technologies, leading to delays in response times and increased risks to life and property. Additionally, the management and maintenance of fire control instrumentation are often decentralized and manual, increasing the likelihood of oversight and system failures.

To address these challenges, there is a growing demand for innovative solutions that integrate Internet of Things (IoT) technology into fire safety systems.

In view of the foregoing discussion, it is portrayed that there is a need to have an IoT-based approach enables centralized monitoring, response, and management of fire incidents, as well as maintenance and oversight of fire control instrumentation. By leveraging IoT sensors, data analytics, and cloud-based platforms, this system can provide real-time alerts, automate maintenance tasks, and enhance overall fire safety measures. Thus, the proposed invention seeks to revolutionize fire safety management by offering a comprehensive system and method for IoT-based centralized monitoring, response, and management of fire incidents, alongside maintenance and management of fire control instrumentation. By integrating advanced technology with established fire safety protocols, this invention aims to mitigate risks, improve response times, and ultimately save lives and property in the event of a fire.

SUMMARY OF THE INVENTION

The present disclosure seeks to provide an IoT-based system and method for centralized fire incident monitoring, response, and maintenance of fire control instrumentation. It offers centralized monitoring, rapid response, and efficient maintenance of fire control instrumentation. By integrating Internet of Things (IoT) technology, the system enables real-time monitoring of fire incidents, automated response protocols, and streamlined maintenance procedures. Key features include centralized control, remote monitoring capabilities, and data-driven insights for improved decision-making. This innovative solution enhances overall fire safety measures by providing timely alerts, facilitating quick response to emergencies, and ensuring the optimal functioning of fire control systems. With its comprehensive approach, the IoT-based system represents a significant advancement in fire safety management, offering enhanced protection for both life and property in various settings.

In an embodiment, an IoT-based system for centralized fire incident monitoring, response, and maintenance of fire control instrumentation is disclosed. The system includes a plurality of sensing nodes installed on a site include smoke detectors, heat detectors, and manual pull stations, each serving to detect and signal potential fire hazards. The system further includes an edge computing-enabled controller interconnected to the sensing nodes through a wiring network to continuously monitor a health status of connected devices, receive signals from the sensing nodes, and process information to detect potential fires or emergencies, wherein potential fires or emergencies are detected by comparing detected smoke and heat values with a threshold smoke and heat values of the site, wherein a fire risk ranking is generated based on the detected potential fires to indicate the severity of the fire, wherein fire risk ranking is indicated from 1-5, in which 1 denotes less fire that which may be controlled by a single person and 5 denotes serious fire that requires fire brigades. The system further includes an alarm activation unit connected to the edge computing-enabled controller to activate audible and/or visual alarms to alert occupants of a building upon detection of a fire or emergency condition. The system further includes a zoning configuration unit coupled to the edge computing-enabled controller to divide the site into a plurality of zones corresponding to specific areas or floors of a building for identifying a location of fire or emergency events. The system further includes a communication channel configured to transmit health status and event data from the edge computing-enabled controller to a cloud server. The system further includes a system diagnostics unit in connection with the edge computing-enabled controller to enable regular testing of connected devices and alert registered personnel of faults or malfunctions. The system further includes a user interface coupled to the edge computing-enabled controller to allow registered personnel to monitor detected potential fire hazards, perform tests, and acknowledge alarms to control fire upon activating water sprinklers on specific areas or floors of the building.

In another embodiment, an IoT-based method for centralized fire incident monitoring, response, and maintenance of fire control instrumentation is disclosed. The method includes detecting presence of smoke particles in the air using a plurality of smoke detectors, signaling a potential fire on a site thereby activating a plurality of heat detectors when a certain temperature threshold is reached, indicating a temperature rise consistent with fire, and activating a plurality of manual pull stations by pulling a handle or breaking the glass to manually initiate a fire alarm.
The method further includes detecting potential fires or emergencies upon receiving presence of smoke particles in the air by an edge computing-enabled controller and monitoring a health status of connected devices, wherein potential fires or emergencies are detected by comparing detected smoke and heat values with a threshold smoke and heat values of the site;
The method further includes generating a fire risk ranking is based on the detected potential fires to indicate the severity of the fire, wherein fire risk ranking is indicated from 1-5, in which 1 denotes less fire that which may be controlled by a single person and 5 denotes serious fire that requires fire brigades.
The method further includes activating audible and/or visual alarms to alert occupants of a building upon detection of a fire or emergency condition using an alarm activation unit.
The method further includes dividing the site into a plurality of zones corresponding to specific areas or floors of a building for identifying a location of fire or emergency events by employing a zoning configuration unit.
The method further includes transmitting health status and event data from the edge computing-enabled controller to a cloud server through a communication channel.
The method further includes enabling regular testing of connected devices and alert registered personnel of faults or malfunctions using a system diagnostics unit.
The method further includes allowing registered personnel to monitor detected potential fire hazards, perform tests, and acknowledge alarms by a user interface to control fire upon activating water sprinklers on specific areas or floors of the building.

An object of the present disclosure is to implement IoT technology to connect fire control panels, enabling seamless communication and data exchange between various fire detection and control components.

Another object of the present disclosure is to establish a cloud-based fire monitoring system to centralize data collection, storage, and analysis, facilitating comprehensive oversight of fire incidents.

Another object of the present disclosure is to integrate AI techniques to conduct health status checks of fire instrumentation, ensuring optimal performance and identifying potential faults or malfunctions in real-time.

Another object of the present disclosure is to enable real-time alerts of fire incidents to relevant stakeholders, including building occupants, emergency responders, and facility managers, to facilitate prompt evacuation and intervention.

Another object of the present disclosure is to utilize geolocation-based technologies for fire monitoring and management, allowing authorities to pinpoint the exact location of fire incidents and deploy resources efficiently for timely mitigation and containment efforts.

Yet another object of the present invention is to deliver an expeditious and cost-effective centralized monitoring and management platform for fire control, providing authorities with real-time access to critical information for effective decision-making and response coordination.

To further clarify the advantages and features of the present disclosure, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail in the accompanying drawings.

BRIEF DESCRIPTION OF FIGURES

These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read concerning the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

Figure 1 illustrates a block diagram of an IoT-based system for centralized fire incident monitoring, response, and maintenance of fire control instrumentation in accordance with an embodiment of the present disclosure;
Figure 2 illustrates a flow chart of an IoT-based method for centralized fire incident monitoring, response, and maintenance of fire control instrumentation in accordance with an embodiment of the present disclosure; and
Figure 3 illustrates an architecture of n IoT-based system for centralized fire incident monitoring, response, and maintenance of fire control instrumentation in accordance with an embodiment of the present disclosure.

Further, skilled artisans will appreciate those elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

DETAILED DESCRIPTION:

To promote an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.

It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.

Reference throughout this specification to “an aspect”, “another aspect” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrase “in an embodiment”, “in another embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises...a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.

Embodiments of the present disclosure will be described below in detail concerning the accompanying drawings.

Referring to Figure 1, a block diagram of an IoT-based system for centralized fire incident monitoring, response, and maintenance of fire control instrumentation is illustrated in accordance with an embodiment of the present disclosure. The system 100 includes a plurality of sensing nodes (102) installed on a site include smoke detectors (102A), heat detectors (102B), and manual pull stations (102C), each serving to detect and signal potential fire hazards.
In an embodiment, an edge computing-enabled controller (104) is interconnected to the sensing nodes (102) through a wiring network to continuously monitor a health status of connected devices, receive signals from the sensing nodes (102), and process information to detect potential fires or emergencies, wherein potential fires or emergencies are detected by comparing detected smoke and heat values with a threshold smoke and heat values of the site wherein a fire risk ranking is generated based on the detected potential fires to indicate the severity of the fire, wherein fire risk ranking is indicated from 1-5, in which 1 denotes less fire that which may be controlled by a single person and 5 denotes serious fire that requires fire brigades.
In an embodiment, an alarm activation unit (106) is connected to the edge computing-enabled controller (104) to activate audible and/or visual alarms to alert occupants of a building upon detection of a fire or emergency condition.
In an embodiment, a zoning configuration unit (108) is coupled to the edge computing-enabled controller (104) to divide the site into a plurality of zones corresponding to specific areas or floors of a building for identifying a location of fire or emergency events.
In an embodiment, a communication channel (110) is configured to transmit health status and event data from the edge computing-enabled controller (104) to a cloud server.
In an embodiment, a system diagnostics unit (112) is in connection with the edge computing-enabled controller (104) to enable regular testing of connected devices and alert registered personnel of faults or malfunctions.
In an embodiment, a user interface (114) is coupled to the edge computing-enabled controller (104) to allow registered personnel to monitor detected potential fire hazards, perform tests, and acknowledge alarms to control fire upon activating water sprinklers on specific areas or floors of the building.

In one embodiment, the wiring network provides a communication pathway between the sensing nodes (102) and edge computing-enabled controller (104), facilitating the transmission of signals and data for fire detection and emergency response purposes, wherein the zoning configuration enables efficient localization of fire or emergency events, aiding in prompt response and evacuation procedures within a building or facility.

The emergency event is detected upon determining thresholds for critical parameters selected from rapid temperature rise, high levels of smoke or gas that signify the occurrence of a fire or emergency situation and activating emergency protocols and response mechanisms upon detection of a potential threat, such as initiating alarms, notifying designated personnel, and activating fire suppression systems.

In another embodiment, a remote monitoring unit (116) allows registered personnel to access and manage the system's functions and data from a remote location for enhanced situational awareness and control.

Yet, in another embodiment, the health status of connected devices is monitored by the edge computing-enabled controller (104) by receiving signals from the sensing nodes (102) securely over the network and capturing signals in real-time from the sensing nodes (102) and store them for further processing thereby parse incoming signals and extract relevant data regarding device health and environmental conditions, wherein machine learning models are further utilized to detect patterns indicative of potential fires or emergencies and implement threshold-based alert mechanisms to trigger notifications when abnormal conditions or potential hazards are detected.

In one embodiment, each input device is assigned with a unique address for identification purposes, wherein the user interface (114) supporting wired or wireless connections using communication protocols such as Wi-Fi, ZigBee, Bluetooth, TCP/IP, Modbus, HTTP(S), MQTT, or CoAP.

In another embodiment, a secure communication module (118) facilitating communication between the edge computing-enabled controller (104) and a cloud-based management system (120), the secure communication module (118) supporting wireless protocols including 2G/4G/5G, LoraWAN, Sigfox, NB IoT, Wi-Fi, and providing secure data transmission and backup communication mechanisms.

In one embodiment, the cloud-based management system (120) comprises a data configurator module configured to receive raw data from the edge computing-enabled controller (104), analyze it using AI-based techniques, and configure it to a standard format for processing remote configuration capability for the data configurator module by a cloud system.
A local processing unit (LPU) comprises a data and event processing module for real-time analysis of edge computing-enabled controller (104) data, based on standard formats determined by the data configurator module.
An inspection module ensuring onsite inspection of the sensing nodes (102) by authorities, utilizing a unique geo-tagged key-sharing mechanism with an inspector's device user interface, and supporting barcode-based or hash-based key sharing through Bluetooth, Wi-Fi, or NFC communication.

In another embodiment, a monitoring and response unit has a plurality of status indicators providing real-time status updates of the sensing nodes (102), accessible through LED displays, LED indicators, inspector's device user interface, and cloud interfaces.

In one embodiment, the smoke detectors (102A) are configured to detect the presence of smoke particles in the air, signaling a potential fire, wherein the heat detectors (102B) are activated when a certain temperature threshold is reached, indicating a temperature rise consistent with fire, and the manual pull stations (102C) are activated by pulling a handle or breaking the glass to manually initiate a fire alarm.

In another embodiment, the edge computing-enabled controller (104) generates the fire risk ranking according to the zone where the sensing node (102) is installed based on the installation location information of the sensing node (102), and the fire risk ranking is given a reference value when the signal information included in the identification information of the sensor is a normal signal, wherein in a case of an abnormal signal, a value higher than the reference value is assigned and calculated, wherein among the plurality of zones in the building, one or more target points to monitor fire are set, and target points are prioritized in consideration of number of floating population and an expected degree of damage in the event of a fire, and the highest priority target point is designated.

In another embodiment, the potential fires or emergencies are detected using a machine learning technique upon extracting a set of features from the data received from the sensing node (102) to help in detecting patterns indicative of fires or emergencies, such as temperature trends, smoke levels, humidity variations, and air quality metrics thereby determining thresholds for abnormal conditions or potential hazards based on historical data analysis and domain knowledge, wherein the machine learning technique is integrated to continuously monitor sensor data streams for patterns indicative of potential fires or emergencies and compare incoming sensor data with established thresholds, wherein if the data surpasses predefined thresholds, trigger an alert mechanism.

In another embodiment, the edge computing-enabled controller (104) further generates an early warning strategy result, pushing real-time early warnings, and determining response strategies within an early warning generation technique, wherein early warning generation technique generates an early warning using logistic regression analysis based on the risk classification label, the real-time early warning pushing unit that pushes early warnings in real-time utilizing WebSocket technology, and the response strategy determination unit determines an optimal countermeasure using a decision matrix.

In another embodiment, the machine learning technique is further configured to determine a target object with a temperature higher than a first preset threshold according to the sensed data and input a sensed data into the machine learning technique to obtain a recognition result that the target object is conventional high-temperature zone or abnormal zone thereby terminating sending fire early warning information of the target area under the condition that the identification result is conventional high-temperature zone, wherein the conventional high-temperature zone is zone with the working temperature exceeding a first preset threshold value; the machine learning technique is obtained after training according to sample sensed data of conventional high-temperature zone and other zone with corresponding labels.

Figure 2 illustrates a flow chart of an IoT-based method for centralized fire incident monitoring, response, and maintenance of fire control instrumentation in accordance with an embodiment of the present disclosure. At step 202, method 200 includes detecting presence of smoke particles in the air using a plurality of smoke detectors (102A), signaling a potential fire on a site thereby activating a plurality of heat detectors (102B) when a certain temperature threshold is reached, indicating a temperature rise consistent with fire, and activating a plurality of manual pull stations (102C) by pulling a handle or breaking the glass to manually initiate a fire alarm.

At step 204, method 200 includes detecting potential fires or emergencies upon receiving presence of smoke particles in the air by an edge computing-enabled controller (104) and monitoring a health status of connected devices, wherein potential fires or emergencies are detected by comparing detected smoke and heat values with a threshold smoke and heat values of the site.

At step 206, method 200 includes generating a fire risk ranking is based on the detected potential fires to indicate the severity of the fire, wherein fire risk ranking is indicated from 1-5, in which 1 denotes less fire that which may be controlled by a single person and 5 denotes serious fire that requires fire brigades.

At step 208, method 200 includes activating audible and/or visual alarms to alert occupants of a building upon detection of a fire or emergency condition using an alarm activation unit (106).

At step 210, method 200 includes dividing the site into a plurality of zones corresponding to specific areas or floors of a building for identifying a location of fire or emergency events by employing a zoning configuration unit (108).

At step 212, method 200 includes transmitting health status and event data from the edge computing-enabled controller (104) to a cloud server through a communication channel (110).

At step 214, method 200 includes enabling regular testing of connected devices and alert registered personnel of faults or malfunctions using a system diagnostics unit (112).

At step 216, method 200 includes allowing registered personnel to monitor detected potential fire hazards, perform tests, and acknowledge alarms by a user interface (114) to control fire upon activating water sprinklers on specific areas or floors of the building.

In another embodiment, the potential fires or emergencies detection further comprises receiving raw data from an edge computing-enabled controller (104) at a data configurator module within a cloud-based management system (120). Then, analyzing the raw data using AI-based techniques within the data configurator module and configuring the raw data to a standard format suitable for processing within the cloud-based management system (120) thereby enabling remote configuration capability for the data configurator module by the cloud system. Then, conducting real-time analysis of edge computing-enabled controller (104) data using a local processing unit (LPU) comprising a data and event processing module. Then, processing the data based on standard formats determined by the data configurator module. Thereafter, conducting an onsite inspection of sensing nodes (102) by authorities utilizing an inspection module and implementing a unique geo-tagged key-sharing mechanism with an inspector's device user interface within the inspection module thereby supporting barcode-based or hash-based key sharing through Bluetooth, Wi-Fi, or NFC communication within the inspection module.
Figure 3 illustrates an architecture of n IoT-based system for centralized fire incident monitoring, response, and maintenance of fire control instrumentation in accordance with an embodiment of the present disclosure. The components are grouped in to 1) Fire control instrumentation on site, 2) Cloud based Management system, and 3) Monitoring & Response system.
The Fire Alarm Control Panel (FACP), also referred to as a fire panel, constitutes a pivotal element within fire alarm systems, tasked with overseeing various fire detection devices including smoke detectors (102A), heat detectors (102B), and manual pull stations (102C). Its primary role is to swiftly detect potential fire hazards and trigger appropriate responses in emergency scenarios.
The operational framework of a fire control panel involves several fundamental principles:
Input Devices (sensing nodes): smoke detectors (102A), heat detectors (102B), and manual pull stations (102C) serve as critical input devices, each designed to detect specific fire-related signals.
Wiring Network: These input devices are interconnected to the fire control panel through a network of wiring, with each device typically assigned with a unique address for precise event location identification.
Monitoring: The fire control panel continuously monitors the operational status of the connected devices, processing incoming signals to assess potential fire or emergency situations.
Alarm Activation: Upon detection of a fire or emergency condition, the fire control panel activates alarm systems, including audible alarms such as sirens or bells, visual alarms such as flashing lights, or a combination thereof, to promptly alert building occupants of the impending danger.
Additionally, fire control panels are characterized by various operational features:
Configuration (Zones): The panel is often subdivided into zones, with each zone corresponding to a specific area or floor within a building. This zoning facilitates the identification of the precise location of fire or emergency events.
System Diagnostics: Fire control panels incorporate diagnostic capabilities to monitor the overall health of the system. This includes conducting regular tests on connected devices and promptly notifying system administrators of any detected faults or malfunctions.
User interface (114): The fire control panel typically features a user interface that allows authorized personnel to monitor system status, conduct tests, and acknowledge alarms. It may also provide relevant information regarding the location of detected events for effective management and response coordination.
The IoT Enabler for FACP represents a groundbreaking innovation absent from conventional fire alarm control panels (FACP). Operating as an edge computing-enabled IoT module, this hardware unit is microprocessor/controller-based, facilitating seamless integration with existing FACP systems. Its primary function involves extracting health status and event data from the FACP, a process vital for comprehensive fire safety management.
Once extracted, the data is transmitted to the cloud via a communication channel (110), facilitating real-time monitoring and analysis. The interface between the FACP and IoT Enabler supports a variety of wired or wireless connections, utilizing communication protocols such as Wi-Fi, ZigBee, Bluetooth, among others. The choice of data protocol includes options like TCP/IP, Modbus, HTTP(S), MQTT, and CoAP, ensuring compatibility and interoperability with diverse systems.
The FACP Interface module serves as a crucial intermediary, managing the connection between the IoT Enabler and the existing FACP. This module facilitates both wired and wireless communication options, enabling flexibility in deployment. Additionally, it identifies and configures data and event tapping points from the FACP, accommodating variations across different FACP models. In wired connections, the FACP interface manages voltage and current levels, ensuring compatibility and safe operation.
The secure communication module (118) plays a pivotal role in connecting the Fire Alarm Control Panel (FACP) to the cloud-based management system (120), ensuring seamless data transmission. Utilizing various wireless protocols such as 2G, 4G, 5G, LoraWAN, Sigfox, NB IoT, Wi-Fi, or other licensed or unlicensed wireless networks, this module establishes robust communication channels. Equipped with an onboard security module, it encrypts data and commands for secure transmission. Furthermore, to enhance reliability, the module includes a backup secondary communication mechanism, which may involve Satcomm, SMS, or unlicensed wireless radios in case of primary communication failure.
The Data Configurator module is responsible for configuring FACP data into a standardized format before transmitting it to the cloud system. Operated remotely by the cloud system, this module initially sends raw data from the FACP for analysis. Using AI-based data analysis and normalization techniques, the cloud system processes the data and sends configuration commands back to the IoT Enabler. The Data Configurator then parses incoming FACP data based on these commands, ensuring compatibility with the cloud system. Additionally, manual configuration via a mobile app is possible for added flexibility.
The Local Processing Unit (LPU) incorporates a Data & Event Processing Module, which receives data from the FACP and analyzes it in real-time. Utilizing a controller/processor, this module converts data into a standardized format determined by the Data Configurator. It continuously monitors FACP health status, alerts locally to any anomalies, and analyzes data for alerts or events, ensuring prompt response to potential emergencies. This comprehensive processing capability enhances the effectiveness and reliability of the overall fire safety system.
The Local Processing Unit (LPU) integrates an Inspection Module (IM) to facilitate regular inspections of the fire alarm system by authorized authorities. The LPU stores and processes data essential for these inspections, ensuring compliance with safety regulations. The IM employs a unique geo-tagged key sharing mechanism, enhancing inspection integrity by preventing falsification. This mechanism involves sharing a barcode or hash-based key with the inspector's mobile device through Bluetooth, Wi-Fi, or NFC communication, guaranteeing physical verification of the Fire Alarm Control Panel (FACP) by authorities.
Status Indicators play a crucial role in conveying various system statuses, including those of the FACP and its modules. These indicators may include LED displays or LED indicators, providing visual cues for system health. They can be accessed via a mobile app or monitored through the cloud app interface, allowing for remote monitoring and management.
The Power Supply System is integral to ensuring continuous operation of the fire alarm system. It includes a standard power supply with all necessary protections as per recommended standards. Additionally, a battery-based backup power source is incorporated to provide uninterrupted power supply during outages or emergencies, safeguarding the system's functionality at all times.
Enclosures & Mountings are designed to provide physical protection to the fire alarm system components. These enclosures feature high IP ratings, ensuring resistance to dust and water ingress. Constructed from fire-resistant materials, they offer enhanced safety in the event of a fire. Furthermore, tamper alerts are enabled to detect and notify of any unauthorized attempts to access or tamper with the system, reinforcing its security measures.
The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.

Benefits, other advantages, and solutions to problems have been described above about specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims. , Claims:1. An IoT-based system for centralized fire incident monitoring, response, and maintenance of fire control instrumentation, the system comprises:
a plurality of sensing nodes (102) installed on a site include smoke detectors (102A), heat detectors (102B), and manual pull stations (102C), each serving to detect and signal potential fire hazards;
wherein said smoke detectors (102A) are configured to detect the presence of smoke particles in the air, signaling a potential fire, wherein said heat detectors (102B) are activated when a certain temperature threshold is reached, indicating a temperature rise consistent with fire, and said manual pull stations (102C) are activated by pulling a handle or breaking the glass to manually initiate a fire alarm;
an edge computing-enabled controller (104) interconnected to said sensing nodes (102) through a wiring network to continuously monitor a health status of connected devices, receive signals from said sensing nodes (102), and process information to detect potential fires or emergencies;
wherein potential fires or emergencies are detected by comparing detected smoke and heat values with a threshold smoke and heat values of the site;
wherein a fire risk ranking is generated based on the detected potential fires to indicate the severity of the fire, wherein fire risk ranking is indicated from 1-5, in which 1 denotes less fire that which may be controlled by a single person and 5 denotes serious fire that requires fire brigades;
an alarm activation unit (106) connected to said edge computing-enabled controller (104) to activate audible and/or visual alarms to alert occupants of a building upon detection of a fire or emergency condition;
a zoning configuration unit (108) coupled to said edge computing-enabled controller (104) to divide said site into a plurality of zones corresponding to specific areas or floors of a building for identifying a location of fire or emergency events;
a communication channel (110) configured to transmit health status and event data from said edge computing-enabled controller (104) to a cloud server;
a system diagnostics unit (112) in connection with said edge computing-enabled controller (104) to enable regular testing of connected devices and alert registered personnel of faults or malfunctions; and
a user interface (114) coupled to said edge computing-enabled controller (104) to allow registered personnel to monitor detected potential fire hazards, perform tests, and acknowledge alarms to control fire upon activating water sprinklers on specific areas or floors of said building.

2. The system as claimed in claim 1, wherein the edge computing-enabled controller (104) generates the fire risk ranking according to the zone where the sensing node (102) is installed based on the installation location information of the sensing node (102), and the fire risk ranking is given a reference value when the signal information included in the identification information of the sensor is a normal signal, wherein in a case of an abnormal signal, a value higher than the reference value is assigned and calculated, wherein among the plurality of zones in the building, one or more target points to monitor fire are set, and target points are prioritized in consideration of number of floating population and an expected degree of damage in the event of a fire, and the highest priority target point is designated.

3. The system as claimed in claim 1, wherein said emergency event is detected upon determining thresholds for critical parameters selected from rapid temperature rise, high levels of smoke or gas that signify the occurrence of a fire or emergency situation and activating emergency protocols and response mechanisms upon detection of a potential threat, such as initiating alarms, notifying designated personnel, and activating fire suppression systems, wherein said wiring network provides a communication pathway between said sensing nodes (102) and edge computing-enabled controller (104), facilitating the transmission of signals and data for fire detection and emergency response purposes, wherein said zoning configuration enables efficient localization of fire or emergency events, aiding in prompt response and evacuation procedures within a building or facility, wherein each input device is assigned with a unique address for identification purposes, wherein said user interface (114) supporting wired or wireless connections using communication protocols such as Wi-Fi, ZigBee, Bluetooth, TCP/IP, Modbus, HTTP(S), MQTT, or CoAP.

4. The system as claimed in claim 1, wherein the health status of connected devices is monitored by the edge computing-enabled controller (104) by receiving signals from the sensing nodes (102) securely over the network and capturing signals in real-time from the sensing nodes (102) and store them for further processing thereby parse incoming signals and extract relevant data regarding device health and environmental conditions, wherein machine learning models are further utilized to detect patterns indicative of potential fires or emergencies and implement threshold-based alert mechanisms to trigger notifications when abnormal conditions or potential hazards are detected.

5. The system as claimed in claim 1, wherein the potential fires or emergencies are detected using a machine learning technique upon extracting a set of features from the data received from the sensing node (102) to help in detecting patterns indicative of fires or emergencies, such as temperature trends, smoke levels, humidity variations, and air quality metrics thereby determining thresholds for abnormal conditions or potential hazards based on historical data analysis and domain knowledge, wherein the machine learning technique is integrated to continuously monitor sensor data streams for patterns indicative of potential fires or emergencies and compare incoming sensor data with established thresholds, wherein if the data surpasses predefined thresholds, trigger an alert mechanism.

6. The system as claimed in claim 1, wherein the edge computing-enabled controller (104) further generates an early warning strategy result, pushing real-time early warnings, and determining response strategies within an early warning generation technique, wherein early warning generation technique generates an early warning using logistic regression analysis based on the risk classification label, the real-time early warning pushing unit that pushes early warnings in real-time utilizing WebSocket technology, and the response strategy determination unit determines an optimal countermeasure using a decision matrix.

7. The system as claimed in claim 1, wherein the machine learning technique is further configured to determine a target object with a temperature higher than a first preset threshold according to the sensed data and input a sensed data into the machine learning technique to obtain a recognition result that the target object is conventional high-temperature zone or abnormal zone thereby terminating sending fire early warning information of the target area under the condition that the identification result is conventional high-temperature zone, wherein the conventional high-temperature zone is zone with the working temperature exceeding a first preset threshold value; the machine learning technique is obtained after training according to sample sensed data of conventional high-temperature zone and other zone with corresponding labels.

8. The system as claimed in claim 1, further comprises:
a remote monitoring unit (116) to allow registered personnel to access and manage the system's functions and data from a remote location for enhanced situational awareness and control;
a secure communication module (118) facilitating communication between said edge computing-enabled controller (104) and a cloud-based management system (120), said secure communication module (118) supporting wireless protocols including 2G/4G/5G, LoraWAN, Sigfox, NB IoT, Wi-Fi, and providing secure data transmission and backup communication mechanisms;
wherein said cloud-based management system (120) comprises:
a data configurator module configured to receive raw data from said edge computing-enabled controller (104), analyze it using AI-based techniques, and configure it to a standard format for processing remote configuration capability for said data configurator module by a cloud system;
a local processing unit (LPU) comprises a data and event processing module for real-time analysis of edge computing-enabled controller (104) data, based on standard formats determined by said data configurator module; and
an inspection module ensuring onsite inspection of the sensing nodes (102) by authorities, utilizing a unique geo-tagged key-sharing mechanism with an inspector's device user interface, and supporting barcode-based or hash-based key sharing through Bluetooth, Wi-Fi, or NFC communication.

9. An IoT-based method for centralized fire incident monitoring, response, and maintenance of fire control instrumentation, the method comprises:
detecting presence of smoke particles in the air using a plurality of smoke detectors (102A), signaling a potential fire on a site thereby activating a plurality of heat detectors (102B) when a certain temperature threshold is reached, indicating a temperature rise consistent with fire, and activating a plurality of manual pull stations (102C) by pulling a handle or breaking the glass to manually initiate a fire alarm;
detecting potential fires or emergencies upon receiving presence of smoke particles in the air by an edge computing-enabled controller (104) and monitoring a health status of connected devices, wherein potential fires or emergencies are detected by comparing detected smoke and heat values with a threshold smoke and heat values of the site;
generating a fire risk ranking is based on the detected potential fires to indicate the severity of the fire, wherein fire risk ranking is indicated from 1-5, in which 1 denotes less fire that which may be controlled by a single person and 5 denotes serious fire that requires fire brigades;
activating audible and/or visual alarms to alert occupants of a building upon detection of a fire or emergency condition using an alarm activation unit (106);
dividing said site into a plurality of zones corresponding to specific areas or floors of a building for identifying a location of fire or emergency events by employing a zoning configuration unit (108);
transmitting health status and event data from said edge computing-enabled controller (104) to a cloud server through a communication channel (110);
enabling regular testing of connected devices and alert registered personnel of faults or malfunctions using a system diagnostics unit (112); and
allowing registered personnel to monitor detected potential fire hazards, perform tests, and acknowledge alarms by a user interface (114) to control fire upon activating water sprinklers on specific areas or floors of said building.

10. The method as claimed in claim 9, wherein the potential fires or emergencies detection further comprises:
receiving raw data from an edge computing-enabled controller (104) at a data configurator module within a cloud-based management system (120);
analyzing said raw data using AI-based techniques within said data configurator module and configuring said raw data to a standard format suitable for processing within said cloud-based management system (120) thereby enabling remote configuration capability for said data configurator module by said cloud system;
conducting real-time analysis of edge computing-enabled controller (104) data using a local processing unit (LPU) comprising a data and event processing module;
processing said data based on standard formats determined by said data configurator module; and
conducting an onsite inspection of sensing nodes (102) by authorities utilizing an inspection module and implementing a unique geo-tagged key-sharing mechanism with an inspector's device user interface within said inspection module thereby supporting barcode-based or hash-based key sharing through Bluetooth, Wi-Fi, or NFC communication within said inspection module.