DE202023104113U1 - Integrated geospatial decision-making system for real-time fire detection and emergency management - Google Patents

Abstract

An integrated spatial decision-making framework for real-time fire detection and emergency management that includes:
a) a variety of AI-based processing units configured to process data from IoT sensors and FLIR early fire detection technology;
b) a network of IoT sensors strategically deployed to collect environmental data, with the sensors measuring temperature, humidity, wind speed and other critical variables;
c) cameras based on FLIR technology to capture thermal images to identify potential fire incidents based on temperature anomalies;
(d) a GIS database containing spatial data for mapping fire incidents and assessing their potential impact on surrounding areas;
e) a central AI system that receives, analyzes and correlates data from multiple AI techniques, IoT sensors, FLIR technology and GIS database to provide real-time situation analysis and emergency management recommendations.

Description

Field of invention:

The present invention relates to the field of fire emergency management and spatial data analysis. In particular, it concerns an integrated spatial decision-making system for fire detection and emergency management in real time.

background

Natural or man-made disasters can have devastating consequences, including loss of life, economic decline and property damage. Fires, one of the most common and dangerous disasters, pose a significant threat worldwide, causing numerous deaths and economic losses every year. Although technological advances such as Internet of Things (IoT) devices and sensors offer potential solutions for disaster management, these technologies have limited use for fire emergency management in India.

Key challenges hindering the full integration of technology into fire department emergency management include the unwillingness to fully adopt cutting-edge technologies, the difficulty of integrating new solutions into existing infrastructure, and the need for significant financial investments.

Various studies have examined fire risk factors in different environments such as: B. informal tent settlements, hospitals and urban areas were examined. It became clear how important accessible and safe accommodation, shared learning and spatial risk assessments are for effective fire prevention and response. However, it is critical to consider the unique social, economic and political contexts in which these threats are embedded in order to develop effective interventions.

In India, the National Smart Cities Mission has shown interest in IoT technologies that aim to create sustainable and livable smart cities through the automation of integrated services. This integration can lead to improved efficiency in various areas, including disaster management.

To address the challenges associated with fire hazards and optimize the integration of IoT technologies and spatial decision-making, this study aims to develop an optimized fire emergency management framework. Through the use of artificial intelligence techniques, Geographic Information System (GIS ), forward looking infrared technology (FLIR) and structured frameworks aim to enable accurate early detection and prediction of potential fire events to significantly reduce risks and improve disaster management capabilities.

In summary, integrating advanced technologies such as IoT, AI and GIS can revolutionize fire emergency management, enabling more effective early detection, situation analysis and resource allocation. By addressing the unique challenges in different environments and adopting state-of-the-art solutions, it is possible to improve disaster preparedness and response and ultimately minimize the impact of fire hazards on life and property.

Summary:

The main objective of the invention is to provide an integrated spatial decision-making framework based on AI that leverages IoT sensors, FLIR technology and GIS for real-time fire detection, mapping and emergency management. Furthermore, the invention aims to compare this AI-based framework with traditional fire management systems to evaluate its performance.

The present disclosure aims to provide an integrated spatial decision-making framework for real-time fire detection and emergency management. The framework includes: a variety of AI-based processing units configured to receive data from IoT sensors and FLIR technology process for early fire detection; a network of IoT sensors strategically deployed to collect environmental data, with the sensors measuring temperature, humidity, wind speed and other critical variables; a variety of cameras based on FLIR technology to capture thermal images to identify potential fire incidents based on temperature anomalies; a GIS database of spatial data to map fire incidents and assess their potential impact on surrounding areas; and a central AI system that collects data from the multitude of AI techniques, IoT sensors, FLIR technology and GIS database receives, analyzes and correlates to generate real-time situational analysis and emergency management recommendations.

The present disclosure also aims to provide a method for evaluating the performance of the integrated spatial decision-making framework for real-time fire detection and emergency management. The method includes: comparing the early detection accuracy of the framework with traditional fire management systems; the analysis of the response time of the framework when responding to fire events compared to traditional fire management systems; and assessing the efficiency of resource allocation during fire crises using the framework compared to traditional fire management systems.

In order to further illustrate the advantages and features of the present disclosure, a more detailed description of the invention will be provided by reference to certain embodiments illustrated in the accompanying drawings. It is understood that these drawings illustrate only typical embodiments of the invention and therefore should not be considered as limiting the scope of the invention. The invention will be described and explained with additional specificity and detail with the accompanying drawings.

Brief description of the drawings

• 1 zeigt ein Blockdiagramm eines integrierten räumlichen Entscheidungsfindungsrahmens für die Branderkennung und das Notfallmanagement in Echtzeit gemäß einer Ausführungsform der vorliegenden Offenbarung; und
• 2 zeigt ein Flussdiagramm eines Verfahrens zur Bewertung der Leistung eines integrierten räumlichen Entscheidungsfindungsrahmens für die Echtzeit-Erkennung und das Notfallmanagement gemäß einer Ausführungsform der vorliegenden Offenlegung.

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

• 1 shows a block diagram of an integrated spatial decision-making framework for real-time fire detection and emergency management in accordance with an embodiment of the present disclosure; and
• 2 shows a flowchart of a method for evaluating the performance of an integrated spatial decision-making framework for real-time detection and emergency management in accordance with an embodiment of the present disclosure.

Those skilled in the art will understand that the elements in the drawings are shown for convenience and are not necessarily drawn to scale. For example, the flowcharts illustrate the method through key steps to enhance understanding of aspects of the present disclosure. In addition, one or more components of the device may be represented in the drawings by conventional symbols, and the drawings show only those specific details relevant to understanding the embodiments of the present disclosure, rather than detailing the drawings which will be readily apparent to those skilled in the art having access to the descriptions contained herein.

Detailed description of the invention

In order to promote an understanding of the invention, reference will now be made to the embodiment shown in the drawings and the same will be described in specific words. It is to be understood, however, that this is not intended to limit the scope of the invention, with consideration being given to such changes and further modifications to the system illustrated and such further applications of the inventive principles set forth therein as would normally occur to one skilled in the art.

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

When reference is made to “an aspect,” “another aspect,” or the like in this description, it means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment included in the present disclosure. Therefore, the expressions “in one embodiment,” “in another embodiment,” and similar expressions in this specification may or may not all refer to the same embodiment.

The terms "comprising", "including" or other variations thereof are intended to cover non-exclusive inclusion, so that a method or method comprising a list of steps not only includes those steps but may also include other steps that are not are expressly listed or belong to such a process or method. Likewise, one or more devices or subsystems or elements or structures or components introduced by “comprises ... a” do not exclude, without further limitation, that there are other devices or other subsystems tems or other elements or other structures or other components or additional devices or additional subsystems or additional elements or additional structures or additional components.

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

Embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

The functional units described in this specification are referred to as devices. A device may be implemented in programmable hardware devices such as processors, digital signal processors, central processing units, field programmable gate arrays, programmable logic arrays, programmable logic devices, cloud processing systems, or the like. The devices can also be implemented in software for execution by various types of processors. An identified device may contain executable code and may include, for example, one or more physical or logical blocks of computer instructions, which may be organized, for example, as an object, procedure, function, or other construct. However, the executable file of an identified device does not have to be physically located in one location, but may consist of various instructions stored in different locations which, when logically linked together, form the device and fulfill the stated purpose of the device.

The executable code of a device or module can be a single instruction or many instructions, and can even be spread across several different code segments, different applications, and multiple storage devices. Similarly, operational data can be identified and represented within the device, in any suitable form and may be organized in any suitable type of data structure. The operational data may be collected as a single data set or distributed across various locations, including various storage devices, and may exist at least in part as electronic signals in a system or network.

When reference is made to “a selected embodiment,” “an embodiment,” or “an embodiment” in this specification, it means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosed subject matter. Therefore, the expressions “a selected embodiment,” “in one embodiment,” or “in one embodiment” used elsewhere in this specification do not necessarily refer to the same embodiment.

Additionally, the features, structures, or properties described may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of the embodiments of the disclosed subject matter. However, those skilled in the art will recognize that the disclosed subject matter may be accomplished without one or more of the specific details or with other methods, components, materials, etc. In other cases, known structures, materials, or processes are not shown or described in detail to avoid obscuring aspects of the subject matter disclosed.

According to exemplary embodiments, the disclosed computer programs or modules may be executed in many exemplary ways, for example, as an application that resides in a device's memory or as a hosted application that runs on a server and interacts with the device application or browser via a A variety of standard protocols such as TCP/IP, HTTP, XML, SOAP, REST, JSON and other sufficient protocols. The disclosed computer programs may be written in exemplary programming languages that are executed from on-device memory or from a hosted server, such as BASIC, COBOL, C, C++, Java, Pascal or scripting languages such as JavaScript, Python, Ruby, PHP, Perl or other sufficient programming languages.

Some of the disclosed embodiments include or involve data transmission over a network, e.g. B. the transmission of various inputs or files over the network. The network can, for example, the Internet, wide area networks (WANs), local area networks (LANs), analog or digital wired and wireless telephone networks (e.g. PSTN, Integrated Services Digital Network (ISDN), a cellular network and Digital Subscriber Line (xDSL)), radio, television, cable, satellite and/or other transmission or tunneling mechanisms for transmitting data. The network can have several Include networks or subnetworks, each of which, for. B. may include a wired or wireless data path. The network may include a circuit-switched voice network, a packet-switched data network, or any other network for the transmission of electronic communications. For example, the network may include networks based on Internet Protocol (IP) or Asynchronous Transfer Mode (ATM), and it may support voice, e.g. B. VoIP, Voice-over-ATM or other comparable protocols are used for voice data communication. In one implementation, the network includes a cellular telephone network configured to enable the exchange of text or SMS messages.

Examples of a network include a Personal Area Network (PAN), a Storage Area Network (SAN), a Home Area Network (HAN), a Campus Area Network (CAN), a Local Area Network (LAN), and a Wide Area Network (WAN), a Metropolitan Area Network (MAN), a Virtual Private Network (VPN), an Enterprise Private Network (EPN), the Internet, a Global Area Network (GAN), and so on.

1 shows a block diagram of an integrated spatial decision-making framework (100) for real-time fire detection and emergency management according to an embodiment of the present disclosure. The framework (100) includes a plurality of AI-based processing units (102) configured to process data from IoT sensors and FLIR technology for early fire detection.

In one embodiment, a network of IoT sensors (104) is strategically deployed to collect environmental data, with the sensors measuring temperature, humidity, wind speed, and other critical variables.

In one embodiment, multiple cameras (106) based on FLIR technology are used to record thermal images in order to identify potential sources of fire based on temperature anomalies.

In one embodiment, a GIS database (108) containing spatial data is used to map fire incidents and assess their potential impact on surrounding areas.

In one embodiment, a central AI system (110) receives, analyzes and correlates data from multiple AI techniques, IoT sensors, FLIR technology and the GIS database to provide real-time situational analysis and emergency management recommendations.

In one embodiment, the AI techniques use pattern recognition, machine learning and deep neural networks to detect and classify fire events in their early stages.

In one embodiment, the IoT sensors are distributed in fire-prone areas to provide real-time data to the central AI system.

In one embodiment, FLIR technology includes one or more FLIR cameras equipped with infrared sensors capable of capturing thermal images.

In one embodiment, the GIS database includes geographic and environmental data, fire incident information, and critical infrastructure information.

In one embodiment, the central AI system processes the incoming data using data fusion techniques to create real-time fire event alerts and situational analysis.

In one embodiment, the framework (100) further includes a communications module (112) configured to disseminate real-time fire event alerts and emergency management recommendations to relevant stakeholders.

In one embodiment, the AI-based processing units (102), the network of IoT sensors (104), the multiple FLIR technology-based cameras (106), the GIS database (108), the central AI system (110 ) and the communication module (112) may be implemented in programmable hardware devices such as processors, digital signal processors, central processing units, programmable gate arrays, programmable array logic, programmable logic devices, cloud processing systems or the like

2 shows a flowchart of a method (200) for evaluating the performance of an integrated spatial decision-making framework for real-time detection and emergency management in accordance with an embodiment of the present disclosure. In step (202), the method (200) includes comparing the early detection accuracy of the frame with conventional fire management systems.

In step (204), the method (200) includes analyzing the frame's response time in responding to fire events compared to traditional fire management systems.

In step (206), the method (200) includes evaluating the efficiency of resource allocation during fire crises using the framework compared to traditional fire management systems.

In one embodiment, a method for evaluating the precision and reliability of FLIR technology and IoT sensors in the integrated spatial decision-making framework for real-time detection and emergency management includes the following steps: evaluating the effectiveness of sensor placement in detecting fire events; Analysis of data quality from IoT sensors and FLIR technology under various environmental conditions; and identifying potential challenges for the sensors in real fire emergency situations.

In one embodiment, a method for integrating spatial analysis methods and GIS data within the integrated spatial decision-making framework for real-time detection and emergency management includes the steps of: using GIS data to map fire events and their spatial relationships; and using spatial analysis methods to optimize resource allocation, evacuation planning, and decision making during fire crises.

1. a) Eine Vielzahl von KI-Techniken:Der Rahmen umfasst fortschrittliche KI-Techniken, die speziell für die Verarbeitung der von IoT-Sensoren und FLIR-Technologie empfangenen Daten programmiert sind.Diese KI-Techniken nutzen hochmoderne Techniken wie Mustererkennung, maschinelles Lernen und tiefe neuronale Netze, um Brandereignisse in ihren frühen Stadien zu erkennen und zu klassifizieren.Durch die kontinuierliche Analyse der Datenströme von IoT-Sensoren und FLIR-Kameras können die KI-Techniken Temperaturanomalien und andere relevante Muster erkennen, die auf potenzielle Brandereignisse hinweisen.
2. b) Netz von IoT-Sensoren: Ein strategisch platziertes Netz von IoT-Sensoren ist ein wesentlicher Bestandteil des Systems.Diese Sensoren werden in brandgefährdeten Gebieten, einschließlich Wohngebieten, Gewerbegebieten und Industriezonen, eingesetzt, um Umweltdaten in Echtzeit zu erfassen. Die IoT-Sensoren messen kritische Variablen wie Temperatur, Luftfeuchtigkeit, Windgeschwindigkeit und andere Umweltbedingungen. Die von diesen Sensoren gesammelten Daten werden zur Analyse und Entscheidungsfindung kontinuierlich an das zentrale KI-System übertragen.
3. c) FLIR-Technologie: Der Rahmen umfasst die Forward-Looking Infrared (FLIR)-Technologie, die mit Wärmebildfunktionen ausgestattet ist. FLIR-Kameras sind in das System integriert und strategisch positioniert, um Wärmebilder der Umgebung zu erfassen. Diese Kameras können Temperaturschwankungen und Wärmesignaturen erkennen, die auf ein Brandereignis hinweisen können. Die von der FLIR-Technologie erfassten Wärmedaten werden zur weiteren Analyse und Korrelation in die KI-Techniken eingespeist.
4. d) GIS-Datenbank: Die Datenbank eines geografischen Informationssystems (GIS) spielt eine entscheidende Rolle in diesem Rahmen.Sie speichert räumliche Daten im Zusammenhang mit Brandereignissen, einschließlich geografischer Koordinaten, topografischer Informationen, Infrastrukturdetails und potenzieller Gefahren.Die GIS-Daten ermöglichen die Kartierung von Brandereignissen und ihrer potenziellen Auswirkungen auf umliegende Gebiete. Durch die Analyse der GIS-Daten kann das System die Ressourcenzuweisung, die Evakuierungsplanung und die Entscheidungsfindung bei Brandkatastrophen optimieren.
5. e) Zentrales KI-System:Das Herzstück des integrierten räumlichen Entscheidungsfindungsrahmens ist ein zentrales KI-System.Dieses System fungiert als Gehirn der gesamten Operation und empfängt, analysiert und korreliert Daten aus verschiedenen Quellen, darunter KI-Techniken, IoT-Sensoren, FLIR-Technologie und die GIS-Datenbank.Das zentrale KI-System verarbeitet die eingehenden Daten kontinuierlich in Echtzeit und erstellt umfassende Situationsanalysen und Empfehlungen für das Notfallmanagement.Diese Empfehlungen sind entscheidend für eine rechtzeitige und fundierte Entscheidungsfindung der relevanten Akteure, einschließlich Feuerwehrleuten, Notfall helfern und Behörden.

Integrated spatial decision-making framework The proposed framework combines AI techniques, IoT sensors, FLIR technology and GIS data to create a comprehensive fire emergency management system. The framework is intended to facilitate real-time data analysis, situation mapping and resource allocation during fire crises. The integrated spatial decision-making framework is a comprehensive system designed to facilitate real-time fire detection, situation analysis and emergency management during fire crises. It includes various interrelated components, each serving a specific purpose to improve the overall efficiency of disaster relief.

1. a) A variety of AI techniques: The framework includes advanced AI techniques that are specifically programmed to process the data received from IoT sensors and FLIR technology. These AI techniques leverage cutting-edge techniques such as pattern recognition, machine learning and deep neural networks to detect and classify fire events in their early stages. By continuously analyzing data streams from IoT sensors and FLIR cameras, the AI techniques can detect temperature anomalies and other relevant patterns that indicate potential fire events.
2. b) Network of IoT Sensors: A strategically placed network of IoT sensors is an essential part of the system. These sensors are deployed in fire-prone areas including residential areas, commercial areas and industrial zones to collect real-time environmental data. The IoT sensors measure critical variables such as temperature, humidity, wind speed and other environmental conditions. The data collected by these sensors is continuously transmitted to the central AI system for analysis and decision making.
3. c) FLIR technology: The frame includes Forward-Looking Infrared (FLIR) technology, which is equipped with thermal imaging capabilities. FLIR cameras are integrated into the system and strategically positioned to capture thermal images of the surrounding area. These cameras can detect temperature fluctuations and heat signatures that can indicate a fire event. Thermal data captured by FLIR technology is fed into AI techniques for further analysis and correlation.
4. d) GIS database: A geographic information system (GIS) database plays a crucial role in this framework. It stores spatial data related to fire events, including geographical coordinates, topographic information, infrastructure details and potential hazards. The GIS data enables mapping of fire events and their potential impact on surrounding areas. By analyzing the GIS data, the system can optimize resource allocation, evacuation planning and fire disaster decision making.
5. e) Central AI System: At the heart of the integrated spatial decision-making framework is a central AI system. This system acts as the brain of the entire operation, receiving, analyzing and correlating data from various sources, including AI techniques, IoT sensors, FLIR Technology and the GIS database. The central AI system continuously processes the incoming data in real time and produces comprehensive situation analyzes and recommendations for emergency management. These recommendations are crucial for a timely and informed response Decision-making by relevant stakeholders, including firefighters, emergency responders and authorities.

The proposed integrated spatial decision-making framework based on AI, IoT sensors, FLIR technology and GIS provides significant advances in real-time fire detection, situational analysis and emergency management. By comparing with conventional fire management systems and evaluating the performance of their components, the Invention represents a comprehensive solution to overcome the challenges in fire emergency management.

The drawings and the preceding description provide examples of embodiments. The person skilled in the art will understand that one or more of the elements described can certainly be combined into a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of the processes described herein may be changed and is not limited to the manner described herein. Additionally, the actions of a flowchart do not have to be performed in the order presented; Not all actions necessarily have to be carried out. The actions that are not dependent on other actions can also be carried out in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples. Numerous variations are possible, regardless of whether they are explicitly listed in the description or not, such as: B. Differences in structure, dimensions and use of materials. The scope of the embodiments is at least as broad as specified in the following claims.

Advantages, other benefits, and solutions to problems have been described above with respect to particular embodiments. However, the advantages, benefits, solutions and components that may cause an advantage, benefit or solution to occur or become more pronounced are not to be construed as a critical, necessary or essential feature or component of any or all of the claims.

CREDENTIALS

100

An integrated spatial decision-making framework for real-time fire detection and emergency management.

102

A variety of AI-based processing units

104

A network of IoT sensors

106

A variety of cameras based on FLIR technology

108

GIS database

110

Central AI system

112

Communication module

200

A method (200) for evaluating the performance of an integrated spatial decision-making framework for real-time detection and emergency management

202

Comparing the early detection accuracy of the framework with traditional fire management systems

204

Analysis of the response time of the framework when responding to fire events compared to traditional fire management systems

206

Assessing the efficiency of fire disaster resource allocation using the framework compared to traditional fire management systems

Claims (7)

An integrated spatial decision-making framework for real-time fire detection and emergency management that includes: a) a variety of AI-based processing units configured to process data from IoT sensors and FLIR early fire detection technology; b) a network of IoT sensors strategically deployed to collect environmental data, with the sensors measuring temperature, humidity, wind speed and other critical variables; c) cameras based on FLIR technology to capture thermal images to identify potential fire incidents based on temperature anomalies; (d) a GIS database containing spatial data for mapping fire incidents and assessing their potential impact on surrounding areas; e) a central AI system that receives, analyzes and correlates data from multiple AI techniques, IoT sensors, FLIR technology and GIS database to provide real-time situation analysis and emergency management recommendations. The integrated spatial decision-making framework according to Claim 1 , where the AI techniques use pattern recognition, machine learning and deep neural networks to identify and classify fire events in their early stages. The integrated spatial decision-making framework according to Claim 1 , with the IoT sensors distributed in fire-prone areas to provide real-time data updates to the central AI system. The integrated spatial decision-making framework according to Claim 1 , where FLIR technology includes one or more FLIR cameras equipped with infrared sensors capable of capturing thermal images. The integrated spatial decision-making framework according to Claim 1 , where the GIS database contains geographical and environmental data, fire incident information and critical infrastructure. The integrated spatial decision-making framework according to Claim 1 , whereby the central AI system processes incoming data using data fusion techniques to produce real-time fire alerts and situational analysis. The integrated spatial decision-making framework according to Claim 1 further includes a communications module configured to disseminate real-time fire event alerts and emergency management recommendations to relevant stakeholders.

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