A system for AI-supported prediction of multiple disasters and intelligent security management for smart homes.

Abstract

A system for AI-powered multi-disaster prediction and intelligent security management for private households, consisting of: a multitude of IoT-based sensors configured to collect real-time data on the environment, structure, and security of a residential setting; a data acquisition and communication module configured to receive sensor data and external disaster-related data sources; an artificial intelligence processing module comprising one or more machine learning models configured to analyze data from multiple sources and predict the occurrence, severity, and risk level of multiple catastrophic events; a decision and control module configured to autonomously initiate adaptive safety measures based on the predicted disaster risk; and a user interaction module configured to provide residents with real-time alerts, safety instructions and evacuation instructions, with the system enabling proactive prediction, monitoring and containment of multiple disasters within a smart home environment.

Description

[0001] The present invention relates generally to the fields of artificial intelligence (AI), the Internet of Things (IoT), and intelligent infrastructure systems. In particular, the invention relates to an AI-enabled system for predicting multiple disasters and managing security in smart homes, which integrates machine learning algorithms, sensor data from multiple sources, and automated control mechanisms to predict, monitor, and mitigate the impact of multiple natural and man-made disasters in residential environments.

[0002] Natural and man-made disasters such as earthquakes, floods, fires, gas leaks, and extreme weather events pose significant risks to human life, residential infrastructure, and economic stability. With increasing urbanization, climate change, and population density, residential buildings become more vulnerable to such hazards. Traditional disaster management approaches rely primarily on centralized warning systems, post-event response mechanisms, or isolated security devices, which often fail to provide timely, localized, and actionable information at the home level. Existing smart home security systems typically focus on monitoring individual functions, such as smoke detectors, fire alarms, or water leak sensors. These systems operate in isolation and usually only react after a hazardous situation has already occurred.Furthermore, current disaster warning platforms typically rely on external agencies or single data sources, such as meteorological or seismic networks, which are not integrated with sensor data from homes and do not account for the specific layout, occupancy, or structural characteristics of individual houses. While recent advances in artificial intelligence and IoT technologies have enabled improved environmental sensing and automation, their application in residential disaster preparedness remains fragmented. Most existing solutions lack capabilities for predicting multiple disasters, real-time data fusion from heterogeneous sources (such as sensors, satellite data, and historical records), or adaptive decision-making tailored to different disaster scenarios.Furthermore, current systems rarely offer personalized emergency instructions, automated safety control measures, or predictive insights that would allow residents to take preventative action before a disaster escalates. Accordingly, there is a clear need for an integrated, AI-powered smart home system capable of predicting multiple disaster types, continuously monitoring environmental and structural parameters, and autonomously managing safety measures. Such a system should provide early warnings, personalized instructions, and automated damage mitigation measures while ensuring data security, scalability, and practical applicability in various residential environments. The present invention addresses these limitations by introducing a comprehensive AI-powered framework for multi-disaster prediction and safety management in smart homes.

[0003] To solve this problem, the present invention offers a system for AI-supported prediction of multiple disasters and security management for smart homes.

[0004] The system can improve safety in residential buildings by proactively predicting, monitoring, and mitigating the impact of multiple disaster events.

[0005] The system can combine artificial intelligence, machine learning, IoT-based sensors and the fusion of data from multiple sources to enable early detection and accurate prediction of disasters such as earthquakes, floods, fires and other hazardous situations at the household level.

[0006] The system can provide a localized, real-time risk assessment by continuously analyzing environmental, structural, and external data sources to generate timely alerts and actionable safety information tailored to individual households.

[0007] The system enables automated and adaptive security measures, autonomously controlling smart home components such as alarm systems, ventilation, power supply, drainage, lighting and access systems to reduce the impact of disasters without manual intervention.

[0008] The system offers personalized emergency instructions and evacuation support, including smart notifications and visual or augmented reality-based evacuation routes to help residents respond effectively in emergency situations.

[0009] The system can support the management of multiple disasters within a single unified platform, thereby eliminating the limitations of existing systems that only deal with isolated hazards or operate independently of each other.

[0010] The system can ensure data protection, cybersecurity and system reliability through secure data processing, encryption and compliance with applicable data protection standards.

[0011] The system offers a scalable, modular and industry-ready solution that can be used in various residential environments, including single-family homes, apartments and smart housing developments in urban and rural areas.

[0012] The present invention relates to a system for AI-supported multi-disaster prediction and intelligent security management for residential buildings, which proactively improves residential building security and disaster preparedness. The system integrates a network of IoT-based environmental and structural sensors with advanced artificial intelligence and machine learning models to continuously monitor, predict, and respond to multiple disaster scenarios, including earthquakes, floods, fires, gas leaks, and extreme environmental conditions. According to the invention, data is collected from heterogeneous sources such as in-house sensors, historical disaster records, real-time weather and seismic data, satellite data, and contextual information.This multimodal data is processed using AI-driven data fusion and predictive analytics to generate highly accurate, localized risk assessments and early warnings. Disaster-specific AI modules analyze patterns and anomalies to estimate severity and potential impact in real time. The system also includes an intelligent decision and control layer that autonomously triggers safety measures, such as activating alarms, controlling ventilation and power supply systems, managing drainage mechanisms, and securing access points to mitigate potential damage. A user-centric interface provides real-time alerts, personalized safety instructions, and guided evacuation assistance, which can include visual or augmented reality-based navigation aids adapted to the building's layout and the type of disaster.The invention prioritizes data privacy and reliability through secure data transmission, encryption, and fail-safe operation. Its modular and scalable architecture enables seamless integration into existing smart home infrastructure and deployment in various residential environments. By combining multi-disaster prediction, automated security management, and personalized emergency response in a single, unified platform, the invention eliminates critical limitations of existing technologies and significantly improves the resilience of households to disasters.

[0013] The present invention relates to a system for AI-supported multi-disaster prediction and security management in smart homes, designed to enable proactive, intelligent, and automated disaster preparedness and residential environments. The system integrates IoT-based sensors, AI-supported analytics, and smart home automation to enable early prediction, continuous monitoring, and real-time mitigation of multiple disaster events such as earthquakes, floods, fires, gas leaks, and extreme environmental conditions.

[0014] According to the invention, a distributed network of IoT sensors is installed inside and around a residential building to continuously collect environmental and structural data. These sensors include seismic sensors for detecting ground vibrations, temperature and smoke sensors for fire detection, gas sensors for identifying hazardous leaks, and water level or humidity sensors for monitoring flooding and leaks. Additional parameters such as humidity, air quality, air pressure, and structural loads can also be monitored. Furthermore, the system can receive external data input from weather forecasting services, seismic monitoring stations, satellite imagery, and historical disaster databases to improve situational awareness and forecast accuracy.The collected data from various sources is processed by an AI processing layer that includes several machine learning and deep learning models. These models are configured to perform disaster-specific predictions and risk analyses using time-series forecasting, pattern recognition, and anomaly detection techniques. For example, seismic data can be analyzed using recurrent neural networks to identify early tremors, while flood risk can be predicted by correlating rainfall data, water level measurements, and historical flood patterns. The system uses data fusion techniques to combine heterogeneous data streams and generate localized, real-time risk assessments for each type of disaster.Based on the predicted risk levels and severity generated by AI models, an intelligent decision-making engine determines appropriate safety measures. The system is capable of autonomously triggering alarms, sending emergency notifications, and controlling connected smart home components to mitigate potential damage. These measures can include shutting off the gas or electricity supply, activating ventilation systems, controlling drainage or pumping mechanisms, activating fire extinguishing systems, and adjusting lighting or access controls to facilitate safe evacuation. These responses are dynamically adapted to the type of disaster, its intensity, and the specific configuration of the residential environment. Furthermore, the invention provides a user-oriented interaction interface that delivers important information to residents in real time.The interface can be accessed via mobile applications, smart displays, or voice-controlled devices and presents a live dashboard showing system status, risk levels, and recommended actions. Personalized emergency instructions and evacuation guides are generated based on the apartment's floor plan and the residents' current location. In certain configurations, augmented reality-based navigation can be used to visually guide residents along the safest evacuation routes in emergency situations.