Drone Fleet PaaS With AI Tracking for Forest Fire Alerts
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Solution Overview
Problem
Current surveillance and forest fire management systems using RPAs face challenges such as inefficient information management, lack of traceability, susceptibility to data loss, high logistical costs for live transmission, and absence of predictive analytics and emergency support systems, leading to decreased productivity and increased risk of information loss and negligence.
Innovation Solution
A platform-as-a-service (PaaS) system that manages and automates drone fleets, allowing for remote piloting and autonomous flights, with features like customizable alerts, cloud-based data storage, machine learning for predictive analytics, and emergency support systems to enhance data-driven decision-making and reduce human resource dependency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If information is stored on physical media such as memory cards, CDs, or hard disks, then data can be backed up, but it requires human resources for traceability control and is susceptible to loss or deletion
Solution Approach 1:
The patent replaces physical media storage with cloud-based digital storage systems. Instead of using memory cards, CDs, or hard disks that require manual handling and physical traceability control, the system stores all drone footage and data in cloud databases accessible through authorized accounts. This eliminates the need for human resources to physically manage and track storage media while maintaining data security through centralized cloud-based access controls and authentication mechanisms.
2Speed
If live RPA recordings are transmitted to television channels, then real-time information dissemination is achieved, but it is economically expensive and requires complex logistics
Solution Approach 1:
The patent creates a multi-functional cloud platform that serves multiple purposes: storing drone footage, enabling real-time streaming to various devices, providing authorized access to different user groups, and allowing data analysis. The same cloud infrastructure that stores data also handles streaming transmissions to television channels, mobile devices, and computers simultaneously. This eliminates the need for separate dedicated transmission systems and reduces overall logistical complexity while maintaining real-time information dissemination capabilities.
3Loss of information
If operators manually report incidents to central offices, then information can be communicated, but significant time is lost in the information chain
Solution Approach 1:
The patent implements an automated feedback system where drones equipped with sensors and AI algorithms automatically detect incidents, analyze the situation, and transmit alerts directly to relevant authorities through the cloud platform. The system provides real-time feedback loops where detected incidents are immediately communicated to central offices and local responders without manual intervention. This automated feedback mechanism eliminates the time-consuming manual reporting process while ensuring complete information transfer through structured data formats that include incident location, type, and severity.
4Reliability
If two operators are required to control the drone system, then comprehensive monitoring is achieved, but human resource requirements and operational complexity increase
Solution Approach 1:
The patent implements autonomous drone systems with onboard AI algorithms that perform self-monitoring and self-diagnosis. The drones automatically track their own operational status, battery levels, and system health, and can autonomously return to charging stations when needed. The cloud platform provides automated mission planning, real-time monitoring, and anomaly detection, reducing the need for multiple human operators. A single operator can manage multiple drones simultaneously through the centralized platform, which handles routine monitoring tasks automatically, thereby maintaining reliable monitoring while simplifying operations.
Data Source
AI summary
The invention discloses a method and a system combining: the detection of parameters by means of unmanned aerial vehicles (RPA) and unmanned aerial systems (UAS), a graphical interface for triggering alerts, the adaptation of a neural network for classifying a plurality of data, a computer sequence for transmitting data, a module for intercommunication between drones, methods and applications for predictive analysis, a method for evaluating activities with artificial intelligence, an autonomous management process in the cloud. The method integrates, tracks the execution procedure of corrective and preventive actions on the detected and transmitted parameters. The system is implemented through a platform (PaaS) to manage, control and record the process, and to combine the activity of a plurality of drones.