Description:Field of the Invention
This invention relates to Gas Leak detection bot.
Background of the Invention
Gas leaks provide serious risks to public health, the environment, and utility infrastructure in a variety of settings, including residential neighbourhoods, commercial buildings, and utility infrastructure. Human error is a result of the inefficiency and length of time associated with traditional approaches like manual inspections and fixed monitoring systems. Whereas gas leaks in residential areas can result in fires, explosions, or poisoning, leaks in industrial sites can produce disastrous events. Gas distribution networks are difficult for utility providers to maintain since not all possible leak sites are covered by fixed sensors. Due to unreported gas emissions that contribute to pollution and the buildup of greenhouse gases, environmental monitoring is extremely difficult. With the use of IoT and AI technologies, the gas leak detection car provides a transportable, autonomous solution that makes it possible to quickly and continuously check for gas leaks in a variety of scenarios.
Using Internet of Things (IoT) and Artificial Intelligence (AI) technology, the gas leak detection automobile can autonomously identify and analyze gas leaks in both residential and industrial settings. To detect, locate, and report gas leaks in real-time, this car is outfitted with cutting-edge sensors, communication modules, and AI-driven data analytics. By improving safety, effectiveness, and precision in gas leak detection, the system seeks to lower the possibility of dangerous situations and environmental harm.
US20210216852A1 Computer-implemented methods, systems, and software of detecting leaks, for example, in a pipeline that conveys a liquid or gas. Embodiments include inputting into a computer system a first set of data acquired (e.g., from the pipeline) during (e.g., normal) operation (e.g., of the pipeline), acquiring a second set of data (e.g., from the pipeline) while simulating leaks (e.g., from the pipeline) by releasing quantities of the liquid or gas (e.g., from the pipeline) from multiple locations (e.g., along the pipeline), inputting into the computer system the second set of data, and training the computer system to detect the leaks (e.g., from the pipeline) including communicating to the computer system that no leaks existed while the first set of data was acquired and communicating to the computer system that leaks existed while the second set of data was acquired
US10203311B2 An emission monitoring system includes at least one gas analyzer for measuring a concentration of a first gas and a concentration of a second gas, a positioning system for determining the location of the at least one gas analyzer when the concentration of the first gas is measured. A method for monitoring emissions at an industrial site and a computer-implemented event detection system applies the steps of detecting the presence of a gas emission event based on a first detection ratio calculated from the measured concentration of the first gas, the measured concentration of the second gas, a background concentration of the first gas and a background concentration of the second gas.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. This invention relates to Gas Leak detection bot.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is 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 with the accompanying drawings.
Multiple gas sensors are being used to monitor and log environmental gas levels in an Internet of things device. A microcontroller in the system gathers and interprets real-time data from every sensor in order to transmit it. The sensors are positioned carefully to measure precise gas concentrations and to provide analog or digital interfaces for the microcontroller to connect with. In order to effectively process and transfer gathered data to a cloud-based platform for storage and analysis, the gadget is outfitted with a Wi-Fi or cellular module. The MQ-2, MQ-7, MQ-135, MH-Z19, MiCS-5524, Figaro TGS 2611, SEN-1327, CCS811, SGP30, and BME680 sensors are critical for monitoring indoor air quality, security, and reliability by detecting various kinds of gases including carbon monoxide (CO), smoke, CO2, methane (CH4), ammonia, etc. Other sensors that produce high-accuracy readings include the MH-Z19, MiCS-5524, SEN-1327, CCS811, and SGP30.
The integration of IoT and AI technology in vehicle gas leak detection systems provides a proactive security solution by detecting hazardous gases like VOCs, CO, and CH4, and transmitting real-time data to the vehicle's central processing unit.
The ecological management system utilizes a central processor that reviews data from multiple sensors to determine compounds like CH4, CO, ammonia, benzene, alcohol, smoke, and CO2. It communicates to a Wi-Fi modem and generates 12V/7Ah power through a solar-powered system, then transmits data to a cloud server for remote maintenance.
AI simplifies auto leak detection by separating normal gas levels and genuine leaks, responding to vehicle features to avoid false warnings and ensure immediate and accurate detection. The technology alerts the driver to a gas leak by means of an application, an audio warning on the dashboard, and both, allowing for prompt action and protection.
The automotive environment is strengthened by AI and IoT-integrated technology, which optimizes vehicle security by anticipating gas leaks. To accurately identify leaks and accelerate emergency response, it interfaces with external platforms. For regular maintenance and quick reaction to threats, the system incorporates fresh data. In order to interpret data from car IoT sensors and record it in a scalable database for further analysis, cloud-based gas leak monitoring systems leverage machine learning, data analytics, and cloud computing.
The data processing procedure for a sensor-based system is illustrated in Figure 2. It involves collecting data from multiple sensors, arranging and filtering it, maintaining it, and implementing it to create models for forecasting and analysis. Through constant learning and model improvement, the system produces visualizations that enable effective monitoring and well-informed decision-making. Utilizing machine learning, the AI system evaluates preprocessed data to determine similarities and measure gas concentrations. It uses both supervised and unsupervised learning techniques to train models for sensitive leak detection. Cloud-based AI identifies irregularities, alerts automobile experts, and estimates gas leaks, delivering reactive vehicle health and safety information as well as data visualization for dashboards and reports.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
Figure 1 System Representation
Figure 2 AI Architecture
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Multiple gas sensors are being used to monitor and log environmental gas levels in an Internet of things device. A microcontroller in the system gathers and interprets real-time data from every sensor in order to transmit it. The sensors are positioned carefully to measure precise gas concentrations and to provide analog or digital interfaces for the microcontroller to connect with. In order to effectively process and transfer gathered data to a cloud-based platform for storage and analysis, the gadget is outfitted with a Wi-Fi or cellular module. The MQ-2, MQ-7, MQ-135, MH-Z19, MiCS-5524, Figaro TGS 2611, SEN-1327, CCS811, SGP30, and BME680 sensors are critical for monitoring indoor air quality, security, and reliability by detecting various kinds of gases including carbon monoxide (CO), smoke, CO2, methane (CH4), ammonia, etc. Other sensors that produce high-accuracy readings include the MH-Z19, MiCS-5524, SEN-1327, CCS811, and SGP30.
The integration of IoT and AI technology in vehicle gas leak detection systems provides a proactive security solution by detecting hazardous gases like VOCs, CO, and CH4, and transmitting real-time data to the vehicle's central processing unit.
The ecological management system utilizes a central processor that reviews data from multiple sensors to determine compounds like CH4, CO, ammonia, benzene, alcohol, smoke, and CO2. It communicates to a Wi-Fi modem and generates 12V/7Ah power through a solar-powered system, then transmits data to a cloud server for remote maintenance.
AI simplifies auto leak detection by separating normal gas levels and genuine leaks, responding to vehicle features to avoid false warnings and ensure immediate and accurate detection. The technology alerts the driver to a gas leak by means of an application, an audio warning on the dashboard, and both, allowing for prompt action and protection.
The automotive environment is strengthened by AI and IoT-integrated technology, which optimizes vehicle security by anticipating gas leaks. To accurately identify leaks and accelerate emergency response, it interfaces with external platforms. For regular maintenance and quick reaction to threats, the system incorporates fresh data. In order to interpret data from car IoT sensors and record it in a scalable database for further analysis, cloud-based gas leak monitoring systems leverage machine learning, data analytics, and cloud computing.
The data processing procedure for a sensor-based system is illustrated in Figure 2. It involves collecting data from multiple sensors, arranging and filtering it, maintaining it, and implementing it to create models for forecasting and analysis. Through constant learning and model improvement, the system produces visualizations that enable effective monitoring and well-informed decision-making. Utilizing machine learning, the AI system evaluates preprocessed data to determine similarities and measure gas concentrations. It uses both supervised and unsupervised learning techniques to train models for sensitive leak detection. Cloud-based AI identifies irregularities, alerts automobile experts, and estimates gas leaks, delivering reactive vehicle health and safety information as well as data visualization for dashboards and reports
A Gas Leak Detection Bot system comprises of a plurality of Sensors (10), Co-processor (11), Battery (12), Wifi modem (13), Cloud Server (14), User access through Web/Mobile App (15), Main Processor (16), Motor Drivers (17), M3 (19), M4 (20), M1 (21) and M2 (22);
Wherein the gas sensors include at least one of the following: MQ-2, MQ-7, MQ-135, MH-Z19, MiCS-5524, Figaro TGS 2611, SEN-1327, CCS811, SGP30, and BME680. The microcontroller is configured to process sensor data in real-time and transmit it to the cloud-based platform.
In another embodiment the microcontroller is configured to transmit sensor data to the cloud-based platform using a Wi-Fi or cellular connection.
In another embodiment the cloud-based platform is configured to store and analyze sensor data using machine learning algorithms.
In another embodiment the system is integrated into a vehicle for gas leak detection.
In another embodiment the system is integrated into an environmental monitoring system.
In another embodiment the system is powered by a renewable energy source.
In another embodiment the system is configured to generate alerts when gas levels exceed predefined thresholds.
In another embodiment the system is configured to provide real-time data visualization on a user interface.
In another embodiment the system is configured to generate alerts and notifications based on abnormal gas levels; and the system is integrated with a vehicle and is configured to detect and alert the driver to gas leaks.

ADVANTAGES OF THE INVENTION
The AI framework improves vehicle safety by collecting gas concentration data, eliminating errors, and enhancing detection accuracy without necessitating modifications.
The system provides immediate notifications and predictive insights for leak detection and preventative maintenance, allowing users to make data-driven decisions with outstanding reports and visualizations.
, Claims:1. A Gas Leak Detection Bot system comprises of a plurality of Sensors (10), Co-processor (11), Battery (12), Wifi modem (13), Cloud Server (14), User access through Web/Mobile App (15), Main Processor (16), Motor Drivers (17), M3 (19), M4 (20), M1 (21) and M2 (22);
Wherein the gas sensors include at least one of the following: MQ-2, MQ-7, MQ-135, MH-Z19, MiCS-5524, Figaro TGS 2611, SEN-1327, CCS811, SGP30, and BME680;
wherein the microcontroller is configured to process sensor data in real-time and transmit it to the cloud-based platform.
2. The system as claimed in claim 1, wherein the microcontroller is configured to transmit sensor data to the cloud-based platform using a Wi-Fi or cellular connection.
3. The system as claimed in claim 1, wherein the cloud-based platform is configured to store and analyze sensor data using machine learning algorithms.
4. The system as claimed in claim 1, wherein the system is integrated into a vehicle for gas leak detection.
5. The system as claimed in claim 1, wherein the system is integrated into an environmental monitoring system.
6. The system as claimed in claim 1, wherein the system is powered by a renewable energy source.
7. The system as claimed in claim 1, wherein the system is configured to generate alerts when gas levels exceed predefined thresholds.
8. The system as claimed in claim 1, wherein the system is configured to provide real-time data visualization on a user interface.
9. The system as claimed in claim 1, wherein the system is configured to generate alerts and notifications based on abnormal gas levels; and the system is integrated with a vehicle and is configured to detect and alert the driver to gas leaks.