AN INTEGRATED ML-BASED WORKFORCE MONITORING SYSTEM FOR VOLUMETRIC FILLING MACHINES IN CHEMICAL INDUSTRIES WITH CLOUD CONNECTIVITY

Abstract

The present invention, MLWM_VFTMote, introduces an integrated ML-based workforce monitoring system for volumetric filling machines in chemical industries. The system utilizes a Raspberry Pi processor, GSM Modem, Camera Module, Neural Stick, DHT Sensor, Touch HMI Display, Speaker, Led Indicator, and Power Supply to capture real-time data, analyze operational performance, and provide AI-driven recommendations for optimizing workforce efficiency. The system facilitates real-time data logging, visual monitoring, and remote access to information, enabling informed decision-making to enhance productivity in chemical manufacturing processes.

Specification

Description:FIELD OF THE INVENTION
This invention relates to workforce monitoring and productivity enhancement in the chemical industry, specifically focusing on volumetric filling machines. It involves real-time data acquisition, analysis, and AI-driven recommendations to optimize workforce efficiency and overall productivity.
BACKGROUND OF THE INVENTION
Efficient workforce management is crucial in the chemical industry, especially for tasks involving volumetric filling machines. These machines play a vital role in chemical manufacturing processes, and their optimal operation significantly impacts overall productivity.
Traditional workforce monitoring systems often lack real-time data analysis capabilities, limiting their ability to provide actionable insights for improvement. This can lead to inefficiencies in task completion, machine utilization, and overall workforce productivity.
Therefore, there is a need for an innovative solution that provides real-time monitoring, analysis, and intelligent recommendations to optimize workforce performance and enhance productivity in chemical industries utilizing volumetric filling machines.
Followings are some prior arts to the present invention:
US10954112B2 The said prior art provides an apparatus for filling containers with fluid includes a volumetric filling valve. The volumetric filling valve includes a valve stem having a gas valve portion and a fluid valve portion. An interior passageway of the gas valve portion is positioned interior of the valve stem, wherein the interior passageway positioned to supply a quantity of gas to a filling nozzle. A gas valve actuator is mechanically connected to the valve stem, wherein the gas valve actuator controls an activation of the gas valve portion. A fluid valve actuator is mechanically connected to the valve stem, wherein the fluid valve actuator controls an activation of the fluid valve portion. The volumetric filling valve may be positioned within a filling head container with the gas valve portion positioned at least partially within a quantity of gas and the fluid valve portion positioned within a quantity of fluid.
US9963288B2 The said prior art provides a dispenser for holding multiple doses of fluids or other substances, and for dispensing the substances, has a vial, a flexible bladder received within the vial, and a variable volume storage chamber formed between the bladder and vial. A filling valve is coupled in fluid communication with the storage chamber and defines (1) a normally closed, fluid-tight position hermetically sealing the storage chamber from the ambient atmosphere, and (2) an open position allowing the passage of fluid through the valve both to evacuate the storage chamber and to introduce fluid through the valve to fill the storage chamber. A pump is coupled in fluid communication with the storage chamber for pumping fluids out of the storage chamber. A dispensing valve is coupled in fluid communication with the pump and defines (1) a normally closed, fluid-tight position preventing the passage of fluid out of the dispenser, and (2) an open position for dispensing pumped fluid therethrough. The sealed, empty dispenser is sterilized, such as by applying gamma radiation thereto. Then, the sterilized, sealed, empty dispenser is filled with fluid by engaging the filling valve with an evacuating/dispensing member to evacuate the storage chamber, and by introducing fluid from the filling member through the open filling valve and into the storage chamber. The filling member is withdrawn from the valve, and a spring moves the valve to a closed position to hermetically seal the fluid within the dispenser.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed.
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.
The present invention, referred to as MLWM_VFTMote, introduces an integrated ML-based workforce monitoring system for volumetric filling machines in chemical industries. The system comprises a Raspberry Pi processor as the central processing unit, a GSM Modem for communication and cloud connectivity, a Camera Module for live video feed, a Neural Stick for AI processing, a DHT Sensor for environmental monitoring, a Touch HMI Display for user interface, a Speaker for audio feedback, a Led Indicator for visual alerts, and a Power Supply.
MLWM_VFTMote captures real-time data from various sensors, live video feeds, and machine on/off schedules. This data is processed using machine learning algorithms to determine working hours, quantify tasks completed, and generate AI-powered recommendations for improvement. The insights are accessible through a user-friendly interface, enabling informed decision-making to optimize workforce efficiency and enhance productivity in chemical manufacturing processes.
To further clarify advantages and features of the present invention, a more particular description of the invention is rendering by reference to specific embodiments thereof, which is illustrated in the appended drawing.
It is appreciated that the drawing depicts only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention is being described and explained with additional specificity and detail with the accompanying drawing.
BRIEF DESCRIPTION OF DRAWINGS
The foregoing detailed description of embodiments is better understood when read in conjunction with the attached drawing. For better understanding, each component is represented by a specific number which is further illustrated as a reference number for the components used with the figures.
Figure 1 represents block diagram of present system
Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the system, one or more components of the system may have been represented in the drawing by conventional symbols, and the drawing may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawing with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.
DETAILED DESCRIPTION OF THE INVENTION
For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawing 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. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skilled in the art to which this invention belongs.
Embodiments of the present invention will be described below in detail with reference to the accompanying drawings.
The present invention, referred to as MLWM_VFTMote, introduces an integrated ML-based workforce monitoring system for volumetric filling machines in chemical industries. The system comprises a Raspberry Pi processor as the central processing unit, a GSM Modem for communication and cloud connectivity, a Camera Module for live video feed, a Neural Stick for AI processing, a DHT Sensor for environmental monitoring, a Touch HMI Display for user interface, a Speaker for audio feedback, a Led Indicator for visual alerts, and a Power Supply.
MLWM_VFTMote captures real-time data from various sensors, live video feeds, and machine on/off schedules. This data is processed using machine learning algorithms to determine working hours, quantify tasks completed, and generate AI-powered recommendations for improvement. The insights are accessible through a user-friendly interface, enabling informed decision-making to optimize workforce efficiency and enhance productivity in chemical manufacturing processes.
The MLWM_VFTMote device operates through the following key components:
• Raspberry Pi Processor: This serves as the central processing unit, coordinating the operations of various modules and executing machine learning algorithms for data analysis.
• GSM Modem: This module enables communication and data transmission to a cloud server, facilitating remote monitoring and analysis.
• Camera Module: This module captures live video feeds of the volumetric filling machine operation, providing visual data for analysis.
• Neural Stick: This module provides specialized hardware acceleration for neural networks, enhancing the efficiency of AI processing.
• DHT Sensor: This sensor monitors environmental parameters, such as temperature and humidity, providing additional data for analysis.
• Touch HMI Display: This display provides a user-friendly interface for on-site monitoring, data visualization, and AI-driven recommendations.
• Speaker: This component provides audio feedback, including alerts and notifications, enhancing operator communication.
• Led Indicator: This indicator provides visual alerts and status updates, complementing the audio feedback.
• Power Supply: This module ensures continuous and reliable operation of the device by providing power to all components.
The MLWM_VFTMote system captures real-time data from the sensors, live video feeds, and machine on/off schedules. This data is transmitted to the cloud server via the GSM Modem. The Raspberry Pi processor, in conjunction with the Neural Stick, processes the data using machine learning algorithms. The processed data is then used to determine working hours, quantify tasks completed, and generate AI-powered recommendations for improvement. These insights are displayed on the Touch HMI Display and are also accessible through a dedicated web dashboard on the cloud server.
While specific language has been used to describe the present invention, any limitations arising on account thereto, are not intended. As would be apparent to a person in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.
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.
ADVANTAGES OF THE PRESENT INVENTION
1. The MLWM_VFTMote is a sophisticated workforce monitoring system made especially for volumetric filling equipment used in the chemical industry. It takes advantage of state-of-the-art hardware, cloud connection, and machine learning algorithms to record data in real-time, assess operational performance, and deliver actionable AI-driven recommendations to maximize efficiency.
2. MLWM_VFTMote's integration of a GSM modem enables smooth data transfer and communication. This function enables the logging of sensor parameters, machine on/off schedules, and vital operational data to a personalized cloud server. This guarantees accessibility and real-time monitoring, assisting in the chemical industries' optimization of personnel efficiency.
3. The Camera Module, which is included into the MLWM_VFTMote, is essential because it uses Clara-based technology to capture live video feeds. By providing a full dataset for analysis, this real-time visual monitoring of volumetric filling equipment in the chemical industries promotes wise decision-making to increase operational efficiency.
4. The Neural Stick integrated into MLWM_VFTMote offers specialized neural network acceleration, improving the system's processing power. This makes it easier to apply machine learning algorithms for the real-time study of volumetric filling machine operations, which helps chemical businesses make wise decisions and maximize worker productivity.
5. An essential part of audio feedback in MLWM_VFTMote is the Speaker, which facilitates efficient contact with operators and sounds an alarm. This enhances the entire monitoring and efficiency optimization of volumetric filling machines in the chemical industries by contributing to an interface that is easier to use.
6. To improve the accuracy of machine learning algorithms in monitoring and optimizing labor productivity for volumetric filling machines in chemical industries, the DHT Sensor included into MLWM_VFTMote gathers vital environmental factors.
7. The Touch HMI Display in MLWM_VFTMote serves as an intuitive user interface that gives operators access to real-time data, AI-driven recommendations, and practical insights. This facilitates effective decision-making in the chemical industries' workforce monitoring and volumetric filling machine optimization.

, C , Claims:1. An Integrated ML-Based Workforce Monitoring system for Volumetric Filling Machines in Chemical Industries with Cloud Connectivity, comprising:
a Raspberry Pi processor as the central processing unit;
a GSM Modem for communication and data transmission to a cloud server;
a Camera Module for capturing live video feeds of the volumetric filling machine operation;
a Neural Stick for accelerating neural network processing;
a DHT Sensor for monitoring environmental parameters;
a Touch HMI Display for user interface and data visualization;
a Speaker for audio feedback and alerts;
a Led Indicator for visual alerts and status updates; and
a Power Supply for powering all components of the system.
2. The system as claimed in claim 1, wherein the GSM Modem facilitates real-time data logging and remote access to workforce monitoring information through a dedicated web dashboard on the cloud server.
3. The system as claimed in claim 1, wherein the Camera Module, in conjunction with Clara-based technology, provides visual data for comprehensive analysis of machine operation and workforce performance.
4. The system as claimed in claim 1, wherein the Neural Stick enhances the processing power of the system, enabling efficient execution of machine learning algorithms for real-time analysis of workforce productivity.
5. The system as claimed in claim 1, wherein the DHT Sensor provides additional environmental data that contributes to the accuracy of machine learning algorithms in monitoring and optimizing workforce efficiency.

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