VISION-BASED EDGED WORKFORCE MONITORING SOLUTION FOR SINGLE PLANETARY MIXER IN BAKERY FOOD PROCESSING

Abstract

ABSTRACT Vision-Based Edged Workforce Monitoring Solution for Single Planetary Mixer in Bakery Food Processing This invention introduces a vision-based IoT-enabled workforce monitoring system for single planetary mixers in bakery food processing. The system integrates a VTEW_SPMMote with a Raspberry Pi Processor, GSM Modem, Camera, and Relay Module to collect and analyze real-time operational data. Leveraging Machine Learning algorithms, the system provides AI-driven recommendations for improving efficiency. The GPU Stick accelerates computational tasks, while the Touch HMI Display offers an intuitive interface for operators. With IoT-based cloud integration, stakeholders gain actionable insights, trend analysis, and real-time visibility, enhancing workforce productivity and operational efficiency in bakery operations.

Specification

Description:FIELD OF THE INVENTION
This invention relates to Vision-Based Edged Workforce Monitoring Solution for Single Planetary Mixer in Bakery Food Processing
BACKGROUND OF THE INVENTION
One significant challenge facing the bakery food processing industry is effectively tracking and optimizing worker activity with regard to individual planetary mixers. In the lack of a specialized and intelligent system, there is inefficient use of resources and limited understanding of operating patterns. The manual process of monitoring mixer scheduling and personnel involvement is laborious, prone to mistakes, and devoid of instantaneous information.
KR20090026356A - A mixing well characterized by an internal volume not exceeding 100 ml; a drive mechanism including a stationary circumferential gear on an inner surface of the mixing well; and a planetary mixing element driven by a mixing element gear which engages the stationary circumferential gear.
Research Gap: Vision-based edge device for workforce monitoring for single planetary mixers in bakery food processing is the novelty of the system.
CN104689738B - The present invention proposes a kind of mixing apparatus, which includes (a) an internal volume not exceeding 100 milliliters of combined shaft; (b) a driving mechanism including a static circumferential gear located on the inner surface of the combined shaft; and (c) a planetary mixing element driven by the hybrid component gear engaged with the static circumferential gear.
Research Gap: Vision-based edge device for workforce monitoring for single planetary mixers in bakery food processing is the novelty of the system.
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 innovative staff monitoring system for a single planetary mixer in bakery food processing offers a comprehensive plan to boost productivity. The device uses a live video stream that is processed by advanced machine learning algorithms to provide real-time insights into what the workers are doing near the mixer. Effectively managing the on/off schedule is the relay module, and a feedback sensor records critical parameters to build an extensive dataset. This data is then sent to a cloud server for analysis, which makes it possible to determine working hours and produce AI-powered suggestions for improving processes.
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.
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 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.
The VTEW_SPMMote revolutionizes worker efficiency monitoring in bakery food processing by integrating hardware components, machine learning (ML) algorithms, cloud-based analytics, and user interfaces. This innovation increases labor efficiency for a single planetary mixer in bakery food processing by combining hardware components, cutting-edge technologies, and cloud-based analytics. The Raspberry Pi CPU, which serves as the central hub coordinating the system's operations, is at the core of this invention. One essential component is the live video feed, which is processed by Machine Learning (ML) algorithms. Through the analysis of live video, these algorithms are able to derive important information on worker activity surrounding the single planetary mixer. The feedback sensor's parameters and this data are then sent to a specially designed cloud server. The information is gathered and processed by the server, enabling in-depth analysis and recommendations for enhancing operational effectiveness. The relay module is essential since it controls the mixer's on/off timing.
A thorough timeline of the mixer's operating patterns is created by entering this data onto the cloud server. By capturing extra metrics, the feedback sensor adds to the comprehensive dataset that the machine learning algorithms can assess. This logged data is used to determine working hours, giving a clear picture of how time is spent around the mixer. The system's processing power is increased by the incorporation of a GPU stick, which makes it easier to do the intricate mathematical operations needed for ML algorithm operations. Accurate timekeeping in sync with real-time clock functions is guaranteed by the RTC module. The SD card module makes it possible to save data securely and keeps track of past events for later use. A customized Web Dashboard, a Mobile App, and a Touch Human-Machine Interface (HMI) Display are the methods by which the user interface is displayed. Additional parameters can be entered by authorized operators, and operators and authorities can get AI-generated recommendations for enhancing productivity. Real-time data transmission made possible by internet connectivity enables stakeholders to watch and control the system from a distance.
BEST METHOD OF WORKING
1. Using cutting-edge machine learning algorithms, the VTEW_SPMMote, which has a Raspberry Pi processor, GSM modem, camera, relay module, feedback sensor, GPU stick, RTC module, SD card module, touch HMI display, and power supply, revolutionizes workforce efficiency monitoring in bakery food processing. It provides real-time insights and AI-driven recommendations through user-friendly interfaces for increased operational productivity.
2. This innovation's Raspberry Pi processor orchestrates the smooth integration of relay-controlled on/off scheduling, feedback sensors, live camera feeds, and machine learning algorithms to enable real-time monitoring and analysis for improved worker productivity in bakery food processing.
3. The GSM modem attached to the VTEW_SPMMote is utilized to enable smooth communication with a customized cloud server, allowing real-time data transmission from the workforce monitoring system. This improves data analysis's accessibility and effectiveness for better decision-making in the food processing industry for bakeries.
4. In order to analyze workforce activities in real-time and provide insightful insights for improved efficiency monitoring and AI-driven recommendations in the context of single planetary mixers in bakery food processing, the camera integrated into VTEW_SPMMote is used to record live footage.
5. The Relay Module, which is connected to the VTEW_SPMMote, is utilized to control the single planetary mixer's on/off schedule. It logs this data to a cloud server and adds it to a large dataset for analysis, which finally optimizes operational efficiency in the food processing of bakeries.
6. To improve workforce efficiency in single planetary mixers used in bakery food processing, the GPU Stick integrated into VTEW_SPMMote is used to boost computational capabilities and efficiently process complex Machine Learning algorithms. This optimizes the analysis of real-time camera feeds and feedback sensor data.
ADVANTAGES OF THE INVENTION
1. The VTEW_SPMMote effortlessly integrates live camera feeds, relay-controlled on/off scheduling, feedback sensors, and cutting-edge machine learning algorithms to transform worker efficiency monitoring in bakery food processing. Through intuitive interfaces, this integration provides AI-driven recommendations and real-time insights, improving operational productivity without the need for separate solutions.
2. The workforce monitoring system may transmit data in real time thanks to the GSM modem's easy connectivity with a customized cloud server. This feature improves data analysis's efficiency and accessibility, which helps bakeries make better decisions about how to produce food.
3. The camera records live video, allowing cutting-edge machine learning algorithms to assess worker activities instantly. This procedure offers insightful information for improved efficiency tracking and AI-driven recommendations, especially when it comes to single planetary mixers used in bakery food processing.
4. The GPU Stick improves computational power and makes it possible to perform sophisticated machine learning algorithms quickly. In single planetary mixers used in bakery food processing, this optimization improves the interpretation of real-time video feeds and feedback sensor data, offering real-time insights and boosting worker efficiency.
, Claims:We Claim:
1. A Vision-Based Edged Workforce Monitoring device for Single Planetary Mixer in Bakery Food Processing, comprises a VTEW_SPMMote equipped with a Raspberry Pi Processor, GSM Modem, Camera, Relay Module, Feedback Sensor, GPU Stick, RTC Module, SD Card Module, Touch HMI Display, and Power Supply, enabling real-time monitoring, AI-driven recommendations, and IoT-based operational insights for improved workforce efficiency and productivity in bakery food processing.
2. The device, as claimed in Claim 1, wherein the Camera integrated into the VTEW_SPMMote captures live video feeds, allowing advanced Machine Learning algorithms to analyze workforce activities in real time, providing insights and recommendations for improving efficiency in single planetary mixers.
3. The device, as claimed in Claim 1, wherein the Relay Module controls the on/off schedule of the single planetary mixer, recording operational patterns and enhancing productivity by leveraging cloud-stored data for analysis and optimization.
4. The device, as claimed in Claim 1, wherein the GPU Stick enhances computational capabilities, enabling efficient processing of complex Machine Learning algorithms, optimizing the analysis of real-time video feeds and feedback sensor data.
5. The device, as claimed in Claim 1, wherein the GSM Modem provides seamless communication with a customized cloud server, facilitating real-time data transmission and accessibility for better decision-making in bakery food processing.
6. The device, as claimed in Claim 1, wherein the Touch HMI Display offers a user-friendly interface for operators and authorities to visualize real-time data, interact with system recommendations, and input additional parameters for enhanced operational control.
7. The device, as claimed in Claim 1, wherein IoT-based cloud integration processes workforce activity data, generating actionable insights and historical analysis to support decision-making and improve operational productivity in bakery food processing.

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