IMPLEMENTATION OF LORA TECHNOLOGY FOR WIRELESS ENERGY MONITORING IN SELF-FEEDING HEAVY-DUTY ELECTRIC CHIPPERS USED IN FORESTRY OPERATIONS WITH CLOUD INTEGRATION

Abstract

An Implementation of LoRa Technology for Wireless Energy Monitoring in Self-Feeding Heavy-Duty Electric Chippers Used in Forestry Operations with Cloud Integration An advanced implementation of LoRa Technology enables wireless energy monitoring for self-feeding heavy-duty electric chippers in forestry operations. The system integrates an LTTDN_HDECMote with a TI MSP430 MCU, LoRa Module, Current Sensor, and Temperature Sensor for real-time data collection and monitoring. It also incorporates an LTRDN_HDECMote featuring a TI MSP430 MCU, LoRa Module, GPRS Module, and TFT Display to transmit data to a customized cloud server. Leveraging IoT-based cloud technology, the system provides energy consumption analysis, real-time notifications, and actionable insights via a user-friendly web dashboard. This innovation enhances operational efficiency, reduces downtime, and ensures proactive maintenance in forestry operations.

Specification

Description:FIELD OF THE INVENTION
This invention relates to Implementation of LoRa Technology for Wireless Energy Monitoring in Self-Feeding Heavy-Duty Electric Chippers Used in Forestry Operations with Cloud Integration
BACKGROUND OF THE INVENTION
This invention aims to address the problem of heavy-duty electric chippers used in forestry operations having energy monitoring systems that are unreliable and useless. Traditional monitoring techniques usually depend on human data collection and analysis, which can lead to errors, inefficiencies, and a delay in identifying possible issues.
US20230311132A1 - A wood chipper comprising a chamber in which wood is chipped, at least one roller for feeding the wood into the chamber, and a hydraulic motor. Hydraulic lines connect a hydrostatic pump to the hydraulic motor, enabling the rotation of the roller for wood feeding. A splined member on the wood chipper engages with splined projections on a member attached to a tractor, transferring mechanical power from the tractor's power take-off to operate the hydrostatic pump for the chipper's functioning.
Research Gap: A Wireless energy consumption monitoring innovation through Lora and IoT Technology for Heavy-Duty Electric Chippers introduces real-time energy monitoring capabilities for forestry equipment, enhancing operational efficiency and enabling predictive energy usage insights through IoT integration.
CN203854078U - The utility model provides an automatic hydraulic wood splitting machine that includes a casing, positioning board, splitting cutter, drive wheels, electric saw, and associated driving air cylinders. The machine's structure facilitates automatic feeding, material breaking, and splitting. Key components, such as the push board and its driving air cylinder, are designed to slide within the material-pushing cavity, while the electric saw is mounted above the feeding opening via a bracket. This system simplifies operations, reduces labor intensity, and improves wood splitting efficiency and safety.
Research Gap: A Wireless energy consumption monitoring innovation through Lora and IoT Technology for Heavy-Duty Electric Chippers integrates IoT and Lora technology, enabling precise energy consumption tracking and optimization for heavy-duty electric chippers, revolutionizing energy efficiency and maintenance planning in forestry applications.
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.
This creative fix increases heavy-duty electric chippers' overall dependability, reduces downtime, and boosts operational efficiency in forestry applications. This development makes it easier to monitor critical data in real-time, such as temperature and current consumption, giving operators useful information to improve performance and avert possible issues. On a specialized cloud server, data is analyzed using machine learning algorithms to generate real-time data charts, energy consumption statistics, and timely notifications.
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.
This innovative method revolutionizes the way energy monitoring in heavy-duty electric chippers used in forestry operations is approached. The system redefines monitoring capabilities. It consists of two key components: the LTTDN_HDECMote and the LTRDN_HDECMote. Each of these components has specialized sensors, microcontrollers, communication modules, and display units. It is the responsibility of the LTTDN_HDECMote to collect critical data from the heavy-duty electric chipper, such as temperature and current values, which are essential for anomaly identification and performance monitoring. The LTTDN_HDECMote effectively gathers and wirelessly transfers this data to the LTRDN_HDECMote, which then processes and forwards it to a customized cloud server using both GPRS and LoRa modules. This is accomplished by the integration of a TI MSP430 MCU, sensors, and a LoRa module.
Using IoT-based cloud technology, this customized cloud server serves as the hub for data analysis, visualization, and storage. Its seamless integration and data accessibility from any internet-connected location are ensured. The system, which is outfitted with machine learning algorithms specifically designed for analyzing data in real-time, produces real-time charts of current data, energy consumption data, and complete analysis to identify important alerts or abnormalities. This insightful knowledge enables operators to better utilize energy, prevent problems before they arise, and increase chipper efficiency. Operators can effortlessly monitor real-time changes, evaluate historical data, and take action on actionable information affecting chipper performance thanks to the bespoke cloud web dashboard's user-friendly layout. The TFT display also makes on-site monitoring easier by giving operators instant feedback and notifications.
BEST METHOD OF WORKING
1. For data collection and transmission, the LTTDN_HDECMote, which is outfitted with a TI MSP430 MCU, Lora Module, Current Sensor, Temperature Sensor, Indicator, and Power Supply, is utilized. It gathers vital data, including temperature and current consumption, from heavy-duty electric chippers used in forestry operations, enabling real-time monitoring and analysis for performance optimization and potential problem detection.
2. Data reception and transmission are handled by the LTRDN_HDECMote, which consists of a TI MSP430 MCU, Lora Module, GPRS Module, TFT Display, Indicator, and Power Supply. Information is wirelessly received from the LTTDN_HDECMote and transmitted to a customized cloud server, enabling real-time monitoring, analysis, and display of crucial data for forestry operations operators.
3. The LoRa module, which is built into both motes, allows for long-range wireless communication between the LTTDN_HDECMote and LTRDN_HDECMote. This makes it easier to transmit data from heavy-duty electric chippers to the cloud server for forestry operations' real-time monitoring and analysis.
4. Measurement of the electrical current used by heavy-duty electric chippers is done by the Current Sensor embedded into both motes. This data is vital for energy monitoring and analysis to optimize performance and identify anomalies in forestry operations.
5. Data acquisition, communication, and processing are made possible by the TI MSP430 MCU, which is integrated into both of the motes and serves as the central processing unit. It regulates and coordinates the operation of various components within the LTTDN_HDECMote and LTRDN_HDECMote, facilitating efficient energy monitoring and analysis in forestry operations.
6. The LTTDN_HDECMote's GPRS module is used to make it easier for data to be transmitted over cellular networks from the LTRDN_HDECMote to the customized cloud server. This allows for the remote monitoring and analysis of heavy-duty electric chippers in forestry operations, even in places without LoRa coverage.
7. To improve on-site monitoring and decision-making skills in forestry operations, the TFT display interfaced on LTRDN_HDECMote is utilized to give operators real-time visual feedback and warnings regarding the performance and status of heavy-duty electric chippers.
ADVANTAGES OF THE INVENTION
1. The LTTDN_HDECMote is a data collection and transmission device that gathers and transmits critical data from heavy-duty electric chippers used in forestry operations, including temperature and current usage. This makes it easier to monitor and analyze data in real time, improving performance and quickly spotting possible problems.
2. The LTRDN_HDECMote wirelessly receives data from the LTTDN_HDECMote and transmits it to a dedicated cloud server, acting as a central hub for data reception and transmission. Forestry operators can benefit from real-time data monitoring, analysis, and display made possible by this procedure.
3. The LoRa module ensures effective data transmission from heavy-duty electric chippers to the cloud server by establishing long-range wireless connection between the LTTDN_HDECMote and LTRDN_HDECMote. Real-time monitoring and analysis-which are crucial for forestry operations-are made possible by this capacity.
4. The Current Sensor provides crucial information for energy monitoring and analysis by precisely measuring the electrical current used by heavy-duty electric chippers. This data is essential for performance optimization and anomaly detection in forestry operations.
5. Data transmission from the LTRDN_HDECMote to the customized cloud server happens without a hitch while using the GPRS module over cellular networks. Even in areas without LoRa coverage, this functionality makes it possible to remotely monitor and analyze heavy-duty electric chippers used in forestry operations.
6. Heavy-duty electric chippers are outfitted with a TFT display, which provides operators with visual feedback and alerts regarding the operation and state of the machines in real time. This improves the capacity for on-site monitoring and gives operators the ability to make wise judgments when conducting forestry operations.
, Claims:1. The system of Implementation of LoRa Technology for Wireless Energy Monitoring in Self-Feeding Heavy-Duty Electric Chippers Used in Forestry Operations with Cloud Integration, comprises an LTTDN_HDECMote (101) equipped with a TI MSP430 MCU (102), LoRa Module (103), Current Sensor (104), Temperature Sensor (105), Indicator (106), and Power Supply (107); and an LTRDN_HDECMote (201) equipped with a TI MSP430 MCU (202), LoRa Module (203), GPRS Module (204), TFT Display (205), Indicator (206), and Power Supply (207). Together, these devices enable data collection from heavy-duty electric chippers, real-time monitoring of temperature and current consumption, and seamless data transmission to a customized cloud server for performance optimization and anomaly detection in forestry operations.
2. The system, as claimed in Claim 1, wherein the LoRa Module (103, 203) facilitates long-range wireless communication between the LTTDN_HDECMote and LTRDN_HDECMote, ensuring effective data transmission for real-time monitoring and analysis in forestry operations.
3. The system, as claimed in Claim 1, wherein the Current Sensor (104) embedded in the LTTDN_HDECMote provides precise measurement of electrical current usage by heavy-duty electric chippers, offering critical insights for energy monitoring, performance optimization, and anomaly detection.
4. The system, as claimed in Claim 1, wherein the GPRS Module (204) in the LTRDN_HDECMote enables data transmission over cellular networks to the customized cloud server, ensuring remote monitoring and analysis in areas lacking LoRa coverage.
5. The system, as claimed in Claim 1, wherein the TFT Display (205) interfaced with the LTRDN_HDECMote provides operators with real-time visual feedback and alerts on the performance and status of heavy-duty electric chippers, enhancing on-site monitoring and decision-making capabilities.

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