Working principle and processing
The operational framework and data processing are segmented into three distinct phases, each contributing uniquely to the research endeavor:
- Transmitting values for external processing.
- Internal processing within the machine.
- Integrate work systems.
These refined stages aim to enhance the precision and efficiency of the research methodology.
1. Transmitting values for external processing:
The systematic transmission of values for external processing is refined into two pivotal components, strategically enhancing user interaction within the research framework. The primary component, known as the local machine, expertly functions as the dedicated interface for seamless reception and response to data. Contrarily, the secondary component, identified as the server machine, assumes the crucial role of conducting comprehensive analyses on the received data, culminating in the delivery of meticulously processed results.
This meticulous delineation between the local and server components not only optimizes the user experience but also ensures a streamlined and effective research workflow, enriching the overall research methodology.
While this process is widely adopted, it does come with inherent limitations, particularly in terms of data transmission, device communication, and the intensive workload on the server. Despite these challenges, it stands out as an effective method for cost reduction in production, making it a favorable approach for reaching a broad user base.
This method’s efficiency in managing production costs positions it as a pragmatic choice, acknowledging the trade-offs involved in achieving scalability and accessibility for a larger user audience.
2. Internal processing within the machine:
Local processing presents a captivating paradigm in working with robots, empowering them to engage in autonomous analytical thinking and independent functionality, mirroring human-like learning capabilities. The innovative design anticipates robots that operate and learn exclusively from their surrounding environment. This approach envisions a level of autonomy and adaptability, akin to human learning, fostering an intriguing frontier in the realm of robotic intelligence.
Local processing, while limited in its capabilities, directly receives data from the server and possesses the unique ability to encapsulate comprehensive knowledge within itself. This distinctive feature sets it apart, creating a reservoir of knowledge that bears a closer resemblance to human cognitive processes than other methodologies. The integration of server data empowers local processing to function with a depth of understanding, offering a promising avenue for advancing the convergence of machine and human-like intelligence.
3. Integrate work systems:
The integration of work systems involves a collaborative approach, where both local and server-based processing work synergistically. The local machine handles initial simple processing tasks, and in cases where answers are elusive, or knowledge is lacking, it seamlessly connects to the server to seek information and learn from its broader database. This methodology mirrors human teamwork dynamics, enabling robots to share information and knowledge, fostering a research-oriented framework that enhances problem-solving capabilities.
Method 3 is anticipated to address and mitigate the limitations associated with points 1 and 2, offering a strategic solution that combines the strengths of both local and server-based processing. This approach aims to optimize efficiency, overcome challenges related to data transmission and communication, and strike a balance between local autonomy and the wealth of knowledge available on the server. By leveraging the advantages of each method, method 3 endeavors to create a more robust and adaptable system, minimizing the drawbacks observed in the earlier stages.