Space-Time-Nodal Signal Processing Engine for Renewable Energy
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Solution Overview
Problem
The processing of large volumes of diverse signals from various sources is resource-demanding, leading to increased complexity, processing time, and costs, particularly in applications like renewable energy integration, where unpredictability and intermittency pose challenges for balancing energy sources and ensuring market stability.
Innovation Solution
The implementation of a space-time-nodal engine for signal processing, which employs a special purpose computing platform to execute spatially, temporally, or nodally-dominant attributes, using techniques such as signal sampling, filtering, encoding, and training models to facilitate efficient processing and analysis of signals from multiple sources, including renewable energy data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional signal processing techniques are used to handle large volumes of diverse signals, then processing capability is maintained, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments signals into discrete spatial-temporal-nodal units called STING cells, each representing a specific segment of signal data with defined spatial, temporal, and nodal attributes. This segmentation allows the system to process large volumes of diverse signals by breaking them down into manageable, standardized units that can be efficiently handled by the computing platform.
Solution Approach 2:
The patent introduces a third dimension of organization by combining spatial, temporal, and nodal attributes into a unified space-time-nodal framework. This multi-dimensional approach enables the system to organize and process signals more efficiently by adding organizational dimensions that traditional two-dimensional (spatial-temporal) approaches cannot achieve, thereby reducing processing time while maintaining capability.
2Loss of information
If comprehensive signal processing is performed to analyze diverse content from multiple sources, then analysis completeness is improved, but processing complexity and costs increase
Solution Approach 1:
The patent creates a universal signal processing framework where a single computing platform can handle diverse signal types from multiple sources by organizing them into standardized STING cells. The space-time-nodal attribute structure serves as a universal container that can accommodate various signal formats and sources, enabling comprehensive analysis without requiring separate processing systems for each signal type, thus reducing overall complexity.
Solution Approach 2:
The patent standardizes signal representation by transforming diverse signals into a common parameter space defined by spatial, temporal, and nodal attributes. This parameter transformation allows the system to maintain analysis completeness across different signal types while reducing processing complexity through uniform handling procedures and standardized data structures.
3Measurement precision
If detailed signal processing is applied to ensure accurate analysis, then measurement precision is improved, but processing costs and resource consumption increase
Solution Approach 1:
The patent performs preliminary organization and standardization of signals into STING cells with defined spatial-temporal-nodal attributes before detailed analysis is performed. This preliminary action prepares the data in an optimized format that reduces the computational resources needed for subsequent precise analysis, thereby achieving high measurement precision while minimizing processing costs through efficient data preparation.
Data Source
AI summary
Example methods, apparatuses, or articles of manufacture are disclosed that may be implemented using one or more computing devices or platforms to facilitate or otherwise support one or more processes or operations associated with a space-time-node engine signal processing.