Data Kernel Transformation for Lossless Large-File Reconstruction
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Solution Overview
Problem
The high cost and time required to store and transport large data stores due to the mismatch between data processing speed and storage/transportation costs, exacerbated by the limitations of traditional data compression methods, hinder the effective utilization of multi-core architecture and exacerbate network congestion.
Innovation Solution
A system utilizing a Data Compiler (DC) to transform large data into a smaller kernel and a Turing Dedekind device (TD) to recalculate the original data from this kernel, leveraging high-speed processors to eliminate the need for storing and transporting large data stores, utilizing equivalence relationships and advanced compression techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of stationary object
If traditional data compression methods are used, then data storage size is reduced, but data reconstruction accuracy deteriorates
Solution Approach 1:
The patent replaces traditional mechanical data compression algorithms with a physics-inspired model using Hamiltonian dynamics and symplectic integrators. This substitution enables lossless compression by transforming data into a compressed phase space representation that preserves all original information through reversible mathematical transformations, rather than discarding data as traditional compression methods do.
Solution Approach 2:
The patent changes the fundamental parameters of data representation by transitioning from fixed-bit compression schemes to dynamic phase space coordinates. By using time-varying Hamiltonian functions and adaptive symplectic integration steps, the system adjusts representation parameters to maintain perfect reconstruction accuracy while achieving high compression ratios.
2Reliability
If large data stores are stored and transported, then data availability is maintained, but storage and transportation costs increase
Solution Approach 1:
The patent creates a compressed copy of the original data in phase space that contains all necessary information for perfect reconstruction. Instead of storing and transporting the full original dataset, the system stores a compressed Hamiltonian model and initial conditions, then reconstructs data on-demand at the destination, significantly reducing storage and transportation volume while maintaining full data availability.
Solution Approach 2:
The patent performs preliminary data transformation into a compressed Hamiltonian representation before storage or transmission. This pre-processing step encodes all essential data information into a compact form that can be rapidly reconstructed when needed, eliminating the need to store or transport large volumes of raw data while ensuring data availability upon reconstruction.
3Speed
If high-speed processors are used for data recalculation, then data processing speed increases, but energy consumption increases
Solution Approach 1:
The patent employs periodic symplectic integration steps that efficiently advance the Hamiltonian system through time. By using optimized integration algorithms with periodic evaluation of Hamiltonian functions, the system achieves high-speed data recalculation while minimizing unnecessary computational operations, thereby reducing energy consumption compared to continuous or less efficient integration methods.
Solution Approach 2:
The patent performs preliminary calculation of Hamiltonian functions and their derivatives during the compression phase, storing these pre-computed values for reuse during reconstruction. This eliminates redundant calculations during high-speed recalculation operations, reducing processor energy consumption while maintaining rapid data processing speed.
Data Source
AI summary
A system transmits a target data file as a set of mathematical functions and data values representative of the target data file to a receiver, the system comprising at least one hardware processor and memory storing computer instructions, the computer instructions when executed by the at least one hardware processor configured to cause the system to identify a target bit pattern of a target data file; generate a set of mathematical functions and data values operative to generate the target bit pattern; and transmit the set of mathematical functions and data values to a receiver, which can use the set of mathematical functions and data values to generate the target data file.