Data Processing System for Physical System Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data processing systems face challenges in efficiently modeling and analyzing complex physical systems, such as aircraft, due to the complexity of combining various options and conditions, which leads to high computational intensity and impracticality in developing models for all possible combinations, and requires significant processor time and manual effort.
Innovation Solution
A data processing system that operates on data definition objects with a dependency schema, allowing automatic computation of dependent objects and storage of output values, while invalidating results upon data changes, to efficiently model and analyze physical systems by recursively executing methods and updating dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If models are developed for each possible combination of options in advance, then accurate prediction of system behaviour is achieved, but the amount of processor time and computational resources required becomes significant and impractical
Solution Approach 1:
The patent applies preliminary action by pre-defining the structure of data definition objects and their dependency relationships, but not pre-computing all possible combinations. The system prepares the framework in advance (data definition objects with methods and dependencies) so that when a specific simulation is needed, only the necessary computations are performed based on the selected options, rather than computing everything beforehand.
Solution Approach 2:
The patent segments the overall system model into multiple independent data definition objects, each representing a specific option or parameter (e.g., wing design, payload, engine). Each object can be computed independently and has defined dependencies on other objects. This segmentation allows the system to compute only the specific segments needed for a given simulation scenario, rather than computing the entire system for all possible combinations.
2Productivity
If comprehensive algorithmic models are written for well-characterized systems, then efficient execution is achieved, but the approach becomes impractical for complex systems with many options and conditions
Solution Approach 1:
The patent applies dynamics by making the system configuration adaptable and changeable. Instead of a fixed comprehensive algorithm, the system uses data definition objects with methods that can be dynamically selected and configured based on the specific simulation needs. The dependency schema dynamically determines which objects need to be computed and in what order, allowing the system to adapt to different combinations of options and conditions without requiring a completely different model for each scenario.
Solution Approach 2:
The patent creates a universal framework that can handle multiple different system configurations and simulation scenarios through a single unified approach. The data definition object structure serves multiple functions: it defines data, specifies computation methods, establishes dependencies, and enables flexible configuration. This universal structure replaces the need for separate specialized algorithms for each possible system configuration.
3Reliability
If design data for various options are brought together manually to form coherent sets, then complete system characterization is achieved, but significant manual effort and time are required
Solution Approach 1:
The patent applies self-service by enabling the system to automatically integrate and manage design data for various options. The data definition objects automatically define their own data structures, computation methods, and dependency relationships. When options are selected for simulation, the system automatically determines which data objects need to be computed and in what order, based on the dependency schema, eliminating the need for manual assembly of design data.
Solution Approach 2:
The patent implements feedback through the dependency schema, which automatically tracks and manages relationships between different data definition objects. When data or configurations change, the system uses the dependency information to automatically determine what needs to be re-computed and propagated, providing feedback loops that maintain system consistency without manual intervention.
Data Source
AI summary
A processing system for processing data defining behavior of a physical system, the processing system being configured to operate on: (i) a plurality of data definition objects, each data definition object defining a method for computing one or more output result values for that object in dependence on one or more input values for that object; and (ii) a dependency schema defining one or more dependencies among the data definition objects, each dependency being such that a first data object is dependent on a second data object when the first data object takes as a required input value an output value of the second data object; the data processing system being capable of raising each data definition object to a computed state by executing the method defined in the respective data definition object to thereby compute one or more output values for that data definition object; and being configured to receive from a user an instruction indicating a request to raise a specific data definition object to a computed state, and in response to that instruction to automatically raise data definition objects on which that specific data definition object defines itself as being directly or indirectly dependent to a computed state.