LCPS Super-Language Context for Asynchronous Algorithm Verification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for identifying and diagnosing illegitimate values or 'bug syntax' in asynchronously generated algorithms by logically connected programs (LCPS) are inadequate, leading to unresolved issues such as system accidents and incomplete algorithm execution.
Innovation Solution
The introduction of the 'LCPS super-language context' mechanism, which captures the necessary information to determine the legitimacy of asynchronously generated algorithms by establishing a context that connects subject names and variable subject names, allowing for the identification of illegitimate values and bug syntax without requiring additional activation parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional verification methods are used for LCPS, then the verification process is simple, but the legitimacy of asynchronously generated algorithms cannot be determined
Solution Approach 1:
The patent introduces 'super-language context information' as an intermediary mechanism that mediates between the LCPS and the verification process. This context information captures the execution state and semantic meaning of the program, enabling legitimacy verification without requiring complex invasive monitoring. The super-language context acts as a bridge that translates LCPS execution states into verifiable legitimacy criteria.
Solution Approach 2:
The patent performs preliminary capture of super-language context information during the execution phase, before legitimacy verification is needed. By pre-capturing the execution state, variable assignments, and control flow information in the super-language context, the system prepares verification data in advance, making the subsequent legitimacy check more efficient and reliable without adding complexity to the verification mechanism itself.
2Loss of information
If activation parameters are imported to capture super-language context, then the context information is complete, but the system requires additional imports and increased complexity
Solution Approach 1:
The patent makes the LCPS self-serve by extracting super-language context information directly from its own execution state without requiring external activation parameters. The verification mechanism utilizes the program's inherent variables, control flow, and execution state to construct the super-language context, eliminating the need for additional parameter imports and reducing system complexity while maintaining information completeness.
Solution Approach 2:
The super-language context mechanism is designed to be universal and work with any LCPS without requiring program-specific activation parameters. The same verification framework can capture context information from different programs by universally monitoring execution state, variable assignments, and control flow, making the system multi-functional and eliminating the need for program-specific parameter configurations.
3Measurement precision
If bug syntax identification is performed without super-language context, then the verification process is faster, but illegitimate values cannot be accurately identified
Solution Approach 1:
The patent performs preliminary capture of super-language context information during normal execution, storing execution state, variable values, and control flow data before verification is needed. This pre-capturing of contextual information ensures that when legitimacy verification occurs, the system immediately has access to accurate data for identifying illegitimate values, maintaining both precision and efficiency without requiring time-consuming analysis during the verification phase.
Data Source
AI summary
The present invention proposes a method for determining whether or not the legitimacy of an algorithm generated by LCPS during operation is established by generating the super-language context of LCPS from the LCPS to be verified by the generalized method of the present invention without using any verification data, and analyzing it by the generalized method of the present invention. With this method, all bug events that LCPS develops during operation are revealed as bug syntaxes in the generalized method of the present invention. If there is no bug syntax in LCPS, the algorithm generated by LCPS when operating is legitimate. The above-described method may be performed manually or by a computer according to a dedicated program.