Three-Dimensional Space Filter for Invalid Performance Data Detection

The patent applies preliminary action by creating a three-dimensional space model from historical data before actual data validation occurs. The edge surface and filter are constructed in advance based on historical patterns, enabling proactive identification of invalid data values before they cause system issues. This prevents the need for complex reactive debugging while maintaining reliability.

The patent transforms traditional one-dimensional or two-dimensional data validation into a three-dimensional space analysis. By analyzing historical data in three dimensions and creating an edge surface in this space, the system can more accurately distinguish valid from invalid data values. This dimensional expansion improves data reliability without requiring complex rule-based validation systems.

The three-dimensional space, edge surface, and filter are all constructed in advance from historical data before actual validation operations. This preliminary setup phase separates the complex computational work from the real-time validation process, allowing accurate detection without excessive processing time during actual data operations.

The patent introduces a filter as an intermediary between the three-dimensional space model and the actual data validation process. This filter simplifies the complex three-dimensional analysis into actionable validation rules, reducing processing complexity while maintaining high detection accuracy. The filter acts as a mediator that translates complex spatial analysis into simple validation logic.

The filter serves as an efficient intermediary that quickly evaluates data against the pre-computed three-dimensional model without requiring exhaustive analysis. This intermediary layer enables continuous monitoring with minimal resource consumption by using the filter's simplified validation logic rather than performing full three-dimensional space analysis on every data point.

The system performs self-service by using its own historical data to automatically construct the three-dimensional space model and filter. Once established, this self-generated model continuously validates new data without requiring external intervention or additional computational resources, maintaining system stability efficiently.