Thermal Energy Data Profiling for Real-Time Error Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current thermal energy management systems lack efficient detection and analysis of errors in data across various stages of thermal energy supply, distribution, and consumption, leading to inefficiencies and unfair billing.
Innovation Solution
An event management method and apparatus that generate error detection profiles, history error profiles, and composite error profiles to identify and classify errors in thermal energy data, transmitting alarms to relevant sites for timely correction and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated real-time energy meter information collection is implemented, then productivity and efficiency of thermal energy management are improved, but reliability of thermal energy data deteriorates due to errors in collected data
Solution Approach 1:
The system implements feedback by continuously monitoring thermal energy data from multiple sources (supply facility, complex facility, accommodation facility) and using error detection profiles to compare actual data against expected patterns. When deviations are detected, the system generates alerts and feeds this information back to relevant facilities for correction, thereby maintaining data reliability while preserving automated real-time collection efficiency.
Solution Approach 2:
The patent introduces an intermediary error detection and analysis system that acts as a mediator between the automated data collection process and the thermal energy management system. This intermediary layer includes error detection profiles, history error profiles, and composite error profiles that filter and validate collected data before it is used for billing and management decisions, thus resolving the contradiction between automated collection and data reliability.
2Measurement precision
If thermal energy data is collected from all stages (supply, distribution, consumption), then completeness and accuracy of billing information are improved, but device complexity increases due to multiple collection points
Solution Approach 1:
The patent applies universality by creating a multi-functional error detection profile system that serves multiple purposes: validating data from supply facilities, complex facilities, and accommodation facilities; detecting various types of errors (malicious manipulation, transmission errors, measurement errors); generating history error profiles; and producing composite error profiles. This single error detection mechanism handles all data collection points, reducing overall system complexity while maintaining comprehensive accuracy.
Solution Approach 2:
The system segments the error detection process into distinct components: error detection profiles for each facility type, history error profiles for pattern recognition, and composite error profiles for overall system analysis. This segmentation allows each component to specialize in specific validation tasks, making the complex multi-point data collection system more manageable and maintainable while preserving measurement precision.
3Reliability
If error detection profiles, history error profiles, and composite error profiles are generated and analyzed, then reliability of thermal energy data is improved, but loss of time increases due to extensive data analysis
Solution Approach 1:
The system performs preliminary action by pre-generating error detection profiles that contain expected data patterns and validation rules before actual thermal energy data is collected. These pre-established profiles enable rapid real-time validation of incoming data without requiring extensive analysis at the moment of data collection, thus maintaining high reliability while minimizing time loss.
Solution Approach 2:
The patent implements partial action by selectively applying different levels of error detection and analysis to different data sources and error types. The system uses error detection profiles for routine validation, history error profiles for pattern-based anomalies, and composite error profiles for critical issues. This selective approach ensures comprehensive reliability checking while avoiding unnecessary analysis time for routine data points.
Data Source
AI summary
An event management method based on thermal energy data is disclosed. The event management method generates profiles in real time for a single error, a composite error, and a history error, respectively, and detects, from the respective generated profiles, an error in thermal energy data that may occur at every stage of thermal energy data supply, distribution, and consumption to manage an event to execute measures in response to the detected error in the thermal energy data.