Power Consumption Anomaly Detection in Mobile Applications
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Solution Overview
Problem
Existing methods for monitoring power consumption in mobile application programs struggle to accurately determine power consumption exceptions due to varying power consumption patterns among users, leading to inaccurate and false determinations.
Innovation Solution
A server-based method that receives report information from terminals, including running information and power consumption data, to determine abnormal power consumption by querying a relationship table and classifying terminal types, thereby sending prompt information to the terminal to manage power consumption exceptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed threshold is set to monitor power consumption, then power consumption monitoring can be implemented, but the accuracy of determining power consumption exceptions deteriorates due to varying user patterns
Solution Approach 1:
The patent changes the monitoring parameter from a fixed threshold to a dynamic baseline that adapts to each user's application usage patterns. The baseline is established by collecting historical data on application running durations and adjusting the threshold based on user-specific behavior, thereby improving determination accuracy without requiring complex manual configuration
Solution Approach 2:
The system automatically establishes power consumption baselines by monitoring and analyzing user application usage patterns over time. The baseline is self-adjusting based on historical data, eliminating the need for manual threshold setting while improving accuracy through personalized adaptation to each user's behavior
2Ease of manufacture
If a fixed threshold is used for all users, then implementation is simple, but false determination of power consumption exceptions increases
Solution Approach 1:
The system performs preliminary data collection during a baseline establishment period, gathering historical information on application running durations and power consumption patterns. This preliminary action creates a personalized reference profile for each user before actual exception detection begins, enabling accurate comparison without complex real-time calculations
Solution Approach 2:
The monitoring threshold transitions from a static fixed value to a dynamic baseline that evolves with user behavior. The system continuously updates the baseline based on new usage data, allowing the threshold to adapt to changing user patterns while maintaining simple automated operation
Data Source
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AI summary
Embodiments of this application provide a power consumption exception prompt method, a server, and a terminal, to accurately identify a power consumption exception in an application program, and reduce falsely determining the power consumption exception in the application program. The method in the embodiments of this application includes: receiving, by a server, report information sent by a terminal, where the report information includes running information of a first application program on the terminal and power consumption of the first application program on the terminal, and the first application program is any application program on the terminal; determining, by the server based on the report information, whether the power consumption of the first application program on the terminal is abnormal; and when determining that the power consumption of the first application program on the terminal is abnormal, sending, by the server, prompt information to the terminal, where the prompt information is used to indicate that the power consumption of the first application program on the terminal is abnormal, and the running information includes at least one of foreground running duration, background running duration, download traffic, a multimedia file download format, and a multimedia file download volume.