Information Placement Using Monotonic Consumption Limits
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Solution Overview
Problem
Traditional methods for determining target consumption limits in information placement often result in low accuracy due to discarding non-monotonic data points, leading to incomplete utilization of historical information and instability in resource allocation.
Innovation Solution
A method that adjusts consumption limits to ensure monotonicity, allowing for real-time determination of target consumption limits based on fresh data and leveraging all available information for improved accuracy and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional methods discard non-monotonic data points to simplify processing, then data processing complexity is reduced, but measurement precision and reliability of target consumption limit determination deteriorate
Solution Approach 1:
The patent extracts and separately processes non-monotonic data points from the historical data set. Instead of discarding them, the system identifies these anomalies and applies specific adjustment operations to restore monotonicity, thereby preserving valuable information while maintaining data quality for accurate target consumption limit determination
Solution Approach 2:
The patent changes the parameter state of consumption limit data by applying monotonicity adjustment operations. When non-monotonic data points are detected, the system modifies their parameter values through specific adjustment rules to ensure the entire data set satisfies monotonicity requirements, enabling more reliable target consumption limit determination without losing data points
2Measurement precision
If all historical data points are utilized for target consumption limit determination, then measurement precision improves, but data processing complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-processing historical consumption limit data to ensure monotonicity before using it for target consumption limit determination. By adjusting the historical data in advance to remove non-monotonic anomalies, the system prepares clean, reliable data that can be directly utilized without complex filtering or validation during the main determination process
Solution Approach 2:
The patent replaces complex data filtering and validation mechanisms with a simpler monotonicity adjustment approach. Instead of using complicated algorithms to handle non-monotonic data, the system applies direct adjustment operations that transform the data to satisfy monotonicity requirements, thereby reducing processing complexity while maintaining data utilization
3Reliability
If non-monotonic data points are adjusted to satisfy monotonicity, then reliability of resource allocation improves, but data processing time increases
Solution Approach 1:
The patent applies local quality by performing targeted adjustments only on specific non-monotonic data points rather than reprocessing the entire data set. When a non-monotonic anomaly is detected, the system locally adjusts only the affected consumption limit values to restore monotonicity, thereby minimizing processing time while ensuring data reliability for resource allocation
Data Source
AI summary
Embodiments of the present disclosure provide a method, device, and medium for placing information. The method comprises obtaining a plurality of pairs of consumption limits and actual consumptions within a period of time, where the consumption limits in the plurality of pairs are not monotonic in a case that the plurality of pairs are sorted by actual consumptions. The method further comprises adjusting the consumption limits in the plurality of pairs such that the adjusted consumption limits are monotonic in the case that the plurality of pairs are sorted by the actual consumptions. In addition, the method further comprises allocating a target consumption limit for placement of target information based on the adjusted plurality of pairs of consumption limits and actual consumptions.