Cloud Server Task Set Estimation for Resource Allocation
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Solution Overview
Problem
Current resource allocation methods in organizations rely on rough estimates and lack real-time, data-driven approaches, making it challenging to accurately plan and prepare for future periods, leading to potential resource shortages.
Innovation Solution
A cloud server-based system that constructs a suggested task set by analyzing historical data to meet a target value, using ratios such as composition, conversion, and addition ratios, and provides real-time monitoring to alert users of discrepancies, enabling informed resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If rough estimates based on experience are used for resource allocation, then the process is simple and quick, but the accuracy and reliability of resource planning deteriorates
Solution Approach 1:
The patent replaces manual experience-based estimation with an automated cloud server system that uses historical data analysis and quantitative algorithms to calculate required resources, substituting the mechanical process of manual calculation with an automated computational system
Solution Approach 2:
The system performs self-service by automatically retrieving historical data from databases, calculating required resources based on predefined algorithms, and generating allocation recommendations without requiring manual intervention, thereby maintaining simplicity while improving accuracy
2Measurement precision
If quantitative methods with historical data analysis are implemented, then the accuracy of resource allocation improves, but the system complexity and computational requirements increase
Solution Approach 1:
The cloud server system performs multiple functions including data retrieval from databases, historical data analysis, calculation of composition ratios and conversion rates, and generation of allocation recommendations, thereby reducing overall system complexity through multi-functionality
Solution Approach 2:
The patent introduces a cloud server as an intermediary between the user and the complex data processing operations, abstracting the complexity away from the user interface while maintaining accurate quantitative analysis through standardized algorithms and database queries
3Reliability
If real-time monitoring and visualization systems are added, then the ability to detect discrepancies and alert users improves, but the system complexity and resource requirements increase
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring actual resource allocation progress against planned allocations, calculating conversion rates and composition ratios in real-time, and generating alerts when discrepancies exceed predetermined thresholds, thereby improving reliability through automated feedback loops
Data Source
AI summary
In an embodiment, described herein is a system and method for creating a suggested task set to meet a target value. A cloud server, in response to receiving a request specifying a target value, retrieves completed task sets from a database. Each completed task set includes a same set of task categories. The cloud server derives a number of ratios from the retrieved completed task sets, including a composition ratio and a conversion rate for each task category, and an addition ratio for the number of completed task sets. Based on the derived ratios and the specified target value, the cloud server constructs the suggested task set, and displays in real-time the suggested task set together with current values for the task categories. The cloud server alerts users of a discrepancy between a current value and the corresponding suggested value for a task category when the discrepancy reaches a predetermined level.