Project Task Assignment Scoring Algorithm
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for project task assignment in enterprises, especially when involving multiple professional skills, lack an objective and efficient system for allocating tasks and executing processes, often relying on experience and subjective judgment, leading to variability in project quality and execution efficiency.
Innovation Solution
A project task assignment method utilizing a system that builds databases for candidate project types and execution terminals, determines task assignment sequences through a scoring mechanism based on feedback data, and transmits project signals to selected execution terminals, enabling objective allocation and sequencing of professional tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual review of professional self-assessment sheets and experiences is used for talent matching, then the process can be simple to implement, but the selection results become subjective and inconsistent
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated computer-based matching system. The processor automatically compares project requirements with talent database information using algorithms, eliminating subjective human judgment while maintaining ease of operation through automated workflows.
Solution Approach 2:
The system implements feedback mechanisms where project outcomes and execution quality are fed back into the database to continuously improve matching algorithms. This creates a closed-loop system that learns from past projects to enhance future matching precision without increasing operational complexity.
2Loss of time
If experience-based professional allocation methods are used, then the approach can be quickly applied, but the project quality and execution efficiency cannot be consistently optimized
Solution Approach 1:
The system performs preliminary actions by pre-building comprehensive databases of talent profiles, project types, and successful allocation patterns before actual project assignment. This preparation enables rapid automated matching that is both fast and consistently accurate, eliminating the trade-off between speed and quality.
Solution Approach 2:
The patent transforms qualitative experience-based allocation into quantitative parameter-driven matching. By converting talent skills, project requirements, and performance metrics into measurable parameters, the system achieves consistent optimization through algorithmic comparison rather than subjective experience.
3Measurement precision
If a comprehensive scoring mechanism considering multiple factors is implemented, then the task assignment accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent segments the complex matching process into distinct modular components: database building module, scoring calculation module, and assignment execution module. Each module handles specific aspects of the matching process independently, making the overall complex system manageable and maintainable while achieving high matching accuracy.
Solution Approach 2:
The system implements a universal scoring mechanism that can evaluate multiple different factors (skills, experience, availability, performance history) through a single integrated framework. This multi-functional approach handles diverse matching criteria without requiring separate complex systems for each factor.
Data Source
AI summary
A project task assignment method executed by a system communicating with a demand terminal and execution terminals. The system includes a storage device and a processor. The processor builds a task assignment database and an execution terminal database. When a project dispatching signal is received from the demand terminal, the processor acquires an assigned project type and project detail data, determines an assigned task assignment sequence, and generates matching scores. The processor acquires operation terminals, transmits one project starting signal to one of the operation terminals, and selectively stores the assigned task assignment sequence into the task assignment database as one of the candidate task assignment sequences. The processor generates one base score associated with the assigned task assignment sequence, computes a representative score of one of the candidate task assignment sequences corresponding to the assigned task assignment sequence, and stores the representative score into the task assignment database.