Flight Attendant Evaluation via Passenger Stress Indicators
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Solution Overview
Problem
Existing techniques for evaluating flight attendants based on customer stress do not provide a comprehensive method to assess and improve service quality, as they primarily focus on detecting stress in individual passengers rather than evaluating the service provided by attendants across assigned areas.
Innovation Solution
A flight attendant evaluation system that uses biological sensors to measure customer stress data, associates it with attendant identifiers, calculates stress indicators, and evaluates flight attendants based on these indicators, providing a comprehensive assessment of service quality and customer satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If biological sensors are used to measure customer stress data and evaluate flight attendants, then service quality assessment capability is improved, but device complexity increases
Solution Approach 1:
The evaluation system is divided into multiple independent components: biological sensors for data collection, stress indicator calculation module, evaluation value calculation module, and display apparatus. Each component performs a specific function, making the overall complex system manageable and maintainable while achieving precise service quality assessment
Solution Approach 2:
The evaluation system serves multiple functions: it monitors customer stress levels, evaluates flight attendant performance, provides feedback for service improvement, and generates statistical data. This multi-functionality justifies the system complexity by delivering comprehensive service quality management in one integrated platform
2Measurement precision
If stress indicators are calculated from individual customer biological data, then evaluation accuracy is improved, but information processing time increases
Solution Approach 1:
The system pre-calculates and stores stress indicators for each customer based on their biological data during the flight. This preliminary processing allows the evaluation value calculation to proceed efficiently by using pre-computed stress indicators rather than processing raw biological data in real-time, thus maintaining accuracy while reducing processing time
Solution Approach 2:
The system calculates stress indicators for all customers served by a flight attendant, even though only aggregated data is needed for evaluation. This excessive action ensures comprehensive data collection that improves evaluation accuracy through larger sample sizes, while the aggregation process efficiently handles the volume of data
3Loss of information
If attendant-associated data is stored with seat identifiers and biological data, then service quality traceability is improved, but data storage requirements increase
Solution Approach 1:
The data storage is segmented into structured tables with specific fields: customer identifier, seat identifier, attendant identifier, biological data, and calculated evaluation values. This segmentation organizes data efficiently, enabling easy retrieval and traceability while optimizing storage space through structured data management
Solution Approach 2:
Instead of storing detailed biological data for every customer and retrieving only what is needed, the system pre-calculates and stores aggregated evaluation values and stress indicators. This inversion reduces storage requirements for raw data while maintaining traceability through the stored evaluation metrics that can be linked back to specific attendants and seats
Data Source
AI summary
A system includes a biological sensor measuring biological data of a plurality of customers, a storage storing a plurality of attendant-associated data records, and a processor. Each attendant-associated data record includes the biological data of a customer, a seat identifier, and an attendant identifier associated with each other. The processor calculates at least one stress indicator of at least one customer. Each stress indicator is calculated based on the biological data in one of at least one attendant-associated data record extracted from the plurality of attendant-associated data records and associated with a first attendant identifier. The processor further calculates an evaluation value indicated by the first attendant identifier based on the at least one stress indicator of the at least one customer. The evaluation value is updated based on a registered evaluation value identified by the first attendant identifier stored in the storage, and the calculated stress indicator.


