CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD. (20240232820). METHOD AND INTERNET OF THINGS SYSTEM FOR DETERMINING AND ALLOCATING GAS MAINTENANCE TASKS BASED ON SMART GAS simplified abstract
Contents
- 1 METHOD AND INTERNET OF THINGS SYSTEM FOR DETERMINING AND ALLOCATING GAS MAINTENANCE TASKS BASED ON SMART GAS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND INTERNET OF THINGS SYSTEM FOR DETERMINING AND ALLOCATING GAS MAINTENANCE TASKS BASED ON SMART GAS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Gas Maintenance Task Allocation System
- 1.13 Original Abstract Submitted
METHOD AND INTERNET OF THINGS SYSTEM FOR DETERMINING AND ALLOCATING GAS MAINTENANCE TASKS BASED ON SMART GAS
Organization Name
CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD.
Inventor(s)
Guanghua Huang of Chengdu (CN)
METHOD AND INTERNET OF THINGS SYSTEM FOR DETERMINING AND ALLOCATING GAS MAINTENANCE TASKS BASED ON SMART GAS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240232820 titled 'METHOD AND INTERNET OF THINGS SYSTEM FOR DETERMINING AND ALLOCATING GAS MAINTENANCE TASKS BASED ON SMART GAS
Simplified Explanation
The patent application describes a method and system for allocating gas maintenance tasks based on smart gas technology.
- Obtaining maintenance personnel information and positioning data for tasks.
- Determining candidate maintenance tasks based on the data.
- Calculating maintenance cost scores for personnel using a prediction model.
- Matching personnel to tasks based on cost scores.
- Sending tasks to selected personnel via a user terminal.
Key Features and Innovation
- Utilizes smart gas technology for efficient allocation of maintenance tasks.
- Predictive model for calculating maintenance cost scores.
- Automated system for matching personnel to tasks based on cost scores.
Potential Applications
This technology can be applied in various industries where maintenance tasks need to be allocated efficiently, such as utilities, manufacturing, and transportation.
Problems Solved
- Streamlines the process of assigning maintenance tasks.
- Ensures tasks are allocated to the most cost-effective personnel.
- Improves overall efficiency and effectiveness of maintenance operations.
Benefits
- Reduces downtime by ensuring timely maintenance.
- Optimizes resource allocation for cost savings.
- Increases productivity by matching tasks to the most suitable personnel.
Commercial Applications
Gas Maintenance Task Allocation System This technology can be used by gas companies, utilities, and other industries with maintenance needs to streamline task allocation processes, improve efficiency, and reduce costs.
Prior Art
Readers interested in prior art related to this technology can explore research papers, industry publications, and patent databases for similar methods and systems for task allocation based on predictive models and historical data.
Frequently Updated Research
Stay updated on advancements in predictive maintenance technologies, IoT systems, and cost prediction models to enhance the efficiency and effectiveness of task allocation processes.
Questions about Gas Maintenance Task Allocation System
How does the predictive model calculate maintenance cost scores?
The predictive model uses task data and historical maintenance information to estimate the cost of each maintenance task for personnel.
What are the key benefits of using smart gas technology for task allocation?
Smart gas technology enables real-time monitoring and data collection, leading to more accurate task allocation and cost-effective maintenance operations.
Original Abstract Submitted
the embodiments of the present disclosure provide a method and an internet of things system for determining and allocating gas maintenance tasks based on smart gas. the method includes: obtaining maintenance personnel information of at least one maintenance personnel; obtaining second positioning information corresponding to at least one maintenance task; determining at least one candidate maintenance task based on the first positioning information and the second positioning information; determining a maintenance cost score of each of the at least one maintenance personnel through a cost prediction model based on task data of the at least one candidate maintenance task and the historical maintenance data of the maintenance personnel; determining a candidate maintenance personnel corresponding to the at least one candidate maintenance task based on the maintenance cost score; and sending the at least one candidate maintenance task to a user terminal of the candidate maintenance personnel.
