Rotation speed control method and system, device, and storage medium
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Solution Overview
Problem
Conventional methods for controlling the rotation speed of compressors in heat pump systems are manual and inaccurate, consuming manpower and material resources, and lack automatic control capabilities, particularly in low-temperature conditions where refrigerant viscosity increases, leading to oil shortage and compressor damage.
Innovation Solution
Implement a method using temperature and pressure sensors to collect data, process it into ratios, match with pre-trained oil content prediction models, and control compressor speed based on predicted oil content data, utilizing a neural network model trained with sample data to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual visual inspection and manual control of compressor speed is used, then the system can operate with simple equipment, but the control accuracy is poor and consumes大量 manpower and material resources
Solution Approach 1:
The system uses temperature and pressure sensors to automatically detect and predict oil content, eliminating the need for manual visual inspection. The control system automatically adjusts compressor speed based on predicted oil content,实现ing self-service operation without human intervention.
Solution Approach 2:
The patent replaces manual mechanical inspection with automated sensing systems (temperature and pressure sensors) and neural network-based prediction algorithms. This substitution transforms the manual control process into an automated electronic control system, improving both accuracy and reducing operational complexity.
2Extent of automation
If automated control system with sensors and neural network is implemented, then the measurement precision and automation level improve, but the device complexity increases
Solution Approach 1:
The system uses a multi-functional integrated approach where temperature and pressure sensors serve dual purposes: monitoring system conditions and providing data for oil content prediction. The neural network model performs multiple functions including pattern recognition, prediction, and control decision-making, reducing the need for separate dedicated components.
Solution Approach 2:
The patent introduces temperature and pressure ratios as intermediary parameters that mediate between raw sensor data and oil content prediction. These intermediary variables simplify the complex relationship between compressor operating conditions and oil content, making the control system more manageable and interpretable.
3Reliability
If compressor speed is not automatically controlled in low-temperature conditions, then the system structure remains simple, but oil return becomes difficult leading to oil shortage and compressor damage
Solution Approach 1:
The system performs preliminary prediction of oil content using neural networks before actual oil shortage occurs. By continuously monitoring temperature and pressure ratios and predicting future oil content levels, the system can proactively adjust compressor speed to prevent oil return problems before they happen.
Solution Approach 2:
The patent implements a closed-loop feedback control system where temperature and pressure sensors continuously monitor system conditions, the neural network predicts oil content based on these readings, and the control system adjusts compressor speed accordingly. This feedback mechanism ensures reliable oil return by continuously adapting to changing operating conditions.
Data Source
AI summary
A rotation speed control method and system, a device, and a storage medium is provided, which is applied to a heat pump system. The heat pump system includes a throttling short tube and a compressor, both sides of the throttling short tube are provided with a temperature sensor and a pressure sensor. The rotation speed control method includes: collecting actual data of the temperature sensors and the pressure sensors, processing to obtain temperature ratio data and pressure ratio data, and obtaining a corresponding oil content prediction model through matching; obtaining predicted oil content data according to the temperature ratio data and the pressure ratio data; and controlling a rotation speed of the compressor according to the predicted oil content data.