Dynamic Prediction Control Method, Apparatus and System for Precision Air Conditioner
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Solution Overview
Problem
Current solutions for data center energy and heat management lack coordination between hardware and software, relying on empirical calculations and not effectively optimizing cooling systems, leading to inefficiencies and increased energy consumption.
Innovation Solution
A dynamic prediction control method and system for precision air conditioners that generates a three-dimensional simulation model based on hardware configuration and layout, uses machine learning to establish a proxy model from dynamic parameters, and predicts temperature distribution, allowing for iterative optimization of control parameters to adjust cooling operations based on real-time data and thermal requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If hardware upgrades or physical accessories are used to optimize cooling, then cooling performance is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces physical hardware modifications with a software-based dynamic prediction control system that uses machine learning models and simulation algorithms to optimize cooling performance. The system substitutes mechanical/physical solutions with computational methods, allowing cooling optimization through software control rather than hardware changes.
Solution Approach 2:
The system optimizes cooling by dynamically adjusting operational parameters such as air conditioner supply temperature, airflow rate, and operational status based on real-time predictions. Instead of changing hardware configuration, the system achieves cooling optimization through continuous parameter adjustment using prediction models that analyze thermal conditions and equipment states.
2Measurement precision
If more physical sensors are deployed for monitoring, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates virtual copies of physical sensors through prediction models and simulation algorithms. Instead of deploying additional physical sensors, the system generates virtual temperature and thermal condition data through computational models that replicate sensor functionality. This allows comprehensive monitoring without increasing physical hardware.
Solution Approach 2:
The system replaces physical sensing hardware with software-based prediction and simulation mechanisms. Machine learning models and CFD simulations substitute for physical sensors, enabling temperature and thermal condition monitoring through computational methods rather than physical measurement devices.
3Ease of operation
If empirical calculations are used for heat management, then ease of operation is maintained, but manufacturing precision and optimization effectiveness deteriorate
Solution Approach 1:
The system enables self-service operation where the dynamic prediction control automatically optimizes cooling parameters without requiring manual intervention or complex user input. The machine learning models and prediction algorithms autonomously analyze conditions and adjust settings, maintaining ease of operation while achieving precise optimization through advanced computational methods.
Solution Approach 2:
The system implements continuous feedback loops where prediction models constantly monitor thermal conditions, compare predicted versus actual states, and automatically adjust cooling parameters. This closed-loop control maintains operational simplicity while achieving high precision through real-time feedback and adaptive optimization based on predicted thermal behavior.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances energy efficiency by reducing unnecessary energy loss, providing early warnings for potential faults, and maintaining optimal operating conditions without requiring excessive physical sensors, thus improving power usage efficiency and reducing downtime.
Implementation Method 1
a dynamic prediction control method for a precision air conditioner... predicting a temperature distribution and change of an application scene... refrigeration of the precision air conditioner
Data Source
AI summary
Various embodiments of the teachings herein include a dynamic prediction control method for a precision air conditioner. An example method includes: generating a three-dimensional simulation model on the basis of a simulation template according to static information of a hardware configuration and layout of an application scene where the precision air conditioner is located, wherein the three-dimensional simulation model and a static model of the hardware configuration and layout of the application scene correspond to each other; running a simulation on the basis of a dynamic parameter of the precision air conditioner corresponding to the three-dimensional simulation model and establishing a correspondence relationship between the dynamic parameter and a simulation result using machine learning, so as to generate a proxy model; and predicting a temperature distribution and change of the application scene on the basis of the proxy model and real-time dynamic data of the precision air conditioner.