Air conditioner, and operation parameter recommendation method, system, and big data server for same

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Solution Overview

Problem

Current air conditioner operation parameters are set manually, which is not convenient or intelligent, lacking in automation and user experience enhancement.

Innovation Solution

An operation parameter recommending method for air conditioners that acquires current application scenario information, compares it with a database to select matched scenarios, generates group-behavior and individual-behavior recommended parameters, and combines them to provide a final recommended parameter for intelligent control.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If operation parameters are set manually by the user, then the user has direct control over the air conditioner, but the convenience and intelligence of control is poor

Engineering Contradiction:

Improveconvenience of controlVSAvoidintelligent control

Core Design Contradiction:

Ease of operationVSExtent of automation

Solution Approach 1:

The air conditioner system automatically recommends and adjusts operation parameters based on user behavior patterns and environmental data without requiring manual input. The system serves itself by collecting data from sensors, analyzing user preferences through machine learning algorithms, and autonomously optimizing temperature, humidity, and airflow parameters to match user needs.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system continuously monitors user interactions with the air conditioner and environmental conditions, then uses this feedback to refine parameter recommendations. By tracking when users adjust settings manually and correlating this with environmental data and time patterns, the system learns and adapts to provide increasingly accurate automatic recommendations.

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If operation parameters are set manually, then the system structure remains simple, but the user experience is not enhanced

Engineering Contradiction:

Improveuser experienceVSAvoidsystem structure

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments the parameter recommendation process into distinct functional modules: data collection from sensors and user interactions, behavioral pattern analysis through machine learning algorithms, environmental condition monitoring, and parameter optimization engines. This modular architecture allows each component to be developed and optimized independently while working together to enhance user experience.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The air conditioner system integrates multiple functions beyond traditional temperature control, including humidity regulation, airflow optimization, energy consumption management, and predictive maintenance scheduling. This multi-functionality is achieved through a unified control platform that processes diverse data types and generates comprehensive operation recommendations across all parameters.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Extent of automation

If automatic parameter recommendation is implemented, then convenience and intelligence are improved, but the system complexity increases

Engineering Contradiction:

Improveautomatic controlVSAvoidsystem structure

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The system introduces an intelligent software intermediary layer that sits between the physical air conditioner hardware and the user. This software layer handles the complex tasks of data collection, machine learning analysis, and parameter optimization, while presenting a simple interface to users and standard control signals to the hardware, thereby isolating complexity from both ends of the system.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11236918B2Air conditioner, and operation parameter recommendation method, system, and big data server for same

Publication Date: 2022.02.01 GD MIDEA AIR CONDITIONING EQUIP CO LTD

AI summary

An operation parameter recommending method includes acquiring current application scenario information of a target air conditioner, comparing the current application scenario information with an application scenario information database to select at least one matched application scenario which matches a current application scenario of the target air conditioner associated with the current application scenario information of the target air conditioner, acquiring an air conditioner operation parameter under each of the at least one matched application scenario, acquiring a group-behavior recommended parameter based on the acquired air conditioner operation parameter, acquiring historical operation records of the target air conditioner through an acquisition server, acquiring an individual-behavior recommended parameter based on the historical operation records, generating a final recommended parameter based on the group-behavior recommended parameter and the individual-behavior recommended parameter, and providing the final recommended parameter to control the target air conditioner to operate according to the final recommended parameter.