Integrated management method and system for kitchen environment using artificial intelligence
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Solution Overview
Problem
Existing kitchen environment management systems lack integrated control over appliances using artificial intelligence, failing to effectively monitor and respond to changes in kitchen atmosphere conditions caused by cooking appliances, such as gas, oil mist, and smoke, which can lead to unpleasant environments and inefficient appliance operations.
Innovation Solution
An integrated management system using artificial intelligence that includes a range hood with sensors and cameras to detect changes in the kitchen atmosphere, a server to determine the need for a kitchen environment management mode, and network-connected appliances to receive control commands, adjusting cooking appliance output and controlling other appliances like dishwashers and air purifiers based on pollution levels and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple environment appliances are controlled independently, then each appliance can be operated separately, but integrated management and coordinated control are lacking
Solution Approach 1:
The patent merges multiple environment appliances (range hood, air conditioner, air purifier, dishwasher) into a single integrated management system controlled by one server. The server receives sensor data and issues coordinated control commands to multiple appliances simultaneously, enabling unified management while maintaining individual appliance functionality. This resolves the contradiction by combining control functions without requiring complex peer-to-peer communication between appliances.
Solution Approach 2:
The server acts as an intermediary between sensors and multiple environment appliances. Instead of direct control connections between appliances, the server mediates all control decisions, receiving atmospheric data from sensors and translating it into appropriate control commands for each appliance based on predefined rules and user preferences. This simplifies the system architecture while enabling integrated management.
2Measurement precision
If kitchen atmosphere changes are monitored continuously, then pollution levels are detected accurately, but energy consumption and system complexity increase
Solution Approach 1:
The system dynamically adjusts sensor operation modes based on cooking appliance status. Sensors operate at high precision during cooking operations when pollution is likely, and reduce measurement frequency or enter low-power mode when no cooking is detected. The server activates monitoring only when cooking appliances are in use, maintaining measurement precision when needed while minimizing energy consumption during idle periods.
Solution Approach 2:
Instead of continuous monitoring, the system employs periodic sensing triggered by cooking appliance operation states. Sensors take measurements at intervals during cooking processes and remain dormant between cooking sessions. This periodic action maintains adequate detection accuracy for pollution events while dramatically reducing overall energy consumption compared to continuous operation.
3Object-generated harmful factors
If cooking appliance output is reduced automatically, then pollution generation is minimized, but cooking efficiency and user control are affected
Solution Approach 1:
The system implements feedback control where sensors continuously monitor pollution levels and feed this information back to the server, which then adjusts cooking appliance output accordingly. When pollution exceeds thresholds, the server automatically reduces cooking power; when pollution is low, full power is restored. This closed-loop feedback maintains cooking efficiency by preserving full power capability while minimizing pollution through adaptive reduction only when necessary.
Solution Approach 2:
The system changes the operating parameters of cooking appliances dynamically based on real-time pollution measurements. Instead of fixed power levels, the cooking power parameter is adjusted continuously according to atmospheric conditions, user preferences, and pollution thresholds. This enables the system to maintain high cooking efficiency during low-pollution periods while automatically reducing power during high-pollution events.
4Productivity
If dishwasher operations are optimized automatically, then washing efficiency is improved, but system complexity and control requirements increase
Solution Approach 1:
The server performs preliminary analysis of pollution data and cooking patterns before initiating dishwasher operations. Based on detected pollution levels and cooking completion status, the server pre-determines appropriate washing cycles, water temperatures, and detergent quantities. This preliminary action simplifies the control logic by making washing parameter decisions before the dishwasher starts, avoiding complex real-time adjustments during the washing cycle while still optimizing efficiency based on actual usage conditions.
Data Source
AI summary
Disclosed are an integrated management method and system for a kitchen environment using artificial intelligence. The integrated management system for the kitchen environment includes: a range hood placed above a cooking appliance including a heater, the range hood including a sensor that measures information on an atmosphere environment changed due to an operation of the cooking appliance; a server determining whether to execute a kitchen environment management mode, on the basis of a result of measurement by the sensor; and multiple environment appliances registered in a user account and cooperating over a network, each of the multiple environment appliance receiving a control command corresponding to the kitchen environment management mode from the server, and operating according to the control command.