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CN-121977265-A - Air conditioner multi-purification-function AI linkage control method based on temperature and humidity data

CN121977265ACN 121977265 ACN121977265 ACN 121977265ACN-121977265-A

Abstract

The invention discloses an air conditioner multi-purification function AI linkage control method based on temperature and humidity data, which belongs to the technical field of air conditioner intelligent control, and comprises the steps of acquiring multi-scene data through a single temperature and humidity sensor, training an AI association model (mould-containing risk prediction sub-model), dynamically linking humidity control based on real-time temperature and humidity data, performing UVC sterilization and fresh air replacement, wherein a high-humidity scene is sterilized after humidity control preferentially, a closed scene is ventilated as required, and a mould high-risk scene is sterilized in advance; meanwhile, the running state (frequency reduction and start and stop as required) of the module is optimized through an AI algorithm, so that the energy consumption is reduced by more than or equal to 10%. The invention solves the problems of redundant sensor, unordered purification function and high energy consumption of the traditional air conditioner without additional sensors, simplifies the system structure and reduces the cost on the premise of ensuring that the mold sterilization rate is more than or equal to 98 percent and the humidity control precision is +/-5 percent RH, and is suitable for multi-scene air health management such as families, offices and the like.

Inventors

  • REN XIAOLIN
  • XIE JINWEN
  • WANG XIAOMING
  • XU ZHILIANG
  • WANG CHONGJIE
  • LUO CHUNYONG

Assignees

  • 中山长虹电器有限公司

Dates

Publication Date
20260505
Application Date
20251231

Claims (7)

  1. 1. The multi-purification-function AI linkage control method based on temperature and humidity data is characterized by comprising the following steps: (1) Acquiring multi-scene temperature and humidity data through a temperature and humidity sensor, and training an AI (analog information) association model integrating feature extraction, mold risk prediction and energy consumption optimization; (2) The AI association model dynamically links and controls the humidity control module, the UVC sterilization module and the fresh air exchange module to operate according to the real-time temperature and humidity data; (3) And the operation states of all modules are optimized through an AI algorithm, so that the cooperative balance of the purification effect and the energy consumption is realized.
  2. 2. The method for controlling the multi-purification function AI of the air conditioner based on temperature and humidity data according to claim 1, wherein in the step (1), the temperature and humidity sensor is installed in a return air area of the air conditioner, the detection precision is +/-5% RH and +/-0.3 ℃, the collection frequency is 1 time/5 minutes under a normal comfortable scene, and when the temperature and humidity fluctuation > +/-5% RH is detected, the collection frequency is increased to 1 time/minute.
  3. 3. The multi-purification-function AI linkage control method based on temperature and humidity data is characterized in that the AI association model in the step (1) comprises three sub-models, namely a feature extraction sub-model is used for extracting three core features of humidity value, humidity duration time and temperature and humidity change rate, a mould risk prediction sub-model is a two-class model, the judging accuracy rate of mould risk is more than or equal to 85% of a medium level, an energy consumption optimization sub-model aims at purifying effect optimum and energy consumption minimum, and a scheduling parameter of fresh air start-stop time duration is output.
  4. 4. The method for controlling the multi-purification function AI linkage of the air conditioner based on temperature and humidity data as set forth in claim 1, wherein in the step (2), the dynamic linkage control logic specifically comprises: (a) Under a high humidity scene (relative humidity is more than 75% RH), the humidity control module is preferentially started to adjust the humidity to 45% -65% RH, and after the humidity is stable (fluctuation is less than or equal to +/-3% RH for 10 minutes), if the mould risk is more than or equal to a medium level, the UVC sterilization module is triggered to operate for 15-30 minutes; (b) Under a closed scene (the temperature and humidity are stabilized at 45% -65% RH, 22-28 ℃ and no fresh air exchange exists in 8 hours, the fresh air exchange module is started according to the standard of 15 minutes of air exchange in each 10m < 2 > space, and the temperature and humidity deviation in the air exchange process is less than or equal to +/-5% RH and +/-1 ℃; (c) Under a mould high-risk scene (the relative humidity is 60% -75% RH and the duration is not less than 4 hours), the UVC sterilization module is started in advance, and meanwhile, the linkage humidity control module slowly dehumidifies.
  5. 5. The multi-purification-function AI linkage control method based on temperature and humidity data is characterized in that the energy consumption collaborative optimization strategy in the step (3) comprises the steps of reducing frequency to 50% -70% of rated frequency after a humidity control module reaches standard, stopping a UVC sterilization module immediately after completion, dynamically adjusting operation time by a fresh air module according to closed time and temperature and humidity change rate, and starting a module with lower energy consumption preferentially when a plurality of scenes are triggered simultaneously.
  6. 6. The air conditioner multi-purification function AI linkage control method based on temperature and humidity data as set forth in claim 1, 3, 4 or 5 is characterized in that the wavelength of the UVC sterilization module is 254nm, the sterilization rate is more than or equal to 98%, the AI association model is deployed on an embedded AI chip, the operation delay is less than or equal to 500ms, the AI chip is connected with each purification module through a dedicated PWM interface, and the interface response time is less than 1 second.
  7. 7. The method of claim 3, wherein the multi-scene in the step (1) comprises a high humidity scene (relative humidity >75% RH), a mold easy-breeding scene (relative humidity 60% -75% RH and lasting more than or equal to 4 hours), a closed scene, and a conventional comfortable scene (temperature and humidity are in the range of 45% -65% RH, 22-28 ℃ and no obvious fluctuation).

Description

Air conditioner multi-purification-function AI linkage control method based on temperature and humidity data Technical Field The invention relates to the technical field of intelligent control of air conditioners, in particular to an AI linkage control method for multiple purification functions of an air conditioner based on temperature and humidity data. Background With the improvement of indoor air quality demand, air conditioners with purification function have become mainstream, but the prior art has the following core defects: 1. The sensor redundancy results in high cost and complex structure, the traditional purifying air conditioner needs to be provided with a plurality of detection devices such as a temperature and humidity sensor, a mould sensor, a carbon dioxide sensor and the like, so that the hardware cost is increased (20% -30% higher than that of a single sensor scheme), and the system failure rate is improved due to complex data fusion of a plurality of sensors, so that the adaptability is poor; 2. The purification function is operated in disorder, the energy consumption is seriously wasted, the humidity control, UVC sterilization and fresh air exchange modules are mainly in independent starting modes (such as manual triggering by a user or fixed time operation), and dynamic cooperative logic is lacked, for example, UVC sterilization is directly started without first controlling humidity in a high-humidity environment, so that the sterilization efficiency is reduced (mould is easy to regenerate in the high-humidity environment), or the fresh air module continuously operates when the temperature and humidity are stable, so that unnecessary energy consumption is caused; 3. the prior art is directly detected by a mould sensor, has lag response and is easy to be interfered by environment, and the mould breeding risk cannot be prejudged in advance according to temperature and humidity changes, so that the purifying time is lag, and the air health effect is affected. In the prior art, although an AI algorithm is adopted to optimize single-function operation (such as independent humidity control or energy-saving strategy) of the air conditioner, a technical scheme of 'humidity control-sterilization-ventilation' full-field Jing Dongtai linkage and energy consumption cooperation can not be realized only through temperature and humidity data. Therefore, the invention provides an AI linkage control method based on temperature and humidity data, which solves the technical pain point. Disclosure of Invention Aiming at the defects of the prior art, the invention aims to provide an air conditioner multi-purification function AI linkage control method based on temperature and humidity data, which is used for realizing dynamic linkage and energy consumption optimization of humidity control, UVC sterilization and fresh air exchange functions by combining an AI association model through collecting data by a single temperature and humidity sensor and is suitable for multi-scene air health management such as families, offices and the like. The invention discloses an air conditioner multi-purification function AI linkage control method based on temperature and humidity data, which comprises the following steps: (1) Acquiring multi-scene temperature and humidity data through a temperature and humidity sensor, and training an AI (analog information) association model integrating feature extraction, mold risk prediction and energy consumption optimization; (2) The AI association model dynamically links and controls the humidity control module, the UVC sterilization module and the fresh air exchange module to operate according to the real-time temperature and humidity data; (3) And the operation states of all modules are optimized through an AI algorithm, so that the cooperative balance of the purification effect and the energy consumption is realized. As a preferred implementation manner, the temperature and humidity sensor in the step (1) is installed in the air conditioner return air area, the detection precision is +/-5% RH and +/-0.3 ℃, the collection frequency is 1 time/5 minutes in a conventional comfort scene, and when the temperature and humidity fluctuation > +/-5% RH is detected, the collection frequency is increased to 1 time/minute. In the step (1), the AI association model comprises three sub-models, wherein the feature extraction sub-model is used for extracting three core features of humidity value, humidity duration time and temperature and humidity change rate, the mould risk prediction sub-model is a two-class model, the judging accuracy rate of mould risk to be equal to or higher than a medium level is equal to or higher than 85%, the energy consumption optimization sub-model aims at 'optimal purification effect and lowest energy consumption', and the scheduling parameters of the humidity control module including the frequency reduction proportion and fresh air start-stop time length are output. A