CN-121979336-A - Intelligent temperature controller adjusting method and system based on multisource environment perception

CN121979336ACN 121979336 ACN121979336 ACN 121979336ACN-121979336-A

Abstract

The invention relates to the field of temperature control adjustment, and discloses an intelligent temperature controller adjustment method and system based on multi-source environment perception, which are used for solving the problems of inaccurate control, poor comfort level and energy waste when a data sense is single, and comprise the steps of calculating a temperature comfort level index, a humidity comfort level index, an air comfort level index and an illumination comfort level index, obtaining a comprehensive comfort level index through weighted summation, comparing the comprehensive comfort level index with a preset threshold value to judge whether to start the temperature control adjustment, when the comprehensive comfort index is greater than or equal to the environmental comfort threshold, the indoor temperature control is not required to be regulated, when the comprehensive comfort index is smaller than the environmental comfort threshold, the indoor temperature control is regulated, a machine learning model is further introduced, the environmental change trend is predicted by utilizing historical data, and the user individuation preference is combined, so that the dynamic and self-adaptive temperature control regulation is realized.

Inventors

WANG LIN
YAO SHUO
LIU YONGCHENG

Assignees

南京精灵智控科技有限公司

Dates

Publication Date: 20260505
Application Date: 20260203

Claims (8)

1. The intelligent temperature controller adjusting method based on multi-source environment sensing is characterized by comprising the following steps of: Step one, acquiring temperature data, humidity data, air quality data and illumination intensity data through a temperature sensor, a humidity sensor, an air quality sensor and an illumination sensor; Calculating a temperature comfort level index, a humidity comfort level index, an air comfort level index and an illumination comfort level index according to the temperature data, the humidity data, the air quality data and the illumination intensity data, calculating the temperature comfort level index, the humidity comfort level index, the air comfort level index and the illumination comfort level index to obtain a comprehensive comfort level index, and judging whether temperature control adjustment is needed at present according to the comprehensive comfort level index; Step three, if the current need of temperature control adjustment is judged, dynamically monitoring environmental change through a machine learning model, and predicting environmental change trend in a period of time in the future to obtain environmental change trend; and step four, according to the environmental change trend and the demand of the user on comfort level, combining the data of environmental comfort level index and individual preference to perform personalized temperature control adjustment.
2. The intelligent regulation method of the temperature controller based on the multi-source environment sensing according to claim 1, wherein the step of obtaining the comprehensive comfort index is as follows: Acquiring temperature data, wherein the temperature data comprises an ambient air temperature, a proper temperature set value, temperature uniformity, a ground temperature value, a radiation temperature value and a heat source temperature value, and evaluating according to the temperature data to obtain a temperature comfort index; Acquiring humidity data, wherein the humidity data comprises an environmental indoor humidity value, a target humidity set value, a humidity gradient value in a current time period, a ground humidity value, air humidity, air flow and local humidity source influence, and evaluating according to the humidity data to obtain a humidity comfort index; Acquiring air quality data, wherein the air quality data comprises fine particulate matter concentration, carbon dioxide concentration, volatile organic compound concentration and air circulation index, and evaluating according to the air quality data to obtain an air comfort index; acquiring illumination intensity data, wherein the illumination intensity data comprises environment illumination perception, target comfortable illumination value, illumination distribution uniformity and natural illumination perception, and evaluating according to the illumination intensity data to obtain an illumination comfort index; And carrying out normalization processing on the temperature comfort index, the humidity comfort index, the air comfort index and the illumination comfort index, and carrying out weighted summation calculation on the temperature comfort index, the humidity comfort index, the air comfort index and the illumination comfort index subjected to normalization processing to obtain the comprehensive comfort index.
3. The intelligent temperature controller adjusting method based on multi-source environment sensing according to claim 1, wherein the temperature comfort index obtaining step comprises the following steps: acquiring the ambient air temperature and a proper temperature set value in the temperature data, and calculating the ratio of the ambient air temperature and the proper temperature set value in the temperature data to obtain a temperature deviation coefficient; Acquiring a proper temperature set value and temperature uniformity in temperature data, and calculating the ratio of the proper temperature set value to the temperature uniformity in the temperature data to obtain a temperature uniformity coefficient; Acquiring temperature uniformity and a ground temperature value in the temperature data, and calculating the ratio of the temperature uniformity and the ground temperature value in the temperature data to obtain a ground temperature coefficient; Acquiring a ground temperature value and a radiation temperature value in the temperature data, and calculating the ratio of the ground temperature value and the radiation temperature value in the temperature data to obtain a radiation temperature coefficient; acquiring a radiation temperature value and a heat source temperature value in the temperature data, and calculating the ratio of the radiation temperature value and the heat source temperature value in the temperature data to obtain a heat source temperature coefficient; Acquiring a heat source temperature value and a proper temperature set value in the temperature data, and calculating the ratio of the heat source temperature value and the proper temperature set value in the temperature data to obtain a heat source adjustment coefficient; And calculating to obtain the temperature comfort index through the temperature deviation coefficient, the temperature uniformity coefficient, the ground temperature coefficient, the radiation temperature coefficient, the heat source temperature coefficient and the heat source adjustment coefficient.
4. The intelligent regulation method of the temperature controller based on the multi-source environment sensing according to claim 1 is characterized in that the acquisition step of the humidity comfort index comprises the steps of acquiring an environment indoor humidity value and a proper humidity range in humidity data, and calculating the ratio of the environment indoor humidity value and the proper humidity range in the humidity data to obtain a humidity deviation coefficient; Acquiring the tolerance of a target humidity set value and a target humidity set value in the humidity data, and calculating the ratio of the tolerance of the target humidity set value and the target humidity set value in the humidity data to obtain a humidity set range coefficient; Acquiring an initial humidity value of a historical time period in the humidity data and a final humidity value of the historical time period, and calculating the ratio of the initial humidity value to the final humidity value in the humidity data to obtain the humidity change rate of the historical time period; Acquiring a humidity gradient value in a current time period, a humidity change rate in the current time period and a humidity change rate in a historical time period in humidity data, and calculating the humidity gradient value in the current time period, the humidity change rate in the current time period and the humidity change rate in the historical time period to obtain a humidity fluctuation amplitude coefficient, wherein the specific acquisition steps are as follows: ; In the middle of As the coefficient of the amplitude of the humidity fluctuation, As the humidity gradient value in the current time period, For the humidity rate of change of the current time period, Humidity change rate for a historical period of time; Obtaining a difference value between a ground humidity value and an environment humidity value in the humidity data, and calculating a ratio of the difference value between the ground humidity value and the environment humidity value in the humidity data to obtain a ground humidity deviation coefficient; acquiring air humidity and air flow speed and humidity stability in humidity data, and calculating the ratio of the air humidity to the air flow speed to the humidity stability in the humidity data to obtain an air humidity and air flow interaction influence coefficient; Acquiring local humidity source influence and humidity source influence intensity in humidity data, and calculating the ratio of the local humidity source influence and the humidity source influence intensity in the humidity data to obtain a local humidity source comfort degree correction coefficient; And carrying out normalization processing on the humidity deviation coefficient, the humidity setting range coefficient, the humidity fluctuation range coefficient, the ground humidity deviation coefficient, the air humidity and air flow interaction influence coefficient and the local humidity source comfort level correction coefficient, and calculating the humidity deviation coefficient, the humidity setting range coefficient, the humidity fluctuation range coefficient, the ground humidity deviation coefficient, the air humidity and air flow interaction influence coefficient and the local humidity source comfort level correction coefficient after normalization processing to obtain a humidity comfort level index.
5. The intelligent regulation method of the temperature controller based on multi-source environment sensing according to claim 1, wherein the air comfort index obtaining step is as follows: acquiring the concentration of fine particles in the air quality data and an air quality standard, and calculating the ratio of the concentration of the fine particles to the air quality standard to obtain an air quality index; Acquiring a carbon dioxide concentration and a carbon dioxide concentration threshold value in the air quality data, and calculating the ratio of the carbon dioxide concentration and the carbon dioxide concentration threshold value in the air quality data to obtain a carbon dioxide influence index; The method comprises the steps of obtaining the concentration of the volatile organic compound in air quality data and a threshold value of the concentration of the volatile organic compound, and calculating the ratio of the concentration of the volatile organic compound in the air quality data to the threshold value of the concentration of the volatile organic compound to obtain an influence index of the volatile organic compound; obtaining an air circulation index and a standard circulation index in the air quality data, and calculating the ratio of the air circulation index and the standard circulation index in the air quality data to obtain an air circulation influence index; And normalizing the air quality index, the carbon dioxide influence index, the organic compound influence index and the air circulation influence index, and calculating the normalized air quality index, carbon dioxide influence index, organic compound influence index and air circulation influence index to obtain the air comfort index.
6. The intelligent regulation method of the temperature controller based on the multi-source environment sensing according to claim 1, wherein the obtaining step of the illumination comfort index is as follows: obtaining an ambient light perception and a target light intensity value in the light intensity data, and calculating the ratio of the ambient light perception and the target light intensity value in the light intensity data to obtain an ambient light perception deviation index; Obtaining a target comfortable illumination value and a standard comfortable illumination range in illumination intensity data, and calculating the ratio of the target comfortable illumination value and the standard comfortable illumination range in the illumination intensity data to obtain an illumination target conformity index; Obtaining illumination distribution uniformity and uniformity standard in illumination intensity data, and enhancing illumination Calculating the ratio of the illumination distribution uniformity in the degree data to the uniformity standard to obtain an illumination uniformity index; Acquiring natural illumination perception and a preset natural illumination standard in illumination intensity data, and calculating the ratio in the illumination intensity data to obtain a natural illumination comfort index; And carrying out normalization processing on the illumination perception deviation index, the illumination target conformity index, the illumination uniformity index and the natural illumination comfort index, and calculating the normalized illumination perception deviation index, the normalized illumination target conformity index, the normalized illumination uniformity index and the normalized natural illumination comfort index to obtain the illumination comfort index.
7. The intelligent temperature controller adjusting method based on multi-source environment sensing according to claim 1, wherein the step of judging whether temperature control adjustment is needed currently according to the comprehensive comfort index is as follows: And comparing the comprehensive comfort index with an environmental comfort threshold, when the comprehensive comfort index is greater than or equal to the environmental comfort threshold, adjusting the indoor temperature control without adjusting, and when the comprehensive comfort index is less than the environmental comfort threshold, adjusting the indoor temperature control.
8. An intelligent temperature controller adjusting system based on multi-source environment sensing, which is used for realizing the intelligent temperature controller adjusting method based on multi-source environment sensing as set forth in any one of claims 1-7, and is characterized in that the system comprises: The data acquisition module is used for acquiring temperature data, humidity data, air quality data and illumination intensity data through the temperature sensor, the humidity sensor, the air quality sensor and the illumination sensor; The data processing module is used for calculating the temperature comfort index, the humidity comfort index, the air comfort index and the illumination comfort index according to the temperature data, the humidity data, the air quality data and the illumination intensity data, calculating the temperature comfort index, the humidity comfort index, the air comfort index and the illumination comfort index to obtain a comprehensive comfort index, and judging whether temperature control adjustment is needed at present according to the comprehensive comfort index; The individualized demand analysis and adjustment module is used for dynamically monitoring environmental changes through a machine learning model and predicting environmental change trend in a period of time in the future to obtain environmental change trend if the current need of temperature control adjustment is judged; and the optimization and feedback adjustment module is used for carrying out personalized temperature control adjustment according to the environmental change trend and the demand of a user on comfort level and combining the data of the environmental comfort level index and the individual preference.

Description

Intelligent temperature controller adjusting method and system based on multisource environment perception Technical Field The invention relates to the field of temperature controller adjustment, in particular to an intelligent temperature controller adjustment method and system based on multisource environment perception. Background The existing temperature control and regulation system generally depends on single environmental data, mainly controls the temperature as the only basis, the method of 'single data perception' has a plurality of limitations, firstly, the temperature is only one important index reflecting the environmental comfort, but is not the only factor, the factors such as humidity, air quality, illumination intensity, air flow and the like in the environment also have important influence on the comfort perception of human bodies, the traditional temperature control system usually ignores the effects of the factors, and regulates heating or refrigerating equipment through the set temperature value, so that the environmental regulation is not accurate enough, the response of the system is generally lag due to ignoring other environmental variables except the temperature, can not be timely adapted to the actual demands, and in the environment with high humidity, although the temperature is proper, however, people may feel uncomfortable, the traditional system is difficult to respond effectively to humidity changes, and finally the overall comfort level is influenced, in addition, because the control is too single, the system is easy to cause energy waste while excessively relying on temperature data, temperature control equipment may still work under the condition that the humidity is higher but the temperature meets a set value, so that energy is unnecessarily consumed, the control mode of 'single data perception' reduces the comfort level of indoor environment, and the energy consumption is increased, so that the overall efficiency and the sustainability of the system are influenced, and therefore, the traditional temperature control system needs to comprehensively sense and adjust various environmental factors to improve the adaptability, the accuracy and the energy utilization efficiency of the system. However, the above technology has at least the following technical problems: The existing temperature control and regulation system has the problem of single data perception, and the problem of inaccurate control, poor comfort level and energy waste can be caused by simply relying on temperature data. Disclosure of Invention In order to overcome the defects in the prior art, the invention provides an intelligent temperature controller adjusting method and system based on multi-source environment sensing, which are used for solving the problems in the background art. In order to achieve the above purpose, the present invention provides the following technical solutions: A temperature controller intelligent adjusting method based on multisource environment sensing comprises the steps of firstly obtaining temperature data, humidity data, air quality data and illumination intensity data through a temperature sensor, a humidity sensor, an air quality sensor and an illumination sensor, secondly obtaining a temperature comfort index, a humidity comfort index, an air comfort index and an illumination comfort index through temperature data, humidity data, air quality data and illumination intensity data through calculation, obtaining a comprehensive comfort index through calculation of the temperature comfort index, the humidity comfort index, the air comfort index and the illumination comfort index, judging whether temperature control adjustment is needed at present according to the comprehensive comfort index, thirdly dynamically monitoring environmental changes through a machine learning model if the temperature control adjustment is needed at present, predicting environmental change trend in a period of time in the future, obtaining environmental change trend, and fourthly carrying out individualized temperature control adjustment according to the environmental change trend and the requirements of users on the comfort index and data of individual preference. The comprehensive comfort index obtaining method comprises the steps of obtaining temperature data, wherein the temperature data comprise an ambient air temperature, a proper temperature set value, temperature uniformity, a ground temperature value, a radiation temperature value and a heat source temperature value, obtaining a temperature comfort index according to temperature data evaluation, obtaining humidity data, wherein the humidity data comprise an ambient indoor humidity value, a target humidity set value, a humidity gradient value in a current time period, a ground humidity value, air humidity, air flow and local humidity source influence, obtaining a humidity comfort index according to humidity data evaluation, obtaining air quali