Temperature and humidity control method and system for smart home

Abstract

The invention relates to the technical field of smart home environment control, in particular to a temperature and humidity control method and system for smart home, and the method comprises the following steps: S1, monitoring the indoor temperature and humidity and the VOC concentration in the air in real time; s2, on the basis of the VOC concentration data collected in the S1, identifying and analyzing peculiar smell components; s3, constructing a temperature and humidity and peculiar smell interaction relation model; s4, generating an adjusting scheme for the current environment according to the temperature and humidity and peculiar smell relation model constructed in the S3; s5, executing corresponding adjustment operation according to the adjustment scheme generated in the step S4; and S6, dynamically updating and optimizing the temperature and humidity and peculiar smell interaction model. According to the method, the interactive relation model of the temperature, the humidity and the VOC concentration is constructed, the adaptive optimization technology is combined, the temperature, the humidity and the air quality are dynamically adjusted, and the accuracy of indoor peculiar smell control and the intelligent level of environment adjustment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home environment control, and in particular to a temperature and humidity control method and system for a smart home. Background Art

[0002] With the rapid development of smart home technology, modern families' requirements for living environment are not only limited to the control of temperature and humidity, but also begin to pay attention to the management of indoor air quality, especially the effective control of odor and volatile organic compounds (VOC) in the air. At present, most smart home systems on the market can monitor temperature, humidity and air quality through sensors, and automatically adjust through air conditioners, humidifiers, air purifiers and other equipment. However, existing technologies mainly focus on single-dimensional temperature, humidity or air quality control, ignoring the complex relationship between temperature, humidity and VOC concentration, and it is difficult to achieve comprehensive and fine control of temperature, humidity and odor, resulting in the comfort and health of users' living environment cannot be effectively guaranteed.

[0003] In the existing technology, temperature, humidity and air quality are usually controlled by independent devices, lacking comprehensive analysis and feedback mechanisms between multi-dimensional data, and unable to adaptively adjust the adjustment plan according to the dynamic changes of the environment. In particular, there are major deficiencies in the management of odor generation, diffusion and accumulation. Changes in temperature and humidity will directly affect the diffusion speed and accumulation risk of odor, and it is difficult for the existing system to build an interactive model between temperature, humidity and odor, making it impossible to formulate accurate adjustment plans for complex environmental conditions. To solve this problem, there is an urgent need for an intelligent control system that can simultaneously monitor and analyze the relationship between temperature, humidity and odor, and dynamically adjust the indoor environment to improve the accuracy and flexibility of air quality management. Summary of the invention

[0004] Based on the above objectives, the present invention provides a temperature and humidity control method and system for a smart home.

[0005] A temperature and humidity control method for a smart home, comprising the following steps: S1: Various sensors deployed to monitor indoor temperature, humidity and VOC concentration in the air in real time; S2: Based on the VOC concentration data collected in S1, identify and analyze the odor components, determine the main source of odor in the air, and analyze the odor diffusion trend in combination with the current temperature and humidity conditions; S3: Based on the temperature and humidity data in S1 and the odor analysis results in S2, a temperature, humidity and odor interaction model is constructed to predict the effects of temperature and humidity on the diffusion, accumulation or reduction of odor in the air; S4: Based on the temperature, humidity and odor relationship model constructed in S3, an adjustment plan for the current environment is generated, which includes temperature and humidity adjustment strategies, as well as a decision on whether to enable air purification equipment or odor removal measures; S5: According to the adjustment plan generated in S4, corresponding adjustment operations are performed to adjust the indoor temperature, humidity and air quality by adjusting the temperature control equipment, humidity control equipment and air purification equipment; S6: After the adjustment plan is executed, continue to monitor new temperature, humidity and odor concentration data, and dynamically update and optimize the temperature, humidity and odor interaction model based on user feedback to improve the adaptive adjustment capability for complex environments.

[0006] Optionally, the S1 specifically includes: S11: Deploy temperature sensors, humidity sensors, and VOC sensors in different areas of the smart home. The temperature sensors are installed at locations away from heat sources and cold sources, the humidity sensors are placed in areas with stable air flow, and the VOC sensors are deployed in areas prone to odor, including kitchens and bathrooms. S12: The temperature sensor collects indoor temperature data at a fixed frequency per second through an integrated thermocouple or resistance temperature detection element; S13: The humidity sensor uses a capacitive humidity detection element to measure the effect of air humidity on capacitance and collect indoor humidity data in real time; S14: VOC sensor is based on photoionization detection technology, which monitors VOC concentration in real time by photoionizing organic volatile molecules in the air; S15: Perform time-series synchronization processing on the collected temperature, humidity and VOC concentration data to ensure that various environmental parameters can be correlated and analyzed on a unified time axis.

[0007] Optionally, the S2 specifically includes: S21: preprocessing the VOC concentration data collected in S1, including signal filtering and noise suppression, to eliminate invalid data caused by external interference or sensor error; S22: Perform component separation analysis on the pre-processed VOC concentration data through the odor component recognition algorithm, and identify the main odor components in the air and determine their sources by comparing the matching degree between the collected VOC components and the known odor component model; S23: Calculate the intensity value of the odor based on the identified odor components and their concentration data, and compare it with the air quality standard to determine the severity of the odor; S24: Based on the current temperature and humidity data collected in S1, a diffusion model is used to calculate the diffusion speed and range of the odor under the current temperature and humidity conditions, and the diffusion trend of the odor in the room is predicted; S25: According to the odor component identification results in S22 and the diffusion trend analysis in S24, the main source area of ​​the odor is determined, and the location of the odor is accurately determined by locating the diffusion starting point or high concentration area.

[0008] Optionally, the S25 specifically includes: S251: Divide the indoor space into multiple grid areas, collect VOC concentration data in each area through sensors, and form a concentration distribution matrix; S252: According to the concentration distribution matrix, identify the grid area where the VOC concentration is higher than the set threshold, and determine the location of the high-concentration area by comparing the VOC concentrations of each area; S253: Using the reverse diffusion model, the diffusion direction of the odor is deduced according to the distribution change of VOC concentration in each grid area, and the diffusion starting point is located; S254: The specific source of the odor is confirmed by comparing the coordinates of the located starting point with the known odor source area, and the accuracy of the inference result is verified by simulating the diffusion trend.

[0009] Optionally, the S3 specifically includes: S31: Based on the temperature and humidity data in S1 and the odor concentration analysis results in S2, the historical data sets of temperature, humidity and VOC concentration in each grid area are collected and normalized to ensure that different physical quantities are analyzed at the same scale; S32: Calculate the correlation coefficient between temperature, humidity and VOC concentration using linear regression or multiple regression analysis methods ,providing a basis for establishing an interaction model; S33: Based on the analysis results of S32, an interactive relationship model between temperature, humidity and VOC concentration is established, and the expression is: ,in, Indicates the VOC concentration at a certain moment, and are temperature and humidity, and is the regression coefficient of temperature and humidity, is the model constant, and the interaction model obtains the interaction between the two by fitting the effects of temperature and humidity on VOC concentration; S34: The constructed interactive relationship model is used to predict the diffusion speed and accumulation range of odor under different temperature and humidity conditions. By analyzing the accelerating or inhibiting effects of temperature and humidity changes on odor diffusion, the diffusion trend of odor in different time periods or whether it accumulates in certain areas can be determined.

[0010] Optionally, the S34 specifically includes: S341: Calculate the diffusion speed of the odor by using the interactive relationship model in combination with the temperature and humidity data; the calculation formula of the diffusion speed is: ,in, is the diffusion rate of the odor, is the diffusion coefficient, is the temperature at the current moment, is the reference temperature, is the humidity at the current moment. The temperature is positively correlated with the diffusion rate, while the humidity is negatively correlated with the diffusion rate. S342: Determination of odor accumulation areas: Based on the changes in humidity and temperature, analyze whether odors are accumulated in different areas. The accumulation trend is determined by calculating the rate of change of VOC concentration in different grid areas. The formula is: ,in, Represents a grid area The rate of change of concentration. If the rate of change is positive and the diffusion rate is slow, the area has a higher risk of odor accumulation; S343: Prediction of odor diffusion trend. By combining the calculation of diffusion velocity in S341, the gas diffusion model is used to predict the diffusion trend of odor in different time periods. Specifically, the diffusion direction and range of odor in different time periods are calculated by the time series changes of temperature and humidity. The prediction formula is: ,in, is the VOC concentration at a future time, is the diffusion rate, is the diffusion radius, is the time step.

[0011] Optionally, the S4 specifically includes: S41: Based on the temperature, humidity and odor interaction model constructed in S3, the current indoor temperature, humidity and VOC concentration data are evaluated in real time to determine whether the current environment meets the preset air quality standards; S42: When the evaluation results show that the temperature and humidity of the current environment have an adverse effect on the diffusion or accumulation of odor, an adjustment strategy for temperature and humidity will be generated according to the regression coefficient in the interactive relationship model, and the adjustment strategy includes setting an upper or lower temperature limit and a humidity range; specifically, when the current temperature is high and the odor diffusion is accelerated, the temperature adjustment strategy is to lower the indoor temperature; when the humidity is high and the odor diffusion is inhibited, the humidity adjustment strategy is to lower the humidity; S43: Based on the evaluation results in S41, if the VOC concentration exceeds the set safety threshold, it will decide whether to activate air purification equipment or deodorization measures, and determine the activation time and duration of the purification equipment based on the prediction results of the odor diffusion trend; S44: Combine the temperature and humidity adjustment strategy in S42 and the air purification equipment activation decision in S43 to generate a final adjustment plan, which includes specific temperature and humidity setting values ​​and the working mode of the air purification equipment.

[0012] Optionally, the S5 specifically includes: S51: According to the adjustment scheme generated in S4, when the system detects that the indoor temperature exceeds the set optimal temperature range, it automatically sends a control signal to the temperature control device of the air conditioner or heater to adjust its output power or working mode to adjust the indoor temperature to a preset target value; S52: When the system detects that the humidity exceeds the set optimal humidity range, a control signal is sent to the humidity control device of the humidifier or dehumidifier to adjust its working state so that the indoor humidity reaches the target value; S53: When the VOC concentration detected in S4 exceeds the set threshold, a start instruction is sent to the air purification device, and the working mode or intensity of the purification device is adjusted according to the degree of pollution; the air purification device reduces the VOC content in the air through high-efficiency filtration technology, and the purification operation continues until the VOC concentration returns to a safe range; S54: During the execution of the adjustment operation, the changes in temperature, humidity and VOC concentration are continuously monitored. When the target environmental conditions are achieved, the operating state or power of the equipment is adjusted to enter the maintenance mode to prevent the temperature, humidity or VOC concentration from exceeding the set range again.

[0013] Optionally, the S6 specifically includes: S61: After the adjustment plan is executed, continuously monitor the new temperature, humidity and VOC concentration data, and collect and record the changes in environmental parameters after adjustment; S62: Collect user feedback information on the comfort level of the current environment. The user feedback is quantified using a preset comfort evaluation standard. Specific evaluation indicators include whether the temperature and humidity are within the acceptable range for the user and whether the air quality meets the comfort requirements. S63: dynamically updating the temperature, humidity and odor interaction relationship model constructed in S3 according to the new environmental data collected in S61 and the user feedback information in S62; S64: After the model update is completed, the parameters of the temperature, humidity and odor interaction model are adjusted through an adaptive optimization algorithm to improve the prediction accuracy.

[0014] A temperature and humidity control system for a smart home, used to implement the above-mentioned temperature and humidity control method for a smart home, comprises the following modules: Environmental parameter acquisition module: used to monitor indoor temperature, humidity and volatile organic compound concentration in real time through multiple deployed sensors; Data processing module: used to pre-process the temperature and humidity data and VOC concentration data collected by the environmental parameter acquisition module, including signal filtering, noise suppression and data normalization processing, and identify and analyze odor components based on the processed data to determine the source of odor in the air and its diffusion trend; Model building and analysis module: used to build an interactive relationship model between temperature, humidity and odor based on the analysis results of the data processing module, so as to analyze the relationship between temperature, humidity and VOC concentration, and predict the diffusion speed, accumulation range and trend of odor under different temperature and humidity conditions based on the model; Adjustment plan generation module: used to generate adjustment plans for the current environment based on the prediction results of the model building and analysis module, including temperature and humidity adjustment strategies and whether to enable air purification equipment or deodorization measures; Execution module: used to generate the adjustment plan of the module according to the adjustment plan, control the working status of the temperature control equipment, humidity control equipment and air purification equipment, and adjust the indoor temperature, humidity and air quality; Feedback and optimization module: It is used to continuously monitor new temperature, humidity and VOC concentration data after the adjustment operation is executed, and dynamically update and adaptively optimize the interactive relationship model of the model building and analysis module based on user feedback information.

[0015] Beneficial effects of the present invention: The present invention realizes the accurate correlation analysis between temperature, humidity and odor concentration in the air by constructing an interactive relationship model between temperature, humidity and volatile organic compound (VOC) concentration. This technology enables the system to dynamically predict the diffusion speed and accumulation range of odor under different temperature and humidity conditions, thereby automatically generating appropriate adjustment plans for complex environments. By real-time monitoring and regulation of temperature, humidity and VOC concentration, the present invention effectively improves the intelligent level of indoor air quality management and enhances the accuracy of odor control.

[0016] The present invention, by continuously collecting environmental data and user feedback and combining it with an adaptive optimization algorithm, can continuously update and optimize the system's adjustment strategy to ensure that the system maintains efficient regulation effects during long-term operation. This dynamic optimization technology not only improves the accuracy of environmental regulation, but also enhances the system's adaptability to changing environments, ensuring that users always have a comfortable and healthy living experience under various environmental conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic diagram of a temperature and humidity control method for a smart home according to an embodiment of the present invention; Figure 2 Schematic diagram of a temperature and humidity control system for a smart home according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0020] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0021] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0022] like Figure 1 As shown, a temperature and humidity control method for a smart home comprises the following steps: S1: Various sensors deployed to monitor indoor temperature, humidity, and VOC (volatile organic compound) concentration in the air in real time; S2: Based on the VOC concentration data collected in S1, identify and analyze the odor components, determine the main source of odor in the air, and analyze the odor diffusion trend in combination with the current temperature and humidity conditions; S3: Based on the temperature and humidity data in S1 and the odor analysis results in S2, a temperature, humidity and odor interaction model is constructed to predict the effects of temperature and humidity on the diffusion, accumulation or reduction of odor in the air; S4: Based on the temperature, humidity and odor relationship model constructed in S3, an adjustment plan for the current environment is generated. The plan includes temperature and humidity adjustment strategies, as well as a decision on whether to enable air purification equipment or odor removal measures to optimize indoor air quality. S5: According to the adjustment scheme generated by S4, corresponding adjustment operations are performed to adjust the indoor temperature, humidity and air quality by adjusting temperature control equipment (such as air conditioners and heaters), humidity control equipment (such as humidifiers and dehumidifiers) and air purification equipment; S6: After the adjustment plan is executed, continue to monitor new temperature, humidity and odor concentration data, and dynamically update and optimize the temperature, humidity and odor interaction model based on user feedback to improve the adaptive adjustment capability for complex environments.

[0023] S1 specifically includes: S11: Deploy temperature sensors, humidity sensors, and VOC sensors in different areas of the smart home. The temperature sensors are installed away from heat and cold sources, the humidity sensors are placed in areas with stable air flow, and the VOC sensors are deployed in areas prone to odor, including kitchens and bathrooms. S12: The temperature sensor collects indoor temperature data at a fixed frequency per second through an integrated thermocouple or resistance temperature detection element; S13: The humidity sensor uses a capacitive humidity detection element to measure the effect of air humidity on capacitance and collect indoor humidity data in real time; S14: VOC sensors are based on photoionization detection (PID) technology. They monitor VOC concentration in real time by photoionizing volatile organic molecules in the air. The built-in light source of the VOC sensor irradiates the air sample at a fixed wavelength to induce the ionization reaction of volatile organic compounds and convert the detected current signal into VOC concentration data. S15: Perform time-series synchronization processing on the collected temperature, humidity and VOC concentration data to ensure that each environmental parameter can be correlated and analyzed on a unified time axis, providing a basis for odor diffusion analysis and model building in subsequent steps.

[0024] S2 specifically includes: S21: preprocessing the VOC concentration data collected in S1, including signal filtering and noise suppression, to ensure the accuracy and stability of the data and eliminate invalid data caused by external interference or sensor error; S22: Perform component separation analysis on the pre-processed VOC concentration data through the odor component recognition algorithm, and identify the main odor components in the air and determine their sources by comparing the matching degree between the collected VOC components and the known odor component model; S23: Based on the identified odor components and their concentration data, the odor intensity value is calculated and compared with the air quality standard to determine the severity of the odor; the calculation formula for the odor intensity value is: ,in, Indicates the intensity value of the odor, Indicates the concentration of VOC, It is an intensity coefficient set according to different odor types. This formula determines the severity of odor by combining the concentration of different odor components and their impact on air quality, and compares it with the air quality standard for classification. S24: Based on the current temperature and humidity data collected in S1, the diffusion model is used to calculate the diffusion speed and range of the odor under the current temperature and humidity conditions, and predict the diffusion trend of the odor in the room; the calculation formula for the diffusion speed is: ,in, is the diffusion rate of the odor, is the diffusion coefficient, is the current indoor temperature, is the standard temperature, is the current relative humidity. This formula predicts the diffusion trend of odor in the current environment by considering the influence of temperature on the movement of air molecules and the effect of humidity on the adsorption of volatile organic compounds; S25: According to the odor component identification results in S22 and the diffusion trend analysis in S24, the main source area of ​​the odor is determined, specifically by locating the diffusion starting point or high concentration area to accurately determine the location of the odor. The above steps can accurately identify the main source of odor in the air through preprocessing, component identification, intensity calculation and diffusion trend analysis, and further predict the diffusion path and impact range of the odor through temperature and humidity data, thereby improving the accuracy and real-time performance of air quality control and providing an accurate basis for subsequent adjustment steps.

[0025] The main source areas of odor identified in S25 include: S251: The indoor space is divided into multiple grid areas. The VOC concentration data in each area is collected by sensors and a concentration distribution matrix is ​​formed. The concentration distribution matrix is ​​expressed as: ,in, Represents the three-dimensional coordinates of the indoor space, is the VOC concentration value at the corresponding coordinate; S252: According to the concentration distribution matrix, identify the grid area where the VOC concentration is higher than the set threshold, and determine the location of the high-concentration area by comparing the VOC concentrations of each area; the identification of the area can be determined by the following formula: ,in, To identify the high concentration areas, is the preset VOC concentration threshold; S253: Using the reverse diffusion model, the diffusion direction of the odor is deduced according to the distribution changes of VOC concentration in each grid area, and the diffusion starting point is located. The formula of the reverse diffusion model is: ,in, Indicates the starting point of odor diffusion. is the rate of change of VOC concentration in each grid area over time. Based on the area with the smallest rate of change, the location where the odor first occurred is inferred; S254: By comparing the coordinates of the starting point of the positioning with the known odor source area (such as kitchen, bathroom, etc.), the specific source of the odor is confirmed, and the accuracy of the inference result is verified by simulating the diffusion trend. The verification process is completed by calculating the distribution simulation of VOC concentration at different times. The formula is: ,in, is the VOC concentration at a certain moment, is the initial concentration, is the diffusion rate, The diffusion coefficient is used to verify the accuracy of the odor source by comparing the actual VOC concentration changes with the concentration changes predicted by the model. The above steps can accurately locate the occurrence location of the odor through grid space division, concentration distribution matrix analysis and application of the reverse diffusion model, and determine the origin of the odor by combining the reverse deduction of the diffusion direction. This technical solution further improves the system's ability to accurately judge the source of odor and ensures the efficiency and reliability of indoor air quality management.

[0026] S3 specifically includes: S31: Based on the temperature and humidity data in S1 and the odor concentration analysis results in S2, the historical data sets of temperature, humidity and VOC concentration in each grid area are collected and normalized to ensure that different physical quantities are analyzed at the same scale; S32: Calculate the correlation coefficient between temperature, humidity and VOC concentration using linear regression or multiple regression analysis methods , the correlation analysis formula is: ,in, is the correlation coefficient between temperature and VOC concentration, and Respectively represent the temperature and VOC concentration at a certain moment, is the number of samples, and the correlation between humidity and VOC concentration can be calculated by a similar formula, which provides a basis for establishing an interaction model; S33: Based on the analysis results of S32, an interactive relationship model between temperature, humidity and VOC concentration is established, and the expression is: ,in, Indicates the VOC concentration at a certain moment, and are temperature and humidity, and is the regression coefficient of temperature and humidity, is the model constant, and the interaction model obtains the interaction between the two by fitting the effects of temperature and humidity on VOC concentration; S34: Use the constructed interactive relationship model to predict the diffusion speed and accumulation range of odor under different temperature and humidity conditions. By analyzing the acceleration or inhibition of temperature and humidity changes on odor diffusion, determine the diffusion trend of odor in different time periods or whether it accumulates in certain areas. Through the above steps, an accurate interactive model of temperature, humidity and VOC concentration is established, which can effectively predict the impact of temperature and humidity changes on odor diffusion and accumulation, provide a scientific basis for air quality regulation, and improve the accuracy and effectiveness of air quality management in smart home environments.

[0027] S34 specifically includes: S341: The diffusion rate of odor is calculated by using the interactive relationship model combined with temperature and humidity data. Temperature accelerates the diffusion of VOC by affecting the movement speed of gas molecules, while humidity adsorbs or inhibits the diffusion of volatile organic compound molecules. The calculation formula for the diffusion rate is: ,in, is the diffusion rate of the odor, is the diffusion coefficient, is the temperature at the current moment, is the reference temperature, is the humidity at the current moment. The temperature is positively correlated with the diffusion rate, while the humidity is negatively correlated with the diffusion rate. S342: Determination of odor accumulation areas. Based on the changes in humidity and temperature, analyze whether odors accumulate in different areas. When the temperature is low and the humidity is high, the diffusion rate of VOC molecules slows down, which will cause odors to accumulate in certain areas. The accumulation trend is determined by calculating the rate of change of VOC concentration in different grid areas. The formula is: ,in, Represents a grid area The rate of change of concentration. If the rate of change is positive and the diffusion rate is slow, the area has a higher risk of odor accumulation; S343: Prediction of odor diffusion trend. By combining the calculation of diffusion velocity in S341, the gas diffusion model is used to predict the diffusion trend of odor in different time periods. Specifically, the diffusion direction and range of odor in different time periods are calculated by the time series changes of temperature and humidity. The prediction formula is: ,in, is the VOC concentration at a future time, is the diffusion rate, is the diffusion radius, is the time step. This formula is used to predict the diffusion path of odor in the future and help predict the diffusion trend of odor in advance.

[0028] S4 specifically includes: S41: Based on the temperature, humidity and odor interaction model constructed in S3, the current indoor temperature, humidity and VOC concentration data are evaluated in real time to determine whether the current environment meets the preset air quality standards: The specific evaluation process determines whether adjustment is required through the following formula: ,in, is the VOC concentration in the current environment, is the set VOC safety threshold; when When , it means that the VOC concentration exceeds the safety threshold, and the system determines that it needs to be adjusted; if , it means that the current environment meets the standard and no adjustment is required; at the same time, combined with the evaluation of temperature and humidity, based on the influence of temperature and humidity on VOC concentration, the adjustment needs are further determined by the following formula: ; ,in, and are the current temperature and humidity, and is the optimal temperature and humidity value set under the air quality standard; or When the absolute value of is greater than the set allowable range, the system determines that the temperature and humidity conditions have an adverse effect on the diffusion of odor and need to be adjusted; S42: When the evaluation results show that the temperature and humidity of the current environment have an adverse effect on the diffusion or accumulation of odor, a temperature and humidity adjustment strategy will be generated based on the regression coefficient in the interactive relationship model. The adjustment strategy includes setting an upper or lower temperature limit and a humidity range to optimize the odor diffusion conditions and achieve the best air quality. Specifically, when the current temperature is high and the odor diffusion is accelerated, the temperature adjustment strategy is to lower the indoor temperature; when the humidity is high and the odor diffusion is suppressed, the humidity adjustment strategy is to lower the humidity to avoid the accumulation of odor in local areas. S43: Based on the evaluation results in S41, if the VOC concentration exceeds the set safety threshold, it will decide whether to activate air purification equipment or deodorization measures. This decision-making process determines whether it is necessary to activate the purification equipment to quickly reduce the VOC content in the air by analyzing the degree of excess of the VOC concentration, and determines the activation time and duration of the purification equipment based on the prediction results of the odor diffusion trend; S44: Combine the temperature and humidity adjustment strategy in S42 and the air purification equipment activation decision in S43 to generate a final adjustment plan, which includes specific temperature and humidity setting values ​​and the working mode of the air purification equipment to ensure that the current environment can quickly reach the ideal state. The plan will be sent to related equipment to ensure control execution; through the above steps, the temperature and humidity can be accurately adjusted dynamically, and it can be determined whether to activate the air purification equipment, thereby optimizing the air quality. This method enhances the intelligent adjustment capability of the system, effectively addresses the problem of odor diffusion or accumulation, and ensures the comfort and health of the indoor environment.

[0029] S5 specifically includes: S51: According to the adjustment scheme generated by S4, when the system detects that the indoor temperature exceeds the set optimal temperature range, it automatically sends a control signal to the temperature control device of the air conditioner or heater to adjust its output power or working mode to adjust the indoor temperature to the preset target value; this process can adjust the temperature output through the PID control algorithm of the temperature control device to ensure that the indoor temperature quickly returns to the set range in the most energy-saving way; If the current indoor temperature is high, the system will lower the cooling setting temperature of the air conditioner or turn off the heating; If the current indoor temperature is low, the system will increase the set temperature of the heater or increase the heating mode of the air conditioner; S52: When the system detects that the humidity exceeds the set optimal humidity range, a control signal is sent to the humidity control device of the humidifier or dehumidifier to adjust its working state so that the indoor humidity reaches the target value; the system optimizes the humidity adjustment efficiency by adjusting the working intensity or time of the device. The humidity adjustment process is based on real-time feedback of the indoor humidity to ensure that the humidity remains within a comfortable range; When the humidity is too high, the system starts the dehumidifier to perform dehumidification operations; When the humidity is too low, the system starts the humidifier to perform humidification operations to ensure that the air humidity reaches the target value; S53: When the VOC concentration detected in S4 exceeds the set threshold, a start instruction is sent to the air purification device, and the working mode or intensity of the purification device is adjusted according to the degree of pollution; the air purification device reduces the VOC content in the air through high-efficiency filtration technology, and the purification operation continues until the VOC concentration returns to a safe range; The system can dynamically adjust the working time of the purification equipment according to the decreasing speed of VOC concentration and optimize energy consumption; When the VOC concentration returns to below the set threshold, the system automatically stops the purification equipment; S54: During the execution of the adjustment operation, the changes in temperature, humidity and VOC concentration are continuously monitored. When the target environmental conditions are achieved, the operating state or power of the equipment is adjusted to enter the maintenance mode to prevent the temperature, humidity or VOC concentration from exceeding the set range again.

[0030] S6 specifically includes: S61: After the adjustment plan is executed, the new temperature, humidity and VOC concentration data are continuously monitored, and the changes in environmental parameters after adjustment are collected and recorded. Specifically, these environmental data are obtained in real time through sensors, and compared with the data before the adjustment plan is executed to ensure accurate feedback on the adjustment effect; S62: Collecting user feedback on the comfort level of the current environment. The user feedback is quantified using a preset comfort evaluation standard. The specific evaluation indicators include whether the temperature and humidity are within the acceptable range for the user and whether the air quality meets the comfort requirements. The feedback information is used to further optimize the adjustment strategy. S63: According to the new environmental data collected in S61 and the user feedback information in S62, the temperature, humidity and odor interaction relationship model constructed in S3 is dynamically updated. The specific update formula is: ,in, is the VOC concentration at a certain moment, and Represents temperature and humidity respectively. and is the regression coefficient of temperature and humidity, is the model constant, and the regression coefficient is recalculated based on the new data and , update the value of the regression coefficient by the least squares method, the formula is: ; ,in, They are the temperature, humidity and VOC concentration data at different sampling times. They are the average values ​​of temperature, humidity and VOC concentration respectively. This update ensures that the model is more in line with the current environmental data; S64: After the model update is completed, the parameters of the temperature, humidity and odor interaction model are adjusted through an adaptive optimization algorithm to improve the prediction accuracy. The optimization process optimizes the model parameters through a gradient descent algorithm; the optimized model parameter update formula is: ,in, are model parameters (such as , , ), is the learning rate, is a loss function, which represents the error between the model prediction value and the actual measurement value. The model is adaptively adjusted through repeated iterative optimization to more accurately reflect the impact of temperature and humidity changes on odor diffusion. The above steps update the regression coefficient of the temperature, humidity and odor interaction model. The system can more accurately reflect the relationship between temperature, humidity and VOC concentration in the current environment. Combined with the adaptive optimization algorithm, the model parameters can be continuously optimized to improve the system's adjustment and prediction capabilities in complex environments, ensuring the accuracy and efficiency of air quality management.

[0031] like Figure 2 As shown, a temperature and humidity control system for a smart home, used to implement the above-mentioned temperature and humidity control method for a smart home, includes the following modules: Environmental parameter acquisition module: used to monitor indoor temperature, humidity and volatile organic compound (VOC) concentration in real time through multiple deployed sensors. The data collected by the sensors are transmitted to the data processing module wirelessly or wiredly; Data processing module: used to pre-process the temperature and humidity data and VOC concentration data collected by the environmental parameter acquisition module, including signal filtering, noise suppression and data normalization processing, and identify and analyze odor components based on the processed data to determine the source of odor in the air and its diffusion trend; Model building and analysis module: used to build an interactive relationship model between temperature, humidity and odor based on the analysis results of the data processing module, so as to analyze the relationship between temperature, humidity and VOC concentration, and predict the diffusion speed, accumulation range and trend of odor under different temperature and humidity conditions based on the model; Adjustment plan generation module: used to generate adjustment plans for the current environment based on the prediction results of the model building and analysis module, including temperature and humidity adjustment strategies and whether to enable air purification equipment or deodorization measures; Execution module: used to generate the adjustment plan of the module according to the adjustment plan, control the working status of temperature control equipment (such as air conditioning, heating), humidity control equipment (such as humidifier, dehumidifier) ​​and air purification equipment, and adjust the indoor temperature, humidity and air quality; Feedback and optimization module: It is used to continuously monitor new temperature, humidity and VOC concentration data after the adjustment operation is executed, and dynamically update and adaptively optimize the interactive relationship model of the model building and analysis module based on user feedback information to ensure that the system's adjustment effect is more accurate.

[0032] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0033] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A temperature and humidity control method for smart home, characterized in that: The following steps are involved: S1: Various sensors deployed to monitor indoor temperature, humidity and VOC concentration in the air in real time; S2: Based on the VOC concentration data collected in S1, identify and analyze the odor components, determine the main source of odor in the air, and analyze the odor diffusion trend in combination with the current temperature and humidity conditions; S3: Based on the temperature and humidity data in S1 and the odor analysis results in S2, a temperature, humidity and odor interaction model is constructed to predict the effects of temperature and humidity on the diffusion, accumulation or reduction of odor in the air; S4: Based on the temperature, humidity and odor relationship model constructed in S3, an adjustment plan for the current environment is generated, which includes temperature and humidity adjustment strategies, as well as a decision on whether to enable air purification equipment or odor removal measures; S5: According to the adjustment plan generated in S4, corresponding adjustment operations are performed to adjust the indoor temperature, humidity and air quality by adjusting the temperature control equipment, humidity control equipment and air purification equipment; S6: After the adjustment plan is executed, continue to monitor new temperature, humidity and odor concentration data, and dynamically update and optimize the temperature, humidity and odor interaction model based on user feedback to improve the adaptive adjustment capability for complex environments.

2. A temperature and humidity control method for smart home according to claim 1, characterized in that: The S1 specifically includes: S11: Deploy temperature sensors, humidity sensors, and VOC sensors in different areas of the smart home. The temperature sensors are installed at locations away from heat sources and cold sources, the humidity sensors are placed in areas with stable air flow, and the VOC sensors are deployed in areas prone to odor, including kitchens and bathrooms. S12: The temperature sensor collects indoor temperature data at a fixed frequency per second through an integrated thermocouple or resistance temperature detection element; S13: The humidity sensor uses a capacitive humidity detection element to measure the effect of air humidity on capacitance and collect indoor humidity data in real time; S14: VOC sensor is based on photoionization detection technology, which monitors VOC concentration in real time by photoionizing organic volatile molecules in the air; S15: Perform time-series synchronization processing on the collected temperature, humidity and VOC concentration data to ensure that various environmental parameters can be correlated and analyzed on a unified time axis.

3. A temperature and humidity control method for smart home according to claim 1, characterized in that: The S2 specifically includes: S21: preprocessing the VOC concentration data collected in S1, including signal filtering and noise suppression, to eliminate invalid data caused by external interference or sensor error; S22: Perform component separation analysis on the pre-processed VOC concentration data through the odor component recognition algorithm, and identify the main odor components in the air and determine their sources by comparing the matching degree between the collected VOC components and the known odor component model; S23: Calculate the intensity value of the odor based on the identified odor components and their concentration data, and compare it with the air quality standard to determine the severity of the odor; S24: Based on the current temperature and humidity data collected in S1, a diffusion model is used to calculate the diffusion speed and range of the odor under the current temperature and humidity conditions, and the diffusion trend of the odor in the room is predicted; S25: According to the odor component identification results in S22 and the diffusion trend analysis in S24, the main source area of ​​the odor is determined, and the location of the odor is accurately determined by locating the diffusion starting point or high concentration area.

4. A temperature and humidity control method for smart home according to claim 3, characterized in that: The S25 specifically includes: S251: Divide the indoor space into multiple grid areas, collect VOC concentration data in each area through sensors, and form a concentration distribution matrix; S252: According to the concentration distribution matrix, identify the grid area where the VOC concentration is higher than the set threshold, and determine the location of the high-concentration area by comparing the VOC concentrations of each area; S253: Using the reverse diffusion model, the diffusion direction of the odor is deduced according to the distribution change of VOC concentration in each grid area, and the diffusion starting point is located; S254: The specific source of the odor is confirmed by comparing the coordinates of the located starting point with the known odor source area, and the accuracy of the inference result is verified by simulating the diffusion trend.

5. A temperature and humidity control method for smart home according to claim 1, characterized in that: The S3 specifically includes: S31: Based on the temperature and humidity data in S1 and the odor concentration analysis results in S2, the historical data sets of temperature, humidity and VOC concentration in each grid area are collected and normalized to ensure that different physical quantities are analyzed at the same scale; S32: Calculate the correlation coefficient between temperature, humidity and VOC concentration using linear regression or multiple regression analysis methods ,providing a basis for establishing an interaction model; S33: Based on the analysis results of S32, an interactive relationship model between temperature, humidity and VOC concentration is established, and the expression is: ,in, Indicates the VOC concentration at a certain moment, and are temperature and humidity, and is the regression coefficient of temperature and humidity, is the model constant, and the interaction model obtains the interaction between the two by fitting the effects of temperature and humidity on VOC concentration; S34: The constructed interactive relationship model is used to predict the diffusion speed and accumulation range of odor under different temperature and humidity conditions. By analyzing the accelerating or inhibiting effects of temperature and humidity changes on odor diffusion, the diffusion trend of odor in different time periods or whether it accumulates in certain areas can be determined.

6. A temperature and humidity control method for smart home according to claim 5, characterized in that: The S34 specifically includes: S341: Calculate the diffusion speed of the odor by using the interactive relationship model in combination with the temperature and humidity data; the calculation formula of the diffusion speed is: ,in, is the diffusion rate of the odor, is the diffusion coefficient, is the temperature at the current moment, is the reference temperature, is the humidity at the current moment. The temperature is positively correlated with the diffusion rate, while the humidity is negatively correlated with the diffusion rate. S342: Determination of odor accumulation areas: Based on the changes in humidity and temperature, analyze whether odors are accumulated in different areas. The accumulation trend is determined by calculating the rate of change of VOC concentration in different grid areas. The formula is: ,in, Represents a grid area The rate of change of concentration. If the rate of change is positive and the diffusion rate is slow, the area has a higher risk of odor accumulation; S343: Prediction of odor diffusion trend. By combining the calculation of diffusion velocity in S341, the gas diffusion model is used to predict the diffusion trend of odor in different time periods. Specifically, the diffusion direction and range of odor in different time periods are calculated by the time series changes of temperature and humidity. The prediction formula is: ,in, is the VOC concentration at a future time, is the diffusion rate, is the diffusion radius, is the time step.

7. A temperature and humidity control method for smart home according to claim 1, characterized in that: The S4 specifically includes: S41: Based on the temperature, humidity and odor interaction model constructed in S3, the current indoor temperature, humidity and VOC concentration data are evaluated in real time to determine whether the current environment meets the preset air quality standards; S42: When the evaluation results show that the temperature and humidity of the current environment have an adverse effect on the diffusion or accumulation of odor, an adjustment strategy for temperature and humidity will be generated according to the regression coefficient in the interactive relationship model, and the adjustment strategy includes setting an upper or lower temperature limit and a humidity range; specifically, when the current temperature is high and the odor diffusion is accelerated, the temperature adjustment strategy is to lower the indoor temperature; when the humidity is high and the odor diffusion is inhibited, the humidity adjustment strategy is to lower the humidity; S43: Based on the evaluation results in S41, if the VOC concentration exceeds the set safety threshold, it will decide whether to activate air purification equipment or deodorization measures, and determine the activation time and duration of the purification equipment based on the prediction results of the odor diffusion trend; S44: Combine the temperature and humidity adjustment strategy in S42 and the air purification equipment activation decision in S43 to generate a final adjustment plan, which includes specific temperature and humidity setting values ​​and the working mode of the air purification equipment.

8. A temperature and humidity control method for smart home according to claim 1, characterized in that: The S5 specifically includes: S51: According to the adjustment scheme generated in S4, when the system detects that the indoor temperature exceeds the set optimal temperature range, it automatically sends a control signal to the temperature control device of the air conditioner or heater to adjust its output power or working mode to adjust the indoor temperature to a preset target value; S52: When the system detects that the humidity exceeds the set optimal humidity range, a control signal is sent to the humidity control device of the humidifier or dehumidifier to adjust its working state so that the indoor humidity reaches the target value; S53: When the VOC concentration detected in S4 exceeds the set threshold, a start instruction is sent to the air purification device, and the working mode or intensity of the purification device is adjusted according to the degree of pollution; the air purification device reduces the VOC content in the air through high-efficiency filtration technology, and the purification operation continues until the VOC concentration returns to a safe range; S54: During the execution of the adjustment operation, the changes in temperature, humidity and VOC concentration are continuously monitored. When the target environmental conditions are achieved, the operating state or power of the equipment is adjusted to enter the maintenance mode to prevent the temperature, humidity or VOC concentration from exceeding the set range again.

9. A temperature and humidity control method for smart home according to claim 1, characterized in that: The S6 specifically includes: S61: After the adjustment plan is executed, continuously monitor the new temperature, humidity and VOC concentration data, and collect and record the changes in environmental parameters after adjustment; S62: Collect user feedback information on the comfort level of the current environment. The user feedback is quantified using a preset comfort evaluation standard. Specific evaluation indicators include whether the temperature and humidity are within the acceptable range for the user and whether the air quality meets the comfort requirements. S63: dynamically updating the temperature, humidity and odor interaction relationship model constructed in S3 according to the new environmental data collected in S61 and the user feedback information in S62; S64: After the model update is completed, the parameters of the temperature, humidity and odor interaction model are adjusted through an adaptive optimization algorithm to improve the prediction accuracy.

10. A temperature and humidity control system for a smart home, used to implement a temperature and humidity control method for a smart home as claimed in any one of claims 1 to 9, characterized in that: Includes the following modules: Environmental parameter acquisition module: used to monitor indoor temperature, humidity and volatile organic compound concentration in real time through multiple deployed sensors; Data processing module: used to pre-process the temperature and humidity data and VOC concentration data collected by the environmental parameter acquisition module, including signal filtering, noise suppression and data normalization processing, and identify and analyze odor components based on the processed data to determine the source of odor in the air and its diffusion trend; Model building and analysis module: used to build an interactive relationship model between temperature, humidity and odor based on the analysis results of the data processing module, so as to analyze the relationship between temperature, humidity and VOC concentration, and predict the diffusion speed, accumulation range and trend of odor under different temperature and humidity conditions based on the model; Adjustment plan generation module: used to generate adjustment plans for the current environment based on the prediction results of the model building and analysis module, including temperature and humidity adjustment strategies and whether to enable air purification equipment or deodorization measures; Execution module: used to generate the adjustment plan of the module according to the adjustment plan, control the working status of the temperature control equipment, humidity control equipment and air purification equipment, and adjust the indoor temperature, humidity and air quality; Feedback and optimization module: It is used to continuously monitor new temperature, humidity and VOC concentration data after the adjustment operation is executed, and dynamically update and adaptively optimize the interactive relationship model of the model building and analysis module based on user feedback information.

Images