Intelligent house temperature and humidity management method under self-adaptive climate condition

Abstract

The invention discloses an intelligent house temperature and humidity management method under a self-adaptive climate condition, and relates to the technical field of intelligent house temperature and humidity management, and the method comprises the following steps: monitoring indoor and outdoor environment temperature and humidity data in real time, and carrying out the data collection through temperature and humidity sensors disposed inside and outside a house; by introducing multi-sensor monitoring, data analysis, an error correction mechanism and a backup sensor, the accuracy and reliability of the temperature and humidity management system are improved, and the failure problem caused by the fault of a single sensor is avoided. In addition, the system optimizes energy use and reduces unnecessary energy consumption by dynamically adjusting the indoor and outdoor temperature and humidity to be matched with climate conditions. Meanwhile, the fault tolerance and the long-term stability of the system are enhanced through sensor redundancy and error correction, and it is ensured that a continuous and comfortable living environment is provided in long-term operation.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent house temperature and humidity management, and in particular to an intelligent house temperature and humidity management method under adaptive climate conditions. Background Art

[0002] Adaptive climate-controlled smart home temperature and humidity management uses advanced sensors, artificial intelligence (AI), and automated control systems to monitor and adjust a home's internal temperature and humidity in real time to adapt to external climate changes. This system automatically adjusts the home's heating, air conditioning, humidifier, dehumidifier, and other equipment based on environmental factors such as external temperature, humidity, and wind speed, maintaining a comfortable and healthy indoor environment. For example, during cold winter months, the system automatically turns on heating to raise the indoor temperature; during the humid rainy season, the system activates the dehumidifier to maintain an appropriate humidity level. This type of intelligent management system can be personalized based on user preferences and the specific characteristics of the home, thereby improving energy efficiency and creating a more comfortable living environment.

[0003] The existing technology has the following shortcomings: In the existing technology's adaptive climate-controlled smart home temperature and humidity management, coordination errors between the temperature and humidity sensor and the backup sensor can have serious consequences. When the primary sensor fails or experiences accuracy deviations, the backup sensor is automatically activated for data comparison and correction. However, if the backup sensor itself has accuracy issues or is calibrated differently from the primary sensor, the system may mistakenly interpret the primary sensor's data as abnormal, resulting in inconsistent data displayed by the two sensors and cumulative deviations during the error correction process. Such errors can cause the automatic adjustment of indoor temperature and humidity to deviate from actual requirements. Over time, this can lead to excessive dryness or humidity in the indoor environment, affecting occupant health and even causing excessive operation of equipment such as air conditioners and humidifiers. This problem is often difficult to detect in the early stages, but once it occurs, it can cause discomfort in the living environment, even leading to equipment failure and energy waste, seriously affecting the stability and overall performance of the system.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method for intelligent house temperature and humidity management under adaptive climate conditions. By incorporating multi-sensor monitoring, data analysis, error correction mechanisms, and backup sensors, the accuracy and reliability of the temperature and humidity management system are improved, avoiding failures caused by single sensor failures. Furthermore, by dynamically adjusting indoor and outdoor temperature and humidity to match climate conditions, the system optimizes energy use and reduces unnecessary energy consumption. Furthermore, sensor redundancy and error correction enhance the system's fault tolerance and long-term stability, ensuring a consistent and comfortable living environment over the long term, thereby addressing the aforementioned issues in the background art.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a method for managing temperature and humidity in an intelligent house under adaptive climate conditions, comprising the following steps:

[0007] Real-time monitoring of indoor and outdoor temperature and humidity data, and data collection through temperature and humidity sensors installed inside and outside the house;

[0008] Use the data analysis module to analyze the collected temperature and humidity data to determine the difference between the indoor environment and the external climate conditions and whether the indoor temperature and humidity need to be adjusted;

[0009] According to the analysis results, the operating status of the temperature and humidity control equipment in the house is automatically adjusted to maintain the appropriate range of indoor temperature and humidity;

[0010] Set up a sensor calibration mechanism to regularly calibrate the temperature and humidity sensors to reduce control deviations caused by sensor accuracy errors;

[0011] When sensor data is abnormal, the backup sensor is automatically activated for real-time data comparison, and the error is corrected through the algorithm to ensure the accuracy of temperature and humidity adjustment decisions;

[0012] Based on the sensor calibration and error correction results, the temperature and humidity management strategy is dynamically optimized to ensure that the indoor environment always remains comfortable and healthy under various climatic conditions.

[0013] Preferably, the temperature and humidity sensor includes a temperature sensor and a humidity sensor. The temperature and humidity sensor is connected to the data analysis module via wireless communication, and transmits the collected data to the data analysis module in real time for real-time data processing and analysis.

[0014] Preferably, the data analysis module uses a machine learning algorithm to predict environmental change trends based on external climate conditions and indoor environmental temperature and humidity data, and adjusts the operating strategy of the indoor temperature and humidity control equipment according to the prediction results;

[0015] The data analysis module performs regression analysis based on historical data to generate a temperature and humidity change trend model, and based on this model, predicts the temperature and humidity changes in the future, thereby adjusting the indoor environment in advance, preventing excessive fluctuations in temperature and humidity, and maintaining a stable and comfortable living environment.

[0016] Preferably, the temperature and humidity control system has an adaptive energy efficiency optimization function, which can automatically adjust the energy usage mode according to indoor and outdoor climate conditions to achieve efficient energy utilization and reduce energy consumption without affecting comfort.

[0017] Preferably, the sensor calibration mechanism includes regularly pushing calibration instructions to each temperature and humidity sensor through a cloud server to ensure that the accuracy of all sensors is always in the optimal state, avoiding sensor errors caused by long-term operation from affecting the stability of the temperature and humidity control system.

[0018] Preferably, the backup sensor has the same working principle and output data format as the main sensor. When the main sensor fails or the data is abnormal, the backup sensor is automatically enabled to ensure that the temperature and humidity control system can continue to operate normally under any circumstances.

[0019] Preferably, the specific steps of sensor data comparison and error correction are as follows:

[0020] Obtain the temperature and humidity data from the primary and backup sensors. Calculate the temperature and humidity differences between the primary and backup sensors based on the obtained data. The calculation expressions are as follows:

[0021] ΔT=|T min -T backup |, ΔH=|H min -H backup |

[0022] , where ΔT is the temperature difference between the main sensor and the backup sensor, T min is the temperature data collected by the main sensor, T backup is the temperature data collected by the backup sensor, ΔH is the humidity difference between the main sensor and the backup sensor, H min is the humidity data collected by the main sensor, H backup It is the humidity data collected by the backup sensor;

[0023] Determine whether the sensor is faulty based on the temperature difference and humidity difference. T Or ΔH>δ H When , it means that the sensor is abnormal, where δ T and δ H are the preset temperature and humidity tolerance thresholds respectively;

[0024] If the sensor is faulty, adjust the temperature and humidity data of the main sensor. The adjustment formula is as follows:

[0025] T adjusted =T backup +(T main -T backup )·α,

[0026] H adjusted =H backup +(H main -H backup )·β, Where, T adjusted is the corrected temperature value of the main sensor, H adjusted is the corrected humidity value of the main sensor, α and β are correction coefficients, α represents the temperature difference ratio between the main sensor and the backup sensor, and β represents the humidity difference ratio between the main sensor and the backup sensor.

[0027] Preferably, the temperature and humidity management system includes a user interface through which the user can view real-time temperature and humidity data, device operating status, and system optimization suggestions, and manually adjust device settings as needed;

[0028] The specific steps for dynamically optimizing the temperature and humidity management strategy are as follows:

[0029] Based on the real-time data of outdoor climate change and indoor temperature and humidity, the target temperature and humidity values ​​are calculated using the weighted average method. The calculation expression is as follows:

[0030] X target =w1·T outside +w2·H outside +w3·T inside +w4·H inside

[0031] , where X target is the target temperature and humidity value, T outside is the outdoor temperature data, H outside is the outdoor humidity data, T inside is the indoor temperature data, H inside is the indoor humidity data, w1 is the weighting coefficient of the outdoor temperature data, w2 is the weighting coefficient of the outdoor humidity data, w3 is the weighting coefficient of the indoor temperature data, and w4 is the weighting coefficient of the indoor humidity data;

[0032] Based on the calculated target temperature and humidity value X target ,Combined with the working capacity of the indoor temperature and humidity control equipment, determine the operating parameters of the equipment, and the calculation expression is as follows:

[0033] P AC =k1·(Ttarget -T inside ),

[0034] P humidifier =k2·(T target -T inside )

[0035] , where P AC is the regulating power of the air conditioner, P humidiftier is the adjustment power of the humidifier, k1 is the air conditioning power adjustment coefficient, and k2 is the humidifier power adjustment coefficient;

[0036] According to the real-time operating status and target temperature and humidity values, the operating parameters are dynamically adjusted through the feedback control mechanism to ensure that the indoor environment always remains within the optimal comfort range.

[0037] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0038] The present invention significantly improves the accuracy and reliability of the temperature and humidity management system by introducing multi-sensor real-time monitoring, data analysis modules, sensor error correction mechanisms and the use of backup sensors. Traditional temperature and humidity management systems often rely solely on a single sensor, which may cause data deviations after long-term operation, resulting in failure of temperature and humidity regulation and affecting indoor comfort. However, the present invention establishes a dual monitoring mechanism of main sensors and backup sensors, and combines real-time data comparison and error correction algorithms. Even if the main sensor fails or there is a data error, the system can automatically enable the backup sensor and correct the temperature and humidity data to ensure accurate and stable temperature and humidity control. This mechanism effectively avoids system failure problems caused by single sensor failures, ensures the continuous and reliable operation of smart houses in any environment, and further improves the comfort of residents and the long-term stability of the system.

[0039] The present invention not only focuses on the accuracy of temperature and humidity regulation, but also pays special attention to energy efficiency optimization. By dynamically adjusting the indoor temperature and humidity to match the external climate conditions, the system can reduce ineffective energy consumption. For example, through temperature and humidity prediction and historical data analysis, the system can adjust the working status of air conditioners, humidifiers and dehumidifiers in advance, avoid activating high-power equipment when it is not necessary, and achieve energy-saving effects. In addition, the system can also automatically adjust the working intensity and duration of the equipment according to real-time climate changes, while maintaining a comfortable environment while minimizing energy consumption. This optimized energy management strategy enables smart houses to not only provide a comfortable environment, but also significantly reduce unnecessary energy waste, which is in line with the concepts of green environmental protection and low-carbon energy conservation.

[0040] The present invention greatly enhances the fault tolerance and long-term operational stability of the temperature and humidity management system by introducing sensor error correction and backup sensor mechanisms. Intelligent temperature and humidity management systems usually need to run uninterruptedly for a long time, which requires its various hardware components to have high stability and durability. Temperature and humidity sensors are often affected by environmental factors (such as dust, moisture, etc.), and may experience a decrease in accuracy or failure. Once a sensor fails in a traditional system, it often leads to temperature and humidity adjustment errors, affecting the comfort of the indoor environment. Through the sensor redundancy and error correction mechanism of the present invention, when the main sensor data is abnormal, the system can automatically switch to the backup sensor and perform data correction in real time to ensure the accuracy of temperature and humidity adjustment. This fault-tolerant design ensures the stability and continuity of the system during long-term operation. Even when there is a problem with the equipment, it can effectively avoid the impact on temperature and humidity control, thereby providing residents with a long-term stable and comfortable environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0042] Figure 1 This is a flow chart of the method for temperature and humidity management of an intelligent house under adaptive climate conditions of the present invention. DETAILED DESCRIPTION

[0043] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0044] The present invention provides Figure 1 The temperature and humidity management method of a smart house under adaptive climate conditions shown includes the following steps:

[0045] Real-time monitoring of indoor and outdoor temperature and humidity data, and data collection through temperature and humidity sensors installed inside and outside the house;

[0046] The temperature and humidity sensor includes a temperature sensor and a humidity sensor. The temperature and humidity sensor is connected to the data analysis module via wireless communication, and transmits the collected data to the data analysis module in real time for real-time data processing and analysis.

[0047] Use the data analysis module to analyze the collected temperature and humidity data to determine the difference between the indoor environment and the external climate conditions and whether the indoor temperature and humidity need to be adjusted;

[0048] The data analysis module uses machine learning algorithms to predict environmental change trends based on external climate conditions (such as temperature, humidity, wind speed, etc.) and indoor environmental temperature and humidity data, and adjusts the operating strategy of indoor temperature and humidity control equipment according to the prediction results.

[0049] The data analysis module performs regression analysis based on historical data to generate a temperature and humidity change trend model, and based on this model, predicts the temperature and humidity changes in the future, thereby adjusting the indoor environment in advance, preventing excessive fluctuations in temperature and humidity, and maintaining a stable and comfortable living environment.

[0050] Based on the analysis results, the operating status of the temperature and humidity control equipment in the house, including air conditioners, humidifiers and dehumidifiers, is automatically adjusted to maintain the appropriate range of indoor temperature and humidity;

[0051] The temperature and humidity control system has an adaptive energy efficiency optimization function, which can automatically adjust the energy usage mode according to indoor and outdoor climate conditions to achieve efficient energy utilization and reduce energy consumption without affecting comfort.

[0052] Set up a sensor calibration mechanism to regularly calibrate the temperature and humidity sensors to reduce control deviations caused by sensor accuracy errors;

[0053] The sensor calibration mechanism involves regularly pushing calibration instructions to each temperature and humidity sensor through a cloud server to ensure that the accuracy of all sensors is always in the optimal state, avoiding sensor errors caused by long-term operation that affect the stability of the temperature and humidity control system.

[0054] When sensor data is abnormal, the backup sensor is automatically activated for real-time data comparison, and the error is corrected through the algorithm to ensure the accuracy of temperature and humidity adjustment decisions;

[0055] The backup sensor has the same working principle and output data format as the main sensor. When the main sensor fails or the data is abnormal, the backup sensor is automatically enabled to ensure that the temperature and humidity control system can continue to operate normally under any circumstances.

[0056] The specific steps for sensor data comparison and error correction are as follows:

[0057] Obtain the temperature and humidity data from the primary and backup sensors. Calculate the temperature and humidity differences between the primary and backup sensors based on the obtained data. The calculation expressions are as follows:

[0058] ΔT=|T min -Tbackup |, ΔH=|H min -H backup |

[0059] , where ΔT is the temperature difference between the main sensor and the backup sensor, T min is the temperature data collected by the main sensor, T backup is the temperature data collected by the backup sensor, ΔH is the humidity difference between the main sensor and the backup sensor, H min is the humidity data collected by the main sensor, H backup It is the humidity data collected by the backup sensor;

[0060] Determine whether the sensor is faulty based on the temperature difference and humidity difference. T Or ΔH>δ H When , it means that the sensor is abnormal, where δ T and δ H are the preset temperature and humidity tolerance thresholds respectively;

[0061] If the sensor is faulty, adjust the temperature and humidity data of the main sensor. The adjustment formula is as follows:

[0062] T adjusted =T backup +(T main -T backup )·α,

[0063] H adjusted =H backup +(H main -H backup )·β

[0064] , where T adjusted is the corrected temperature value of the main sensor, H adjusted is the corrected humidity value of the main sensor, α and β are correction coefficients, α represents the temperature difference ratio between the main sensor and the backup sensor, and β represents the humidity difference ratio between the main sensor and the backup sensor.

[0065] Dynamically optimize temperature and humidity management strategies based on sensor calibration and error correction results to ensure the indoor environment remains comfortable and healthy under various climate conditions.

[0066] The temperature and humidity management system includes a user interface through which users can view real-time temperature and humidity data, equipment operating status, and system optimization suggestions, and manually adjust equipment settings as needed.

[0067] The specific steps for dynamically optimizing the temperature and humidity management strategy are as follows:

[0068] Based on the real-time data of outdoor climate changes (such as temperature, humidity, wind speed, etc.) and indoor temperature and humidity, the target temperature and humidity values ​​are calculated using the weighted average method. The calculation expression is as follows:

[0069] X target =w1·T outside +w2·H outside +w3·H inside +w4·H inside

[0070] , where X target is the target temperature and humidity value, which represents the comprehensive target value based on indoor and outdoor temperature and humidity data and climatic conditions. outside is the outdoor temperature data, H outside is the outdoor humidity data, T inside is the indoor temperature data, H inside is the indoor humidity data, w1 is the weighting coefficient of the outdoor temperature data, w2 is the weighting coefficient of the outdoor humidity data, w3 is the weighting coefficient of the indoor temperature data, and w4 is the weighting coefficient of the indoor humidity data;

[0071] Based on the calculated target temperature and humidity value X target ,Combined with the working capacity of the indoor temperature and humidity control equipment, determine the operating parameters of the equipment, and the calculation expression is as follows:

[0072] P AC =k1·(T target -T inside ),

[0073] P humidifier =k2·(T target -T inside )

[0074] , where P AC is the regulating power of the air conditioner, P humidiftier is the adjustment power of the humidifier, k1 is the air conditioning power adjustment coefficient, and k2 is the humidifier power adjustment coefficient;

[0075] According to the real-time operating status and target temperature and humidity values, the operating parameters are dynamically adjusted through the feedback control mechanism to ensure that the indoor environment always remains within the optimal comfort range.

[0076] Specific embodiment 1: In this embodiment, a climate-adaptive smart house temperature and humidity management system integrates temperature and humidity sensors with a data analysis module to fully implement real-time collection and processing of indoor and outdoor environmental data. Specifically, the system uses a multi-point deployment of temperature and humidity sensors, which are distributed in different locations indoors and outdoors to collect temperature and humidity data. Outdoor sensors are used to monitor external climate conditions, such as external temperature, humidity, and wind speed, while indoor sensors continuously monitor indoor temperature and humidity conditions. The sensors are connected to the data analysis module via a wireless communication module, and the collected data is transmitted to the central processing system in real time.

[0077] The data analysis module uses advanced data analysis technologies (such as machine learning and artificial intelligence) to deeply process the real-time data collected by temperature and humidity sensors. First, the system will determine whether indoor temperature and humidity need to be adjusted based on changes in the outdoor climate. For example, the system can monitor changes in external temperature in real time and, based on a set algorithm, automatically activate air conditioning and heating equipment when the temperature drops sharply; when the external humidity is high, the system will automatically activate the dehumidifier to adjust the humidity. At the same time, the data analysis module can determine whether there is a temperature and humidity imbalance by comparing indoor and outdoor temperature and humidity data, and promptly adjust the operating status of equipment such as air conditioners, humidifiers, and dehumidifiers to ensure that the indoor environment is always maintained within an appropriate range.

[0078] Furthermore, the real-time transmission and processing of sensor data goes beyond simple temperature and humidity control and can also be integrated with intelligent home control systems. For example, when multiple smart home devices are deployed, the temperature and humidity management system can intelligently adjust the opening and closing of curtains, the operating status of heating equipment, and the start and stop of ventilation systems based on temperature and humidity control needs, comprehensively improving the comfort of the entire indoor environment. This comprehensive data collection and processing capability ensures that smart homes can quickly respond to external climate changes and actual indoor needs.

[0079] To ensure the stability and accuracy of the system during long-term operation, the present invention also introduces a regular calibration mechanism for the temperature and humidity sensors. During operation, the sensors may experience accuracy deviations or performance degradation due to environmental factors (such as moisture, dust, temperature fluctuations, etc.). The system will regularly push calibration instructions through the cloud server to automatically calibrate each temperature and humidity sensor to ensure that their output data is always within the accurate range. Through this mechanism, the problem of temperature and humidity regulation failure caused by sensor errors during long-term use can be effectively avoided.

[0080] To further enhance system reliability, a redundant mechanism for temperature and humidity sensors has been designed, with at least two sensors deployed at each key location. If the output data from one sensor exhibits an anomaly, the system automatically uses the real-time data from the backup sensor to ensure accurate data collection. This redundant design effectively prevents system crashes or misregulation caused by a single sensor failure.

[0081] In summary, the integration of temperature and humidity sensors and data analysis modules addresses the issues of unstable temperature and humidity control and sensor failure that can arise in adaptive climate-controlled smart home temperature and humidity management through real-time data acquisition and processing, intelligent algorithm optimization and control, and sensor calibration and redundancy. The system not only provides precise temperature and humidity regulation but also improves long-term stability and reliability, ensuring a consistently comfortable living environment for residents regardless of climate conditions.

[0082] Specific embodiment 2: Ensuring the stability and reliability of the intelligent temperature and humidity management system by introducing sensor error correction and the use of backup sensors. Temperature and humidity sensors may exhibit errors over long periods of operation, leading to deviations in the system's temperature and humidity regulation, impacting indoor comfort and equipment energy efficiency. Therefore, this embodiment designs a temperature and humidity management system with high reliability and fault tolerance, ensuring stable operation even in the event of sensor failure or errors.

[0083] In practice, the system uses a primary sensor and a backup sensor to monitor indoor and outdoor temperature and humidity data, respectively. Each sensor operates on identical principles and data formats, ensuring consistent data collection. When the primary sensor is operating normally, the system adjusts the temperature and humidity based on its real-time data. However, after a period of use, the sensor may develop errors due to environmental influences (such as dust, moisture, or temperature fluctuations), resulting in inaccurate output data. To prevent this from affecting the temperature and humidity regulation, the present invention incorporates an error correction mechanism.

[0084] When the system detects that the difference between the temperature and humidity data output by the main sensor and the backup sensor exceeds a preset threshold, the system automatically initiates the error correction process. Specifically, the system compares the temperature and humidity data of the main sensor and the backup sensor to determine whether the difference is outside the tolerance range (for example, the temperature difference is greater than 1°C, and the humidity difference is greater than 5%). If it exceeds the set threshold, the system will determine that the main sensor may have a fault or error. At this time, the system will adjust the output data of the main sensor through a preset correction algorithm.

[0085] The correction algorithm adjusts the temperature and humidity data from the primary sensor to values ​​close to those of the backup sensor through weighted averaging, linear interpolation, or other data processing methods. For example, the system calculates the error between the primary and backup sensors and corrects the primary sensor's data by a certain percentage, ensuring that temperature and humidity control decisions are based on accurate data. Furthermore, the system regularly performs sensor self-tests to determine if there are any faults and switches to the backup sensor as needed, ensuring that the temperature and humidity management system remains in normal operation.

[0086] Throughout the correction process, the system performs real-time data comparison and feedback, ensuring data accuracy and precise temperature and humidity control through multiple correction iterations. Corrected data is also fed back to the data analysis module in real time for further optimization of the control strategy. This effectively prevents system failures caused by single-point sensor failures or errors, ensuring the stability and sustainability of the intelligent temperature and humidity management system.

[0087] Furthermore, to enhance the system's fault tolerance, data error correction and redundancy mechanisms are designed into the system. For example, if consistency issues arise across multiple sensors, the system compares data collected by sensors in different locations and types to perform more accurate data corrections. This multi-layered error correction mechanism significantly improves the reliability of the temperature and humidity management system, ensuring that the equipment continues to function properly under any abnormal circumstances.

[0088] Through the above-mentioned sensor error correction and backup sensor mechanism, this embodiment significantly improves the stability of the smart house temperature and humidity management system, can ensure the accuracy of temperature and humidity adjustment under any circumstances, and effectively avoid the negative impact caused by sensor failure or error.

[0089] Specific embodiment 3: This invention utilizes a dynamic temperature and humidity management strategy. By combining real-time data analysis with external climate forecasts, it predicts temperature and humidity trends in advance and optimizes adjustments. Unlike traditional adjustment methods that rely solely on real-time sensor data, the dynamic temperature and humidity management strategy proactively adjusts indoor temperature and humidity based on historical data and future climate forecasts, providing a more precise and comfortable environment for residents.

[0090] First, the system uses machine learning algorithms to analyze historical temperature and humidity data and build models of temperature and humidity trends. These models capture the patterns of climate change and the characteristics of indoor environmental responses, providing a basis for future temperature and humidity adjustments. For example, based on temperature and humidity fluctuations over the past few days, the system predicts climate trends for the next few hours and automatically adjusts the operating status of air conditioners, humidifiers, and dehumidifiers. This predictive control approach allows preventive measures to be taken before sudden climate changes occur, avoiding excessive temperature and humidity fluctuations and maintaining a stable indoor environment.

[0091] Secondly, the system uses external climate forecast data to proactively adjust temperature and humidity. For example, if the meteorological department predicts a rapid rise in humidity, the system can activate the dehumidifier in advance to prevent moisture accumulation, mold growth, and furniture damage. Similarly, if the outdoor temperature suddenly rises, the air conditioning system will activate in advance to prevent excessive indoor temperatures from causing discomfort.

[0092] In order to ensure the real-time and accuracy of the adjustment strategy, the system also introduces a feedback control mechanism. During the operation of the temperature and humidity equipment, the system will monitor the indoor temperature and humidity data in real time and compare it with the set target temperature and humidity. If there is a deviation,

[0093] The system immediately adjusts the operating status of the equipment to compensate. For example, based on real-time data feedback, the system might adjust the air conditioner's fan speed, the humidifier's output, or the dehumidifier's operating hours, thereby achieving precise temperature and humidity control.

[0094] Furthermore, this implementation emphasizes energy efficiency optimization. During the temperature and humidity adjustment process, the system not only considers temperature and humidity comfort but also comprehensively considers energy efficiency. By analyzing the relationship between the energy consumption of temperature and humidity control equipment and external climate conditions, the system can minimize energy consumption while ensuring living comfort. For example, when the outdoor temperature is suitable, the system may shut down or reduce the operation of air conditioning equipment and switch to energy-saving measures such as natural ventilation, thereby achieving energy conservation and environmental protection goals.

[0095] In summary, the dynamic temperature and humidity management strategy achieves optimization and intelligent control of temperature and humidity regulation by combining real-time data, historical trends and external climate forecasts, ensuring a stable and comfortable indoor environment, improving the system's energy efficiency and reducing energy waste.

[0096] The present invention significantly improves the accuracy and reliability of the temperature and humidity management system by introducing multi-sensor real-time monitoring, data analysis modules, sensor error correction mechanisms and the use of backup sensors. Traditional temperature and humidity management systems often rely solely on a single sensor, which may cause data deviations after long-term operation, resulting in failure of temperature and humidity regulation and affecting indoor comfort. However, the present invention establishes a dual monitoring mechanism of main sensors and backup sensors, and combines real-time data comparison and error correction algorithms. Even if the main sensor fails or there is a data error, the system can automatically enable the backup sensor and correct the temperature and humidity data to ensure accurate and stable temperature and humidity control. This mechanism effectively avoids system failure problems caused by single sensor failures, ensures the continuous and reliable operation of smart houses in any environment, and further improves the comfort of residents and the long-term stability of the system.

[0097] The present invention not only focuses on the accuracy of temperature and humidity regulation, but also pays special attention to energy efficiency optimization. By dynamically adjusting the indoor temperature and humidity to match the external climate conditions, the system can reduce ineffective energy consumption. For example, through temperature and humidity prediction and historical data analysis, the system can adjust the working status of air conditioners, humidifiers and dehumidifiers in advance, avoid activating high-power equipment when it is not necessary, and achieve energy-saving effects. In addition, the system can also automatically adjust the working intensity and duration of the equipment according to real-time climate changes, while maintaining a comfortable environment while minimizing energy consumption. This optimized energy management strategy enables smart houses to not only provide a comfortable environment, but also significantly reduce unnecessary energy waste, which is in line with the concepts of green environmental protection and low-carbon energy conservation.

[0098] The present invention greatly enhances the fault tolerance and long-term operational stability of the temperature and humidity management system by introducing sensor error correction and backup sensor mechanisms. Intelligent temperature and humidity management systems usually need to run uninterruptedly for a long time, which requires its various hardware components to have high stability and durability. Temperature and humidity sensors are often affected by environmental factors (such as dust, moisture, etc.), and may experience a decrease in accuracy or failure. Once a sensor fails in a traditional system, it often leads to temperature and humidity adjustment errors, affecting the comfort of the indoor environment. Through the sensor redundancy and error correction mechanism of the present invention, when the main sensor data is abnormal, the system can automatically switch to the backup sensor and perform data correction in real time to ensure the accuracy of temperature and humidity adjustment. This fault-tolerant design ensures the stability and continuity of the system during long-term operation. Even when there is a problem with the equipment, it can effectively avoid the impact on temperature and humidity control, thereby providing residents with a long-term stable and comfortable environment.

[0099] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0100] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0101] It should be noted that, in this document, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0102] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0103] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0104] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0105] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0106] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0107] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0108] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. A method for managing temperature and humidity in an intelligent house under adaptive climate conditions, characterized in that: The following steps are involved: Real-time monitoring of indoor and outdoor temperature and humidity data, and data collection through temperature and humidity sensors installed inside and outside the house; Use the data analysis module to analyze the collected temperature and humidity data to determine the difference between the indoor environment and the external climate conditions and whether the indoor temperature and humidity need to be adjusted; According to the analysis results, the operating status of the temperature and humidity control equipment in the house is automatically adjusted to maintain the appropriate range of indoor temperature and humidity; Set up a sensor calibration mechanism to regularly calibrate the temperature and humidity sensors to reduce control deviations caused by sensor accuracy errors; When sensor data is abnormal, the backup sensor is automatically activated for real-time data comparison, and the error is corrected through the algorithm to ensure the accuracy of temperature and humidity adjustment decisions; Based on the sensor calibration and error correction results, the temperature and humidity management strategy is dynamically optimized to ensure that the indoor environment always remains comfortable and healthy under various climatic conditions.

2. The method for managing temperature and humidity in an intelligent house under adaptive climate conditions according to claim 1, characterized in that: The temperature and humidity sensor includes a temperature sensor and a humidity sensor. The temperature and humidity sensor is connected to the data analysis module via wireless communication, and transmits the collected data to the data analysis module in real time for real-time data processing and analysis.

3. The method for managing temperature and humidity in an intelligent house under adaptive climate conditions according to claim 1, wherein: The data analysis module uses machine learning algorithms to predict environmental change trends based on external climate conditions and indoor temperature and humidity data, and adjusts the operating strategy of indoor temperature and humidity control equipment based on the prediction results; The data analysis module performs regression analysis based on historical data to generate a temperature and humidity change trend model, and based on this model, predicts the temperature and humidity changes in the future, thereby adjusting the indoor environment in advance, preventing excessive fluctuations in temperature and humidity, and maintaining a stable and comfortable living environment.

4. The method for managing temperature and humidity in an intelligent house under adaptive climate conditions according to claim 1, wherein: The temperature and humidity control system has an adaptive energy efficiency optimization function, which can automatically adjust the energy usage mode according to indoor and outdoor climate conditions to achieve efficient energy utilization and reduce energy consumption without affecting comfort.

5. The method for managing temperature and humidity of an intelligent house under adaptive climate conditions according to claim 1, wherein: The sensor calibration mechanism involves regularly pushing calibration instructions to each temperature and humidity sensor through a cloud server to ensure that the accuracy of all sensors is always in the optimal state, avoiding sensor errors caused by long-term operation that affect the stability of the temperature and humidity control system.

6. The method for managing temperature and humidity in an intelligent house under adaptive climate conditions according to claim 1, characterized in that: The backup sensor has the same working principle and output data format as the main sensor. When the main sensor fails or the data is abnormal, the backup sensor is automatically enabled to ensure that the temperature and humidity control system can continue to operate normally under any circumstances.

7. The method for managing temperature and humidity in an intelligent house under adaptive climate conditions according to claim 1, characterized in that: The specific steps for sensor data comparison and error correction are as follows: Obtain the temperature and humidity data from the primary and backup sensors. Calculate the temperature and humidity differences between the primary and backup sensors based on the obtained data. The calculation expressions are as follows: ΔT=|T min -T backup |,ΔH=|H min -H backup |, Where ΔT is the temperature difference between the primary sensor and the backup sensor, T min is the temperature data collected by the main sensor, T backup is the temperature data collected by the backup sensor, ΔH is the humidity difference between the main sensor and the backup sensor, H min is the humidity data collected by the main sensor, H backup It is the humidity data collected by the backup sensor; Determine whether the sensor is faulty based on the temperature difference and humidity difference. T Or ΔH>δ H When , it means that the sensor is abnormal, where δ T and δ H are the preset temperature and humidity tolerance thresholds respectively; If the sensor is faulty, adjust the temperature and humidity data of the main sensor. The adjustment formula is as follows: T adjusted =T backup +(T main -T backup )·α, H adjusted =H backup +(H main -H backup )·β, Where, T adjusted is the corrected temperature value of the main sensor, H adjusted is the corrected humidity value of the main sensor, α and β are correction coefficients, α represents the temperature difference ratio between the main sensor and the backup sensor, and β represents the humidity difference ratio between the main sensor and the backup sensor.

8. The method for managing temperature and humidity in an intelligent house under adaptive climate conditions according to claim 1, wherein: The temperature and humidity management system includes a user interface, through which users can view real-time temperature and humidity data, equipment operating status, and system optimization suggestions, and manually adjust equipment settings as needed; The specific steps for dynamically optimizing the temperature and humidity management strategy are as follows: Based on the real-time data of outdoor climate change and indoor temperature and humidity, the target temperature and humidity values ​​are calculated using the weighted average method. The calculation expression is as follows: X target =w1·T outside +w2·H outside +w3·T inside +w4·H inside , Where, X target is the target temperature and humidity value, T outside is the outdoor temperature data, H outside is the outdoor humidity data, T inside is the indoor temperature data, H inside is the indoor humidity data, w1 is the weighting coefficient of the outdoor temperature data, w2 is the weighting coefficient of the outdoor humidity data, w3 is the weighting coefficient of the indoor temperature data, and w4 is the weighting coefficient of the indoor humidity data; Based on the calculated target temperature and humidity value X target ,Combined with the working capacity of the indoor temperature and humidity control equipment, determine the operating parameters of the equipment, and the calculation expression is as follows: P AC =k1·(T target -T inside ), P humidifier =k2·(T target -T inside ), Where, P AC is the regulating power of the air conditioner, P humidifier is the adjustment power of the humidifier, k1 is the air conditioning power adjustment coefficient, and k2 is the humidifier power adjustment coefficient; According to the real-time operating status and target temperature and humidity values, the operating parameters are dynamically adjusted through the feedback control mechanism to ensure that the indoor environment always remains within the optimal comfort range.

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