Fresh air linkage control method and system for household appliances

Abstract

The present invention relates to the field of intelligent device technology, and discloses a fresh air linkage control method and system for household appliances, which involves obtaining the working state data and indoor air quality parameters of household appliances, calculating the load index and air quality index according to these data, and starting the fresh air system when the load index exceeds the threshold and the air quality does not meet the standard; by dynamically adjusting the air volume adjustment value, the present invention can optimize the working state of the fresh air system according to the difference in indoor and outdoor air quality and the change in the load index in real time; at the same time, an adaptive control strategy is adopted, based on the user's historical air quality preference, the air volume and working mode are intelligently adjusted, and the energy efficiency and indoor air quality of household appliances are improved. The present invention has a high level of intelligence, can effectively improve air quality, and save energy and reduce emissions.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent devices, and in particular to a fresh air linkage control method and system for household appliances. Background Art

[0002] With the popularization of household appliances and the rapid development of smart home technology, how to improve household energy efficiency and optimize indoor air quality has become an important topic that has attracted much attention. At present, most of the fresh air systems on the market are started at fixed time intervals or manually set, but this control method cannot intelligently adjust the air volume dynamically according to the load status of household appliances and changes in indoor air quality, so it has certain limitations. In addition, the change of indoor air quality is not only affected by a single factor, but it is closely related to multiple factors such as temperature, humidity, PM2.5 concentration, carbon dioxide concentration, etc. In view of this, there is an urgent need for a fresh air linkage control method that can realize intelligent and automatic adjustment, so as to more effectively balance the relationship between indoor air quality and energy consumption, thereby providing residents with a healthier and more comfortable living environment. Summary of the invention

[0003] In view of this, the present invention proposes a fresh air linkage control method and system for household appliances, which aims to dynamically adjust the air volume of the fresh air system through intelligent perception of the operating status of household appliances and indoor air quality parameters, so as to achieve precise control of indoor air quality.

[0004] The present invention proposes a fresh air linkage control method for household appliances, comprising:

[0005] Acquire the working status data of the household appliance, and calculate the load index of the appliance according to the working status data; wherein the working status data includes the start and stop status, load power, working mode and start and stop time of the household appliance;

[0006] At the same time, indoor air quality parameters are obtained, and an air quality index is calculated according to the indoor air quality parameters, and whether to start the fresh air system is determined according to the load index, the air quality index and its preset threshold value;

[0007] When the judgment result is that the fresh air system needs to be started, the adjustment value of the air volume is determined according to the air quality index and the load index; wherein the indoor air quality parameters include carbon dioxide concentration, temperature, humidity and PM2.5 value;

[0008] Adjust the air volume of the fresh air system in real time according to the difference in indoor and outdoor air quality and changes in the load index.

[0009] Preferably, when calculating the load index of the electrical appliance according to the working status data, it includes:

[0010] The load index is calculated using the following formula:

[0011] ;

[0012] Where LI represents the load index; P i represents the load power of the i-th household appliance; t i represents the working time of the i-th household appliance; n represents the number of appliances.

[0013] Preferably, the air quality index is calculated by the following formula:

[0014] ;

[0015] Among them, AQI stands for air quality index; C i Indicates the real-time value of each air quality parameter, C i C CO2 , C T , C H , C PM2.5 , respectively, carbon dioxide concentration, temperature, humidity and PM2.5 concentration; C i,min and C i,max Indicates the minimum and maximum values ​​of the parameter; α i Indicates air quality parameter C i The weight coefficient of .

[0016] Preferably, when the load index is greater than a preset threshold and the air quality index does not reach a predetermined standard, the fresh air system is started.

[0017] The setting of the load index threshold is based on the dynamic relationship between the load power of household appliances and environmental parameters, and the formula is expressed as:

[0018] ;

[0019] Among them, LI threshold represents the load index threshold; κ represents the adjustment coefficient.

[0020] Preferably, the adjustment value of the air volume is determined according to the air quality index and the load index, and the adjustment value is calculated by the following calculation formula:

[0021] ;

[0022] Among them, V f represents the adjustment value; η represents the air volume adjustment coefficient.

[0023] Preferably, the air volume of the fresh air system is adjusted in real time according to the difference in indoor and outdoor air quality and the change in load index, and the following calculation formula is used for dynamic adjustment:

[0024] ;

[0025] Among them, V adjusted Indicates the adjusted air volume; α and β indicate the adjustment coefficient; AQI current Indicates the current air quality index, AQI target Indicates the target air quality index, LI current Indicates the current load index, LI threshold Indicates the load index threshold.

[0026] Preferably, user behavior learning is performed through the home appliance control platform, and the air volume and working mode are automatically adjusted according to the indoor air quality preferences set by the user in history;

[0027] Update the control strategy by:

[0028] ;

[0029] Among them, S adaptive represents the adaptive control strategy, AQI historical Represents the historical air quality index, LI historical represents the historical load index, T historical Indicates historical temperature.

[0030] Preferably, the adaptive control strategy is based on a deep learning algorithm, which analyzes user historical data to predict future trends in indoor air quality and load index, thereby automatically adjusting the working mode, air volume, and start and stop time of the fresh air system to optimize air quality and energy consumption; the adaptive control strategy includes:

[0031] Collect and store historical air quality index, load index and temperature data; analyze historical data through deep learning models to learn users' air quality preferences and load index changes; dynamically adjust air volume adjustment values ​​and working modes based on prediction results, and adjust the system's energy efficiency accordingly when the load index changes.

[0032] Preferably, the deep learning model adopts a time series data prediction algorithm and is trained and optimized in combination with the dynamic relationship between air quality and load index; the training process includes:

[0033] The convolutional neural network is trained through historical data to identify the relationship between air quality index, load index and indoor environmental changes; the model is regularly optimized and calibrated.

[0034] The present invention also provides a fresh air linkage control system for household appliances, comprising:

[0035] A load monitoring module, used to collect the working status data of household appliances and calculate the load index of the appliances according to the working status data;

[0036] Air quality monitoring module, used to collect indoor air quality parameters and calculate the air quality index;

[0037] A control unit is used to determine whether to start the fresh air system according to the load index, air quality index and preset threshold values, and calculate the air volume adjustment value;

[0038] Fresh air system, used to adjust the air volume according to the air volume adjustment value of the control unit;

[0039] The intelligent platform interface is used to provide remote control and intelligent notification functions, supporting users to adjust the working status of the fresh air system in real time according to their preferences.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The present invention relates to an innovative household appliance fresh air linkage control method, which has the function of intelligently adjusting the air volume of the fresh air system according to the working state of the household appliance, the load index and the indoor air quality parameters. By analyzing and processing the relationship between the load index and the air quality index in real time, the system can flexibly start the fresh air system and adjust the air volume according to the current indoor environmental conditions, thereby optimizing the indoor air quality while effectively saving energy consumption. In addition, the system also adopts an adaptive control strategy, which can achieve more accurate and personalized adjustment by learning the user's historical behavior preferences, thereby significantly improving the user's comfort experience and further enhancing the intelligence level of the entire system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0043] Figure 1 A flow chart of a fresh air linkage control method for household appliances;

[0044] Figure 2 The figure is a functional block diagram of a fresh air linkage control system for household appliances. DETAILED DESCRIPTION

[0045] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0046] See also Figure 1 This embodiment provides a fresh air linkage control method for household appliances, including:

[0047] Acquire the working status data of the household appliance, and calculate the load index of the appliance according to the working status data; wherein the working status data includes the start and stop status, load power, working mode and start and stop time of the household appliance;

[0048] At the same time, indoor air quality parameters are obtained, and an air quality index is calculated according to the indoor air quality parameters, and whether to start the fresh air system is determined according to the load index, the air quality index and its preset threshold value;

[0049] When the judgment result is that the fresh air system needs to be started, the adjustment value of the air volume is determined according to the air quality index and the load index; wherein the indoor air quality parameters include carbon dioxide concentration, temperature, humidity and PM2.5 value;

[0050] Adjust the air volume of the fresh air system in real time according to the difference in indoor and outdoor air quality and changes in the load index.

[0051] It can be seen that this embodiment proposes an innovative household appliance fresh air linkage control method, which aims to improve indoor air quality while reasonably allocating and utilizing appliance resources. The specific steps are as follows:

[0052] First, the working status data of household appliances are obtained through intelligent sensing devices. These data record in detail key information such as the start and stop status, load power, working mode, and start and stop time of household appliances;

[0053] At the same time, the system will simultaneously monitor relevant parameters of indoor air quality, including but not limited to carbon dioxide concentration, temperature, humidity and PM2.5 value, and then calculate a comprehensive air quality index based on these parameters;

[0054] When the calculated electrical load index exceeds the preset load index threshold and the indoor air quality parameters indicate that the air quality does not meet the health standard, the system will automatically start the fresh air system to improve the indoor air quality;

[0055] If it is determined that the fresh air system needs to be started, the system will further determine the air volume adjustment value of the fresh air system based on the current air quality index and load index to ensure that the air quality can be effectively improved while avoiding excessive energy consumption;

[0056] Finally, the system will continuously monitor the difference in indoor and outdoor air quality and changes in the load index, and dynamically adjust the air volume output of the fresh air system based on these real-time data to achieve more intelligent and energy-saving environmental control.

[0057] It is understandable that the advantage of this embodiment is that it not only realizes the intelligent linkage between household appliances and the fresh air system, but also fully considers the reasonable allocation and utilization of electrical resources, avoiding unnecessary energy waste. By accurately calculating the load index of the appliance, the system can more accurately determine when the fresh air system needs to be started and how much air volume is needed to meet the current indoor air quality requirements. This intelligent control method not only improves the indoor air quality, but also brings a more comfortable and energy-saving living environment to users. In addition, the method also has strong adaptability and flexibility, and can be personalized and adjusted according to different household appliances and indoor environments to meet the diverse needs of users.

[0058] In some embodiments of the present application, when calculating the load index of the electrical appliance according to the working status data, it includes:

[0059] The load index is calculated using the following formula:

[0060] ;

[0061] Where LI represents the load index; P i represents the load power of the i-th household appliance; t i represents the working time of the i-th household appliance; n represents the number of appliances.

[0062] It can be seen that this embodiment provides a specific and operable load index calculation method, which combines the load power and working time of household appliances and obtains a comprehensive load index through weighted summation or more complex algorithms. This calculation method can more intuitively reflect the overall energy consumption status of household appliances, and provides an important basis for the subsequent start-up of the fresh air system and air volume adjustment. At the same time, the calculation formula also takes into account the number of appliances, making the calculation of the load index more comprehensive and accurate.

[0063] It is understandable that the advantage of this embodiment is that it provides a simple and effective means of calculating the load index, so that the system can quickly and accurately evaluate the energy consumption of household appliances. This not only helps the fresh air system to make timely and reasonable startup decisions, but also effectively avoids the energy waste caused by the fresh air system starting when it is not needed, or improper air volume adjustment when it is needed. In addition, the design of the calculation formula also fully considers the energy consumption differences of different household appliances, so that the calculation results of the load index are closer to the actual situation, and the accuracy and efficiency of the fresh air system control are improved. In this way, this embodiment not only realizes the intelligent linkage between household appliances and the fresh air system, but also further improves the intelligence level of indoor air quality control, bringing users a more comfortable, healthy and energy-saving living environment.

[0064] In some embodiments of the present application, the air quality index is calculated by the following formula:

[0065] ;

[0066] Among them, AQI stands for air quality index; C i Indicates the real-time value of each air quality parameter, C i C CO2 , C T , C H , C PM2.5 , respectively, carbon dioxide concentration, temperature, humidity and PM2.5 concentration; C i,min and C i,max Indicates the minimum and maximum values ​​of the parameter; α i Indicates air quality parameter C i The weight coefficient of .

[0067] It can be seen that this embodiment also provides a specific and comprehensive method for calculating the air quality index. This method comprehensively considers multiple air quality parameters such as carbon dioxide concentration, temperature, humidity and PM2.5 concentration, and derives a comprehensive air quality index by assigning corresponding weight coefficients to each parameter. This calculation method can more comprehensively reflect the indoor air quality conditions and provide a more accurate basis for the subsequent fresh air system air volume adjustment. At the same time, the calculation formula also takes into account the minimum and maximum values ​​of each parameter, making the calculation of the air quality index more flexible and accurate.

[0068] It can be seen that the advantage of this embodiment is that the air quality index calculation method it provides is simple and effective, and can quickly and accurately evaluate the indoor air quality. This not only helps the fresh air system to make more accurate air volume adjustment decisions, but also effectively avoids energy waste or poor fresh air system effects caused by inaccurate air quality assessment. In this way, this embodiment further improves the intelligent level of indoor air quality control and brings users a healthier, more comfortable and energy-saving living environment.

[0069] In some embodiments of the present application, when the load index is greater than a preset threshold and the air quality index does not reach a predetermined standard, the fresh air system is started.

[0070] The setting of the load index threshold is based on the dynamic relationship between the load power of household appliances and environmental parameters, and the formula is expressed as follows:

[0071] ;

[0072] Among them, LI threshold represents the load index threshold; κ represents the adjustment coefficient.

[0073] It can be seen that the present embodiment further considers the setting of the load index threshold. This setting is not arbitrary, but is based on the dynamic relationship between the load power of household appliances and environmental parameters, and is finely adjusted by introducing the adjustment coefficient κ. The introduction of the adjustment coefficient κ makes the setting of the load index threshold more flexible and accurate, and can better meet the needs of different household appliances and indoor environments. In this way, the system can more accurately determine when the fresh air system needs to be started to avoid unnecessary energy waste and ensure that the indoor air quality is always maintained at a healthy level. This refined control strategy not only improves the efficiency of the fresh air system, but also brings users a more intelligent living environment experience.

[0074] It can be seen that the advantage of this embodiment is that it realizes the intelligent startup and air volume adjustment of the fresh air system by comprehensively considering the load conditions of household appliances and the indoor air quality. This intelligent control strategy not only ensures the continued health of indoor air quality, but also fully considers the rational allocation and utilization of energy. In addition, by introducing the specific calculation method of the load index and the air quality index, as well as the dynamic setting of the load index threshold, this embodiment further improves the accuracy and efficiency of the fresh air system control. These innovative designs and optimizations enable this embodiment to maximize the use of energy while providing a comfortable and healthy indoor environment, bringing users a more energy-saving and environmentally friendly life experience.

[0075] In some embodiments of the present application, the adjustment value of the air volume is determined according to the air quality index and the load index, and the adjustment value is calculated by the following calculation formula:

[0076] ;

[0077] Among them, V f represents the air volume adjustment value; η represents the air volume adjustment coefficient.

[0078] It can be seen that the present embodiment also provides a specific calculation method for the air volume adjustment value. This calculation method is based on the air quality index and the load index, and an accurate air volume adjustment value is obtained by introducing the air volume adjustment coefficient η. This method can ensure that the fresh air system can accurately adjust the air volume according to the real-time indoor air quality conditions and the load conditions of household appliances. This dynamic adjustment strategy not only ensures the continuous circulation and renewal of indoor air, but also avoids problems such as energy waste or poor indoor air quality caused by excessive or insufficient air volume. In this way, the present embodiment further improves the control accuracy and efficiency of the fresh air system, and brings users a more intelligent and personalized living environment experience.

[0079] It can be seen that the advantage of this embodiment is that it realizes intelligent control of the air volume output of the fresh air system by accurately calculating the air volume adjustment value. This control method not only ensures the continuous health of the indoor air quality, but also fully considers the reasonable allocation and utilization of energy. The introduction of the air volume adjustment coefficient η makes the calculation of the air volume adjustment value more flexible and accurate, and can better meet the needs of different household appliances and indoor environments. Through this dynamic adjustment strategy, the system can automatically adjust the air volume output of the fresh air system according to the real-time indoor air quality status and the load of household appliances to ensure that the indoor air quality is always maintained in the best state. This intelligent control method not only improves the use efficiency of the fresh air system, but also brings users a more comfortable and energy-saving living environment experience. In addition, the design of the calculation method also fully considers the differences between different household appliances and indoor environments, so that the air volume adjustment result is closer to the actual situation, and further improves the accuracy and efficiency of the fresh air system control.

[0080] In some embodiments of the present application, the air volume of the fresh air system is adjusted in real time according to the difference in indoor and outdoor air quality and the change in load index, and the following calculation formula is used for dynamic adjustment:

[0081] ;

[0082] Among them, V adjusted Indicates the adjusted air volume; α and β indicate the adjustment coefficient; AQI current Indicates the current air quality index, AQItarget Indicates the target air quality index, LI current Indicates the current load index, LI threshold Indicates the load index threshold.

[0083] It can be seen that this embodiment further considers the impact of indoor and outdoor air quality differences and load index changes on the air volume regulation of the fresh air system. By introducing adjustment coefficients α and β, as well as the current air quality index AQI current , Target Air Quality Index AQI target , Current load index LI current and load index threshold LI threshold The system uses multiple parameters such as , to achieve dynamic adjustment of the air volume of the fresh air system. This adjustment method not only takes into account the difference in indoor and outdoor air quality, but also combines the load conditions of household appliances, making the air volume adjustment of the fresh air system more accurate and intelligent. The introduction of adjustment coefficients α and β makes the air volume adjustment process more flexible and controllable, and can better adapt to changes in different environments and needs. In this way, the system can automatically adjust the air volume output of the fresh air system according to the real-time indoor and outdoor air quality conditions and the load conditions of household appliances to ensure that the indoor air quality is always maintained in the best state, while achieving the rational use of energy. This intelligent dynamic adjustment strategy not only improves the efficiency and accuracy of the fresh air system, but also brings users a more comfortable, healthy and energy-saving living environment experience.

[0084] It can be seen that the advantage of this embodiment is that by comprehensively considering the difference in indoor and outdoor air quality and the changes in the load index, the dynamic adjustment of the air volume of the fresh air system is achieved. This adjustment strategy is not only more sophisticated and intelligent, but also can better adapt to changes in different environments and needs. By introducing adjustment coefficients α and β, as well as multiple related air quality index and load index parameters, this embodiment makes the air volume adjustment process of the fresh air system more flexible and controllable. This intelligent dynamic adjustment method not only ensures the continued health of indoor air quality, but also fully considers the rational allocation and utilization of energy, bringing users a better quality living environment experience. In addition, the design of the adjustment strategy also fully considers the differences between different household appliances and indoor environments, so that the air volume adjustment results are closer to the actual situation, further improving the accuracy and efficiency of the fresh air system control.

[0085] In some embodiments of the present application, user behavior learning is performed through the home appliance control platform, and the air volume and working mode are automatically adjusted according to the indoor air quality preferences set by the user historically;

[0086] Update the control strategy by:

[0087] ;

[0088] Among them, S adaptive represents the adaptive control strategy, AQI historical Represents the historical air quality index, LI historical represents the historical load index, T historical Indicates historical temperature.

[0089] It can be seen that this embodiment further combines user behavior learning to optimize the control strategy of the fresh air system. Through the home appliance control platform, the system can learn the user's historical indoor air quality preferences, such as the user's preferred air quality index range, temperature, etc., so as to automatically adjust the air volume and working mode of the fresh air system to better meet the user's personalized needs. In addition, the system can also adjust the air quality index based on the historical air quality index AQI historical , Historical load index LI historical And the historical temperature T historical etc., continuously updating and improving the adaptive control strategy S adaptive This control strategy optimization method based on user behavior learning makes the control of the fresh air system more intelligent and personalized, and can better adapt to the living habits and preferences of different users. In this way, the system can not only provide more accurate and comfortable fresh air services, but also improve user satisfaction and loyalty, bringing greater commercial value to home appliance manufacturers and service providers.

[0090] It can be seen that the advantage of this embodiment is that it realizes personalized optimization of the control strategy of the fresh air system by combining user behavior learning. This optimization method not only takes into account the user's indoor air quality preferences, but also combines historical environmental data, making the control of the fresh air system more accurate and intelligent. Through the home appliance control platform, the system can learn the user's preferences and habits in real time, thereby automatically adjusting the air volume and working mode of the fresh air system to ensure that the indoor air quality always meets the user's expectations. In addition, the control strategy update method based on historical data enables the system to continuously learn and progress, and better adapt to the changes of different users and environments. This intelligent control strategy optimization method not only improves the efficiency and accuracy of the use of the fresh air system, but also brings a more comfortable and personalized living environment experience to users. Through this innovative design and optimization, this embodiment not only provides a healthy and comfortable indoor environment, but also realizes the maximum utilization of energy, bringing users a more energy-saving and environmentally friendly life experience.

[0091] In some embodiments of the present application, the adaptive control strategy is based on a deep learning algorithm, which analyzes user historical data to predict future trends in indoor air quality and load index, thereby automatically adjusting the working mode, air volume, and start and stop time of the fresh air system to optimize air quality and energy consumption; the adaptive control strategy includes:

[0092] Collect and store historical air quality index, load index and temperature data; analyze historical data through deep learning models to learn users' air quality preferences and load index changes; dynamically adjust air volume adjustment values ​​and working modes based on prediction results, and adjust the system's energy efficiency accordingly when the load index changes.

[0093] It can be seen that this embodiment also adopts an adaptive control strategy based on a deep learning algorithm, which further improves the intelligence level of the fresh air system. By collecting and storing historical air quality index, load index, temperature and other data, the system can use a deep learning model to analyze and learn these data, so as to grasp the user's air quality preferences and load index change rules. Based on these learning results, the system can predict the future trend of indoor air quality and load index changes, and automatically adjust the working mode, air volume and start and stop time of the fresh air system accordingly. This predictive adjustment method not only ensures that the indoor air quality is always maintained at the optimal value set by the user, but also can adjust the energy efficiency of the system in real time according to the change of the load index to maximize the use of energy. In this way, while providing a healthy and comfortable indoor environment, this embodiment also fully considers energy conservation and environmental protection, bringing users a more energy-saving and sustainable life experience. This adaptive control strategy based on a deep learning algorithm not only improves the intelligence and automation level of the fresh air system, but also brings users a more convenient and efficient use experience.

[0094] It can be seen that the advantage of this embodiment is that by introducing a deep learning algorithm, the predictive optimization of the fresh air system control strategy is achieved. This optimization method not only takes into account the learning and analysis of historical data, but also combines the future trend of indoor air quality and load index changes, making the control of the fresh air system more forward-looking and accurate. Through the analysis and learning of the deep learning model, the system can grasp the user's air quality preferences and load index change rules, thereby predicting future trends, and automatically adjust the working mode, air volume and start and stop time of the fresh air system accordingly. This predictive adjustment method not only improves the intelligence and automation level of the system, but also makes the control of the fresh air system more in line with the expectations and needs of users. In addition, the adaptive control strategy based on the deep learning algorithm can also adjust the energy efficiency of the system in real time according to the changes in the load index, maximize the use of energy, and bring users a more energy-saving and sustainable life experience. This innovative design and optimization not only improves the efficiency and accuracy of the use of the fresh air system, but also brings users a more comfortable, healthy and personalized living environment experience.

[0095] In some embodiments of the present application, the deep learning model uses a time series data prediction algorithm and is trained and optimized in combination with the dynamic relationship between air quality and load index; the training process includes:

[0096] The convolutional neural network is trained through historical data to identify the relationship between air quality index, load index and indoor environmental changes; the model is regularly optimized and calibrated.

[0097] It can be seen that this embodiment also uses a time series data prediction algorithm to train and optimize the deep learning model to further improve the intelligence and accuracy of the fresh air system. By using historical data to train the convolutional neural network, the system can more accurately identify the relationship between the air quality index, the load index and the indoor environmental changes. This prediction algorithm based on time series data enables the system to better grasp the changing laws of indoor air quality and load index, thereby providing users with more accurate and reliable fresh air services. In addition, this embodiment also emphasizes the importance of regular optimization and calibration of the model to ensure that the model can continue to maintain efficient and accurate prediction capabilities. Through this innovative design and optimization, this embodiment not only provides a healthy and comfortable indoor environment, but also realizes the continuous optimization and improvement of the fresh air system control strategy, bringing users a more intelligent and personalized user experience.

[0098] It can be seen that the advantage of this embodiment is that the deep learning model is trained and optimized through the time series data prediction algorithm, so as to realize the precision and efficiency of the fresh air system control strategy. This optimization method not only improves the prediction ability of the system, but also makes the control of the fresh air system more in line with the actual needs of users. By training the convolutional neural network with historical data, the system can accurately identify the relationship between the air quality index, the load index and the indoor environmental changes, so as to provide users with more reliable fresh air services. In addition, by regularly optimizing and calibrating the model, this embodiment can ensure that the system continues to maintain efficient and accurate prediction capabilities, and provide users with more stable and reliable fresh air control. This innovative design and optimization not only improves the intelligence level of the fresh air system, but also brings users a more comfortable, healthy and personalized living environment experience, further satisfying the pursuit of high-quality life by modern families.

[0099] This embodiment also provides a fresh air linkage control system for household appliances, comprising:

[0100] A load monitoring module, used to collect the working status data of household appliances and calculate the load index of the appliances according to the working status data;

[0101] Air quality monitoring module, used to collect indoor air quality parameters and calculate the air quality index;

[0102] A control unit is used to determine whether to start the fresh air system according to the load index, air quality index and preset threshold values, and calculate the air volume adjustment value;

[0103] Fresh air system, used to adjust the air volume according to the air volume adjustment value of the control unit;

[0104] The intelligent platform interface is used to provide remote control and intelligent notification functions, supporting users to adjust the working status of the fresh air system in real time according to their preferences.

[0105] It can be seen that this embodiment builds an efficient and intelligent fresh air linkage control system by integrating the load monitoring module, the air quality monitoring module, the control unit, the fresh air system and the intelligent platform interface. The load monitoring module can collect the working status data of household appliances in real time, accurately calculate the load index of the appliances, and provide key load information for the system. The air quality monitoring module is responsible for collecting indoor air quality parameters, calculating the air quality index, and ensuring that the system can timely understand the indoor air quality status. The control unit, as the core of the system, intelligently determines whether to start the fresh air system according to the load index, the air quality index and its preset threshold, and accurately calculates the air volume adjustment value to achieve indoor air quality optimization and energy saving. The fresh air system automatically adjusts the air volume according to the air volume adjustment value of the control unit to ensure that the indoor air quality is always maintained in the optimal state set by the user. The intelligent platform interface provides remote control and intelligent notification functions, allowing users to adjust the working status of the fresh air system in real time according to their preferences and enjoy a more convenient and personalized user experience.

[0106] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0107] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0108] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0109] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A fresh air linkage control method for household appliances, characterized in that: include: Acquire the working status data of the household appliance, and calculate the load index of the appliance according to the working status data; wherein the working status data includes the start and stop status, load power, working mode and start and stop time of the household appliance; At the same time, indoor air quality parameters are obtained, and an air quality index is calculated according to the indoor air quality parameters, and whether to start the fresh air system is determined according to the load index, the air quality index and its preset threshold value; When the judgment result is that the fresh air system needs to be started, the adjustment value of the air volume is determined according to the air quality index and the load index; wherein the indoor air quality parameters include carbon dioxide concentration, temperature, humidity and PM2.5 value; Adjust the air volume of the fresh air system in real time according to the difference in indoor and outdoor air quality and changes in the load index.

2. The fresh air linkage control method for household appliances according to claim 1, characterized in that: When calculating the load index of the electrical appliance according to the working status data, it includes: The load index is calculated using the following formula: ； Where LI represents the load index; P i represents the load power of the i-th household appliance; t i represents the working time of the i-th household appliance; n represents the number of appliances.

3. The fresh air linkage control method for household appliances according to claim 2, characterized in that: The air quality index is calculated using the following formula: ； Among them, AQI stands for air quality index; C i Indicates the real-time value of each air quality parameter, C i C CO2 , C T , C H , C PM2.5 , respectively, carbon dioxide concentration, temperature, humidity and PM2.5 concentration; C i,min and C i,max Indicates the minimum and maximum values ​​of the parameter; α i Indicates air quality parameter C i The weight coefficient of .

4. The fresh air linkage control method for household appliances according to claim 3, characterized in that: When the load index is greater than the preset threshold and the air quality index does not reach the preset standard, the fresh air system is started. The setting of the load index threshold is based on the dynamic relationship between the load power of household appliances and environmental parameters, and the formula is expressed as: ； Among them, LI threshold represents the load index threshold; κ represents the adjustment coefficient.

5. The fresh air linkage control method for household appliances according to claim 4, characterized in that: The adjustment value of the air volume is determined according to the air quality index and the load index, and the adjustment value is obtained by calculating the following formula: ； Among them, V f represents the adjustment value; η represents the air volume adjustment coefficient.

6. The fresh air linkage control method for household appliances according to claim 5, characterized in that: According to the difference in indoor and outdoor air quality and the change in load index, the air volume of the fresh air system is adjusted in real time, and the following calculation formula is used for dynamic adjustment: ； Where V adjusted Indicates the adjusted air volume; α and β indicate the adjustment coefficient; AQI current Indicates the current air quality index, AQI target Indicates the target air quality index, LI current Indicates the current load index, LI threshold Indicates the load index threshold.

7. The fresh air linkage control method for household appliances according to claim 6, characterized in that: The home appliance control platform learns user behavior and automatically adjusts the air volume and working mode based on the indoor air quality preferences set by the user in history; Update the control strategy by: ； Among them, S adaptive represents the adaptive control strategy, AQI historical Represents the historical air quality index, LI historical represents the historical load index, T historical Indicates historical temperature.

8. The fresh air linkage control method for household appliances according to claim 7, characterized in that: The adaptive control strategy is based on a deep learning algorithm. By analyzing historical user data, it predicts the future trend of indoor air quality and load index, and automatically adjusts the working mode, air volume, and start and stop time of the fresh air system to optimize air quality and energy consumption. The adaptive control strategy includes: Collect and store historical air quality index, load index and temperature data; Through deep learning models, historical data is analyzed to learn users’ air quality preferences and load index changes. Air volume adjustment values ​​and working modes are dynamically adjusted based on the prediction results, and the system’s energy efficiency is adjusted accordingly when the load index changes.

9. The fresh air linkage control method for household appliances according to claim 8, characterized in that: The deep learning model uses a time series data prediction algorithm and combines the dynamic relationship between air quality and load index for training and optimization; the training process includes: The convolutional neural network is trained through historical data to identify the relationship between air quality index, load index and indoor environmental changes; the model is regularly optimized and calibrated.

10. A fresh air linkage control system for household appliances, using the control method according to any one of claims 1 to 9, characterized in that: The fresh air linkage control system comprises: A load monitoring module, used to collect the working status data of household appliances and calculate the load index of the appliances according to the working status data; Air quality monitoring module, used to collect indoor air quality parameters and calculate the air quality index; A control unit is used to determine whether to start the fresh air system according to the load index, air quality index and preset threshold values, and calculate the air volume adjustment value; Fresh air system, used to adjust the air volume according to the air volume adjustment value of the control unit; The intelligent platform interface is used to provide remote control and intelligent notification functions, supporting users to adjust the working status of the fresh air system in real time according to their preferences.

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