Intelligent house indoor noise air quality monitoring and adjusting method

Abstract

The invention discloses an intelligent house indoor noise and air quality monitoring and adjusting method, which relates to the technical field of environment monitoring and comprises the following steps of: monitoring an indoor noise level and an air quality index in real time through a sensor; the intelligent adjusting system is linked with the air purifier through the window, noise control and air quality optimization are achieved on the basis of multi-objective optimized equipment adjustment and airflow management, and a healthy and comfortable living environment is provided. The system automatically adjusts equipment according to real-time data, energy waste is avoided, the service life of the equipment is prolonged, and fault and maintenance cost is reduced. By means of flexible adjustment, comfort is improved, health risks are reduced, energy efficiency is optimized, energy consumption and equipment abrasion are reduced, and therefore the long-term operation cost is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of environmental monitoring, and in particular to a method for monitoring and regulating indoor noise and air quality in an intelligent house. Background Art

[0002] The smart house indoor noise and air quality monitoring and adjustment system refers to the real-time monitoring of noise and air quality in the indoor environment through a set of integrated smart devices and sensors, and automatically adjusting environmental conditions based on the monitoring data to improve the living experience. Specifically, the system will continuously monitor the indoor noise level, automatically identify and adjust indoor audio equipment, window opening status or other noise sources to maintain a quiet environment. At the same time, it will also monitor harmful substances in the air (such as PM2.5, carbon dioxide, volatile organic compounds, etc.) and air humidity, and automatically adjust the air purifier, ventilation equipment or air conditioning system to ensure that the indoor air is fresh and healthy. This intelligent system usually relies on artificial intelligence algorithms and Internet of Things technology, which can automatically optimize the indoor environment according to user needs or preset scenarios, and improve living comfort and health.

[0003] The prior art has the following deficiencies:

[0004] In the existing smart house indoor noise and air quality monitoring and adjustment process, there is a conflict management problem between noise and air quality adjustment. Specifically, when the system adjusts noise control (such as window switches, air conditioning noise adjustment), it may conflict with air quality adjustment. For example, in order to reduce noise, the smart system may automatically close windows or reduce ventilation, but this will directly affect indoor air circulation and cause air quality to deteriorate. Long-term air stagnation and insufficient ventilation may cause increased carbon dioxide concentrations, reduce indoor oxygen levels, and may even cause harm to the health of residents, especially at night or in a closed environment for a long time. If such conflicts are not identified and adjusted in time, they may invisibly increase indoor air pollution and even cause health problems such as dizziness and difficulty breathing. In severe cases, they may cause discomfort in the indoor living environment or even a health crisis.

[0005] 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 constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a method for monitoring and regulating indoor noise and air quality in an intelligent house. The intelligent regulation system ensures that indoor noise is controlled while maintaining good air quality, and provides a healthy and comfortable living environment through the linkage control of windows and air purification equipment, equipment regulation based on multi-objective optimization, and indoor airflow management. The system adaptively adjusts equipment operation according to real-time data to avoid energy waste, extend equipment life, and reduce failure rate and maintenance costs. Through flexible adjustment, the system not only improves living comfort and reduces health risks, but also optimizes energy efficiency, reduces unnecessary energy consumption and equipment wear, thereby reducing long-term operating costs to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a method for monitoring and adjusting indoor noise and air quality of a smart house, comprising the following steps:

[0008] Real-time monitoring of indoor noise levels and air quality indicators through sensors;

[0009] Based on real-time monitoring data, use preset rules to evaluate current noise control needs and air quality adjustment needs to determine whether there is a conflict;

[0010] Adopting multi-objective optimization algorithm, the priority of noise control and air quality regulation is automatically calculated according to indoor environmental conditions, and the working state of home appliances is adjusted according to the optimization results to ensure the balance between noise control and air quality regulation;

[0011] According to the user's living habits and preset scenarios, the algorithm automatically adjusts the noise and air quality control strategy;

[0012] Based on feedback data, the adjustment algorithm is continuously optimized, and the control strategies and parameters are adjusted to ensure that the system can adaptively adjust according to long-term environmental changes and the actual needs of users, thereby improving long-term use effects and living health.

[0013] Preferably, the specific steps of real-time monitoring of indoor noise level and air quality index by sensors are as follows:

[0014] First, real-time data of the indoor environment is collected through various sensors;

[0015] When the sensor data is uploaded to the central control system, the data is pre-processed;

[0016] After data processing and analysis, the current environmental conditions are evaluated based on real-time feedback information and adjustment strategies are prepared;

[0017] Based on the analysis results and adjustment strategies, the working status of home appliances is automatically adjusted.

[0018] Preferably, according to the real-time monitoring data, using preset rules, evaluating the current noise control requirements and air quality adjustment requirements, and determining whether there is a conflict are as follows:

[0019] Get data collected by sensors in real time;

[0020] After data preprocessing is completed, the current noise and air quality control requirements are determined according to preset rules;

[0021] After evaluating the needs for noise control and air quality regulation, determine whether there is a conflict between the two based on the set priorities and actual environmental conditions;

[0022] When a conflict between noise control and air quality regulation is identified, it is resolved through a decision-making mechanism.

[0023] Preferably, a multi-objective optimization algorithm is used to automatically calculate the priority of noise control and air quality regulation according to indoor environmental conditions, and the working state of home appliances is adjusted according to the optimization results to ensure a balance between noise and air quality regulation. The specific steps are as follows:

[0024] Identify optimization objectives and constraints based on assessed noise and air quality regulation needs;

[0025] After determining the optimization objectives and constraints, a multi-objective optimization model is constructed;

[0026] Multi-objective optimization algorithms will be used to solve optimization problems;

[0027] Automatically adjust the working status of home appliances based on the optimal solution calculated by the optimization algorithm.

[0028] Preferably, the specific steps of automatically adjusting the noise and air quality adjustment strategy in combination with the algorithm according to the user's living habits and preset scenarios are as follows:

[0029] First, we define parameters related to user living habits and preset scenarios. In the model, we assign a set of weights to each scenario, indicating the priority of the scenario for noise and air quality. By inputting these living habit parameters, we can dynamically adjust the control priority of noise and air quality. The scenario fitness calculation expression is as follows:

[0030]

[0031] In the formula, S scene is the scene adaptability, W i is the scene weight, P i is the user preference score corresponding to the scenario, i is an index, indicating different adjustment requirements;

[0032] According to the calculated scene fitness function Sscene Dynamically adjust the priority of noise control and air quality regulation, using a weighted method to adjust the scene adaptability S scene Combined with the noise control requirements and air quality adjustment requirements, the adjustment priority is calculated as follows:

[0033]

[0034] Where P priority is the adjustment priority, N need is the noise control requirement, A need is the gas quality regulation demand, W noise is the priority weight of noise control, W air is the priority weight of air quality regulation;

[0035] Based on the priority calculation, an optimization model of the adjustment strategy is formulated according to the needs and priorities of the current scenario. The adjustment strategy of the system is represented by the following objective function:

[0036] F opt =α·(P priority ΔN adjust +β·ΔA adjust )

[0037] In the formula, F opt is the optimization objective function, ΔN adjust is the adjustment amplitude of noise control, ΔA adjust is the adjustment amplitude of air quality regulation, α and β are coefficients in the regulation strategy, which are used to adjust the contribution of noise and air quality regulation to the overall optimization strategy respectively;

[0038] Adjustments are made based on real-time feedback data to optimize the execution effect of the regulation strategy, based on the real-time monitored noise level N actual and air quality A actual The difference from the target value dynamically adjusts the parameters in the optimization model and makes adaptive adjustments through the feedback mechanism. If it is detected that the noise control adjustment fails to achieve the expected target, the adjustment amplitude ΔN will be increased. adjust The value is reduced, otherwise it decreases. The calculation expression is as follows:

[0039] ΔN adjust =γ·(N target -N actual )

[0041] ΔA adjust =δ·(A target -A actual )

[0042] Where N targetis the target noise value, N actual is the actual noise value, γ is the feedback coefficient of noise regulation, A target is the target air quality value, A actual is the actual air quality value, and δ is the feedback coefficient of air quality regulation.

[0043] Preferably, according to the feedback data, the adjustment algorithm is continuously optimized, and the control strategy and parameters are adjusted to ensure that the system can be adaptively adjusted according to long-term environmental changes and the actual needs of users, thereby improving the long-term use effect and living health. The specific steps are as follows:

[0044] The real-time data collected by the sensors include noise level, air quality index and comfort feedback. These data will be used for real-time analysis to evaluate the current adjustment effect. By calculating the weighted sum of the feedback data, it provides a basis for the subsequent optimization. The calculation expression is as follows:

[0045] D total =w 1 Dn oise +w 2 D air_quality +w 3 D comfort

[0046] Where D total is the comprehensive score, D noise is the noise level, D air_quality It is an air quality index, D comfort is the comfort feedback, w 1 、w 2 and w 3 The noise level, air quality index and comfort feedback are weight coefficients;

[0047] Based on the collected comprehensive rating feedback data D total , dynamically adjust the weight coefficient of the control strategy according to the current environment and user needs. Different modes will affect the priority of different adjustment targets. The calculation expression is as follows:

[0048] W noise =f(D total ,mode noise ), w air_quality =f(D total ,mode air_quality )

[0049] In the formula, w noise is the weight coefficient of noise control, mode noise is the noise requirement in the current environment mode, w air_quality is the weight coefficient of air quality regulation, modeair_quality is the demand for air quality under the current environmental mode, and f represents a function based on feedback data and mode;

[0050] After updating the weight coefficients, the control parameters of the equipment are calculated according to these new weights to optimize the adjustment scheme of noise and air quality. The adjustment parameters of each equipment are combined with the corresponding weights to calculate the optimal control scheme. The calculation expression is as follows:

[0051]

[0052] Where P adjust is the adjusted parameter value of each device, P i，noise and P j,air_quality represent the working parameters of the i-th device under noise control and air quality regulation, respectively, and n is the total number of devices;

[0053] After adjustment, the adjustment effect is continuously evaluated, and adaptive adjustments are made based on long-term environmental changes and user feedback. Indicators such as noise and air quality are monitored, and the algorithm is further optimized based on historical feedback data. The long-term optimization target calculation expression is as follows:

[0054]

[0055] In the formula, E long_term is the long-term optimization effect of the system, D noise (t) and D comfort (t) are the noise, air quality and comfort feedback data within the long-term time period T, respectively. F, G and H are the weight coefficients of noise, air quality and comfort within the long-term time period T, respectively.

[0056] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0057] The present invention uses intelligent windows, linkage control of air purification systems, equipment adjustment based on multi-objective optimization algorithms, and coordinated control of indoor airflow management and noise monitoring. The system can provide a comfortable and healthy living environment while ensuring that the indoor noise level is moderate and the air quality is good. Whether in an external environment with high noise or in a situation of poor air quality, the system can flexibly adjust window switches, air conditioners and air purification equipment to achieve the optimal balance of the indoor environment. For example, in the night sleep mode, the system will automatically close the windows to reduce external noise and start the air purifier to ensure fresh air; while in the daily activity mode, the system will automatically adjust the working state of the equipment according to real-time data to ensure air circulation without excessive noise. This intelligent adjustment greatly improves the comfort of the living environment, especially for families who are exposed to noise pollution or air pollution for a long time, it can effectively reduce the risk of health problems such as respiratory diseases, anxiety and stress. In addition, through the adaptive adjustment function, the system can automatically optimize according to the user's living habits and environmental changes, and provide personalized air quality and noise control solutions, so that residents can enjoy a healthy and comfortable living experience at any time.

[0058] The present invention greatly improves energy utilization efficiency and extends the service life of equipment through efficient energy consumption management and precise equipment control. In traditional environmental control methods, noise and air quality regulation are often carried out separately, which easily leads to energy waste. By adopting a multi-objective optimization algorithm, the system can intelligently adjust the operating state of the equipment while ensuring noise control and air quality improvement. For example, when the external environmental noise is low or the air quality is good, the system will automatically reduce the operating intensity of equipment such as air conditioners and air purifiers to reduce unnecessary energy consumption; when the air quality needs to be improved, the system will centrally adjust the relevant equipment to ensure the rational use of energy. This intelligent adjustment not only reduces energy waste, but also avoids frequent switching of equipment or long-term high-load operation, reduces equipment wear, and thus extends the service life of the equipment. At the same time, the system performs adaptive optimization according to environmental changes and user feedback, avoids excessive use of equipment, reduces equipment failure rate, and reduces maintenance and replacement costs. Ultimately, this optimized energy management and equipment control method not only improves living comfort, but also reduces operating costs during long-term use. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced 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.

[0060] Figure 1 The present invention is a method flow chart of the method for monitoring and regulating indoor noise and air quality in a smart house. DETAILED DESCRIPTION

[0061] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of 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 the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0062] The present invention provides Figure 1 The method for monitoring and regulating indoor noise and air quality of a smart house shown in the figure comprises the following steps:

[0063] Real-time monitoring of indoor noise levels and air quality indicators through sensors;

[0064] The specific steps for real-time monitoring of indoor noise levels and air quality indicators through sensors are as follows:

[0065] First, real-time data of the indoor environment is collected through various sensors;

[0066] The noise sensor detects the noise level in each area of ​​the room and records the current noise intensity in decibels (dB); the air quality sensor continuously monitors pollutants in the air, including carbon dioxide (CO 2 ), PM2.5, volatile organic compounds (VOCs), humidity and temperature. These sensors are connected to the central processing system and regularly transmit the measured data to the cloud or local processing unit. During the data transmission process, the system needs to ensure the real-time and accuracy of the data and avoid any delays or packet loss that may cause errors. In this way, the system can obtain and update the indoor environmental conditions at any time, providing reliable raw data for subsequent data processing and analysis.

[0067] When the sensor data is uploaded to the central control system, the data is pre-processed;

[0068] This process includes cleaning, formatting and denoising the data to ensure the accuracy and consistency of the data. For example, some abnormal data caused by sensor failure or external environmental interference (such as occasional noise peaks) may be filtered out. Next, the system will conduct a preliminary analysis of the processed data to determine the current noise level and air quality status. For example, if the indoor carbon dioxide concentration is detected to exceed the preset safety threshold, the system will identify the poor air quality; at the same time, if the noise exceeds the predetermined range, the system will also identify the risk of noise pollution. At this point, the system will generate a preliminary environmental assessment report based on these analysis results.

[0069] After data processing and analysis, the current environmental conditions are evaluated based on real-time feedback information and adjustment strategies are prepared;

[0070] This process requires the system to automatically calculate the priority of noise and air quality control based on environmental data. If the noise level is high and the air quality is poor, the system may choose to adjust both aspects at the same time; if only one aspect exceeds the standard, the system will decide the focus of the adjustment according to the preset rules. For example, in sleep mode, noise reduction is prioritized; while in daytime work mode, more attention may be paid to air quality adjustment. This decision-making process is based on a large amount of historical data, user habits, and environmental change models to ensure that the adjustment strategy matches actual needs, thereby optimizing the living experience.

[0071] Automatically adjust the working status of home appliances based on analysis results and adjustment strategies;

[0072] For noise control, the noise may be reduced by adjusting the opening status of windows, air conditioning wind speed, sound system or other noise sources; for air quality adjustment, the system will control the working status of air purifiers, the switch of ventilation equipment, humidity adjustment of humidifiers, etc. according to real-time data. At this time, the adjustment operation is not only real-time, but also optimized according to different scenarios. For example, at night, the system may choose to close the windows to reduce the interference of external noise, while maintaining air circulation through window ventilation or air conditioning system. In this process, the system will also continuously collect feedback data, fine-tune and optimize the adjustment strategy to ensure that the indoor environment is always in a healthy and comfortable state. Through this automated and real-time adjustment, the smart house system can effectively resolve the conflict between noise and air quality management, and provide a healthier and more comfortable living environment.

[0073] Based on real-time monitoring data, use preset rules to evaluate current noise control needs and air quality adjustment needs to determine whether there is a conflict;

[0074] Based on real-time monitoring data, using preset rules, evaluate the current noise control needs and air quality adjustment needs, and determine whether there is a conflict. The specific steps are as follows:

[0075] Get data collected by sensors in real time;

[0076] Including data from noise sensors (such as noise intensity) and data from air quality sensors (such as carbon dioxide concentration, PM2.5, humidity, etc.). The acquired data will first be preliminarily screened to remove invalid or abnormal data (such as sensor failure or abnormal values ​​of instantaneous fluctuations). This ensures that the data for subsequent processing is of high quality. For example, the system may filter out noise peaks or sudden changes in air quality caused by instantaneous sensor errors. The screened data provides a reliable basis for the system to ensure the accuracy of subsequent rule judgments and conflict analysis.

[0077] After data preprocessing is completed, the current noise and air quality control requirements are determined according to preset rules;

[0078] These rules are pre-set by the system and usually include threshold settings under different environmental conditions. For example, if the noise level exceeds a certain decibel value (such as 70dB), the system will consider the noise to be too high and need to be adjusted; if the indoor carbon dioxide concentration exceeds 400ppm, the air quality also needs to be adjusted. At this time, the system will quantify the needs of noise and air quality, and give the priority or weight of noise control needs and air quality adjustment needs respectively. The purpose of the preset rules is to trigger the system's adjustment mechanism under specific conditions to help the system make real-time decisions.

[0079] After evaluating the needs for noise control and air quality regulation, determine whether there is a conflict between the two based on the set priorities and actual environmental conditions;

[0080] If the system needs to close windows to reduce noise, closing windows may affect air circulation and reduce indoor air quality. At this time, the system needs to detect these potential conflicts, especially the negative impacts that need to be avoided, such as noise control may affect air flow or air quality. Through logical judgment, the system will comprehensively consider the current noise level and air quality to determine whether the priority needs to be adjusted. If the system detects a conflict, it will go to the next step and solve the problem through optimization algorithms.

[0081] When conflicts between noise control and air quality regulation are identified, resolve them through decision-making mechanisms;

[0082] The system will call a multi-objective optimization algorithm (such as a genetic algorithm or a particle swarm optimization algorithm) to perform conflict analysis and calculate the optimal balance between noise control and air quality regulation. At this point, the system needs to evaluate the effects of each adjustment scheme and find the best compromise between noise and air quality. For example, the system may slightly relax noise control while giving priority to air quality, or reduce noise without significantly affecting air quality. Ultimately, the system will adjust the working status of home appliances based on the optimization results to achieve a balanced environmental adjustment solution.

[0083] Adopting multi-objective optimization algorithm, the priority of noise control and air quality regulation is automatically calculated according to indoor environmental conditions, and the working state of home appliances is adjusted according to the optimization results to ensure the balance between noise control and air quality regulation;

[0084] Using a multi-objective optimization algorithm, the priority of noise control and air quality regulation is automatically calculated according to the indoor environmental conditions, and the working state of home appliances is adjusted according to the optimization results to ensure a balance between noise and air quality regulation. The specific steps are as follows:

[0085] Identify optimization objectives and constraints based on assessed noise and air quality regulation needs;

[0086] The system will first evaluate the priority of noise and air quality needs based on real-time data and preset rules. For example, if the indoor noise exceeds the set threshold, the system may set noise control as the priority target; if the air quality (such as carbon dioxide concentration, PM2.5) reaches the warning level, the air quality adjustment demand is set as the priority target. Based on these evaluation results, the system will build an optimization model, taking noise control and air quality adjustment as the two main optimization goals to ensure that the algorithm can make a reasonable balance between the two.

[0087] On this basis, the system will further limit the optimization goal according to some constraints. For example, if the system needs to keep the windows closed to reduce external noise, then the system must ensure that sufficient ventilation is still provided when the windows are closed; if the air purifier makes noise when running, it must consider how to balance its noise impact with the improvement of air quality. These constraints ensure that the optimization algorithm does not deviate from the reasonable operating range during execution.

[0088] After determining the optimization objectives and constraints, a multi-objective optimization model is constructed;

[0089] This model treats noise control and air quality regulation as two separate but related goals. Each goal has a corresponding objective function that quantifies the degree to which the goal is achieved. For example, the objective function for noise control may be the minimization of noise intensity (dB), while the objective function for air quality regulation may be the minimization of the concentration of harmful substances in the air (such as carbon dioxide or PM2.5).

[0090] In order to comprehensively consider these two goals, the system will introduce weighting coefficients or other methods to balance the relative importance of the two goals. The weighting coefficients can be dynamically adjusted according to real-time environmental conditions. For example, noise control may be given a higher weight in a quiet environment, while air quality regulation may be given a higher weight in poor air quality conditions. In this way, the system can flexibly adjust priorities according to the actual conditions of the environment and quantify the regulation effect through the objective function, thereby providing input for subsequent optimization algorithms.

[0091] Multi-objective optimization algorithms (such as particle swarm optimization algorithm, genetic algorithm, etc.) will be used to solve optimization problems;

[0092] At this stage, the algorithm will find the best operation plan based on the objective functions of noise control and air quality regulation, taking into account the priorities and constraints of both. The core of the optimization algorithm is to adjust the working status of different home appliances (such as adjusting window switches, air conditioning speed, air purifiers, etc.) through iterative calculations so that the results of the two objective functions reach the optimal balance.

[0093] During the optimization process, the system simulates the effects of different adjustment schemes and gradually optimizes the equipment configuration by evaluating the pros and cons of each scheme. The algorithm evaluates the effect of the scheme and updates the strategy in each iteration until a scheme is found that achieves the ideal level of noise control and air quality adjustment. At this point, the system outputs an optimal solution to determine which equipment needs to be turned on, adjusted to what working state, and how to maximize the balance between the two without causing environmental degradation.

[0094] Automatically adjust the working status of home appliances according to the optimal solution calculated by the optimization algorithm;

[0095] The system may adjust the opening and closing status of windows, the working intensity of ventilation equipment, the wind speed of air conditioners, and the operating mode of air purifiers to ensure that air quality is maintained or improved while achieving noise control. In the process of making these adjustments, the system will monitor the indoor noise and air quality levels in real time, obtain feedback data, and make dynamic adjustments based on the actual results.

[0096] During the execution process, the system will also adapt according to different usage scenarios (such as night mode, work mode, etc.) to ensure that the optimization strategy is consistent with actual needs. If the system finds that the adjustment effect of some devices is not good during operation, or there are new environmental changes, the algorithm will continue to make adaptive adjustments. In this way, the system can respond in real time and continuously optimize to ensure the best living environment for a long time.

[0097] According to the user's living habits and preset scenarios, the algorithm automatically adjusts the noise and air quality control strategy;

[0098] The specific steps of automatically adjusting the noise and air quality adjustment strategy based on the user's living habits and preset scenarios are as follows:

[0099] First, define parameters related to the user's living habits and preset scenarios. These parameters include but are not limited to the user's work and rest time, activity mode (such as sleep, work, entertainment, etc.), noise tolerance, air quality preference, etc. For example, assuming that the user is particularly sensitive to noise in sleep mode, but pays more attention to air quality in work mode. In the model, a set of weights is assigned to each scenario, indicating the priority of the scenario for noise and air quality. Suppose that in sleep mode, the weight of noise sensitivity is W sleep =0.7, the weight of air quality preference is W air_sleep =0.3, while in working mode, air quality has a higher priority, and the weight is set to W work =0.6 and W air_work =0.4, these parameters will be used to calculate the adjustment strategy for each scenario. By inputting these living habit parameters, the control priority of noise and air quality can be dynamically adjusted. The scenario fitness calculation expression is as follows:

[0100]

[0101] In the formula, S scene is the scene adaptability, indicating the priority of needs set according to the user's living habits and scenes, W i is the scene weight, P i is the user preference score corresponding to the scenario (such as noise tolerance, air quality requirements, etc.), which is used to represent the user's acceptance of noise and air quality in the current mode. i is an index, indicating different adjustment requirements;

[0102] According to the calculated scene fitness function S scene Dynamically adjust the priority of noise control and air quality regulation, using a weighted method to adjust the scene adaptability S scene Combined with the noise control requirements and air quality regulation requirements, the regulation priority is calculated as follows:

[0103]

[0104] Where P priority is the adjustment priority, N need is the noise control requirement, A need is the gas quality regulation demand, W noise is the priority weight of noise control, W air is the priority weight of air quality regulation;

[0105] During the calculation process, the system will dynamically adjust W according to different scenarios (such as sleep mode, work mode, etc.). noise and W air If it is sleep mode, the weight of noise control may be higher, while in work mode, the weight of air quality may be higher. In this way, the system will adjust the priority of noise and air quality control according to needs in each scene, thus providing a basis for subsequent adjustment decisions.

[0106] Based on the priority calculation, an optimization model of the adjustment strategy is formulated according to the needs and priorities of the current scene. This optimization model needs to consider how to balance noise control and air quality adjustment to ensure that the needs of users are met in different scenarios. The adjustment strategy of the system is represented by the following objective function:

[0107] F opt =α·(P priority ΔN adjust +β·ΔA adjust )

[0108] In the formula, F opt It is the optimization objective function used to evaluate and measure the effect of noise control and air quality regulation, ΔN adjust is the adjustment amplitude of noise control, indicating the degree to which the noise level needs to be changed to meet the current demand, ΔA adjust is the adjustment range of air quality regulation, which indicates the specific adjustment range that needs to be made to improve air quality. α and β are coefficients in the regulation strategy, which are used to adjust the contribution of noise and air quality regulation to the overall optimization strategy respectively.

[0109] The optimization objective function aims to minimize the error of the noise and air quality adjustment scheme and maximize the user's comfort, ensuring that the noise and air quality adjustment effects match the priority in different scenarios. adjust and ΔA adjust ), achieve balanced control of noise and air quality, and ultimately find an optimal regulation strategy.

[0110] Adjustments are made based on real-time feedback data to optimize the execution effect of the regulation strategy, based on the real-time monitored noise level N actual and air quality A actual The difference from the target value dynamically adjusts the parameters in the optimization model and makes adaptive adjustments through the feedback mechanism. If it is detected that the noise control adjustment fails to achieve the expected target, the adjustment amplitude ΔN will be increased. adjust The value is reduced, otherwise it decreases. The calculation expression is as follows:

[0111] ΔN adjust =γ·(Ntarget -N actual )

[0113] ΔA adjust =δ·(A target -A actual )

[0114] Where N target is the target noise value, which refers to the noise level that the system hopes to achieve, N actual is the actual noise value, which refers to the current noise level monitored by the system in real time. γ is the feedback coefficient of noise regulation, which indicates the sensitivity of the system to noise regulation. target is the target air quality value, which refers to the air quality level that the system hopes to achieve. actual is the actual air quality value, which refers to the current air quality level monitored by the system in real time; δ is the feedback coefficient of air quality regulation, which indicates the sensitivity of the system to air quality regulation;

[0115] Through this dynamic feedback mechanism, the system can adjust the regulation strategy in real time to ensure that the noise and air quality regulation always remain in the best state under the ever-changing indoor environment and user needs, thereby providing a more comfortable and healthy living experience.

[0116] Based on feedback data, continuously optimize the adjustment algorithm, adjust the control strategy and parameters to ensure that the system can be adaptively adjusted according to long-term environmental changes and actual needs of users, thereby improving long-term use effects and living health;

[0117] Based on the feedback data, the adjustment algorithm is continuously optimized, and the control strategy and parameters are adjusted to ensure that the system can be adaptively adjusted according to long-term environmental changes and the actual needs of users, thereby improving long-term use effects and living health. The specific steps are as follows:

[0118] The real-time data collected by the sensors include noise level, air quality index and comfort feedback. These data will be used for real-time analysis to evaluate the current adjustment effect. By calculating the weighted sum of the feedback data, it provides a basis for the subsequent optimization. The calculation expression is as follows:

[0119] D total =w 1 D noise +w 2 D air_quality +w 3 D comfort

[0120] , where D total is a comprehensive score, indicating the effect of current environmental regulation, D noise is the noise level, Dair_quality is an air quality indicator, D comfort is the comfort feedback, w 1 、w 2 and w 3 The noise level, air quality index and comfort feedback are weight coefficients;

[0121] By weighting and calculating these feedback data, the system can obtain a comprehensive score D total , as a comprehensive evaluation of the current regulatory effect. This comprehensive score D total It will be used as input parameter for subsequent adjustments.

[0122] Based on the collected comprehensive rating feedback data D total , dynamically adjust the weight coefficient of the control strategy according to the current environment and user needs. Different modes (such as sleep mode, work mode, etc.) will affect the priority of different adjustment targets. For example, when the noise is more serious, the weight coefficient of noise control will increase; when the air quality is poor, the weight coefficient of air quality adjustment will increase. The calculation expression is as follows:

[0123] w noise =f(D total , mode noise ), w air_quality =f(D total , mode air_quality )

[0124] , where w noise is the weight coefficient of noise control, which measures the urgency of noise control needs. noise It is the demand for noise in the current environment mode, such as work mode, sleep mode, entertainment mode, etc. Different modes correspond to different noise tolerances. air_quality is the weight coefficient of air quality regulation, which measures the priority of air quality control needs. air_quality is the demand for air quality in the current environmental mode, such as sleep mode, work mode, entertainment mode, etc., and f represents a function based on feedback data and mode;

[0125] Function f will be based on the current D total Adjust the weight coefficients, for example, if D total If the air quality is poor and the noise control requirement is high, then w noise and w air_quality The values ​​of will be adjusted appropriately, with priority given to ensuring a balance between the two.

[0126] After updating the weight coefficients, the control parameters of the equipment are calculated according to these new weights to optimize the adjustment scheme of noise and air quality. The adjustment parameters of each equipment are combined with the corresponding weights to calculate the optimal control scheme. The calculation expression is as follows:

[0127]

[0128] Where P adjust is the adjusted parameter value of each device, P i，noise and P j，air_quality They represent the working parameters of the i-th device under noise control and air quality regulation (such as wind speed, operation mode, on / off status, etc.), and n is the total number of devices;

[0129] After adjustment, the adjustment effect is continuously evaluated, and adaptive adjustments are made based on long-term environmental changes (such as seasonal changes) and user feedback. Indicators such as noise and air quality are monitored, and the algorithm is further optimized based on historical feedback data. The long-term optimization goal is to ensure that the system can provide stable adjustment effects based on long-term changes. The long-term optimization goal calculation expression is as follows:

[0130]

[0131] In the formula, E long_term is the long-term optimization effect of the system, D noise (t) and D comfort (t) are the noise, air quality and comfort feedback data within the long-term time period T, respectively. F, G and H are the weight coefficients of noise, air quality and comfort within the long-term time period T, respectively.

[0132] The weighted sum of these feedback data represents the long-term effect of system regulation, and the system will further adjust the control strategy and parameters based on these results.

[0133] Specific implementation method 1: linkage control of smart windows and air purification system

[0134] In this implementation, the smart window and the air purification system are linked to effectively resolve the conflict between noise and air quality. The system monitors the indoor noise level in real time through the noise sensor, and monitors the indoor air quality indicators (such as carbon dioxide concentration, PM2.5, volatile organic compounds, etc.) through the air quality sensor. When the noise exceeds the preset threshold, the smart window will automatically close to reduce the interference of external noise, and the air purification system will start immediately to refresh the air and maintain a good indoor air quality.

[0135] For example, if the external noise is very high (such as traffic noise or construction noise), the windows will automatically close based on the feedback from the noise sensor to ensure a quiet indoor environment. However, closing the windows may result in poor air circulation, which may affect the indoor air quality. At this time, the air purifier will start and automatically adjust the wind speed based on the feedback from the air quality sensor. The air purifier may increase the wind speed to minimize the concentration of harmful substances in the air and ensure the freshness of the indoor air. If the system detects that the air quality standard is still not met after the air purifier is running, the system will further adjust the status of the fan or ventilation equipment.

[0136] To ensure efficient operation, the system will automatically adjust the working status of window switches and air purifiers according to different time periods and usage scenarios. For example, in sleep mode, the system will choose to close the windows and turn on the air purifier to provide a quiet and healthy environment; in daily activities, the system may slightly open the windows to reduce noise and ensure indoor air circulation. Smart windows will adjust the opening degree according to external weather and noise conditions to ensure that indoor air is kept circulating while minimizing noise pollution.

[0137] In addition, the linkage mechanism between smart windows and air purifiers also takes into account the external air quality. If the external air quality is poor, the system will give priority to closing the windows and turning on the air purifier. The system will automatically adjust the working state of the equipment based on the changes in external air quality and indoor air quality. For example, if the external air quality is good, the system may choose to slightly open the windows to maintain air circulation and reduce noise; if the external air quality is poor, the system will close the windows and rely on the air purifier to maintain indoor air quality.

[0138] In order to further optimize the energy efficiency and regulation effect of the equipment, the system can also perform intelligent learning based on the user's living habits and environmental changes. For example, during long-term use, the system can adjust the regulation strategy by analyzing the user's behavior patterns (such as sleep time, time away from home, etc.). The system will not only automatically adjust the working status of windows and air purifiers during specific time periods, but also intelligently adjust according to different seasonal changes (such as different ventilation requirements in summer and winter). Through this intelligent adjustment, the system can efficiently ensure noise control while maximizing good air quality.

[0139] In summary, the linkage control of smart windows and air purification systems achieves a balance between noise and air quality. While reducing noise, the system also ensures that air quality does not deteriorate due to closed windows. Through real-time monitoring and intelligent adjustment, the living environment is always kept in the most suitable state. This solution not only effectively improves the quality of life, but also achieves efficient use of energy.

[0140] Specific implementation method 2: Smart home device adjustment based on multi-objective optimization

[0141] In the second implementation, the system intelligently adjusts the working status of home appliances based on a multi-objective optimization algorithm and real-time monitoring data of noise and air quality. Specifically, the system dynamically calculates the priority of noise control and air quality adjustment based on the feedback from noise sensors and air quality sensors, and determines the adjustment strategy of home appliances through a multi-objective optimization algorithm.

[0142] First, the system will evaluate the current environmental conditions by monitoring noise levels and air quality indicators in real time. If the indoor noise exceeds the set threshold (for example, 70dB), the system will set noise control as a high priority target; if the carbon dioxide concentration, PM2.5 concentration or other harmful substances in the air exceed the safety standard, the system will set air quality regulation as a priority target. By evaluating these real-time data, the system will dynamically adjust the priorities of the two goals. For example, if the noise is particularly high and the air quality is poor, the system will prioritize noise adjustment; if the air quality is particularly poor, the system may choose to increase ventilation or turn on the air purifier to prioritize improving air quality.

[0143] In actual applications, the system will adjust the priority of the device according to the set scene mode. For example, in the night sleep mode, the system will give priority to noise control, the windows may be completely closed, the air conditioner is adjusted to low wind speed, and the air purifier is set to run at low wind speed to ensure a quiet and healthy sleeping environment. In the working mode, the system may give priority to air quality, increase the intensity of ventilation and air purification, while keeping the noise within the range that does not interfere with work. The system can flexibly adjust the device status according to user needs and environmental changes to ensure the balance between noise and air quality in different scenarios.

[0144] The multi-objective optimization algorithm plays a core role in this process. The system continuously optimizes the balance between noise control and air quality regulation through the algorithm. The algorithm calculates the best combination of different regulation strategies based on real-time environmental data and preset rules, and continuously adjusts through the feedback mechanism. After each adjustment, the system will evaluate whether the current environment meets the expected goals based on the user's feedback data and the monitoring results of the sensors. Through continuous optimization, the system can adapt to the user's personalized needs and continuously improve the regulation accuracy during long-term operation to ensure a comfortable living environment.

[0145] In addition, the system can also automatically adjust strategies according to the external environment (such as weather changes). For example, when the external air quality is poor, the system may close the windows to enhance indoor air purification; when the external air quality is good, the system may choose to partially open the windows to reduce the burden of air conditioning and enhance indoor air circulation. This flexible adjustment method not only optimizes the living experience, but also improves energy efficiency. Ultimately, the system can find the best balance between noise and air quality through a multi-objective optimization algorithm to ensure that users enjoy a healthy and comfortable indoor environment.

[0146] In summary, the smart home device adjustment solution based on multi-objective optimization can achieve a balance between noise and air quality and improve the quality of life of residents by dynamically calculating the priority of noise and air quality and flexibly adjusting the working status of the equipment according to real-time environmental changes. This method is both flexible and efficient and can adapt to the needs of different time periods and environmental conditions.

[0147] Specific implementation method 3: Coordinated control of indoor airflow management and noise monitoring

[0148] The third implementation method solves the conflict between noise and air quality through the coordinated control of indoor airflow management and noise monitoring. This method combines the intelligent air conditioning system and HVAC (heating, ventilation and air conditioning) equipment, monitors the changes in noise and air quality in real time, and flexibly adjusts the operating status of air conditioning and HVAC equipment to ensure that the noise is reduced without affecting the air quality, and vice versa.

[0149] During implementation, the system will monitor the indoor noise level through noise sensors and the indoor PM2.5 and CO through air quality sensors. 2 When the system detects that the indoor noise is high, it will automatically adjust the air conditioner wind speed or change the air flow direction to reduce the noise output of the air conditioner; at the same time, to avoid deterioration of air quality, the system will enhance the air purification function, increase the running speed of the air purifier or enable the air circulation function to improve the indoor air quality. When the system detects that the air quality is poor (such as high carbon dioxide concentration), the ventilation mode of the air conditioner and HVAC system will automatically turn on to increase air circulation and ensure fresh indoor air.

[0150] The intelligent adjustment of air conditioning and HVAC systems not only considers noise control and air quality, but also the comfort of indoor temperature and humidity. The system monitors environmental changes in real time through integrated temperature and humidity sensors, and adjusts the equipment status in time according to changes in temperature and humidity. For example, in warm weather conditions, the system may prioritize turning on the air conditioner and increase the air flow to reduce the indoor temperature; while in cold weather, the air conditioner's wind speed may be automatically reduced to avoid excessive noise. The flexible adjustment of the system ensures that residents can enjoy the best indoor environment in different seasons and different time periods.

[0151] In addition, the system can also make intelligent adjustments based on the activity patterns of the occupants. For example, in work mode, the system may prioritize air quality to ensure sufficient fresh air; while in rest mode, the system will prioritize noise control to reduce the noise output of the air conditioner.

[0152] The system can continuously optimize indoor airflow and noise management under various environmental conditions, ultimately ensuring comfort and health without compromise.

[0153] In summary, the coordinated control of indoor airflow management and noise monitoring can achieve the best balance between noise and air quality by flexibly adjusting the working status of air conditioning and HVAC systems through real-time data analysis. This implementation not only improves environmental comfort, but also makes intelligent adjustments based on user activity patterns and environmental changes, ensuring a healthy and quiet living environment for a long time.

[0154] The present invention provides a comfortable and healthy living environment by means of linkage control of intelligent windows and air purification systems, equipment adjustment based on multi-objective optimization algorithms, and coordinated control of indoor airflow management and noise monitoring. The system can provide a comfortable and healthy living environment under the premise of ensuring that the indoor noise level is moderate and the air quality is good. Whether in an external environment with high noise or in the case of poor air quality, the system can flexibly adjust window switches, air conditioners and air purification equipment to achieve the optimal balance of the indoor environment. For example, in the night sleep mode, the system will automatically close the windows to reduce external noise and start the air purifier to ensure fresh air; while in the daily activity mode, the system will automatically adjust the working state of the equipment according to real-time data to ensure air circulation without excessive noise. This intelligent adjustment greatly improves the comfort of the living environment, especially for families who are exposed to noise pollution or air pollution for a long time, it can effectively reduce the risk of health problems such as respiratory diseases, anxiety and stress. In addition, through the adaptive adjustment function, the system can automatically optimize according to the user's living habits and environmental changes, and provide personalized air quality and noise control solutions, so that residents can enjoy a healthy and comfortable living experience at any time.

[0155] The present invention greatly improves energy utilization efficiency and extends the service life of equipment through efficient energy consumption management and precise equipment control. In traditional environmental control methods, noise and air quality regulation are often carried out separately, which easily leads to energy waste. By adopting a multi-objective optimization algorithm, the system can intelligently adjust the operating state of the equipment while ensuring noise control and air quality improvement. For example, when the external environmental noise is low or the air quality is good, the system will automatically reduce the operating intensity of equipment such as air conditioners and air purifiers to reduce unnecessary energy consumption; when the air quality needs to be improved, the system will centrally adjust the relevant equipment to ensure the rational use of energy. This intelligent adjustment not only reduces energy waste, but also avoids frequent switching of equipment or long-term high-load operation, reduces equipment wear, and thus extends the service life of the equipment. At the same time, the system performs adaptive optimization according to environmental changes and user feedback, avoids excessive use of equipment, reduces equipment failure rate, and reduces maintenance and replacement costs. Ultimately, this optimized energy management and equipment control method not only improves living comfort, but also reduces operating costs during long-term use.

[0156] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for monitoring and regulating indoor noise and air quality in a smart house, characterized in that: The following steps are involved: Real-time monitoring of indoor noise levels and air quality indicators through sensors; Based on real-time monitoring data, use preset rules to evaluate current noise control needs and air quality adjustment needs to determine whether there is a conflict; Adopting multi-objective optimization algorithm, the priority of noise control and air quality regulation is automatically calculated according to indoor environmental conditions, and the working state of home appliances is adjusted according to the optimization results to ensure the balance between noise control and air quality regulation; According to the user's living habits and preset scenarios, the algorithm automatically adjusts the noise and air quality control strategy; Based on feedback data, the adjustment algorithm is continuously optimized, and the control strategies and parameters are adjusted to ensure that the system can adaptively adjust according to long-term environmental changes and the actual needs of users, thereby improving long-term use effects and living health.

2. The method for monitoring and adjusting indoor noise and air quality of a smart house according to claim 1, characterized in that: The specific steps for real-time monitoring of indoor noise levels and air quality indicators through sensors are as follows: First, real-time data of the indoor environment is collected through various sensors; When the sensor data is uploaded to the central control system, the data is pre-processed; After data processing and analysis, the current environmental conditions are evaluated based on real-time feedback information and adjustment strategies are prepared; Based on the analysis results and adjustment strategies, the working status of home appliances is automatically adjusted.

3. The method for monitoring and adjusting indoor noise and air quality of a smart house according to claim 1, characterized in that: Based on real-time monitoring data, using preset rules, evaluate the current noise control needs and air quality adjustment needs, and determine whether there is a conflict. The specific steps are as follows: Get data collected by sensors in real time; After data preprocessing is completed, the current noise and air quality control requirements are determined according to preset rules; After evaluating the needs for noise control and air quality regulation, determine whether there is a conflict between the two based on the set priorities and actual environmental conditions; When a conflict between noise control and air quality regulation is identified, it is resolved through a decision-making mechanism.

4. The method for monitoring and regulating indoor noise and air quality of a smart house according to claim 1, characterized in that: Using a multi-objective optimization algorithm, the priority of noise control and air quality regulation is automatically calculated according to the indoor environmental conditions, and the working state of home appliances is adjusted according to the optimization results to ensure a balance between noise and air quality regulation. The specific steps are as follows: Identify optimization objectives and constraints based on assessed noise and air quality regulation needs; After determining the optimization objectives and constraints, a multi-objective optimization model is constructed; Multi-objective optimization algorithms will be used to solve optimization problems; Automatically adjust the working status of home appliances based on the optimal solution calculated by the optimization algorithm.

5. The method for monitoring and regulating indoor noise and air quality of a smart house according to claim 1, characterized in that: The specific steps of automatically adjusting the noise and air quality adjustment strategy based on the user's living habits and preset scenarios are as follows: First, we define parameters related to user living habits and preset scenarios. In the model, we assign a set of weights to each scenario, indicating the priority of the scenario for noise and air quality. By inputting these living habit parameters, we can dynamically adjust the control priority of noise and air quality. The scenario fitness calculation expression is as follows: In the formula, S scene is the scene fitness, W i is the scene weight, P i is the user preference score corresponding to the scenario, i is an index, indicating different adjustment requirements; According to the calculated scene fitness function S scene Dynamically adjust the priority of noise control and air quality regulation, using a weighted method to adjust the scene adaptability S scene Combined with the noise control requirements and air quality adjustment requirements, the adjustment priority is calculated using the following formula: Where P priority is the adjustment priority, N need is the noise control requirement, A need is the gas quality regulation demand, W noise is the priority weight of noise control, W air is the priority weight of air quality regulation; Based on the priority calculation, an optimization model of the adjustment strategy is formulated according to the needs and priorities of the current scenario. The adjustment strategy of the system is represented by the following objective function: F opt =α·(P priority ·ΔN adjust +β·ΔA adjust ) In the formula, F opt is the optimization objective function, ΔN adjust is the adjustment amplitude of noise control, ΔA adjust is the adjustment amplitude of air quality regulation, α and β are coefficients in the regulation strategy, which are used to adjust the contribution of noise and air quality regulation to the overall optimization strategy respectively; Adjustments are made based on real-time feedback data to optimize the execution effect of the regulation strategy, based on the real-time monitored noise level N actual and air quality A actual The difference from the target value dynamically adjusts the parameters in the optimization model and makes adaptive adjustments through the feedback mechanism. If it is detected that the noise control adjustment fails to achieve the expected target, the adjustment amplitude ΔN will be increased. adjust The value is reduced, otherwise it decreases. The calculation expression is as follows: ΔN adjust =γ·(N target -N actual ), A adjust =δ·(A target -A actual ) Where N target is the target noise value, N actual is the actual noise value, γ is the feedback coefficient of noise regulation, A target is the target air quality value, A actual is the actual air quality value, and δ is the feedback coefficient of air quality regulation.

6. The method for monitoring and regulating indoor noise and air quality of a smart house according to claim 1, characterized in that: Based on the feedback data, the adjustment algorithm is continuously optimized, and the control strategy and parameters are adjusted to ensure that the system can be adaptively adjusted according to long-term environmental changes and the actual needs of users, thereby improving long-term use effects and living health. The specific steps are as follows: The real-time data collected by the sensors include noise level, air quality index and comfort feedback. These data will be used for real-time analysis to evaluate the current adjustment effect. By calculating the weighted sum of the feedback data, it provides a basis for the subsequent optimization. The calculation expression is as follows: D total =w1D noise +w2D air-quality +w3D comfort Where D total is the comprehensive score, D noise is the noise level, D air-quality is an air quality indicator, D comfort is the comfort feedback, w1, w2 and w3 are the noise level, air quality index and comfort feedback are weight coefficients respectively; Based on the collected comprehensive rating feedback data D total ,The weight coefficient of the control strategy is dynamically adjusted according to the current environment and user needs. Different modes will affect the priority of different adjustment targets. The calculation expression is as follows: w noise =f(D total ,mode noise ),w air-quality =f(D total ,mode air-quality ) In the formula, w noise is the weight coefficient of noise control, mode noise is the noise requirement in the current environment mode, w air-quality is the weight coefficient of air quality regulation, mode air-quality is the demand for air quality under the current environmental mode, and f represents a function based on feedback data and mode; After updating the weight coefficients, the control parameters of the equipment are calculated according to these new weights to optimize the adjustment scheme of noise and air quality. The adjustment parameters of each equipment are combined with the corresponding weights to calculate the optimal control scheme. The calculation expression is as follows: Where P adjust is the adjusted parameter value of each device, P i,noise and P j,air-quality represent the working parameters of the i-th device under noise control and air quality regulation, respectively, and n is the total number of devices; After adjustment, the adjustment effect is continuously evaluated, and adaptive adjustments are made based on long-term environmental changes and user feedback. Indicators such as noise and air quality are monitored, and the algorithm is further optimized based on historical feedback data. The long-term optimization target calculation expression is as follows: In the formula, E long_term is the long-term optimization effect of the system, D noise (t) and D comfort (t) are the noise, air quality and comfort feedback data within the long-term time period T, respectively. F, G and H are the weight coefficients of noise, air quality and comfort within the long-term time period T, respectively.

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