Intelligent home management method based on machine learning optimization
By applying machine learning optimization methods in smart home systems, the limitations of traditional smart home management methods in comfort adjustment, energy efficiency optimization and fault detection are solved, and accurate prediction and control of the home environment is achieved, the intelligence and adaptability of the system is improved, and energy consumption is significantly reduced and personalized user experience is provided.
Patent Information
- Application Number
- CN202510225374.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-27
- Publication Date
- 2025-06-06
AI Technical Summary
Traditional smart home management methods have limitations in comfort adjustment, energy efficiency optimization, equipment coordination and fault detection, and cannot fully consider environmental changes and user needs. They lack real-time energy consumption monitoring and dynamic adjustment capabilities, resulting in waste of energy consumption and shortening of equipment service life.
Using machine learning-based optimization methods, through environmental data acquisition, feature extraction, machine learning model training and automated control, accurate prediction and control of the home environment are achieved, and comfort, energy-saving effects and equipment collaborative work are optimized.
It improves the intelligence and adaptability of smart home systems, realizes high-precision prediction and precise control of the home environment, significantly reduces energy consumption, extends the service life of the equipment, and provides a personalized user experience.
Smart Images
Figure CN120103701A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of smart home technology, and in particular to a smart home management method based on machine learning optimization. Background Art
[0002] With the rapid development of smart home technology, smart home systems have played an important role in improving the quality of life and energy efficiency of users. Smart home systems usually include the collection of environmental parameters such as temperature, humidity, light, and air quality, and obtain real-time data through different types of sensors in order to adjust the operating status of home devices. However, with the continuous increase in devices and the complexity of the usage environment, traditional smart home management methods face many technical challenges.
[0003] Most current smart home systems rely on preset control rules or simple automation logic for device management. Although this approach can achieve some basic functions, it has certain limitations in terms of comfort adjustment, energy efficiency optimization, device coordination, and fault detection. For example, in terms of comfort adjustment, traditional methods often fail to fully consider environmental changes and user's personalized needs; in terms of energy efficiency optimization, the lack of real-time monitoring and dynamic adjustment capabilities of equipment energy consumption leads to energy waste; and in terms of equipment fault detection, traditional methods cannot detect potential faults in real time or perform maintenance in advance, which affects the service life of the equipment and the stability of the system. With the advancement of artificial intelligence and machine learning technologies, smart home management is gradually moving towards a more intelligent and personalized direction. By using machine learning algorithms, changes in environmental data can be analyzed and predicted, and more accurate and flexible device adjustment decisions can be made based on this. At the same time, machine learning can also help realize functions such as collaborative optimization between devices, energy efficiency prediction and control, fault detection and prediction, etc., thereby greatly improving the intelligence level and operation efficiency of smart home systems. Summary of the invention
[0004] The purpose of the present invention is to provide a smart home management method based on machine learning optimization, which can achieve accurate prediction and control of the home environment by using environmental data collection, feature extraction, machine learning model training, automatic control and decision optimization, thereby optimizing comfort, energy saving effect and collaborative work of equipment. Specifically, the technical solution of the present invention includes the following key steps.
[0005] To achieve the above object, the present invention proposes the following technical solution: a smart home management method based on machine learning optimization, comprising the following steps:
[0006] Step 1: Environmental data collection and preprocessing: collect environmental data in real time through sensors installed in smart home devices. Environmental data includes temperature, humidity, light, and air quality. Preprocess these data, including missing value filling and outlier detection.
[0007] Step 2: Environmental data analysis and feature extraction: Analyze the collected data and extract meaningful features. These features can be used for subsequent machine learning model training. The features of multiple sensor data are X = {x 1 , x 2 , ..., x n}, reduce the dimension of high-dimensional data through feature extraction method, Z = XW, where W is the transformation matrix and Z is the eigenvector after dimension reduction. The goal of PCA is to make the data after dimension reduction retain the variance of the original data to the greatest extent;
[0008] Step 3: Use the machine learning model to predict changes in the home environment based on sensor data, predict the target variable through the regression model, and optimize the parameters of the regression model;
[0009] Step 4: Automatically adjust the operating status of home appliances based on the prediction results and optimize the control strategy through reinforcement learning;
[0010] Step 5: Smart home device collaboration and multi-objective optimization. Different home devices work together through the smart home platform. The multi-objective optimization algorithm can optimize the device collaboration plan based on multiple objectives and set multiple optimization objectives. 1 , O 2 , …, O m , optimized by weighted sum:
[0011] min λ 1 O 1 +λ 2 O 2 +…+λ m O m ;
[0012] O 1 , O 2 , …, O m : Multiple optimization objectives, λ 1 ,λ 2 , …, λ m : The weight of each goal;
[0013] Step 6: Dynamic energy consumption prediction and optimization. Use machine learning models to predict the energy consumption of equipment and dynamically adjust the equipment operation time according to the prediction results to achieve energy saving. Set the energy consumption prediction model as a regression model to predict energy consumption E:
[0014] E = f(X) = WX + b;
[0015] Use real-time feedback to adjust control strategies and minimize energy consumption:
[0016] Among them, E real is the actual energy consumption, E pred It is to predict energy consumption, and the loss function is optimized using mean square error;
[0017] Step 7: Real-time fault detection and adaptive maintenance, using machine learning algorithms to detect and predict equipment faults, using anomaly detection models to promptly detect equipment faults and perform adaptive maintenance, and using support vector machines for fault detection:
[0018] x: sample data to be tested, α i : Lagrange multiplier of support vector machine, y i : Label of training sample, K(xi,x): kernel function, used to calculate the similarity between samples, b: bias term, f(x): classification result, normal or faulty;
[0019] Step 8: Continuous learning and system updates. The system can continuously learn based on new environmental data and user behavior, and continuously optimize control strategies and equipment collaboration solutions.
[0020] Furthermore, in the present invention, the process of missing value filling and outlier detection in step 1 is as follows:
[0021] Assume that the data collected by the sensor is X = {x 1 , x 2 , ..., x n}, where x i For the data collected by the i-th sensor, the interpolation method is used to fill in the missing values:
[0022]
[0023] For outlier detection, the standard deviation method is used: x i : the i-th sensor data, μ: the mean of the data, σ: the standard deviation of the data, z i : Standardized data, if |z i ∣>3 is considered an outlier.
[0024] Furthermore, in the present invention, the specific process of step 3 is as follows:
[0025] Prediction model training based on machine learning, using machine learning algorithms, builds a prediction model, predicts changes in the home environment based on sensor data, predicts target variable Y through sensor data X, and uses a regression model: Y = WX + b, where W is the weight vector and b is the bias term. The mean square error is used as the loss function to optimize the model: L: loss function, Y i : actual value, Predicted value, n: sample size;
[0026] Optimize W and b by gradient descent: Where η is the learning rate, is the gradient of the loss function with respect to the weights.
[0027] Furthermore, in the present invention, the specific process of step 4 is as follows:
[0028] Automated control and decision optimization: According to the prediction results of the trained model output, the operating status of home appliances is automatically adjusted. Through machine learning, the control strategy is optimized to adjust the operation of the equipment to maximize comfort or energy saving. The model output is Y pred , the control strategy u(t) is determined by the prediction results and preset rules, and reinforcement learning (such as Q learning) is used to optimize the control strategy:
[0029]
[0030] Q(s t , a t ): In state s t Next, perform action a t The expected return, α: learning rate, controls the influence of new information on Q value update, r t+1 : In state s t Execute action a t The immediate reward after, γ: discount factor, measuring the importance of future rewards, a′: the action in the next state.
[0031] Furthermore, in the present invention, in step 8, an incremental learning algorithm is used to update the model parameters:
[0032] Among them, θ t is the model parameter at time t, η is the learning rate, L(θ t ) is the loss function.
[0033] Beneficial effects: The technical solution of this application has the following technical effects:
[0034] 1. Improved intelligence and adaptability: By introducing machine learning algorithms, smart home systems can dynamically adjust based on real-time environmental data. The system can not only adapt to changing environmental conditions, but also continuously learn and optimize based on changes in user behavior to ensure that optimal comfort and energy efficiency are always provided.
[0035] 2. Improved environmental prediction accuracy: The machine learning model can achieve high-precision prediction of changes in the home environment through in-depth analysis of sensor data. With more accurate environmental data prediction, the system can make decisions in advance, reduce manual intervention, and achieve more precise control.
[0036] 3. Optimization of energy-saving effects: The dynamic energy consumption prediction and optimization mechanism can predict the energy consumption of equipment based on real-time data, and dynamically adjust the operating time and status of the equipment, significantly reducing unnecessary energy consumption and improving the overall energy efficiency of the system.
[0037] 4. Balance between comfort and energy saving: By optimizing control strategies through machine learning, smart home systems can minimize energy consumption while maintaining user comfort. For example, during periods of low user activity, the system can automatically reduce the power of air conditioning or heating equipment to save energy while maintaining a comfortable living environment.
[0038] 5. Optimization of equipment coordination: The multi-objective optimization algorithm enables the various devices in the smart home to work better in coordination, avoiding conflicts between devices and unnecessary waste of resources. The system can reasonably schedule the devices according to multiple optimization goals (such as temperature, humidity, energy consumption, etc.), thereby achieving the overall optimal operation effect.
[0039] 6. Real-time fault detection and adaptive maintenance: Through machine learning algorithms to predict equipment faults and monitor them in real time, the system can promptly detect potential equipment problems, conduct early warnings and fault diagnosis, and automatically adjust equipment operation or perform maintenance. This not only improves the stability and reliability of the equipment, but also extends the service life of the equipment and reduces the maintenance costs of users.
[0040] 7. Continuous learning and optimization: The system has the ability to learn incrementally and can continuously optimize its control strategy and equipment collaboration plan based on new environmental data and user behavior. This continuous learning capability ensures that the system can always make the most appropriate adjustments based on the changing environment and user needs, improving the long-term effectiveness of the system.
[0041] 8. Personalized user experience: Machine learning algorithms can be personalized according to the behavior patterns and preferences of different users to ensure that each user can experience the smart home service that best suits their needs. For example, the temperature, lighting and other home device settings can be adjusted according to the user's living habits to provide a more customized living experience.
[0042] 9. Enhanced system stability and scalability: By using machine learning models to optimize the management of smart homes, the system can maintain good stability and response speed in the face of an increase in the types and number of devices. At the same time, the machine learning framework has strong scalability and can easily integrate more devices and functional modules to meet the development needs of future smart homes.
[0043] The smart home management method based on machine learning optimization of the present invention not only improves the automation and intelligence level of the system, but also greatly improves the performance in many aspects such as energy efficiency, comfort, equipment coordination, and fault prevention, ultimately providing users with a more intelligent, energy-saving and comfortable living experience.
[0044] It should be appreciated that all combinations of the foregoing concepts, as well as additional concepts described in greater detail below, may be considered to be part of the inventive subject matter of the present disclosure, provided such concepts are not mutually inconsistent.
[0045] The foregoing and other aspects, embodiments and features of the present invention can be more fully understood from the following description in conjunction with the accompanying drawings. Other additional aspects of the present invention, such as the features and / or beneficial effects of the exemplary embodiments, will be apparent from the following description or learned from the practice of the specific embodiments according to the present invention. BRIEF DESCRIPTION OF THE DRAWINGS
[0046] The drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component shown in various figures may be represented by the same reference numeral. For clarity, not every component is labeled in every figure. Embodiments of various aspects of the present invention will now be described by way of example and with reference to the accompanying drawings, in which:
[0047] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION
[0048] In order to better understand the technical content of the present invention, specific embodiments are cited and described as follows in conjunction with the accompanying drawings. Various aspects of the present invention are described in this disclosure with reference to the accompanying drawings, in which many illustrative embodiments are shown. The embodiments of the present disclosure are not necessarily defined to include all aspects of the present invention. It should be understood that the various concepts and embodiments introduced above, as well as those described in more detail below, can be implemented in any of many ways, because the concepts and embodiments disclosed in the present invention are not limited to any implementation. In addition, some aspects disclosed in the present invention can be used alone, or in any appropriate combination with other aspects disclosed in the present invention.
[0049] like Figure 1As shown, the present invention provides a smart home management method based on machine learning optimization, comprising the following steps:
[0050] Step 1: Environmental data collection and preprocessing: collect environmental data in real time through sensors installed in smart home devices. Environmental data includes temperature, humidity, light, and air quality. Preprocess these data, including missing value filling and outlier detection.
[0051] Step 2: Environmental data analysis and feature extraction: Analyze the collected data and extract meaningful features. These features can be used for subsequent machine learning model training. The features of multiple sensor data are X = {x 1 , x 2 , ..., x n}, reduce the dimension of high-dimensional data through feature extraction method, Z = XW, where W is the transformation matrix and Z is the feature vector after dimension reduction. The goal of PCA is to make the data after dimension reduction retain the variance of the original data to the greatest extent; use principal component analysis (PCA) to reduce the dimension of high-dimensional data and extract the most representative features. By reducing the number of features, the efficiency and stability of subsequent algorithms are improved. PCA can effectively reduce redundant features, improve computational efficiency, and reduce the risk of overfitting by maximizing the variance retention of data.
[0052] Step 3: Use the machine learning model to predict changes in the home environment based on sensor data, predict the target variable through the regression model, and optimize the parameters of the regression model;
[0053] Step 4: Automatically adjust the operating status of home appliances based on the prediction results and optimize the control strategy through reinforcement learning;
[0054] Step 5: Smart home device collaboration and multi-objective optimization. Different home devices work together through the smart home platform. The multi-objective optimization algorithm can optimize the device collaboration plan based on multiple objectives and set multiple optimization objectives. 1 , O 2 , …, O m , optimized by weighted sum:
[0055] min λ 1 O 1 +λ 2 O 2 +…+λ m O m ;
[0056] O 1 , O 2 , …, O m : Multiple optimization objectives, λ 1 ,λ 2 , …, λ m:The weight of each goal; balance optimization among multiple goals through the weighted sum method. For example, in a smart home system, it may be necessary to balance comfort and energy saving goals. Multi-objective optimization can make trade-offs between different goals, provide a global optimal solution, and adapt to complex real-world needs
[0057] Step 6: Dynamic energy consumption prediction and optimization. Use machine learning models to predict the energy consumption of equipment and dynamically adjust the equipment operation time according to the prediction results to achieve energy saving. Set the energy consumption prediction model as a regression model to predict energy consumption E:
[0058] E = f(X) = WX + b;
[0059] Use real-time feedback to adjust control strategies and minimize energy consumption: Use regression models to predict the energy consumption of home appliances to implement energy-saving control strategies. Regression models are simple and effective, and can predict energy consumption based on actual data, facilitating subsequent control optimization.
[0060] Among them, E real is the actual energy consumption, E pred It is to predict energy consumption, and the loss function is optimized using mean square error; by minimizing the energy consumption prediction error, the energy consumption management strategy is optimized. The simple mean square error loss function can effectively quantify the prediction error, and then reduce the error through the optimization algorithm to provide accurate energy consumption prediction.
[0061] Step 7: Real-time fault detection and adaptive maintenance, using machine learning algorithms to detect and predict equipment faults, using anomaly detection models to promptly detect equipment faults and perform adaptive maintenance, and using support vector machines for fault detection:
[0062] x: sample data to be tested, α i : Lagrange multiplier of support vector machine, y i : Label of training samples, K(xi, x): kernel function, used to calculate the similarity between samples, b: bias term, f(x): classification result, normal or faulty; Support vector machine (SVM) is used for fault detection to determine whether the equipment is faulty in real time. SVM can effectively process high-dimensional data, is suitable for fault detection problems, and also shows good performance for small sample data.
[0063] Step 8: Continuous learning and system updates. The system can continuously learn based on new environmental data and user behavior, and continuously optimize control strategies and equipment collaboration solutions.
[0064] Furthermore, in the present invention, the process of missing value filling and outlier detection in step 1 is as follows:
[0065] Assume that the data collected by the sensor is X = {x 1 , x 2 , ..., x n}, where x i For the data collected by the i-th sensor, the interpolation method is used to fill in the missing values:
[0066] The above steps are used for data filling, especially for dealing with missing values in sensor data. Missing sensor data is filled by taking the average of adjacent data. Linear interpolation is simple and effective, especially when data is missing and there is no obvious nonlinear relationship, and it can effectively recover missing data.
[0067] For outlier detection, the standard deviation method is used: x i : the i-th sensor data, μ: the mean of the data, σ: the standard deviation of the data, z i : Standardized data, if |z i ∣>3 is considered an outlier. The standard deviation method is used to detect outliers in the data (i.e. points exceeding 3 times the standard deviation). Through standardization, the data is converted to zero mean and unit variance, which can detect extreme abnormal data and help discover sensor failures or data collection errors.
[0068] Furthermore, in the present invention, the specific process of step 3 is as follows:
[0069] Prediction model training based on machine learning, using machine learning algorithms, build a prediction model, predict changes in the home environment based on sensor data, predict the target variable Y through sensor data X, use the regression model: Y = WX + b, where W is the weight vector and b is the bias term, and use the linear regression model to predict the target variable. Learn the weight W and bias b through training data, minimize the error, and make accurate predictions. Simple, effective, and easy to understand. Linear regression can provide intuitive prediction results, and the training and calculation process is relatively efficient.
[0070] Use mean squared error as the loss function to optimize the model: L: loss function, Y i : actual value, Prediction value, n: number of samples; optimize model parameters by minimizing mean square error. Mean square error is a commonly used loss function in regression problems. It is intuitive and easy to optimize, and can effectively measure the prediction performance of the model.
[0071] Optimize W and b by gradient descent: Where η is the learning rate, is the gradient of the loss function with respect to the weights.
[0072] Furthermore, in the present invention, the specific process of step 4 is as follows:
[0073] Automated control and decision optimization: According to the prediction results of the trained model output, the operating status of home appliances is automatically adjusted. Through machine learning, the control strategy is optimized to adjust the operation of the equipment to maximize comfort or energy saving. The model output is Y pred , the control strategy u(t) is determined by the prediction results and preset rules, and reinforcement learning (such as Q learning) is used to optimize the control strategy:
[0074]
[0075] Q(s t , a t ): In state s t Next, perform action a t The expected return, α: learning rate, controls the influence of new information on Q value update, r t+1 : In state s t Execute action a t The immediate reward after the action, γ: discount factor, measuring the importance of future rewards, a′: action in the next state. Use the Q-learning reinforcement learning algorithm to optimize the decision-making strategy of the smart home system to maximize long-term returns. Control home devices by learning the optimal action in each state. Q-learning does not require knowing the environment model (i.e. the dynamics of the environment) in advance, and gradually learns the optimal strategy through interaction with the environment. It is very suitable for complex and dynamic control problems.
[0076] Furthermore, in the present invention, in step 8, an incremental learning algorithm is used to update the model parameters:
[0077] Among them, θ t is the model parameter at time t, η is the learning rate, L(θ t ) is the loss function. The incremental learning algorithm is used to continuously optimize the model and update the parameters to adapt it to new environmental data or user behavior. The incremental learning algorithm can dynamically update the model without retraining the entire model, and can gradually improve the accuracy of prediction and control in long-term use.
[0078] Also give the following examples
[0079] The user lives in a smart residential community equipped with a variety of smart devices, including smart air conditioners, smart lights, smart curtains, smart door locks, and environmental sensors (temperature, humidity, air quality, light intensity, etc.). The family hopes that the system can not only automatically adjust the environmental comfort, but also optimize energy consumption and ensure safety.
[0080] Target:
[0081] Dynamically adjust the home environment to improve comfort.
[0082] Achieve energy-saving control and reduce household energy consumption.
[0083] Improve the adaptability of home systems through machine learning.
[0084] Continuously optimize smart home management strategies through real-time data analysis and model training.
[0085] Implementation steps
[0086] Step 1: Environmental data collection and preprocessing
[0087] Equipment deployment: Install temperature and humidity sensors, air quality sensors, light intensity sensors and smart electricity meters. All data is transmitted to the home's central control unit via Wi-Fi.
[0088] Data collection:
[0089] Assuming that data is collected once every minute, the collected environmental data includes:
[0090] Temperature (°C), humidity (%), air quality (PM2.5, CO2), light intensity (Lux), power consumption (kWh);
[0091] Data preprocessing: If some sensors are offline or malfunction, linear interpolation is used to fill in the missing data.
[0092] Detect abnormal values. For example, if the temperature value exceeds the preset threshold (such as exceeding 40°C or below 5°C), it is considered abnormal and needs to be removed or marked.
[0093] Step 2: Environmental data analysis and feature extraction
[0094] Feature extraction:
[0095] The collected data is reduced in dimension and the most important features are extracted using PCA (Principal Component Analysis). The features extracted from the raw data include:
[0096] Comprehensive index of ambient temperature and humidity, maximum and minimum light intensity, air quality index (AIQ), comprehensive PM2.5, CO 2 Concentration, etc.
[0097] Data Dimensionality Reduction:
[0098] Through PCA, high-dimensional data (such as multidimensional data from various sensors) can be reduced to 2-3 main components, reducing redundant information and improving the efficiency of subsequent models.
[0099] Step 3: Machine learning-based prediction model training
[0100] Model selection: A linear regression model is used to predict indoor temperature and humidity changes, taking into account influencing factors including external weather and the operating status of internal equipment (such as air conditioners, humidifiers, etc.).
[0101] For energy consumption prediction, a regression model is used to predict the energy consumption of each device and is trained based on time, usage patterns, and environmental conditions such as temperature and humidity.
[0102] Model training: Use the data from the past week for training. The training data includes environmental data (temperature, humidity, light, etc.) and the historical status of the equipment (the on / off status of air conditioners, lights, etc.).
[0103] Train regression model: Y pred =WX+b, where Y pred is the predicted temperature, humidity or energy consumption, X is the input feature, W is the weight, and b is the bias.
[0104] Optimization goal: Minimize the mean square error (MSE) to optimize the model:
[0105] Step 4: Automated control and decision optimization
[0106] Control strategy: Based on the predicted environmental data and energy consumption data, the smart home system optimizes the on / off status of air conditioners, lights, curtains and other equipment through Q-learning. For example, in summer, the system dynamically adjusts the temperature setting of the air conditioner based on the predicted indoor temperature and humidity. When the outdoor temperature is high, the air conditioner will start in advance to maintain indoor comfort.
[0107] Q-learning optimization: state s t Contains information such as the current environment's temperature, humidity, and air quality. Action a t To adjust the status of air conditioners, lights, curtains and other equipment (such as turning on / off, adjusting temperature, brightness, etc.).
[0108] The system gradually optimizes the strategy through reinforcement learning algorithm (Q-learning):
[0109] Among them, r t+1 It is an instant reward. For example, a positive reward is given when the system reaches the preset comfort or energy-saving target, and a negative reward is given otherwise.
[0110] Step 5: Smart home device collaboration and multi-objective optimization
[0111] Multi-objective optimization: The system optimizes multiple objectives simultaneously: improving comfort (temperature, humidity, etc.), saving energy (reducing energy consumption), and ensuring safety (preventing excessive energy consumption, equipment failure, etc.). For example, a trade-off between comfort and energy consumption: min λ 1 O 1 +λ 2 O 2 +…+λ m O m Among them, O 1 represents comfort index (such as temperature deviation), O 2 represents energy consumption, λ 1 ,λ 2 is the weight coefficient of the target. By adjusting the weight, the system can dynamically adjust the balance between comfort and energy saving.
[0112] Equipment collaboration:
[0113] Synergy between smart curtains and air conditioners: When the light intensity is high, the curtains close automatically to reduce the entry of solar radiation and reduce the burden on the air conditioner.
[0114] Based on the predicted indoor temperature, the system automatically adjusts the operation of curtains and air conditioning to ensure energy saving and comfort.
[0115] Step 6: Dynamic energy consumption prediction and optimization
[0116] Energy consumption prediction: The system predicts the energy consumption of each device through a regression model. The model will be dynamically adjusted according to user behavior and environmental changes (such as temperature changes, air conditioning usage time, etc.): E = WX + b, where E is the energy consumption prediction value, X is the feature (such as temperature, humidity, equipment operating status, etc.), W is the weight of the regression model, and b is the bias.
[0117] Energy consumption optimization: Based on energy consumption forecasts, the system can adjust the on / off time of devices in a timely manner. For example, when the user is not at home, the air conditioner automatically lowers the temperature and the lights automatically turn off, minimizing energy waste.
[0118] Step 7: Real-time fault detection and adaptive maintenance
[0119] Fault detection: The system uses support vector machines (SVM) to monitor the operating status of the equipment in real time and perform fault detection: Among them, x: sample data to be tested, α i : Lagrange multiplier of support vector machine, y i: Label of training samples, K(xi, x): kernel function, used to calculate the similarity between samples, b: bias term, f(x): classification result, normal or faulty; Support vector machine (SVM) is used for fault detection to determine whether the equipment is faulty in real time. SVM can effectively process high-dimensional data, is suitable for fault detection problems, and also shows good performance for small sample data.
[0120] Maintenance strategy: The system can detect equipment failure risks in advance (such as dirty air conditioning filters, light bulbs that are about to break, etc.) and notify users to perform maintenance to prevent equipment failures from affecting the home environment.
[0121] Step 8: Continuous learning and system updates
[0122] Incremental learning: The system continuously collects new data and continuously optimizes the model through incremental learning. Over time, the system can more accurately predict user needs and environmental changes, thereby continuously improving decision-making efficiency.
[0123] Among them, among them, θ t is the model parameter at time t, η is the learning rate, L(θ t ) is the loss function. The incremental learning algorithm is used to continuously optimize the model and update the parameters to adapt it to new environmental data or user behavior. The incremental learning algorithm can dynamically update the model without retraining the entire model, and can gradually improve the accuracy of prediction and control in long-term use.
[0124] The results and effects are as follows: Improved comfort: The system can automatically adjust parameters such as temperature and humidity according to user preferences and environmental changes to ensure the comfort of the user's living environment. Energy-saving effect: By intelligently controlling the on and off status of air conditioners, curtains, lights and other equipment, the system significantly reduces the energy consumption of the family, with an average energy saving effect of 20%-30%. Intelligent and adaptive capabilities: The system can predict user needs based on real-time data and historical behavior, gradually optimize home management strategies, and make the system more intelligent and adaptive. Fault warning: Equipment failures are detected and warned in a timely manner, avoiding potential equipment damage or energy waste.
[0125] Through this smart home system optimized based on machine learning, users not only enjoy a more comfortable living environment, but also achieve significant energy-saving effects and can better manage the maintenance and use of home appliances.
[0126] Although the present invention has been disclosed as above with preferred embodiments, it is not intended to limit the present invention. A person with ordinary knowledge in the technical field to which the present invention belongs may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the definition of the claims.
Claims
1. A smart home management method based on machine learning optimization, characterized in that: The steps include: Step 1: Environmental data collection and preprocessing: collect environmental data in real time through sensors installed in smart home devices. Environmental data includes temperature, humidity, light, and air quality. Preprocess these data, including missing value filling and outlier detection. Step 2: Environmental data analysis and feature extraction: Analyze the collected data and extract meaningful features. These features can be used for subsequent machine learning model training. The features of multiple sensor data are X = {x1, x2, ..., x n }, reduce the dimension of high-dimensional data through feature extraction method, Z = XW, where W is the transformation matrix and Z is the eigenvector after dimension reduction. The goal of PCA is to make the data after dimension reduction retain the variance of the original data to the greatest extent; Step 3: Use the machine learning model to predict changes in the home environment based on sensor data, predict the target variable through the regression model, and optimize the parameters of the regression model; Step 4: Automatically adjust the operating status of home appliances based on the prediction results and optimize the control strategy through reinforcement learning; Step 5: Smart home device collaboration and multi-objective optimization. Different home devices work together through the smart home platform. The multi-objective optimization algorithm can optimize the device collaboration plan based on multiple objectives and set multiple optimization objectives O1, O2, ..., O m , optimized by weighted sum: min λ1O1+λ2O2+…+λ m The m ; O1, O2, ..., O m : Multiple optimization objectives, λ1, λ2, …, λ m : The weight of each goal; Step 6: Dynamic energy consumption prediction and optimization. Use machine learning models to predict the energy consumption of equipment and dynamically adjust the equipment operation time according to the prediction results to achieve energy saving. Set the energy consumption prediction model as a regression model to predict energy consumption E: E = f(X) = WX + b; Use real-time feedback to adjust control strategies and minimize energy consumption: Among them, E real is the actual energy consumption, E pred It is to predict energy consumption, and the loss function is optimized using mean square error; Step 7: Real-time fault detection and adaptive maintenance, using machine learning algorithms to detect and predict equipment faults, using anomaly detection models to promptly detect equipment faults and perform adaptive maintenance, and using support vector machines for fault detection: x: sample data to be tested, α i : Lagrange multiplier of support vector machine, y i : Label of training sample, K(xi,x): kernel function, used to calculate the similarity between samples, b: bias term, f(x): classification result, normal or faulty; Step 8: Continuous learning and system updates. The system can continuously learn based on new environmental data and user behavior, and continuously optimize control strategies and equipment collaboration solutions.
2. The smart home management method based on machine learning optimization according to claim 1, characterized in that: The process of missing value filling and outlier detection in step 1 is as follows: Assume that the data collected by the sensor is X = {x1, x2, ..., x n }, where x i For the data collected by the i-th sensor, the interpolation method is used to fill in the missing values: For outlier detection, the standard deviation method is used: x i : the i-th sensor data, μ: the mean of the data, σ: the standard deviation of the data, z i : Standardized data, if |z i ∣>3 is considered an outlier.
3. The smart home management method based on machine learning optimization according to claim 1, characterized in that: The specific process of step 3 is as follows: Prediction model training based on machine learning, using machine learning algorithms, builds a prediction model, predicts changes in the home environment based on sensor data, predicts target variable Y through sensor data X, and uses a regression model: Y = WX + b, where W is the weight vector and b is the bias term. The mean square error is used as the loss function to optimize the model: L: loss function, Y i : actual value, Predicted value, n: sample size; Optimize W and b by gradient descent: Where η is the learning rate, is the gradient of the loss function with respect to the weights.
4. The smart home management method based on machine learning optimization according to claim 1, characterized in that: The specific process of step 4 is as follows: Automated control and decision optimization: According to the prediction results of the trained model output, the operating status of home appliances is automatically adjusted. Through machine learning, the control strategy is optimized to adjust the operation of the equipment to maximize comfort or energy saving. The model output is Y pred , the control strategy u(t) is determined by the prediction results and preset rules, and reinforcement learning (such as Q learning) is used to optimize the control strategy: Q(s t , a t ): In state s t Next, perform action a t The expected return, α: learning rate, controls the influence of new information on Q value update, r t+1 : In state s t Execute action a t The immediate reward after, γ: discount factor, measuring the importance of future rewards, a′: the action in the next state.
5. The smart home management method based on machine learning optimization according to claim 1, characterized in that: In step 8, the incremental learning algorithm is used to update the model parameters: Among them, θ t is the model parameter at time t, η is the learning rate, L(θ t ) is the loss function.