Smart home control system and method

Abstract

The invention discloses a smart home control system and method, and relates to the field of energy utilization, and the system comprises the following components: a data analysis module which receives data transmitted by an energy monitoring module, carries out the real-time analysis and processing, carries out the deep mining and analysis of the household energy use condition through an advanced algorithm and model, and carries out the real-time analysis of the data; according to the method, equipment and time periods with high energy consumption are identified, a basis is provided for optimizing an energy use mode, the mode and trend of household energy use are analyzed through an analysis module support vector machine (SVM), the energy use modes of different time periods, different equipment and different family members are effectively identified, and the future energy use condition is predicted; therefore, an energy-saving strategy and a control measure can be accurately formulated, meanwhile, adjustment and optimization can be carried out through a cross validation method so as to adapt to different data and scenes, and the prediction accuracy and reliability are improved.

Description

Technical Field

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

[0002] With the rapid development of science and technology, smart home systems have become an important part of modern family life. Smart home control systems use residential housing as a platform and use integrated wiring technology, network communication technology, security technology, automatic control technology, and audio and video technology to efficiently integrate facilities related to home life, and build an efficient control and management system for residential facilities and family schedule affairs to enhance the intelligence, safety, convenience, and comfort of the home, and realize an environmentally friendly and energy-saving comprehensive smart home network control system platform.

[0003] However, although traditional smart home management systems can achieve remote control and automated management of equipment, they cannot automatically adjust operating modes and parameters according to actual needs. They have problems with low efficiency and high energy consumption in terms of energy use, and cannot combine renewable energy utilization technology to convert renewable energy into electricity or heat energy to provide families with clean and sustainable energy supply. At the same time, traditional home energy management methods often lack real-time monitoring and analysis of home energy usage, further exacerbating the problem of energy waste.

[0004] Therefore, how to develop a smart home control system and method that can monitor and analyze household energy usage in real time, optimize energy usage patterns, and reduce energy consumption has become an important direction for the current development of smart home technology. However, the research on energy conservation in smart home systems on the market is still insufficient. Therefore, developing a smart home control system and method that can comprehensively realize the above-mentioned characteristics has important social value and environmental significance for improving household energy efficiency, reducing energy costs, and promoting the popularization of sustainable development and environmental protection concepts. Summary of the invention

[0005] The purpose of the present invention is to make up for the deficiencies of the prior art and to provide a smart home control system and method. The system and method integrate advanced energy monitoring, data analysis, intelligent control and renewable energy utilization technologies to achieve real-time monitoring, analysis and optimal control of household energy usage, reduce energy consumption, improve energy utilization efficiency, and promote green and low-carbon operation of the home environment.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a smart home management system, characterized in that the system includes the following components:

[0007] Energy monitoring module: used to collect and transmit real-time monitoring of household electricity and water consumption data. Through sensors installed in key locations in the home, energy consumption data and water consumption data of electrical equipment are obtained in real time, and the data is transmitted to the data analysis module;

[0008] Data analysis module: Receives data transmitted by the energy monitoring module, performs real-time analysis and processing, and uses advanced algorithms and models to conduct in-depth mining and analysis of household energy usage, identify high-energy-consuming devices and time periods, and provide a basis for optimizing energy usage patterns;

[0009] Control module: Based on the results of the data analysis module, the control algorithm generates corresponding control instructions to intelligently control home devices. This module can automatically adjust the operation mode and parameters of the equipment to achieve efficient use of energy;

[0010] Renewable energy utilization module: Combine renewable energy utilization technology to achieve green and low-carbon operation of the home environment. By installing renewable energy power generation equipment, renewable energy is converted into electrical energy or thermal energy to provide a clean and sustainable energy supply for the family.

[0011] Furthermore, the specific steps of the energy monitoring module are:

[0012] (1) Data collection: installing sensors to monitor the energy consumption and water consumption data of electrical equipment in real time;

[0013] (2) Data transmission: The collected data is transmitted to the data analysis module via wired or wireless means.

[0014] Furthermore, the data analysis module specifically comprises the following steps:

[0015] (1) Data reception and processing: Receive real-time data transmitted from the energy monitoring module, and use algorithms and models to clean, organize, and analyze the received data to identify devices and time periods with high energy consumption;

[0016] (2) Model identification and result output: By comparing historical data and real-time data, the patterns and trends of household energy use are analyzed, the analysis results are output in a visual form, and transmitted to the control module.

[0017] Furthermore, the control module specifically comprises the following steps:

[0018] (1) Control algorithm application: Receive optimization instructions from the data analysis module and generate corresponding control strategies through the control algorithm according to the instructions;

[0019] (2) Device control and feedback: Send control instructions to home devices through the smart home network, automatically adjust the operating modes and parameters of the devices, monitor the device response, and ensure that the control instructions are correctly executed.

[0020] Furthermore, the specific steps of the renewable energy utilization module are as follows:

[0021] (1) Device installation: Install renewable energy power generation devices at suitable positions in the home;

[0022] (2) Energy conversion and supply: Use these devices to convert renewable energy into electrical energy or thermal energy, connect the converted clean energy to the home power grid, and provide electricity or thermal energy for the home;

[0023] (3) Monitoring and maintenance: Real-time monitor the operating status of renewable energy devices to ensure their normal operation, and perform regular maintenance.

[0024] Furthermore, the analysis module analyzes the patterns and trends of home energy use by using the Support Vector Machine (SVM). The formula of the Support Vector Machine (SVM) is: The standardization formula for data preprocessing is: Among them, x is the original data, μ is the mean value, σ is the standard deviation, and normalization is to scale the data to the interval [0, 1]. For the training of the SVM model, the objective function needs to be constructed first. The formula of the objective function is: Among them, C is the penalty coefficient, ξi is the slack variable, and the constraint condition is added: y i (w·x i +b)≥1-ξ i ,ξ i ≥0, Through the Lagrange multiplier method, introduce the Lagrangian function and solve its dual problem to construct the dual objective function. The formula is: Add the constraint condition: 0≤a i ≤C, Use the quadratic programming solver to solve the above optimization problem to obtain the value of α, and then solve the model parameters: Among them, j is the subscript that satisfies 0 <α j <C. Use the established model to predict the energy use situation. For the new data point x, its predicted label is: Predicted label = sign(w·x + b). According to the prediction results, analyze the different patterns of home energy use and observe the changing trends of these patterns over time.

[0025] Furthermore, the control module helps the smart home control system identify and analyze device usage patterns and energy consumption through the K-Means clustering algorithm. The calculation formula of the K-Means clustering algorithm is: for a data point x = (x1, x2, ..., x n ) and cluster centroid c i =(c i1 , c i2 , …, c in ), Euclidean distance d(x, c i ) is calculated as: For cluster i, the set of data points it contains is S i ={x1, x2, ..., x mi}, where m i is the number of data points in cluster i, and the new cluster centroid c′ i The calculation formula is: Specifically, if cluster i has n features, the new cluster centroid c′ i =(c′ i1 , c′ i2 ,…,c′ in ), where each coordinate c′ ij The calculation formula is: x j is the jth eigenvalue of data point x. The iterative process will repeat the steps of assigning data points to clusters and updating centroids until a certain stopping condition is met. The common stopping condition can be expressed as: Centroid change threshold: If the change in the centroids of all clusters is less than a predetermined threshold, the iteration is stopped. The centroid change can be measured by the Euclidean distance between the new and old centroids, and its calculation formula is: Maximum number of iterations: If the algorithm has performed T iterations (T is the preset maximum number of iterations), the iteration is stopped regardless of whether the centroid converges. The expression is: current number of iterations ≥ T. Through calculation, the K-Means clustering algorithm can accurately calculate the distance between the data point and the cluster centroid, and update the position of the cluster centroid, thereby realizing the clustering analysis of the usage pattern and energy consumption of smart home devices, and then generating effective control strategies.

[0026] Furthermore, the renewable energy utilization module converts renewable energy into electrical energy or thermal energy through renewable energy power generation equipment to provide clean and sustainable energy supply for households. At the same time, it cooperates with the energy monitoring module, the data analysis module and the control module to use the energy demand prediction algorithm and the energy supply optimization algorithm to adjust the operating parameters of the renewable energy equipment and the energy allocation strategy to achieve the optimal energy supply. The energy demand prediction algorithm uses a linear regression mathematical model to calculate various factors that affect energy demand. The calculation formula is: y=β0+β1x1+β2x2+…+βn x n , where y is the predicted energy demand, x1,x2,…,x n are various factors that affect energy demand, β0,β1,…,β n is the regression coefficient. For the energy supply optimization algorithm, a linear optimization mathematical model is used, and its objective function is: z = c1x1 + c2x2 + ... + c n x n , the constraints can be expressed as a series of inequalities:

[0027] Among them, x1,x2,…,x n are decision variables, c1, c2, …, c n is the coefficient of the objective function, a ij and b i It is the coefficient and limit value of the constraint conditions. According to the results of the optimization calculation, the operating parameters of the renewable energy equipment and the energy allocation strategy are adjusted to achieve the optimal energy supply.

[0028] Furthermore, the renewable energy utilization module includes an energy storage unit for storing the converted renewable energy to provide supplementary energy during peak energy demand periods or when renewable energy supply is insufficient.

[0029] Furthermore, the specific steps of the method are:

[0030] (1) Energy data monitoring: Start the energy monitoring module to monitor household electricity and water consumption data in real time.

[0031] (2) Data transmission and analysis: The monitored data is transmitted to the data analysis module in real time for in-depth analysis and processing.

[0032] (3) Control instruction generation: Based on the data analysis results, the control module generates corresponding control instructions and sends the control instructions to home devices through the smart home network to achieve intelligent control of the devices.

[0033] (4) Renewable energy utilization: Start the renewable energy utilization module and use renewable energy equipment to provide clean energy for the home.

[0034] (5) Monitoring and feedback: Continuously monitor household energy usage and equipment operating status to ensure the stable operation of the entire system, and continuously optimize the control strategy based on feedback data.

[0035] Compared with the prior art, the control system and method of the smart home have the following beneficial effects:

[0036] 1. The present invention uses the renewable energy power generation equipment of the renewable energy utilization module to convert renewable energy into electrical energy or thermal energy, thereby providing clean and sustainable energy supply for households and achieving energy self-sufficiency and low-carbon operation. At the same time, it cooperates with the energy monitoring module, the data analysis module and the control module to use the energy demand prediction algorithm and the energy supply optimization algorithm to adjust the operating parameters of the renewable energy equipment and the energy allocation strategy, thereby achieving comprehensive monitoring and optimized control of household energy usage, reducing energy consumption and improving energy efficiency.

[0037] 2. The present invention monitors and analyzes energy usage in real time through an energy monitoring module and a data analysis module, accurately identifies high-energy-consuming equipment and time periods through a K-Means clustering algorithm, and then generates corresponding control instructions through an intelligent control algorithm to automatically adjust the operating mode and parameters of the equipment, thereby not only avoiding energy waste, but also making energy use more efficient and achieving a significant reduction in energy consumption.

[0038] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 The present invention is a flow chart of a control system and method for a smart home. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Example 1

[0043] A control system and method for a smart home, the system comprising the following components:

[0044] Energy Monitoring Module: It is used for the collection and transmission of real-time monitoring data of household electricity and water consumption. Through sensors installed at various key positions in the home, it can obtain the energy consumption data and water consumption data of electrical appliances in real time, and transmit the data to the Data Analysis Module;

[0045] Data Analysis Module: Receives the data transmitted by the Energy Monitoring Module, conducts real-time analysis and processing. Through advanced algorithms and models, it deeply mines and analyzes the household energy usage situation, identifies high-energy-consuming devices and time periods, and provides a basis for optimizing the energy usage pattern;

[0046] Control Module: According to the results of the Data Analysis Module, it generates corresponding control instructions through control algorithms to intelligently control household devices. This module can automatically adjust the operation mode and parameters of the devices to achieve efficient utilization of energy;

[0047] Renewable Energy Utilization Module: Combines renewable energy utilization technologies to achieve green and low-carbon operation of the home environment. By installing renewable energy power generation devices, it converts renewable energy into electrical energy or heat energy to provide clean and sustainable energy supply for the home.

[0048] The Analysis Module analyzes the patterns and trends of household energy usage by using Support Vector Machine (SVM). The formula of Support Vector Machine (SVM) is: The standardization formula for data preprocessing is: Among them, x is the original data, μ is the mean value, σ is the standard deviation, and normalization is to scale the data to the interval [0, 1]. For the training of the SVM model, first, the objective function needs to be constructed. The formula of the objective function is: Among them, C is the penalty coefficient, ξi is the slack variable, and the constraint condition is added: y i (w·x i +b)≥1 - ξ i , ξ i ≥0, Through the Lagrange multiplier method, the Lagrangian function is introduced, and its dual problem is solved to construct the dual objective function. The formula is: The constraint condition is added: 0≤a i ≤C, Use a quadratic programming solver to solve the above optimization problem to obtain the value of α, and then solve the model parameters: Among them, j is an arbitrary subscript that satisfies 0 < α j < C. Use the established model to predict the energy usage situation. For a new data point x, its predicted label is: Predicted label = sign(w·x + b). According to the prediction results, analyze different patterns of household energy usage and observe the changing trends of these patterns over time.

[0049] The support vector machine (SVM) analysis module analyzes the patterns and trends of household energy use, effectively identifies the energy use patterns of different time periods, different devices and different family members, and predicts future energy use, which helps to accurately formulate energy-saving strategies and control measures. At the same time, it can be adjusted and optimized through cross-validation methods to adapt to different data and scenarios, thereby improving the accuracy and reliability of predictions.

[0050] Example 2

[0051] The system consists of the following components:

[0052] Energy monitoring module: used to collect and transmit real-time monitoring of household electricity and water consumption data. Through sensors installed in key locations in the home, energy consumption data and water consumption data of electrical equipment are obtained in real time, and the data is transmitted to the data analysis module;

[0053] Data analysis module: Receives data transmitted by the energy monitoring module, performs real-time analysis and processing, and uses advanced algorithms and models to conduct in-depth mining and analysis of household energy usage, identify high-energy-consuming devices and time periods, and provide a basis for optimizing energy usage patterns;

[0054] Control module: Based on the results of the data analysis module, the control algorithm generates corresponding control instructions to intelligently control home devices. This module can automatically adjust the operation mode and parameters of the device to achieve efficient use of energy;

[0055] Renewable energy utilization module: Combine renewable energy utilization technology to achieve green and low-carbon operation of the home environment. By installing renewable energy power generation equipment, renewable energy is converted into electrical energy or thermal energy to provide clean and sustainable energy supply for the family.

[0056] The control module helps the smart home control system identify and analyze device usage patterns and energy consumption through the K-Means clustering algorithm. The calculation formula of the K-Means clustering algorithm is: For a data point x = (x1, x2, ..., x n ) and cluster centroid c i =(c i1 , c i2 , …, c in ), Euclidean distance d(x, c i ) is calculated as: For cluster i, the set of data points it contains is S i ={x1, x2, ..., x mi}, where m i is the number of data points in cluster i, and the new cluster centroid c′ i The calculation formula is: Specifically, if cluster i has n features, the new cluster centroid c′ i =(c′ i1 , c′ i2 ,…,c′ in ), where each coordinate c′ ij The calculation formula is: x j is the jth eigenvalue of data point x. The iterative process will repeat the steps of assigning data points to clusters and updating centroids until a certain stopping condition is met. The common stopping condition can be expressed as: Centroid change threshold: If the change in the centroids of all clusters is less than a predetermined threshold ∈, the iteration is stopped. The centroid change can be measured by the Euclidean distance between the new and old centroids, and its calculation formula is: Maximum number of iterations: If the algorithm has performed T iterations (T is the preset maximum number of iterations), the iteration is stopped regardless of whether the centroid converges. The expression is: current number of iterations ≥ T. Through calculation, the K-Means clustering algorithm can accurately calculate the distance between the data point and the cluster centroid, and update the position of the cluster centroid, thereby realizing the clustering analysis of the usage pattern and energy consumption of smart home devices, and then generating effective control strategies.

[0057] The control module uses the K-Means clustering algorithm to accurately divide the usage patterns and energy consumption of household devices into different clusters. Each cluster represents similar usage patterns or energy consumption characteristics, allowing the control module to formulate more accurate and effective control strategies for different clusters to achieve efficient use of energy. At the same time, the number of clusters (K value) can be adjusted according to actual needs to adapt to the energy usage characteristics of different households and different devices, helping households achieve more accurate and efficient energy management and energy-saving control.

[0058] Example 3

[0059] The steps of the system based on Example 1 and Example 2 are as follows:

[0060] (1) Energy data monitoring: Start the energy monitoring module to monitor household electricity and water consumption data in real time.

[0061] (2) Data transmission and analysis: The monitored data is transmitted to the data analysis module in real time for in-depth analysis and processing.

[0062] (3) Control instruction generation: Based on the data analysis results, the control module generates corresponding control instructions and sends the control instructions to home devices through the smart home network to achieve intelligent control of the devices.

[0063] (4) Renewable energy utilization: Start the renewable energy utilization module and use renewable energy equipment to provide clean energy for the home.

[0064] (5) Monitoring and feedback: Continuously monitor household energy usage and equipment operating status to ensure the stable operation of the entire system, and continuously optimize the control strategy based on feedback data.

[0065] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A smart home control system and method, characterized in that: The system consists of the following components: Energy monitoring module: used to collect and transmit real-time monitoring of household electricity and water consumption data. Through sensors installed in key locations in the home, energy consumption data and water consumption data of electrical equipment are obtained in real time, and the data is transmitted to the data analysis module; Data analysis module: Receives data transmitted by the energy monitoring module, performs real-time analysis and processing, and uses advanced algorithms and models to conduct in-depth mining and analysis of household energy usage, identify high-energy-consuming devices and time periods, and provide a basis for optimizing energy usage patterns; Control module: Based on the results of the data analysis module, the control algorithm generates corresponding control instructions to intelligently control home devices. This module can automatically adjust the operation mode and parameters of the device to achieve efficient use of energy; Renewable energy utilization module: Combine renewable energy utilization technology to achieve green and low-carbon operation of the home environment. By installing renewable energy power generation equipment, renewable energy is converted into electrical energy or thermal energy to provide clean and sustainable energy supply for the family.

2. The smart home control system and method according to claim 1, characterized in that: The specific steps of the energy monitoring module are: (1) Data collection: installing sensors to monitor the energy consumption and water consumption data of electrical equipment in real time; (2) Data transmission: The collected data is transmitted to the data analysis module via wired or wireless means.

3. The smart home control system and method according to claim 1, characterized in that: The data analysis module has the following specific steps: (1) Data reception and processing: Receive real-time data transmitted from the energy monitoring module, and use algorithms and models to clean, organize, and analyze the received data to identify equipment and time periods with high energy consumption; (2) Model identification and result output: By comparing historical data and real-time data, the patterns and trends of household energy use are analyzed, the analysis results are output in a visual form, and transmitted to the control module.

4. The smart home control system and method according to claim 1, characterized in that: The control module has the following specific steps: (1) Control algorithm application: Receive optimization instructions from the data analysis module and generate corresponding control strategies through the control algorithm according to the instructions; (2) Device control and feedback: Through the smart home network, control commands are sent to home devices to automatically adjust the device's operating mode and parameters, monitor the device's response, and ensure that the control commands are executed correctly.

5. The smart home control system and method according to claim 1, characterized in that: The specific steps of the renewable energy utilization module are as follows: (1) Equipment installation: Install renewable energy power generation equipment at appropriate locations in the home; (2) Energy conversion and supply: These devices are used to convert renewable energy into electrical energy or thermal energy, and the converted clean energy is connected to the home power grid to provide electricity or thermal energy for the home; (3) Monitoring and maintenance: Monitor the operating status of renewable energy equipment in real time to ensure its normal operation and perform regular maintenance.

6. The smart home control system and method according to claim 3, characterized in that: The data analysis module analyzes the patterns and trends of household energy use by using a Support Vector Machine (SVM). The formula of the Support Vector Machine (SVM) is: The formula for data preprocessing standardization is: where x is the original data, μ is the mean, σ is the standard deviation, and normalization is to scale the data to the interval [0, 1]. For the training of the SVM model, the objective function needs to be constructed first. The formula of the objective function is: where C is the penalty coefficient and ξi is the slack variable, and the constraint conditions are added: y i (w·x i +b)≥1 - ξi, ξi≥0, Through the Lagrange multiplier method, the Lagrangian function is introduced, and its dual problem is solved to construct the dual objective function. The formula is: The constraint conditions are added: The quadratic programming solver is used to solve the above optimization problem to obtain the value of α, and then the model parameters are solved: where j is an arbitrary subscript that satisfies 0 < α j <C. The established model is used to predict the energy usage situation. For a new data point x, its predicted label is: Predicted label = sign(w·x + b). According to the prediction results, different patterns of household energy use are analyzed, and the changing trends of these patterns over time are observed.

7. The smart home control system and method according to claim 4, characterized in that: The control module helps the smart home control system identify and analyze device usage patterns and energy consumption through the K-Means clustering algorithm. The calculation formula of the K-Means clustering algorithm is: for a data point x=(x1, x2, ..., x n ) and cluster centroid c i =(c i1 , c i2 , …, c in ), Euclidean distance d(x, c i ) is calculated as: For cluster i, the set of data points it contains is S i ={x1, x2, …, x mi }, where m i is the number of data points in cluster i, and the new cluster centroid c i The calculation formula of ′ is: Specifically, if cluster i has n features, the new cluster centroid c i ′=(c i ′1,c i ′2,…,c i ' n ), where each coordinate c′ ij The calculation formula is: x j is the jth eigenvalue of data point x. The iterative process will repeat the steps of assigning data points to clusters and updating centroids until a certain stopping condition is met. The common stopping condition can be expressed as: Centroid change threshold: If the change in the centroids of all clusters is less than a predetermined threshold, the iteration is stopped. The centroid change can be measured by the Euclidean distance between the new and old centroids, and its calculation formula is: Maximum number of iterations: If the algorithm has performed T iterations (T is the preset maximum number of iterations), the iteration is stopped regardless of whether the centroid converges. The expression is: current number of iterations ≥ T. Through calculation, the K-Means clustering algorithm can accurately calculate the distance between the data point and the cluster centroid, and update the position of the cluster centroid, thereby realizing the clustering analysis of the usage patterns and energy consumption of smart home devices, and then generating effective control strategies.

8. The smart home control system and method according to claim 5, characterized in that: The renewable energy utilization module converts renewable energy into electrical energy or thermal energy through renewable energy power generation equipment to provide clean and sustainable energy supply for households. At the same time, it cooperates with the energy monitoring module, the data analysis module and the control module to use the energy demand prediction algorithm and the energy supply optimization algorithm to adjust the operating parameters of the renewable energy equipment and the energy allocation strategy to achieve the optimal energy supply. The energy demand prediction algorithm uses a linear regression mathematical model to calculate various factors that affect energy demand. The calculation formula is: y=β0+β1x1+β2x2+…+β n x n , where y is the predicted energy demand, x1,x2,…,x n are various factors that affect energy demand, β0,β1,…,β n is the regression coefficient. For the energy supply optimization algorithm, a linear optimization mathematical model is used, and its objective function is: z = c1x1 + c2x2 + ... + c n x n , the constraints can be expressed as a series of inequalities: Among them, x1,x2,…,x n are decision variables, c1, c2, …, c n is the coefficient of the objective function, a ij and b i It is the coefficient and limit value of the constraint conditions. According to the results of the optimization calculation, the operating parameters of the renewable energy equipment and the energy allocation strategy are adjusted to achieve the optimal energy supply.

9. The smart home control system and method according to claim 1, characterized in that: The renewable energy utilization module includes an energy storage unit for storing the converted renewable energy to provide supplementary energy during peak energy demand periods or when renewable energy supply is insufficient.

10. The smart home control system and method according to claim 1, characterized in that: The specific steps of the method are: (1) Energy data monitoring: Start the energy monitoring module to monitor household electricity and water consumption data in real time. (2) Data transmission and analysis: The monitored data is transmitted to the data analysis module in real time for in-depth analysis and processing. (3) Control instruction generation: Based on the data analysis results, the control module generates corresponding control instructions and sends the control instructions to home devices through the smart home network to achieve intelligent control of the devices. (4) Renewable energy utilization: Start the renewable energy utilization module and use renewable energy equipment to provide clean energy for the home. (5) Monitoring and feedback: Continuously monitor household energy usage and equipment operating status to ensure the stable operation of the entire system, and continuously optimize the control strategy based on feedback data.

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