Household intelligent control system and method based on embedded software

Abstract

The invention discloses a household intelligent control system and method based on embedded software, and relates to the technical field of intelligent home furnishing.The system comprises a data processing module, an energy consumption management module, a data processing module, an intelligent control module and a control module, the energy consumption limit setting module is used for setting the energy consumption limit of each household appliance according to the prediction result, the parameter adjusting module is used for intelligently adjusting the operation parameters of the household appliances in the peak period of power consumption, and the feedback optimization module is used for optimizing the energy consumption management strategy of the household appliances through real-time monitoring feedback; according to the intelligent home control system and method based on the embedded software, the overall energy consumption is reduced, the electricity utilization cost is saved for families, carbon emission is reduced, sustainable development is promoted, and energy consumption of household appliances is intelligently regulated and controlled so that the problems of predicting electricity utilization peak according to use habits and achieving energy conservation can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home, and in particular to a smart home control system and method based on embedded software. Background Art

[0002] With the rapid development of smart home technology, home control systems have gradually shifted from traditional remote control and timing control to more intelligent management modes, especially the efficient regulation of energy consumption of home appliances, which has become one of the important ways to improve energy efficiency and reduce carbon emissions. As the core of smart home device control, embedded software technology provides more accurate and flexible energy consumption monitoring and optimization strategies. In traditional home control systems, the operation of home appliances often lacks intelligent regulation, and most of them rely on users to manually set time or mode to adjust energy consumption, resulting in excessive power consumption of home appliances during peak power consumption periods or when users are not at home. As for the prediction and regulation of energy consumption, traditional systems usually only rely on preset timers or simple operation logic, and fail to make full use of users' actual usage habits and environmental information for intelligent prediction and regulation. Therefore, how to accurately predict peak power consumption and intelligently regulate the energy consumption of home appliances has become an important challenge for energy saving and optimizing home electricity consumption.

[0003] With the development of artificial intelligence, the Internet of Things, and big data analysis technology, smart home control methods based on embedded software have been increasingly widely used. By collecting information such as the usage data of home appliances, user behavior patterns, and environmental factors, and using machine learning algorithms to analyze and process the data, it is possible to accurately predict the user's peak electricity consumption, optimize the operating timing and mode of home appliances, and thus achieve energy saving. For example, combined with factors such as the user's work and rest habits, weather changes, and historical electricity consumption data, the operation mode of home appliances can be predicted and adjusted to avoid unnecessary energy waste. Existing smart home energy consumption control methods usually use simple schedule control or adjust through single sensor data. These methods are less intelligent and are easily affected by environmental changes or user behavior uncertainty. Therefore, how to accurately realize energy consumption prediction and intelligent control of home appliances based on embedded software systems, and then achieve energy saving goals, is still a problem in the existing technology. Summary of the invention

[0004] The purpose of the present invention is to provide a home intelligent control system and method based on embedded software to solve the problem of how to intelligently regulate the energy consumption of home appliances to predict peak electricity consumption according to usage habits and achieve energy saving.

[0005] To achieve the above object, the present invention provides the following technical solution: a home intelligent control system based on embedded software, the system comprising:

[0006] A data processing module, used to predict the peak power consumption time of home appliances based on user usage habit data;

[0007] An energy consumption management module connected to the data processing module is used to set energy consumption limits for each household appliance according to the prediction results;

[0008] The parameter adjustment module connected to the energy consumption management module is used to intelligently adjust the operating parameters of household appliances during peak power consumption periods, including collecting the current energy consumption demand of the equipment, comparing it with the allocated energy consumption limit, calculating the error, calculating the adjustment amount, and dynamically optimizing the equipment operating parameters. The specific formula is:

[0009] u(t)=K p ×e(t);

[0010] Among them, u(t) represents the equipment control quantity calculated according to the current energy consumption demand, K p represents the sensitivity coefficient for adjusting the device parameters, e(t) represents the difference between the actual energy consumption demand of the current device and the energy consumption limit allocated by the system, and t represents time;

[0011] The feedback optimization module connected to the data processing module, the energy consumption management module and the parameter adjustment module is used to optimize the energy consumption management strategy of the household appliance through real-time monitoring feedback.

[0012] Preferably, the data processing module predicts the peak power consumption time of the household appliance based on the user usage habit data, including:

[0013] Record the peak electricity consumption time of users every day to form a behavior data sequence. According to the time interval of peak electricity consumption and the data time span, calculate the speed of change of electricity consumption behavior. The specific formula is: v = d / T;

[0014] Among them, v represents the speed of change of user's electricity consumption behavior, d represents the interval between peak electricity consumption times, and T represents the time span of user behavior data;

[0015] The next peak time is estimated based on the speed of change and the result is sent to the energy consumption management module.

[0016] Preferably, the energy consumption management module sets the energy consumption limit of each household appliance according to the prediction result, including:

[0017] Record the energy consumption changes of each household appliance under different energy consumption limits, and calculate the elasticity of the equipment to the energy consumption limit. The specific formula is: A = ΔQ / ΔP;

[0018] Among them, A represents the flexibility of the equipment to the energy consumption limit, ΔQ represents the change in energy consumption, and ΔP represents the change in the limit.

[0019] Preferably, the feedback optimization module optimizes the energy consumption management strategy of household appliances through real-time monitoring feedback, including assigning energy consumption priority weights to each device, calculating the overall energy consumption management cost, and analyzing the optimization results. The specific formula is:

[0020] Where C represents the overall energy consumption management cost, d i represents the actual operating parameters of the i-th household appliance under current conditions, w i It represents the energy consumption priority of the i-th household appliance, i represents the index number of the household appliance, and n represents the total number of all appliances.

[0021] Preferably, S1 also includes collecting and analyzing user daily usage habit data, using historical data to build a user behavior model, predicting the usage frequency of each household appliance in different time periods based on the user behavior model, and predicting the peak electricity consumption period in the next week in combination with the electricity demand pattern.

[0022] Preferably, the prediction of the usage frequency of each household appliance in different time periods based on the user behavior model includes analyzing the specific operation time of each household appliance by the user, summarizing and calculating the historical power-on time and power consumption of each device, analyzing and generating an average power consumption curve of each household appliance, and if L×P>E, marking this period of time as a high energy consumption stage, wherein L represents the predicted duration, P represents the power consumption per minute, and E represents the preset energy threshold value.

[0023] Preferably, the analysis generates an average power consumption curve for each household appliance, including classifying the types of household appliances, and establishing a data table for the corresponding category of equipment, drawing a total power graph of all related appliances at different time points each day, and based on this graph drawing an average load graph for each type of appliance in a typical week, setting a threshold for the type of equipment, and if the average daily power of a certain type of equipment exceeds the threshold for that category, then increased attention will be paid to that category in the future.

[0024] Preferably, the data processing module also includes a storage unit for recording the user's historical electricity usage data and extracting electricity usage habit characteristics.

[0025] Preferably, the parameter adjustment module includes a proportional control unit and a real-time monitoring unit, the proportional control unit calculates the equipment adjustment amount according to a proportional control formula, and the real-time monitoring unit is used to collect the current energy consumption state of the equipment and feed back the adjusted operating parameters.

[0026] A home intelligent control method based on embedded software, using the home intelligent control system based on embedded software, the method comprising:

[0027] S1. Predict the peak electricity consumption time of household appliances based on user usage habit data;

[0028] S2. Set the energy consumption limit of each household appliance according to the prediction results;

[0029] S3, intelligently adjust the operating parameters of household appliances during peak power consumption periods;

[0030] S4. Optimize the energy consumption management strategy of household appliances through real-time monitoring feedback.

[0031] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0032] The home intelligent control system and method based on embedded software predicts the peak power consumption time of home appliances based on user usage habit data, sets the energy consumption limit of each home appliance according to the prediction result, intelligently adjusts the operation parameters of the home appliances during the peak power consumption period, optimizes the energy consumption management strategy of the home appliances through real-time monitoring feedback, effectively predicts the peak power consumption time of the user, thereby providing a scientific basis for energy consumption optimization, improving the accuracy and reliability of the prediction, making the equipment operation more intelligent, and reducing the dependence on manual intervention of the user. According to the peak power consumption prediction result and the energy consumption priority of each device, the energy consumption limit is reasonably allocated to avoid energy waste caused by the operation of the device during the peak period or when the user is not at home, thereby realizing efficient use of energy, performing customized regulation according to the specific needs of different home users, providing energy-saving strategies and power consumption suggestions that are more in line with personalized needs, being able to adjust the operation mode of the home appliances in real time, adapting to different environmental changes, further improving the regulation efficiency, reducing the overall energy consumption, saving electricity costs for the family, while helping to reduce carbon emissions and promote sustainable development, and performing intelligent regulation on the energy consumption of home appliances to solve the problem of predicting peak power consumption according to usage habits and achieving energy saving. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a connection diagram of the system modules of the present invention;

[0034] Figure 2 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0036] like Figure 1 As shown, the present invention provides a technical solution: a home intelligent control system based on embedded software, the system comprising:

[0037] A data processing module, used to predict the peak power consumption time of home appliances based on user usage habit data;

[0038] An energy consumption management module connected to the data processing module is used to set energy consumption limits for each household appliance according to the prediction results;

[0039] The parameter adjustment module connected to the energy consumption management module is used to intelligently adjust the operating parameters of household appliances during peak power consumption periods, including collecting the current energy consumption demand of the equipment, comparing it with the allocated energy consumption limit, calculating the error, calculating the adjustment amount, and dynamically optimizing the equipment operating parameters. The specific formula is:

[0040] u(t)=K p ×e(t);

[0041] Among them, u(t) represents the equipment control quantity calculated according to the current energy consumption demand, K p represents the sensitivity coefficient for adjusting the device parameters, e(t) represents the difference between the actual energy consumption demand of the current device and the energy consumption limit allocated by the system, and t represents time;

[0042] The feedback optimization module connected to the data processing module, the energy consumption management module and the parameter adjustment module is used to optimize the energy consumption management strategy of the household appliance through real-time monitoring feedback.

[0043] In the above system, the data processing module first predicts the future peak electricity consumption time by analyzing the historical data of the user's electricity consumption behavior. Data mining algorithms are used in the prediction process, such as models based on time series analysis, to ensure the accuracy of predictions during peak time periods. The energy consumption management module calculates the energy consumption limit allocation value for each device based on the prediction results of the data processing module. The allocation strategy adopts a dynamic allocation algorithm to set limits based on the importance of the equipment, historical electricity consumption, and current energy supply and demand conditions, so as to reasonably allocate limited resources during peak electricity consumption periods. The parameter adjustment module collects the real-time energy consumption requirements of household appliances and compares them with the allocated energy consumption limits. The error value e(t) is calculated and then the formula u(t)=K is used. p×e(t) calculates the adjustment amount u(t), thereby dynamically adjusting the operating parameters of the equipment, such as adjusting the temperature setting of the air conditioner, adjusting the lighting brightness, etc. The adjustment process combines the sensitivity coefficient to control the response speed to ensure that the adjustment process is smooth and stable. The feedback optimization module further optimizes the energy consumption management strategy by monitoring the adjustment results and real-time data. Its core is to establish a closed-loop feedback system based on real-time data, dynamically adjust the quota allocation and control strategy to adapt to changes in actual operating conditions. Through user habit data analysis and peak power consumption prediction, the energy consumption of household appliances is managed in advance, reducing the pressure of power consumption during peak hours. The parameter adjustment module dynamically optimizes according to the real-time error, improves the flexibility and accuracy of energy consumption management, and can effectively reduce energy waste. The energy consumption quota of each device is reasonably allocated through the energy consumption management module, while ensuring user experience, the energy utilization efficiency is improved. The introduction of the feedback optimization module enables the system to adjust adaptively, further improving the stability and efficiency of the system.

[0044] The data processing module predicts the peak power consumption time of home appliances based on user usage habit data, including:

[0045] Record the peak electricity consumption time of users every day to form a behavior data sequence. According to the time interval of peak electricity consumption and the data time span, calculate the speed of change of electricity consumption behavior. The specific formula is: v = d / T;

[0046] Among them, v represents the speed of change of user's electricity consumption behavior, d represents the interval between peak electricity consumption times, and T represents the time span of user behavior data;

[0047] The next peak time is estimated based on the speed of change and the result is sent to the energy consumption management module.

[0048] In the above implementation, the data processing module records the user's daily peak electricity consumption time, constructs a behavior data sequence, and processes the data in combination with time series analysis. The peak time interval d and the data time span T are used to calculate the user's electricity consumption behavior change speed v. The change speed v indicates how fast the peak time changes, and this indicator can quantify the dynamic change trend of the user's electricity consumption behavior. For example, when the v value is small, it means that the user's electricity consumption behavior is more regular and the peak electricity consumption time is stable; when the v value is large, it indicates that the user's peak electricity consumption time has significant fluctuations. Based on the calculated change speed, the data processing module uses a prediction algorithm, such as a linear regression or exponential smoothing algorithm, to infer the user's next possible peak electricity consumption time. The prediction results will be sent to the energy consumption management module in real time to update the energy consumption quota allocation strategy of household appliances, so as to regulate the electrical appliances in advance and optimize energy efficiency. By calculating the change speed v of the user's electricity consumption behavior, the stability of the user's behavior rules can be effectively quantified, providing an accurate basis for peak time prediction. Based on the real-time analysis and prediction of behavioral data, the energy consumption management strategy can be quickly adjusted when the user's behavior changes significantly, thereby improving the flexibility and adaptability of the system. By predicting the next peak electricity consumption time and adjusting the energy consumption management plan, the peak electricity load can be reduced and the overall utilization efficiency of power resources can be improved. Dynamic analysis of user electricity consumption behavior helps to understand their electricity consumption habits and provide a reference for further optimization of smart home systems.

[0049] The energy consumption management module sets the energy consumption limit of each household appliance according to the prediction results, including:

[0050] Record the energy consumption changes of each household appliance under different energy consumption limits, and calculate the elasticity of the equipment to the energy consumption limit. The specific formula is: A = ΔQ / ΔP;

[0051] Among them, A represents the flexibility of the equipment to the energy consumption limit, ΔQ represents the change in energy consumption, and ΔP represents the change in the limit.

[0052] In the above implementation, the energy consumption management module first collects the operating data of household appliances under different energy consumption limits, and records the corresponding energy consumption change ΔQ and energy consumption limit change ΔP. Then, the energy consumption elasticity A of the device is calculated according to the formula A=ΔQ / ΔP. Among them, the energy consumption elasticity A represents the sensitivity of the energy consumption of the device to the change of the energy consumption limit. When the A value is large, it means that the energy consumption of the device is highly sensitive to the change of the limit. For example, high-power consumption equipment, such as air conditioners, will produce significant energy consumption changes when the limit changes. When the A value is small, it means that the energy consumption of the device is not sensitive to the change of the limit. For example, low-power consumption equipment, such as LED lights, has a weak response to the change of the limit. According to the calculated energy consumption elasticity, the energy consumption management module can dynamically adjust the energy consumption limit of each household appliance. For example, a stricter limit is assigned to a high-elasticity device to control the total energy consumption, and a loose limit is assigned to a low-elasticity device to improve the user experience. Through this process, the system can achieve refined management of household energy use while optimizing overall power efficiency. By calculating the energy consumption elasticity A of the equipment, we can clearly quantify the response capabilities of different household appliances to the adjustment of energy consumption limits, and provide a scientific basis for quota allocation. Based on the elasticity of the equipment, we can reasonably allocate energy consumption limits, effectively control the energy consumption of high-power consumption equipment, and ensure the normal operation of low-power consumption equipment, thereby improving the overall energy efficiency of the family. Through the flexible allocation strategy, we can avoid excessive inconvenience to users due to limit adjustments, and ensure the user experience while achieving energy-saving goals. The system can adjust the energy consumption allocation strategy according to real-time data, adapt to different power consumption scenarios and changing needs, and improve the flexibility and stability of the system.

[0053] The feedback optimization module optimizes the energy consumption management strategy of household appliances through real-time monitoring feedback, including assigning energy consumption priority weights to each device, calculating the overall energy consumption management cost, and analyzing the optimization results. The specific formula is:

[0054] Where C represents the overall energy consumption management cost, d i represents the actual operating parameters of the i-th household appliance under current conditions, w i It represents the energy consumption priority of the i-th household appliance, i represents the index number of the household appliance, and n represents the total number of all appliances.

[0055] In the above embodiment, the feedback optimization module collects the operating parameters of the household appliance in real time. i , and assign energy consumption priority weight w according to the importance of the equipment or the degree of energy consumption impact i These weights are usually adjusted dynamically based on the device type, user needs, and power usage scenarios. For example, high-energy-consuming and critical devices (such as refrigerators) will be given higher weights, while low-energy-consuming devices, such as night lights, will be given lower weights. The feedback optimization module calculates the overall energy management cost C. This cost value takes into account the actual operating parameters and priority weights of each device and is used to evaluate the efficiency of the current energy management strategy. When analyzing the optimization results, the system compares the current cost value C with the set target cost value to determine whether the current strategy needs to be adjusted. If the cost is too high, the feedback optimization module modifies the device operating parameters d i Or adjust the priority w i , re-optimize the energy consumption allocation strategy to ensure that the overall system achieves the lowest energy consumption management cost while meeting user needs. By introducing the priority weight w i and run parameter d i The system can fully quantify the contribution of each device to the overall energy management cost, thereby achieving accurate cost assessment. The feedback optimization module dynamically adjusts the energy allocation strategy based on real-time data to ensure that the system can adapt to changing operating conditions and improve the flexibility and accuracy of energy management. Through weight allocation and cost calculation, it effectively prioritizes the operation of key equipment, while optimizing the energy consumption of low-priority equipment and improving energy utilization efficiency. The system dynamically adjusts priority weights based on user preferences. i , while achieving energy-saving goals and meeting user needs to the greatest extent possible.

[0056] S1 also includes collecting and analyzing the user's daily usage habit data, using historical data to build a user behavior model, predicting the usage frequency of each household appliance in different time periods based on the user behavior model, and predicting the peak power consumption period in the next week in combination with the power demand pattern. In the above implementation, the system first collects the user's daily behavior data on the use of household appliances for a long time through the sensor module or intelligent device of the embedded software, such as the start-up time, usage duration and usage scenario of each household appliance. Subsequently, the system constructs a user behavior model based on the collected historical data using statistical methods, such as cluster analysis, or machine learning algorithms, such as decision trees or time series prediction models. The user behavior model reflects the user's typical usage habits for household appliances, such as the daily usage frequency, usage time period and associated usage patterns of the equipment, such as the linkage use of air conditioners and televisions. Based on the user behavior model, the system predicts the usage frequency of each household appliance in different time periods. For example, by analyzing the usage pattern of air conditioners in the high temperature period in summer, its high-frequency usage period is predicted. At the same time, the system combines the overall power demand pattern, such as the fluctuation trend of the daily power load of the whole family, to further predict the possible peak power consumption period in the next week. Finally, the prediction results will be sent to the energy consumption management module to optimize the energy consumption quota allocation and parameter adjustment strategy of each household appliance to better adapt to future electricity demand. By collecting and analyzing the user's daily usage habit data, the system can deeply understand the user's electricity demand, establish a personalized user behavior model, and improve the accuracy of the prediction. Based on the combined analysis of the user behavior model and the electricity demand pattern, the prediction of the peak electricity consumption period in the next week is more accurate, providing a reliable basis for the optimization of the energy consumption management strategy. By reasonably allocating energy consumption limits and optimizing equipment parameters, the system can reduce energy waste while meeting user needs and improve the overall energy saving effect. After predicting the peak period, the system can adjust the equipment operation mode in advance, reduce electricity conflicts during peak periods, and improve user convenience and comfort.

[0057] Predicting the usage frequency of each household appliance in different time periods based on the user behavior model includes analyzing the specific operation time of each household appliance by the user, summarizing and calculating the historical startup time and power consumption of each device, analyzing and generating the average power consumption curve of each household appliance, and if L×P>E, marking this period as a high energy consumption stage, where L represents the predicted time, P represents the power consumption per minute, and E represents the preset energy threshold value. In the above implementation, the system first collects the specific operation time data of the user on the household appliance through real-time monitoring and historical data recording, including the device's startup time, shutdown time, and continuous operation time. Then, the system calculates the historical power consumption value of each device based on the historical startup time data of each device and the corresponding power consumption record, and further generates an average power consumption curve. The curve reflects the average power consumption of the device in each time period of a day, for example, during peak hours, such as lunch or dinner time, the device consumption is higher, and during non-peak hours, the power is lower. In the prediction process, the system uses the user behavior model to predict the operation time L and the power consumption per minute P of each device, and calculates the energy consumption value L×P. If the calculation result exceeds the set energy threshold value E, the system marks the time period as a high energy consumption stage. The marking result of the high energy consumption stage will serve as an important basis for the subsequent energy consumption management module to adjust the strategy, such as reducing the energy consumption allocation limit or optimizing the equipment operation parameters. Through the formula L×P>E, the system can accurately identify the high energy consumption stage and provide a clear optimization target for energy consumption management. Based on the user's operation time and the historical power consumption data of the equipment, the system can dynamically generate an average power consumption curve to provide scientific data support. After marking the high energy consumption stage, the system can take optimization measures for high energy consumption equipment first, such as reducing the equipment's operating power or limiting the operating time, to maximize the energy saving effect. Based on the user behavior model and specific operation data, the system realizes personalized energy consumption management of household appliances, which not only meets user needs but also effectively controls energy consumption. By introducing the power consumption curve and the energy threshold value E, the accuracy of the high energy consumption stage prediction is improved and misjudgment is reduced.

[0058] The analysis and generation of the average power consumption curve of each household appliance includes classifying the types of household appliances, establishing a data table of the corresponding category of equipment, drawing a total power diagram of all related electrical appliances at different time points every day, and drawing a graph of the average load of each type of electrical appliance in a typical week based on this graph, setting a threshold for the type of equipment, and if the average daily power of a certain type of equipment exceeds the threshold of the category, then the attention to the category in the future days will be increased. In the above implementation, the system first classifies all household appliances by type, for example, air conditioners, refrigerators, etc. are classified as high-energy-consuming equipment, and lighting lamps, smart sockets, etc. are classified as low-energy-consuming equipment, and a corresponding data table is established for each category. The data table contains the operating time, power consumption, and time-related operation records of each device. Then, the system draws a total power diagram of all related electrical appliances at different time points every day based on the collected data. The diagram reflects the concentration and fluctuation trend of equipment power in different time periods. For example, during lunch time, air conditioners and kitchen appliances may run at the same time, resulting in an increase in power value. Based on the overall power diagram, the system further integrates the data within a week and draws an average load diagram of each type of electrical appliance in a typical week. For example, by summarizing the daily usage data of air conditioners in a week, the average power curve of the air conditioner equipment in different time periods can be drawn to intuitively display its typical operating load.

[0059] In order to optimize system operation and energy consumption management, the system sets a daily average power threshold for each device category. For example, air conditioning equipment may be set to a daily average power threshold of 5kWh. When the daily average power of a certain type of equipment, such as air conditioning, exceeds the threshold of the category, the system will mark the category as a high-energy consumption category and increase the attention to the equipment in this category in the future. Specific measures include increasing the frequency of real-time monitoring of equipment operation, optimizing the energy consumption limit allocation strategy, or reminding users to take energy-saving actions. By classifying household appliances by category and establishing data tables, the system realizes the refined management of different types of equipment, providing a clear data basis for subsequent analysis and optimization. Based on the overall power diagram and average load diagram, the system can dynamically grasp the regularity and fluctuation trend of equipment operation, providing a scientific basis for the identification of high-energy consumption categories. Setting device type thresholds and increasing attention to high-energy consumption categories enables the system to actively optimize equipment operation strategies and reduce overall energy consumption. By paying attention to high-energy consumption categories, the system can combine prediction algorithms and real-time feedback mechanisms to adjust energy consumption management strategies in a timely manner to ensure efficient and economical energy utilization. The system can guide users to pay attention to the usage habits of high-energy-consuming devices through interface prompts or notifications, thereby further improving energy-saving effects.

[0060] The data processing module also includes a storage unit for recording the user's historical electricity consumption data and extracting electricity consumption habit features. In the above embodiment, the storage unit is one of the core components of the data processing module, which is used to record and store the user's historical electricity consumption data. These data are derived from the operation records of household appliances, including the on / off time, operation time, power consumption value and frequency of use of the equipment. The data processing module extracts the user's electricity consumption habit features through the long-term data recorded by the storage unit, combined with statistical analysis methods, such as time series analysis or feature extraction algorithms, such as cluster analysis and principal component analysis. These features include but are not limited to: the operation time period of the user's commonly used equipment, such as the high-frequency use period of the day, such as the operation of the air conditioner from 8 to 10 pm, the use priority of different equipment, such as the continuous operation of the refrigerator, the intermittent use of air conditioning and lighting, seasonal or periodic electricity consumption patterns, such as high power consumption of air conditioning in summer and increased frequency of use of heating equipment in winter. The extracted electricity consumption habit features will serve as the basic data for further predicting the user's electricity demand and optimizing the energy consumption management strategy. For example, in the process of predicting peak electricity consumption, combining these features can more accurately judge the future electricity consumption trend. In addition, the long-term recording function of the storage unit also supports retrospective analysis of power consumption data, providing a basis for optimizing equipment operation efficiency. Through the storage unit, the system can record and retain the user's historical power consumption data, providing a data basis for subsequent analysis and optimization. Combined with statistical analysis and feature extraction algorithms, the system can clearly reveal the user's power consumption habits and provide a scientific basis for the formulation of equipment control strategies. Based on the characteristics of user power consumption habits, the accuracy and reliability of the system in power peak prediction and energy consumption management optimization are significantly improved. The extracted power consumption habit characteristics support the formulation of personalized energy consumption management plans, enabling the system to more flexibly adapt to the needs and behavior patterns of different users. The historical record function of the storage unit supports retrospective analysis of user power consumption behavior, providing strong support for power consumption anomaly detection and energy saving potential mining.

[0061] The parameter adjustment module includes a proportional control unit and a real-time monitoring unit. The proportional control unit calculates the equipment adjustment amount according to the proportional control formula. The real-time monitoring unit is used to collect the current energy consumption status of the equipment and feedback the adjusted operating parameters. In the above embodiment, the proportional control unit and the real-time monitoring unit of the parameter adjustment module work together to achieve intelligent adjustment of the equipment operating parameters. The proportional control unit adjusts the operating status of the equipment based on the proportional control formula. Assume that the proportional control formula is: Δu = K p ×e, where Δu represents the adjustment amount of the equipment, K pis the proportional gain coefficient, which indicates the response sensitivity of the control system to the deviation, and e indicates the error between the actual energy consumption of the current device and the target energy consumption. When the actual energy consumption state of the device deviates from the target energy consumption, the proportional control unit dynamically adjusts the device operating parameters, such as operating power or working time, by calculating the adjustment amount Δu, so as to restore the energy consumption state of the device to the target range as soon as possible. The real-time monitoring unit is responsible for collecting the current energy consumption state data of the device, such as power consumption, current, voltage and other parameters, and sending the collected data to the proportional control unit for error calculation. At the same time, the real-time monitoring unit will feedback the adjusted operating parameters to ensure that the adjusted device operating state meets the expected target. If a new deviation is found, the real-time monitoring unit will trigger the proportional control unit to calculate the adjustment amount again to achieve closed-loop control. The overall working mechanism is similar to the proportional control system, the P controller, which achieves rapid response and adjustment through proportional control and real-time monitoring to ensure the stability of the device operation and the optimization of energy consumption. Through the rapid calculation of the proportional control unit, the system can respond to the energy consumption deviation in real time, dynamically adjust the device operating parameters, and ensure that the device operating state is close to the target value. The real-time monitoring unit can collect the energy consumption state data of the device in real time and feedback the adjusted operating effect, thereby improving the control accuracy of the system. The combination of the proportional control unit and the real-time monitoring unit constitutes a closed-loop control system, ensuring that the adjustment results can be verified and further optimized in a timely manner to achieve efficient energy consumption management. p The system can flexibly set the response sensitivity according to different devices or operating conditions to meet the control needs of various scenarios. Based on precise adjustment and real-time monitoring, the system can effectively reduce energy waste, improve the operating efficiency of home appliances, and reduce the overall energy consumption of the family.

[0062] A home intelligent control method based on embedded software is also provided, using the home intelligent control system based on embedded software, the method comprising:

[0063] S1. Predict the peak electricity consumption time of household appliances based on user usage habit data;

[0064] S2. Set the energy consumption limit of each household appliance according to the prediction results;

[0065] S3, intelligently adjust the operating parameters of household appliances during peak power consumption periods;

[0066] S4. Optimize the energy consumption management strategy of household appliances through real-time monitoring feedback.

[0067] In this method, the system collects the user's daily electricity consumption data through the data processing module, combines historical records and electricity consumption habits, and uses time series analysis or machine learning algorithms to predict the future peak electricity consumption time. Subsequently, the energy consumption management module dynamically sets the energy consumption limit of each household appliance according to the prediction results, and weighs the importance and usage priority of the equipment through the allocation algorithm. During the peak electricity consumption period, the parameter adjustment module calculates the adjustment amount of the operating parameters through the proportional control formula, and optimizes the equipment operation status in combination with the feedback of the real-time monitoring unit to ensure that the energy consumption meets the set limit requirements. This method can also be applied to the door and window intelligent system. For example, during the peak electricity consumption period, the system can optimize the air conditioning operation efficiency and reduce power consumption by real-time monitoring of the door and window opening status and indoor temperature. When the door and window are detected to be open, the system will immediately reduce the air conditioning operation load or issue a reminder to close the door and window to avoid energy waste. At the same time, the sensor data of the door and window system can also be combined with the electricity consumption prediction model to dynamically adjust the equipment operation parameters to further optimize the energy efficiency. Finally, the feedback optimization module continuously adjusts the energy consumption management strategy through the closed-loop control of real-time data to adapt to the actual operating conditions, forming a full-process intelligent energy consumption management system from prediction to adjustment to feedback optimization. Through this method, the system realizes a complete closed-loop control mechanism from peak power consumption prediction to energy consumption quota allocation, operation parameter adjustment and feedback optimization, which significantly improves energy utilization efficiency and system flexibility. The system can give priority to the operation of key equipment during peak power consumption, reduce energy waste, and take into account the actual needs of users to improve the home experience. In addition, the real-time monitoring and feedback optimization functions effectively reduce the deviation of energy consumption strategy execution, ensure system stability, and provide users with accurate energy-saving support, further improving the overall performance and reliability of the smart home system.

[0068] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A home intelligent control system based on embedded software, characterized in that: The system comprises: A data processing module, used to predict the peak power consumption time of home appliances based on user usage habit data; An energy consumption management module connected to the data processing module is used to set energy consumption limits for each household appliance according to the prediction results; The parameter adjustment module connected to the energy consumption management module is used to intelligently adjust the operating parameters of household appliances during peak power consumption periods, including collecting the current energy consumption demand of the equipment, comparing it with the allocated energy consumption limit, calculating the error, calculating the adjustment amount, and dynamically optimizing the equipment operating parameters. The specific formula is: u(t)=K p ×e(t); Among them, u(t) represents the equipment control quantity calculated according to the current energy consumption demand, K p represents the sensitivity coefficient for adjusting the device parameters, e(t) represents the difference between the actual energy consumption demand of the current device and the energy consumption limit allocated by the system, and t represents time; The feedback optimization module connected to the data processing module, the energy consumption management module and the parameter adjustment module is used to optimize the energy consumption management strategy of the household appliance through real-time monitoring feedback.

2. A home intelligent control system based on embedded software according to claim 1, characterized in that: The data processing module predicts the peak power consumption time of the household appliance based on the user usage habit data, including: Record the peak electricity consumption time of users every day to form a behavior data sequence. According to the time interval of peak electricity consumption and the data time span, calculate the speed of change of electricity consumption behavior. The specific formula is: v = d / T; Among them, v represents the speed of change of user's electricity consumption behavior, d represents the interval between peak electricity consumption times, and T represents the time span of user behavior data; The next peak time is estimated based on the speed of change and the result is sent to the energy consumption management module.

3. The home intelligent control system based on embedded software according to claim 1, characterized in that: The energy consumption management module sets the energy consumption limit of each household appliance according to the prediction result, including: Record the energy consumption changes of each household appliance under different energy consumption limits, and calculate the elasticity of the equipment to the energy consumption limit. The specific formula is: A = ΔQ / ΔP; Among them, A represents the flexibility of the equipment to the energy consumption limit, ΔQ represents the change in energy consumption, and ΔP represents the change in the limit.

4. The home intelligent control system based on embedded software according to claim 1, characterized in that: The feedback optimization module optimizes the energy consumption management strategy of household appliances through real-time monitoring feedback, including assigning energy consumption priority weights to each device, calculating the overall energy consumption management cost, and analyzing the optimization results. The specific formula is: Where C represents the overall energy consumption management cost, d i represents the actual operating parameters of the i-th household appliance under current conditions, w i It represents the energy consumption priority of the i-th household appliance, i represents the index number of the household appliance, and n represents the total number of all appliances.

5. The home intelligent control system based on embedded software according to claim 1, characterized in that: The S1 also includes collecting and analyzing user daily usage habit data, using historical data to build a user behavior model, predicting the usage frequency of each home appliance in different time periods based on the user behavior model, and predicting the peak power consumption period in the next week based on the power demand pattern.

6. A home intelligent control system based on embedded software according to claim 5, characterized in that: The method of predicting the usage frequency of each household appliance in different time periods based on the user behavior model includes analyzing the specific operation time of each household appliance by the user, summarizing and calculating the historical startup time and power consumption of each device, and analyzing and generating an average power consumption curve of each household appliance. If L×P>E, this period of time is marked as a high energy consumption stage, where L represents the predicted duration, P represents the power consumption per minute, and E represents the preset energy threshold value.

7. The home intelligent control system based on embedded software according to claim 6, characterized in that: The analysis generates an average power consumption curve for each household appliance, including classifying the types of household appliances, establishing a data table for the corresponding category of equipment, drawing a total power graph of all related appliances at different time points each day, and based on this graph drawing an average load graph for each type of appliance in a typical week, setting a threshold for the type of equipment, and if the average daily power of a certain type of equipment exceeds the threshold for that category, increasing attention to that category in the future.

8. The home intelligent control system based on embedded software according to claim 1, characterized in that: The data processing module also includes a storage unit for recording the user's historical electricity usage data and extracting electricity usage habit characteristics.

9. The home intelligent control system based on embedded software according to claim 1, characterized in that: The parameter adjustment module includes a proportional control unit and a real-time monitoring unit. The proportional control unit calculates the equipment adjustment amount according to a proportional control formula. The real-time monitoring unit is used to collect the current energy consumption state of the equipment and feed back the adjusted operating parameters.

10. A home intelligent control method based on embedded software, using the home intelligent control system based on embedded software according to any one of claims 1 to 9, characterized in that: The method comprises: S1. Predict the peak electricity consumption time of household appliances based on user usage habit data; S2. Set the energy consumption limit of each household appliance according to the prediction results; S3, intelligently adjust the operating parameters of household appliances during peak power consumption periods; S4. Optimize the energy consumption management strategy of household appliances through real-time monitoring feedback.

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