Intelligent household electrical appliance cluster power-saving control method based on user behavior analysis

Abstract

The invention discloses an intelligent household electrical appliance cluster power-saving control method based on user behavior analysis, and the method comprises the following steps: 1, collecting the behavior data of a user using an intelligent household electrical appliance through a sensor disposed on each intelligent household electrical appliance and a user interaction interface; 2, processing and analyzing the collected behavior data through a user behavior analysis module, and establishing a user use habit model; step 3, home appliance operation mode optimization: combining the energy consumption characteristics of each intelligent home appliance to formulate a plurality of alternative operation modes for each intelligent home appliance, and calculating the operation energy consumption of the intelligent home appliance in different alternative operation modes; and 4, comprehensively considering the alternative operation modes and the operation energy consumption of the intelligent household appliances and the real-time requirements of the user on the use of the household appliances, and coordinately controlling the intelligent household appliances to switch the corresponding alternative operation modes so as to form an operation combination which has the lowest energy consumption of the whole intelligent household appliances and does not influence the user experience.

Description

Technical Field

[0001] This invention relates to the field of energy-saving methods, and more specifically to an intelligent home appliance cluster energy-saving control method based on user behavior analysis. Background Technology

[0002] With the development of technology and the improvement of people's living standards, smart home appliances are becoming increasingly popular, and the number of smart home appliances in households is also increasing day by day. However, the simultaneous operation of a large number of smart home appliances has led to a sharp increase in household energy consumption, which not only increases the economic burden on users, but is also detrimental to energy conservation and environmental protection.

[0003] Currently, most existing energy-saving control methods for smart home appliances focus on individual appliances, optimizing their operating parameters to reduce energy consumption. However, this single-unit control approach fails to consider the collaborative relationships between multiple appliances, making it difficult to achieve optimal energy-saving results for the entire appliance cluster. Furthermore, some energy-saving control methods may negatively impact the user experience, such as excessively reducing the performance of appliances. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides a smart home appliance cluster energy-saving control method based on user behavior analysis, comprising the following steps: Step 1: User behavior analysis: Collect user behavior data on smart home appliances through sensors installed on each smart home appliance and user interaction interfaces; Step 2: Process and analyze the collected behavior data through a user behavior analysis module to establish a user usage habit model, which is used to determine the user's usage preferences and needs for each appliance at different time periods; Step 3: Appliance operation mode optimization: Based on the user usage habit model and combined with the energy consumption characteristics of each smart home appliance, formulate several alternative operation modes for each smart home appliance and calculate the operating energy consumption of the smart home appliance under different alternative operation modes; Step 4: Cluster collaborative control: Comprehensively consider the alternative operation modes and operating energy consumption of each smart home appliance, as well as the user's real-time needs for appliance use, coordinate and control the smart home appliances to switch to the corresponding alternative operation modes, so as to form an operating combination with the lowest overall energy consumption of smart home appliances without affecting the user experience.

[0005] Furthermore, in step one, the sensors include a temperature sensor, a humidity sensor, a current sensor, a voltage sensor, and an infrared sensor. The temperature sensor and humidity sensor are used to monitor the temperature and humidity of the home appliance's operating environment in real time. The current sensor and voltage sensor are used to collect the current and voltage changes during the operation of the smart home appliance. The infrared sensor is used to detect whether the user is active near the smart home appliance.

[0006] Furthermore, in step one, the user interaction interface includes a control panel for smart home appliances, a mobile terminal for connecting smart home appliances, and a voice interaction module. The control panel is used by the user to directly operate the smart home appliances, the mobile terminal is used by the user to control the smart home appliances remotely, and the voice interaction module is used to recognize the user's voice commands and convert these voice commands into analyzable data.

[0007] Furthermore, in step two, the user behavior analysis module includes a data preprocessing subunit and a user behavior analysis subunit. The data preprocessing subunit is used to clean, filter, and transform the collected raw behavior data; the user behavior analysis subunit is used to analyze the preprocessed behavior data using machine learning algorithms to form a user habit model.

[0008] Furthermore, step three includes an energy consumption characteristic analysis subunit and an operation mode optimization subunit. The energy consumption characteristic analysis subunit is used to analyze the energy consumption characteristics of each smart home appliance under different operation modes by combining the model and specifications of the smart home appliances and the data obtained in advance through energy consumption testing. The operation mode optimization subunit is used to generate multiple alternative operation modes for each smart home appliance based on the user habit model derived by the user behavior analysis subunit and the home appliance energy consumption data provided by the energy consumption characteristic analysis subunit.

[0009] Furthermore, the energy consumption characteristics include the relationship between power, operating time, and energy consumption under different operating modes.

[0010] Furthermore, in step four, when coordinating and controlling smart home appliances to switch to the corresponding alternative operating modes, a genetic algorithm and a multi-objective optimization algorithm are used.

[0011] Furthermore, it also includes step five, which involves real-time monitoring of user usage and operating status of smart home appliances. When user behavior or smart home appliance operating status changes, user behavior analysis, appliance operating mode optimization, and cluster collaborative control are re-performed to dynamically adjust the operating mode of smart home appliances.

[0012] Furthermore, it also includes a real-time monitoring subunit and a user feedback processing subunit. The real-time monitoring subunit is used to monitor user behavior, the operating status of smart home appliances, and the power grid supply situation, including electricity price fluctuations and power load information. The user feedback processing subunit is used to establish a user feedback channel to receive user feedback on the operating performance of home appliances.

[0013] Compared with the prior art, the beneficial effects of the present invention are:

[0014] 1. Achieving clustered energy saving: This invention coordinates the operation modes of multiple home appliances and takes into account the synergistic relationship between them, thereby achieving the optimal energy-saving effect of the entire home appliance cluster. Compared with the energy-saving control of a single home appliance, the energy-saving effect is more significant.

[0015] 2. No impact on user experience: The user habit model established based on user behavior analysis can accurately grasp the user's usage needs and preferences. When implementing energy-saving control, it ensures that the operating mode of home appliances conforms to user habits and does not affect the user's normal user experience.

[0016] 3. Strong dynamic adaptability: Through real-time monitoring and adjustment, it can respond promptly to changes in user behavior and appliance operating status, dynamically adjust the operating mode of appliances, and ensure optimal energy-saving effect and good user experience under various conditions.

[0017] Additional aspects and advantages of the invention will be set forth in the description which follows, and in some respects will be obvious from the description or may be learned by practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of the smart home appliance cluster energy-saving control method based on user behavior analysis according to the present invention. Detailed Implementation

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] The present invention will now be described in more detail. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should be noted that when an element is described as being "fixed to" another element, it can be directly on the other element, or one or more intermediate elements may exist between them. When an element is described as being "connected to" another element, it can be directly connected to the other element, or one or more intermediate elements may exist between them.

[0022] In the description of this invention, it should be noted that directional terms such as "front," "rear," "up," "down," "left," "right," "horizontal," "vertical," "horizontal," and "top," "bottom," etc., indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the scope of protection of this invention. The directional terms "inner" and "outer" refer to the inner or outer contours of each component itself. In the description of this invention, it should be noted that the use of terms such as "first" and "second" to define components is merely for the convenience of distinguishing the corresponding components. Unless otherwise stated, these terms have no special meaning and therefore should not be construed as limiting the scope of protection of this invention. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0023] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention.

[0024] Furthermore, the technical features involved in the different embodiments of this application described below can be combined with each other as long as they do not conflict with each other.

[0025] The preferred embodiments of the present invention will now be further described with reference to the accompanying drawings. A smart home appliance cluster energy-saving control method based on user behavior analysis includes the following steps: Step 1: User behavior analysis: Collect user behavior data on smart home appliances through sensors installed on each smart home appliance and user interaction interfaces; Step 2: Process and analyze the collected behavior data through a user behavior analysis module to establish a user usage habit model. The user usage habit model is used to determine the user's usage preferences and needs for each home appliance at different time periods; Step 3: Home appliance operation mode optimization: Based on the user usage habit model and combined with the energy consumption characteristics of each smart home appliance, formulate several alternative operation modes for each smart home appliance and calculate the operating energy consumption of the smart home appliance under different alternative operation modes; Step 4: Cluster collaborative control: Taking into account the alternative operation modes and operating energy consumption of each smart home appliance, as well as the user's real-time needs for home appliance use, coordinate and control the smart home appliances to switch to the corresponding alternative operation modes to form an operating combination with the lowest overall energy consumption of the smart home appliances without affecting the user experience.

[0026] As can be seen from the above embodiments, compared with the prior art, the present invention has the following beneficial effects: Achieving clustered energy saving: By coordinating the operating modes of multiple home appliances and considering the collaborative relationship between them, the present invention can achieve the optimal energy-saving effect of the entire home appliance cluster. Compared with the energy-saving control of a single home appliance, the energy-saving effect is more significant. Not affecting user experience: The user habit model established based on user behavior analysis can accurately grasp the user's usage needs and preferences. When performing energy-saving control, it ensures that the operating mode of the home appliances conforms to user habits and does not affect the user's normal user experience. Strong dynamic adaptability: Through real-time monitoring and adjustment steps, it can respond promptly to changes in user behavior and home appliance operating status, dynamically adjust the operating mode of the home appliances, and ensure that optimal energy-saving effect and a good user experience can be achieved under various conditions.

[0027] Building upon the above embodiments, sensors are distributed across various smart home appliances, including temperature sensors, humidity sensors, current sensors, voltage sensors, and infrared sensors. Temperature and humidity sensors monitor the temperature and humidity of the appliance's operating environment in real time, such as the ambient temperature around the air conditioner and the internal temperature of the refrigerator, providing data to determine whether the appliance's operation meets environmental requirements. Current and voltage sensors accurately collect changes in current and voltage during appliance operation, thereby calculating the appliance's real-time power and energy consumption. Infrared sensors detect whether a user is active near the appliance, helping to determine the user's intention to use the appliance; for example, when an infrared sensor detects a user approaching a television, it can predict that the user may want to turn on the television.

[0028] Building upon the above embodiments, the user interaction interface unit includes the appliance's control panel, a mobile terminal (e.g., a mobile phone) connected to the appliance, and a voice interaction module. The control panel allows users to directly operate the appliance, such as setting washing programs for the washing machine and adjusting the air conditioner temperature, while simultaneously recording user commands and settings. The mobile phone serves as a remote control interface, enabling users to control the appliance remotely and simultaneously recording their remote operation data, such as the time a user can pre-start the water heater via their phone. The voice interaction module can recognize user voice commands, such as "turn on the living room lights" or "set the air conditioner to 26 degrees," and convert these commands into analyzable data, while also recording the time and content of the commands.

[0029] Building upon the above embodiments, in step two, the user behavior analysis module includes a data preprocessing subunit and a user behavior analysis subunit. The data preprocessing subunit cleans, filters, and transforms the collected raw data. Since data acquisition may be subject to interference, resulting in outliers or missing values, this subunit removes outlier data and fills in missing data appropriately to ensure data integrity and accuracy. For example, it filters out abnormal current data caused by instantaneous fluctuations in the current sensor and makes reasonable inferences to supplement occasionally missing operation records in the user behavior data. Simultaneously, it converts data from different formats into a unified format to facilitate subsequent analysis and processing. The user behavior analysis subunit uses machine learning algorithms (such as decision tree algorithms, neural network algorithms, recurrent neural networks, etc.) to perform in-depth analysis of the preprocessed user behavior data. By learning from data such as the time, frequency, and settings of users' use of home appliances, it establishes a user habit model to accurately identify users' usage preferences and needs for various home appliances in different time periods and scenarios. For example, the analysis revealed that users frequently use coffee machines and bread machines between 7 and 8 a.m. on weekdays, and have a specific preference for the toasting level of the bread machine; it also identified patterns such as users preferring to set the air conditioner temperature to around 25 degrees Celsius between 8 and 10 p.m.

[0030] Building upon the above embodiments, step three includes an energy consumption characteristic analysis subunit and an operation mode optimization subunit. Combining the appliance model, specifications, and data obtained through pre-testing, the subunit analyzes the energy consumption characteristics of each appliance under different operation modes. It establishes the correlation between appliance operation modes and energy consumption, clarifying the correspondence between power, operating time, and energy consumption under different modes. It also considers the impact of environmental factors on energy consumption, such as the impact of outdoor temperature changes on air conditioner energy consumption, thus providing accurate energy consumption data support for subsequent operation mode optimization. The operation mode optimization subunit, based on user habit models and the energy consumption characteristics of each appliance, develops multiple alternative operation modes for each appliance and calculates the energy consumption of each alternative mode. The energy consumption characteristics of each appliance include the relationship between power, operating time, and energy consumption under different operation modes, obtained in advance through energy consumption testing. For example, air conditioners can be set with alternative operating modes such as energy-saving mode, standard mode, and powerful mode, with different power and energy consumption in different modes; washing machines can be set with alternative operating modes such as different washing programs and spin speeds, with varying energy consumption.

[0031] In this embodiment, the sensors, distributed across various smart home appliances, collect real-time environmental parameters (such as ambient temperature around the air conditioner and internal humidity of the refrigerator), operational status parameters (such as real-time current and voltage of the appliances), and user interaction signals (such as the infrared sensor's signal when a user approaches the television). Simultaneously, the user interaction interface unit collects user operation commands (such as setting washing programs for the washing machine and adjusting the air conditioner temperature via voice commands), set parameters (such as air conditioner temperature settings and washing machine spin speed), and operation times through the appliance control panel, mobile app, and voice interaction module. This raw data is transmitted to the user behavior analysis module via communication lines. Upon receiving the raw data, the data preprocessing subunit processes it. This subunit removes abnormal data caused by sensor interference (such as instantaneous fluctuations in current sensor data), appropriately fills in missing data (such as supplementing occasionally missed appliance operation records), and converts data of different formats into a unified format to prepare for subsequent analysis. Next, the user behavior analysis subunit uses machine learning algorithms such as decision trees, neural networks, and recurrent neural networks to conduct in-depth analysis of the preprocessed user behavior data. By mining user patterns in appliance usage (e.g., using a coffee machine between 7-8 am on weekdays), frequency characteristics (e.g., using a vacuum cleaner 3 times a week), and setting preferences (e.g., air conditioner temperature is always set to 25 degrees Celsius), a user habit model is established to clarify user needs for each appliance in different scenarios. Simultaneously, the energy consumption characteristic analysis subunit, combining appliance models, specifications, and pre-acquired energy consumption test data, analyzes the energy consumption characteristics of each appliance under different operating modes, establishing the correlation between operating modes and energy consumption. It also considers the impact of environmental factors (e.g., outdoor temperature) on appliance energy consumption, providing accurate energy consumption data for subsequent operating mode optimization. The operating mode optimization subunit, based on the user habit model and appliance energy consumption characteristic data, generates multiple alternative operating modes for each appliance (e.g., "temporary heating" and "nighttime silent energy-saving" modes for refrigerators), calculates the energy consumption of each mode, and dynamically adjusts the alternative modes according to real-time user needs and environmental changes.

[0032] Building upon the aforementioned embodiments, in step four, when coordinating the switching of smart home appliances to their respective alternative operating modes, a genetic algorithm and a multi-objective optimization algorithm are employed. This allows for comprehensive consideration of each appliance's alternative operating modes, energy consumption, and real-time user needs, enabling coordinated control of the operation of multiple appliances. The optimal combination of appliances is determined with the goal of minimizing overall energy consumption without impacting user experience. For example, when it is detected that a user is simultaneously using an electric water heater and an electric oven, this subunit analyzes the potential high-energy-consumption coupling problem caused by both operating at high power simultaneously, and then rationally arranges their operating time or adjusts their operating power to avoid energy consumption accumulation. Simultaneously, an energy-consumption coupling model between appliances is established to monitor the mutual influence of multiple appliances operating simultaneously in real time and adjust the control strategy accordingly.

[0033] Building upon the above embodiments, the method further includes step five: real-time monitoring of user usage and operating status of smart home appliances. When user behavior or smart appliance operating status changes, user behavior analysis, appliance operating mode optimization, and cluster collaborative control are re-performed to dynamically adjust the operating mode of the smart home appliances. Step five includes a real-time monitoring subunit and a user feedback processing subunit. The real-time monitoring subunit monitors user behavior, smart appliance operating status, and power grid supply conditions, including electricity price fluctuations and power load information. The user feedback processing subunit establishes a user feedback channel to receive user feedback on the appliance operating effects. In addition to monitoring user behavior and appliance operating status, the real-time monitoring subunit also monitors power grid supply conditions, including electricity price fluctuations and power load information. By tracking this data in real time, the system can formulate differentiated energy-saving strategies for different electricity consumption periods. For example, during peak electricity price periods, high-energy-consuming appliances are prioritized to enter low-power operating modes. Simultaneously, the operating parameters of the appliances are continuously monitored to ensure they are within normal ranges, such as the compressor operating frequency of air conditioners and the speed of washing machines, ensuring safe and stable operation of the appliances. The user feedback processing subunit establishes user feedback channels, such as feedback interfaces within mobile apps and feedback buttons on appliance control panels, to receive user evaluations and suggestions regarding appliance performance, energy efficiency, and user experience. It categorizes and processes user feedback; for example, if a user reports that the temperature is too low in the air conditioner's energy-saving mode, this subunit will transmit this information to the data processing and analysis module and the intelligent decision-making and control module as an important basis for adjusting the air conditioner's operating mode parameters.

[0034] Building upon the above embodiments, the method can further utilize a real-time monitoring subunit to continuously track fluctuations in grid electricity prices (e.g., peak-hour price of 1.5 yuan / kWh and off-peak price of 0.5 yuan / kWh) and users' appliance usage habits (e.g., users typically use washing machines and water heaters between 8:00 PM and 10:00 PM). The system combines this data to automatically adjust the operating times of high-energy-consuming appliances, shifting the operating time of devices such as washing machines and water heaters from peak electricity price periods to off-peak periods.

[0035] For example, if a user previously used a water heater for 1 hour (2000W power) at 9 PM daily, the peak-hour electricity cost would be 2000W × 1 hour × 1.5 yuan / kWh = 3 yuan. After adjusting the operation to 2 AM (off-peak hour), the cost drops to 2000W × 1 hour × 0.5 yuan / kWh = 1 yuan, saving 2 yuan per day and approximately 730 yuan per year. Simultaneously, the system can automatically generate peak-valley electricity consumption analysis reports, assisting users in applying for peak-valley electricity pricing packages from the power grid company to further maximize energy-saving benefits.

[0036] The details of the exemplary embodiments described above are provided, and the invention can be implemented in other specific forms without departing from the spirit or essential characteristics 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. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the invention.

Claims

1. A method for energy-saving control of intelligent home appliance clusters based on user behavior analysis, characterized in that, Includes the following steps: Step 1: User Behavior Analysis: Collect user behavior data by using sensors installed on various smart home appliances and through user interaction interfaces; Step 2: Process and analyze the collected behavioral data through the user behavior analysis module to establish a user habit model. The user habit model is used to determine the user's usage preferences and needs for various home appliances at different time periods. Step 3: Optimize the operation mode of home appliances: Based on the user habit model and the energy consumption characteristics of each smart home appliance, formulate several alternative operation modes for each smart home appliance, and calculate the operating energy consumption of the smart home appliance under different alternative operation modes. Step 4: Cluster Collaborative Control: Taking into account the alternative operating modes and energy consumption of each smart home appliance, as well as the user's real-time needs for appliance use, coordinate and control the smart home appliances to switch to the corresponding alternative operating modes, so as to form an operating combination of the smart home appliances with the lowest overall energy consumption and without affecting the user experience.

2. The smart home appliance cluster energy-saving control method based on user behavior analysis according to claim 1, characterized in that, In step one, the sensors include a temperature sensor, a humidity sensor, a current sensor, a voltage sensor, and an infrared sensor. The temperature sensor and humidity sensor are used to monitor the temperature and humidity of the home appliance's operating environment in real time. The current sensor and voltage sensor are used to collect the current and voltage changes during the operation of the smart home appliance. The infrared sensor is used to detect whether the user is active near the smart home appliance.

3. The intelligent home appliance cluster energy-saving control method based on user behavior analysis according to claim 1, characterized in that, In step one, the user interaction interface includes a control panel for smart home appliances, a mobile terminal for connecting smart home appliances, and a voice interaction module. The control panel is used by the user to directly operate the smart home appliances, the mobile terminal is used by the user to control the smart home appliances remotely, and the voice interaction module is used to recognize the user's voice commands and convert these voice commands into analyzable data.

4. The intelligent home appliance cluster energy-saving control method based on user behavior analysis according to claim 1, characterized in that, In step two, the user behavior analysis module includes a data preprocessing subunit and a user behavior analysis subunit. The data preprocessing subunit is used to clean, filter, and transform the collected raw behavior data. The user behavior analysis subunit is used to analyze the preprocessed behavior data using machine learning algorithms to form a user habit model.

5. The intelligent home appliance cluster energy-saving control method based on user behavior analysis according to claim 4, characterized in that, Step three includes an energy consumption characteristic analysis subunit and an operation mode optimization subunit. The energy consumption characteristic analysis subunit is used to analyze the energy consumption characteristics of each smart home appliance under different operation modes by combining the model and specifications of the smart home appliances and the data obtained in advance through energy consumption testing. The operation mode optimization subunit is used to generate multiple alternative operation modes for each smart home appliance based on the user habit model derived by the user behavior analysis subunit and the home appliance energy consumption data provided by the energy consumption characteristic analysis subunit.

6. The intelligent home appliance cluster energy-saving control method based on user behavior analysis according to claim 5, characterized in that, The energy consumption characteristics include the relationship between power, operating time and energy consumption under different operating modes.

7. The intelligent home appliance cluster energy-saving control method based on user behavior analysis according to claim 1, characterized in that, In step four, when coordinating and controlling smart home appliances to switch to the corresponding alternative operating modes, a genetic algorithm and a multi-objective optimization algorithm are used.

8. The intelligent home appliance cluster energy-saving control method based on user behavior analysis according to claim 1, characterized in that, It also includes step five, which involves real-time monitoring of user usage and operating status of smart home appliances. When user behavior or smart home appliance operating status changes, user behavior analysis, appliance operating mode optimization, and cluster collaborative control are re-performed to dynamically adjust the operating mode of smart home appliances.

9. The intelligent home appliance cluster energy-saving control method based on user behavior analysis according to claim 8, characterized in that, It also includes a real-time monitoring subunit and a user feedback processing subunit. The real-time monitoring subunit is used to monitor user behavior, the operating status of smart home appliances, and the power grid supply situation, including electricity price fluctuations and power load information. The user feedback processing subunit is used to establish a user feedback channel to receive user feedback on the operating performance of home appliances.

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