Smart home energy management optimization method in Internet of Things environment

Abstract

The invention discloses a smart home energy management optimization method in an Internet of Things environment. According to the method, the Internet of Things technology is utilized, intelligent identification and classified management are carried out on equipment, multiple energy management strategies are preset, and a user can conveniently and autonomously adjust; energy data is monitored in real time through a sensor and an intelligent electric meter, an energy consumption rule is mined by using a big data analysis technology, and a demand trend is predicted; constructing an intelligent energy dispatching center, and formulating a dynamic energy distribution scheme by using an optimization algorithm; a mobile phone application and an intelligent control panel are developed, and a user is supported to check data, control equipment and carry out personalized customization; and meanwhile, equipment firmware upgrading and remote management are realized. According to the invention, energy consumption can be reduced, cost can be controlled, equipment collaboration and energy distribution efficiency can be improved, user experience can be enhanced, system maintenance and upgrading are facilitated, and intelligent household energy management is more efficient, intelligent and flexible so as to adapt to diversified household requirements.

Description

Technical Field

[0001] The present invention belongs to the field of smart home energy management, and specifically is a smart home energy management optimization method based on the Internet of Things. The present invention aims to achieve efficient utilization and management of home energy by integrating multiple links such as device identification and management, energy monitoring, intelligent scheduling, data analysis and prediction, and user interaction, so as to improve energy utilization efficiency, reduce energy consumption, enhance user experience and provide intelligent energy management solutions. Background Art

[0002] With the rapid development of Internet of Things technology, smart home devices have been widely used in modern families, covering many fields such as lighting, home appliances, security, entertainment, etc., greatly improving people's quality of life. However, the popularity of smart homes has also brought about a rapid increase in household energy consumption, exposing many problems in energy management. Many smart home devices have defects in energy consumption management, such as excessive standby energy consumption, lack of flexibility in operation mode, and inability to adjust intelligently according to environmental changes and user habits, resulting in a large amount of energy waste. For example, some smart home appliances continue to consume power when in standby mode, and lighting equipment cannot automatically adjust brightness according to personnel activities and natural light. At the same time, it is difficult for users to accurately grasp the energy consumption of their homes, and the interaction with the devices is not convenient and intelligent enough, so they cannot effectively participate in energy management.

[0003] The ability of devices to work together is insufficient, and the dynamic changes in energy supply (such as peak and valley electricity prices, fluctuations in renewable energy generation) and the diverse needs of users (such as the priority of using different devices and comfort requirements) are not fully considered, making it impossible to achieve optimal energy allocation. In addition, the existing smart home energy management system has poor flexibility and is difficult to adapt to the energy usage scenarios and user demand changes of different households, and cannot provide personalized energy management solutions.

[0004] The rise of related technologies such as the Internet of Things and big data has brought new opportunities to solve the problem of smart home energy management. Through the Internet of Things, devices can be interconnected, data can be transmitted and shared in real time, and it provides a technical foundation for the realization of intelligent energy management. However, at present, how to fully tap the potential of the Internet of Things and build an efficient and intelligent smart home energy management system still faces many challenges. Summary of the invention

[0005] The purpose of the present invention is to provide a smart home energy management optimization method in an Internet of Things environment, overcome the shortcomings of existing smart home energy management technologies, achieve efficient utilization of household energy, reduce energy consumption, improve user experience, and ensure that the system can adapt to diverse household energy usage scenarios and changes in user needs.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0007] A smart home energy management optimization method in an Internet of Things environment comprises the following steps: (S1) Intelligent identification and classification management of equipment. By using IoT technology and other related technologies, the physical characteristics of the equipment (such as equipment appearance, size, interface, etc.), communication protocol characteristics (such as protocol type, port number, data format, etc.), and operation data characteristics (such as energy consumption mode, operation frequency, working time, etc.) are comprehensively analyzed to automatically identify and accurately classify smart home devices, covering lighting equipment, household appliances, energy storage equipment, security equipment, entertainment equipment and other types, and the boundaries of each category are clear and the basis for distinction is clear. A rich set of energy management strategies is pre-set for different types of equipment. Lighting equipment can automatically and continuously adjust the brightness, color temperature and switch status according to ambient light intensity, personnel activities and time information; household appliances can intelligently optimize the operating power, duration and start and stop time according to factors such as real-time electricity cost, equipment life prediction, and user usage habit model. Users can independently adjust some or all parameters in the energy management strategy set of various types of equipment. The adjustment methods include manually entering parameter values, selecting preset templates, or automatically generating suggestions based on user behavior habits and confirming them. (S2) Real-time energy monitoring and data analysis. Rationally arrange and install a variety of monitoring equipment such as smart meters, temperature sensors, humidity sensors, light sensors, human infrared sensors, smoke sensors, air quality sensors, water flow sensors, etc., to collect all-round data related to household energy consumption in real time, including the power consumption of each device, power consumption, power consumption time, power consumption waveform, ambient temperature, humidity, light intensity, personnel activity location and trajectory, smoke concentration, air quality indicators, water flow and water equipment information, etc. Use big data analysis technologies such as cluster analysis, association rule mining, decision tree analysis, neural network algorithms, etc. to conduct in-depth mining and analysis of massive real-time and historical data. Explore the energy consumption patterns, abnormal situations and complex relationships between influencing factors under different seasons, months, date types, time periods, family areas, personnel combinations, and equipment combinations, and build a multivariate prediction model to predict the energy demand trend, consumption peak and valley time points and corresponding energy consumption type distribution in different time scales in the future (short-term 1-7 days, medium-term 1-3 months, long-term 1-12 months). (S3) Intelligent energy dispatching and optimization algorithms. Build an intelligent energy dispatching center with high intelligence, adaptability and distributed collaboration capabilities to obtain and comprehensively consider energy supply conditions in real time, including the dynamic division of peak and valley electricity price periods of the power grid, the power generation, electricity, efficiency and remaining electricity of renewable energy power generation equipment (solar panels, wind turbines, hydropower generators, etc.), the electricity, charge and discharge status, power, efficiency and health status of energy storage equipment (battery energy storage, capacitor energy storage, etc.), and user diversified needs, such as equipment use priority (multi-level settings such as critical, commonly used, and occasionally used equipment), quantitative indicators of comfort requirements (temperature, humidity, light intensity range, etc.), energy saving and cost control target setting (energy saving percentage within a preset time period, electricity budget, etc.). Use optimization algorithms such as mixed integer linear programming algorithms, dynamic programming algorithms, and reinforcement learning algorithms to formulate energy allocation plans that meet multiple constraints (energy supply, equipment operation, and user demand constraints). During peak power periods, energy storage and renewable energy equipment are prioritized for power supply. The operating power, mode, and start / stop status of air conditioners, water heaters, lighting, and other equipment are accurately adjusted based on the user's real-time location, activity status, and historical habit data. During valley power periods, energy storage charging, equipment maintenance, and low-priority tasks are reasonably arranged. The energy allocation plan can be dynamically adjusted in seconds based on real-time changes in energy supply and user demand. The adjustment methods include real-time recalculation, automatic adjustment based on preset rules, or instant modification based on real-time user intervention instructions. (S4) User interaction and personalized settings. Develop mobile applications that support mainstream operating systems such as iOS, Android, and Hongmeng, or intelligent control panels with multiple interactive methods such as touch, voice, and gesture operations, to build a two-way interactive channel between users and the energy management system. Users can use this to view detailed and intuitive energy consumption data reports (statistics of power consumption, electricity costs, etc. by device type, region, and time), animated demonstrations of device operation status (graphical display of device start and stop, operation mode, fault status, etc.), energy management strategy execution effect evaluation reports (quantitative evaluation of energy saving and cost control effects), and perform manual and flexible control (remote switching of devices, adjustment of parameters, etc.) and personalized settings. Personalized settings cover multi-scenario energy-saving modes (sleep, party, outing mode, etc., each mode contains different device preset energy-saving parameter sets), custom device operation schedules (accurate to seconds, supporting periodic and one-time settings), device priority settings (sorted according to user rules), energy usage reminder threshold settings (power consumption, electricity fee thresholds, automatic reminders when reaching the thresholds), energy management rule creation (automatically executing device operations or adjusting strategies under specific conditions), etc. The system automatically and intelligently adjusts energy management strategies according to user settings, the mobile application presents data in a variety of visual forms, and the smart control panel is easy to operate. (S5) Equipment firmware upgrade and remote management. Establish a safe, reliable, efficient and stable equipment firmware upgrade mechanism, support automatic detection and update, manual trigger update and scheduled update, etc. During the upgrade process, the firmware file is encrypted and transmitted, integrity checked and version compatibility checked to ensure the security of equipment data, stable operation and functional compatibility, so that the equipment maintains the best performance and energy saving state. Realize the remote management function, users or managers can remotely monitor the equipment energy management parameters (real-time energy consumption, operating status, fault code, health, energy conversion efficiency, etc.) in real time, and make precise adjustments remotely when necessary (modify operating parameters, start self-test program, etc.). The system regularly uses advanced detection technology to conduct comprehensive and in-depth monitoring of the equipment. If a fault or abnormality (hardware fault, software abnormality, communication interruption, performance degradation, security vulnerability, etc.) is found, it will immediately send an alarm to the user in a variety of ways (SMS, push notification, sound and light alarm, email notification, etc.) and provide detailed diagnostic suggestions (fault cause analysis, solution, maintenance guidance, etc.). Firmware upgrades can be automatically pushed or manually selected according to user preset rules to ensure efficient and flexible equipment management.

[0008] Compared with the prior art, the present invention has the following beneficial effects: (1) Significantly reduce standby energy consumption through intelligent equipment management and optimized operation mode. The intelligent lighting system automatically adjusts the brightness and switch status according to the ambient light intensity, personnel activities and time information, avoiding unnecessary energy consumption. Household appliances use real-time electricity costs, equipment life predictions and user usage habit models to optimize operating power, duration and start and stop time. For example, high-energy consumption equipment is operated during low electricity price periods, and the equipment automatically enters a low-power state when idle. With the continuous development of technology and the gradual expansion of application scenarios, there is potential for further improvement in actual energy-saving effects. (2) Real-time monitoring and data analysis provide users and systems with rich and accurate data and in-depth insights into patterns. Users can clearly understand the energy usage status of each device and the family as a whole based on detailed energy consumption data, and then adjust device usage habits in a targeted manner, such as reducing the use of high-energy-consuming devices during peak energy consumption periods. Relying on advanced big data analysis technology, the system accurately predicts key information such as energy demand trends, the time points of peak and valley consumption, so as to reasonably plan energy allocation in advance. Taking into account the current energy market price fluctuation patterns, research results on user electricity behavior patterns, and the unique advantages of the present invention in optimizing energy management strategies, it is expected to effectively reduce household electricity bills, help users achieve energy-saving goals, and reasonably control household energy costs. (3) The intelligent energy dispatch center realizes dynamic energy distribution based on multiple factors and achieves efficient collaboration between devices. When renewable energy generation is sufficient, electricity is allocated to devices suitable for flexible electricity use first to improve the utilization rate of clean energy; when energy supply is tight, the stable operation of key equipment is guaranteed to ensure that basic family life and network functions are not affected. User interaction and personalized settings cover a variety of practical functions. The multi-scenario energy-saving mode can switch energy-saving configurations with one click according to different life scenarios. The customized device operation schedule can be accurate to the second level to meet the special needs of users. The device priority setting allows users to flexibly arrange the operation order of devices according to their own habits and needs. These functions fully meet the diverse and personalized needs of users, effectively improve users' participation and satisfaction in smart home energy management, and thus effectively improve the quality of life of users. (4) Device firmware upgrades and remote management mechanisms give the system better maintainability and good scalability. Regular device status monitoring can detect potential problems in a timely manner, and the remote management function allows technicians to remotely diagnose faults and accurately adjust device parameters, reducing the manpower, material costs and time consumption of on-site maintenance. A variety of flexible firmware upgrade methods such as automatic detection updates, manual trigger updates and scheduled updates ensure that the device can obtain the latest functions and performance optimizations in a timely manner, extend the service life of the device, and reduce maintenance costs caused by device failures or performance degradation, so that the system can continue to adapt to the dynamic changes in home energy management needs and the evolutionary trend of technological development, and always maintain an efficient and stable operating state. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 This is a system layered architecture diagram of the method of the present invention.

[0010] Figure 2 This is a flow chart of energy monitoring data analysis in the present invention.

[0011] Figure 3 This is a schematic diagram of intelligent energy scheduling in the present invention. DETAILED DESCRIPTION

[0012] Equipment installation planning. Determine the installation locations of smart meters, various sensors, smart appliances, lighting equipment, sockets, switches, and energy storage devices based on the home layout and energy management needs. For example, install smart meters and sensors in appropriate locations in main rooms such as living rooms and bedrooms to ensure accurate monitoring of energy consumption and environmental information; install smart sockets and switches near electrical equipment to facilitate control of equipment power.

[0013] Device connection and configuration. Use a combination of wired (Ethernet, RS-485, etc.) and wireless (Bluetooth, Wi-Fi, ZigBee, LoRaWAN, NB-IoT, etc.) methods to connect devices and build a smart home device network. During the connection process, set the device ID, network parameters (IP address, subnet mask, gateway, etc.) and communication protocol parameters to ensure that the device can stably access the IoT communication network and realize data interaction with the energy management center. For example, smart home appliances connect to the home network via Wi-Fi, sensors communicate with the smart gateway via the ZigBee network, and the smart gateway transmits data to the energy management center.

[0014] Smart meter data collection. Smart meters use high-precision measurement chips to monitor the total household power consumption and the power consumption of each branch circuit in real time, and collect data such as power consumption, power consumption time, and power consumption waveform at set time intervals (such as once a minute). The collected data is initially processed (format conversion, encryption, etc.) and transmitted to the data collection module of the energy management center through the network communication layer.

[0015] Sensor data collection. Sensors such as temperature, humidity, light, human infrared, smoke, air quality, and water flow collect data in real time. For example, the temperature sensor collects ambient temperature once a second, and the human infrared sensor detects human activity in real time. The collected data is processed and converted inside the sensor and then transmitted to the energy management center via wireless communication (such as ZigBee and Wi-Fi).

[0016] Data cleaning and preprocessing. After receiving the data, the data acquisition module removes abnormal values ​​(such as incorrect power readings, unreasonable temperature values) and noise data (sensor fluctuation data), unifies the data format and performs normalization processing (such as converting power consumption to kilowatt-hours and normalizing temperature to the range of 0-1) to facilitate subsequent analysis.

[0017] Feature extraction and analysis. Use data analysis technology to extract key features, such as equipment energy consumption characteristics (average energy consumption, peak, valley), environmental characteristics (average temperature, humidity trend, light duration), time characteristics (weekdays / weekends, time periods), analyze the relationship between energy consumption patterns and the environment, time, and equipment operating status, and provide a basis for strategy formulation.

[0018] Data mining and pattern exploration. Use cluster analysis, association rule mining, decision tree analysis, neural network algorithms, etc. to mine data. Cluster analysis classifies energy consumption patterns; association rule mining discovers equipment usage associations; decision tree analysis makes optimal decisions; neural network algorithm modeling and prediction, and builds a prediction model to predict future energy demand trends, consumption peak and valley time points, and corresponding energy consumption type distribution.

[0019] Strategy formulation and optimization. Based on the data analysis results, the strategy formulation module formulates strategies such as equipment operating power adjustment, operating time adjustment, energy allocation priority, equipment maintenance, and energy procurement. For example, when energy demand peaks and electricity prices are high, the power of non-critical equipment is reduced; maintenance tasks are scheduled based on equipment life prediction. Strategies are updated and optimized in real time based on user needs and real-time changes in energy supply.

[0020] Generation and transmission of control instructions. The device control module generates control instructions based on the energy management strategy, including equipment start and stop, operation mode adjustment (such as air conditioning cooling and heating mode switching, wind speed adjustment), operation power setting (such as washing machine washing power adjustment), etc. After the instructions are encrypted and encoded, they are transmitted to the corresponding device at the device layer through the network communication layer.

[0021] Equipment execution and feedback. Smart home appliances, lighting equipment, sockets, switches, etc. at the equipment level receive instructions and parse and execute them. For example, smart air conditioners adjust the working state of the compressor according to the temperature setting and operation mode instructions. After the equipment executes, it returns the execution result feedback information (such as whether it is successfully executed and the current operation status) to the energy management center for subsequent monitoring and adjustment.

[0022] Energy dispatch execution. Dynamically allocate energy according to energy supply and demand. For example, during peak power periods, energy storage devices are used first to supply power, reducing dependence on high-priced power from the power grid. The system monitors the power of energy storage devices in real time. When the power is lower than the set threshold, the charging time is reasonably arranged according to the real-time power price of the power grid and the household power demand, such as charging during valley power periods. When renewable energy generation is sufficient (such as solar power generation reaching a certain level), electricity is allocated first to low-priority but high-energy-consuming devices (such as electric water heaters and electric vehicle chargers), making full use of clean energy and reducing the consumption of traditional energy. At the same time, according to the device priority and real-time demand set by the user, ensure that key equipment (such as medical equipment, security monitoring equipment, etc.) can obtain sufficient energy supply at any time, and for non-critical equipment, appropriate power limitation or shutdown operations are performed when energy is tight, so as to achieve efficient use and reasonable allocation of energy.

[0023] Mobile application functions are realized. The mobile application provides functions such as energy consumption data display (charts, curves, 3D models to display power consumption, electricity charges, etc.), remote control of equipment (switches, parameter adjustment), personalized settings (multi-scenario energy-saving mode, equipment operation schedule, priority setting, reminder threshold setting, energy management rule creation) and system information push (real-time energy consumption reminder, energy-saving suggestions, fault warning, report push, upgrade notification). Users can operate anytime and anywhere to achieve in-depth participation in smart home energy management.

[0024] Smart control panel operation. The smart control panel uses touch, voice, gesture operation and other methods, and has a simple device control interface (one-touch operation button, slider control), energy status display (real-time power, electricity price, consumption trend) and emergency stop function. Users can easily control devices and check energy status at home, and can quickly cut off the power supply of the device to ensure safety in an emergency.

[0025] Equipment status monitoring and alarm. The system regularly monitors the equipment status, including real-time energy consumption, operating status, fault code, health, energy conversion efficiency, etc. When a fault or abnormality (hardware failure, software abnormality, communication interruption, performance degradation, security vulnerability, etc.) is found, an alarm is sent to the user via SMS, push notification, sound and light alarm, email notification, etc., and detailed diagnostic suggestions (fault cause analysis, solution, maintenance guidance) are provided.

[0026] Firmware upgrade management. Device firmware upgrade supports automatic detection and update (regular check and push), manual trigger update (user manually check and choose to upgrade) and scheduled update (user set the device to automatically upgrade during idle time). During the upgrade process, the firmware file is encrypted for transmission, integrity check and version compatibility check to ensure device data security, stable operation and functional compatibility. After the upgrade is completed, the device function is automatically tested to ensure normal operation and energy management function optimization.

Claims

1. A smart home energy management optimization method in an Internet of Things environment, characterized in that: The following steps are involved: Equipment intelligent identification and classification management steps (S1): Use IoT and other related technologies to achieve comprehensive and automated identification of smart home devices. By analyzing one or more of the physical characteristics, communication protocol characteristics, and operating data characteristics of the devices, the devices are classified into multiple types, such as lighting equipment, household appliances, energy storage equipment, security equipment, and entertainment equipment. Each category has a clear and unique basis for distinction. Energy management strategy sets containing multiple control parameters and operating modes are pre-set for each type of equipment. Lighting equipment can automatically and continuously adjust brightness, color temperature and switch status according to changes in ambient light intensity, personnel activities and time information. Home appliances can intelligently optimize operating power, operating time and start and stop time based on one or more factors in real-time electricity costs, equipment life predictions, and user usage habit models. Users can independently adjust some or all parameters in the energy management strategy set for each type of equipment. Real-time energy monitoring and data analysis steps (S2): Reasonably arrange and install various types of monitoring equipment such as smart meters, temperature sensors, humidity sensors, light sensors, human infrared sensors, smoke sensors, air quality sensors, water flow sensors, etc. to collect all-round data related to household energy consumption in real time, including but not limited to the power consumption, power consumption, power consumption time, power consumption waveform, ambient temperature, ambient humidity, light intensity, personnel activity location and trajectory, smoke concentration, air quality indicators, water flow and water-using equipment information of each device; Using big data analysis technologies including but not limited to cluster analysis, association rule mining, decision tree analysis, and neural network algorithms, we conduct multi-dimensional in-depth mining and detailed analysis of the massive real-time and historical data collected to find out the complex relationship between energy consumption patterns, abnormal energy consumption, and factors affecting energy consumption under different seasons, months, date types (weekdays, weekends, holidays), time periods (hourly subdivisions), household areas, personnel combinations, and equipment combinations, and build a multivariate prediction model based on this to predict the future at different time scales (short-term: 1-7 days; Medium term: 1-3 months; Long-term (1-12 months) energy demand trends, the time points of energy consumption peaks and valleys, and the corresponding energy consumption type distribution; Smart energy scheduling and optimization algorithm steps (S3): Build an intelligent energy dispatching center with high intelligence, adaptability and distributed collaboration capabilities. The center obtains and comprehensively considers the current energy supply status in real time, including but not limited to the real-time dynamic division of peak and valley electricity price periods of the power grid, the real-time power generation power, power generation, power generation efficiency and remaining power of renewable energy power generation equipment (solar panels, wind turbines, hydropower generators, etc.), the current power, charge and discharge status, charge and discharge power, charge and discharge efficiency and health status of energy storage equipment (battery energy storage, capacitor energy storage, etc.), and the diverse needs of users, such as multi-level setting of equipment use priority (critical equipment, commonly used equipment, occasionally used equipment, etc.), quantitative indicators of comfort requirements (temperature range, humidity range, light intensity range, etc.), energy saving target setting (energy saving percentage within a preset time period), and cost control target setting (electricity fee budget within a preset time period); Use carefully designed, optimized and dynamically adaptive algorithms (such as mixed integer linear programming algorithms, dynamic programming algorithms, reinforcement learning algorithms, etc.) to develop a scientific, reasonable, energy-efficient and multi-constraint (energy supply constraints, equipment operation constraints, user demand constraints) energy allocation plan. During peak power periods, energy storage equipment and renewable energy equipment are preferentially and intelligently called for power supply. At the same time, according to the user's real-time location information, activity status information and historical habit data, the operating power, operation mode and start-stop status of various equipment such as air conditioners, water heaters, and lighting equipment are accurately adjusted. During valley power periods, energy storage equipment charging, equipment maintenance tasks and low-priority tasks are reasonably arranged, and the energy allocation plan can respond to adjustments in seconds according to real-time changes in energy supply and user demand. User interaction and personalization step (S4): Develop mobile applications (supporting mainstream operating systems such as iOS, Android, and Hongmeng) or smart control panels (with multiple interaction methods such as touch operation, voice operation, and gesture operation) that are highly user-friendly, fully functional, and cross-platform compatible, and build an efficient, real-time, and two-way interaction channel between users and energy management systems; With the help of the above-mentioned interactive channels, users can view detailed and intuitive energy consumption data reports (including electricity consumption and electricity charges classified by device type, region, and time), equipment operation status animation demonstration (graphically displaying the start and stop, operation mode, fault and other status of the equipment), energy management strategy execution effect evaluation report (using quantitative indicators to evaluate energy-saving effects, cost control effects, etc.), and perform manual and flexible control (remote switching of equipment, adjustment of equipment parameters, etc.) and personalized setting operations anytime and anywhere, among which personalized settings cover a wide variety of functional options, such as setting multi-scenario energy-saving modes (sleep mode, party mode, outing mode, etc., each mode contains preset energy-saving parameter sets for different devices), custom equipment operation schedules (with time intervals accurate to seconds, supporting periodic and one-time settings), setting equipment priorities (based on user-defined rules for equipment Priority sorting), setting energy usage reminder thresholds (power thresholds, electricity fee thresholds, etc., automatically reminding users when the thresholds are reached), creating energy management rules (automatically executing equipment operations or adjusting energy management strategies according to specific conditions), etc. The system will automatically and intelligently adjust the energy management strategy based on the various parameters set by the user to ensure that energy management is perfectly matched with user needs. The mobile application can present energy consumption data and statistical analysis results in various visual forms such as charts (bar charts, line charts, pie charts, etc.), curves (energy consumption trend curves, power change curves, etc.), and three-dimensional models (family energy consumption distribution models, etc.). The smart control panel has a concise and easy-to-operate equipment control interface (one-touch operation buttons, slider controls, etc.) and a clear and accurate energy status display function (real-time power display, electricity price display, etc.); Equipment firmware upgrade and remote management steps (S5): The system establishes a safe, reliable, efficient and stable device firmware upgrade mechanism, which supports multiple firmware upgrade methods such as automatic detection and update, manual trigger update and scheduled update for smart home devices. During the upgrade process, the firmware files are encrypted for transmission, integrity check and version compatibility check to ensure data security, stable operation and functional compatibility of the device during the upgrade process, so as to continuously update energy management related functions and keep the device in the best performance and energy-saving state at all times; At the same time, it realizes powerful and easy-to-operate remote management functions. Users or professional managers can remotely monitor the energy management parameters of the equipment in real time (including but not limited to real-time energy consumption, operating status, fault codes, equipment health, energy conversion efficiency, etc.), and make remote precise adjustments when necessary (remotely modify equipment operating parameters, start equipment self-test programs, etc.). The system regularly uses advanced detection technologies (such as equipment performance testing technology, fault diagnosis technology, communication link detection technology, etc.) to conduct comprehensive and in-depth status monitoring of the equipment. Once a device failure or abnormality is detected (covering hardware failure, software anomaly, communication interruption, performance degradation, security vulnerability and other types), it immediately sends alarm information to the user in various ways (such as SMS, push notification, sound and light alarm, email notification, etc.), and simultaneously provides detailed, accurate and targeted fault diagnosis suggestions (including fault cause analysis, possible solutions, maintenance guidance, etc.). Firmware upgrades can be automatically pushed according to user-preset rules (such as automatic detection and update rules, scheduled update plans, etc.) or manually selected by the user according to actual needs to ensure the efficiency and flexibility of equipment management.

2. A hierarchical architecture for implementing the smart home energy management optimization method according to claim 1, characterized in that: include: (1) Equipment layer: includes smart meters, various high-precision and high-sensitivity sensors (such as temperature sensors, humidity sensors, light sensors, human infrared sensors, smoke sensors, air quality sensors, water flow sensors, etc.), smart home appliances (such as smart refrigerators, smart air conditioners, smart washing machines, smart TVs, etc.), smart lighting equipment (such as smart bulbs, smart light strips, smart lamps, etc.), smart sockets, smart switches, energy storage devices (such as battery energy storage systems, capacitor energy storage devices, etc.), security equipment (such as smart cameras, smart door locks, smart alarms, etc.), entertainment equipment (such as smart speakers, smart game consoles, etc.), and each device is connected through wired (such as The scientific and reasonable physical connection relationship combining Ethernet, RS-485, etc. and wireless (such as Bluetooth, Wi-Fi, ZigBee, LoRaWAN, NB-IoT, etc.) builds a close-cooperation and efficient smart home device network, in which sensors are responsible for real-time and accurate collection of environmental and device operation-related data, smart meters accurately monitor power consumption, and other devices implement specific functions and energy consumption according to system instructions. All devices have good compatibility and stability, and can operate stably in complex IoT environments. At the same time, they support the plug-and-play function of devices, and new devices can be automatically recognized and configured by the system when connected; (2) Network communication layer: covers high-speed and stable IoT communication networks (such as Bluetooth, Wi-Fi, ZigBee, LoRaWAN, NB-IoT, CoAP, MQTT, etc.). Its core function is to achieve fast, stable and secure transmission of massive data between the device layer and the energy management center layer, ensuring the real-time, accuracy and integrity of the data. The network communication protocol has high compatibility and scalability, and can adapt to the data interaction requirements between different types of devices and systems. It supports data breakpoint resumption, encrypted transmission, multicast transmission and other functions. At the same time, it has network congestion control, traffic optimization and automatic fault switching mechanisms to ensure the high reliability of the communication network. (3) Energy management center layer: It is equipped with data acquisition module, data analysis module, strategy formulation module and equipment control module. The data acquisition module is responsible for efficiently receiving data transmitted by the equipment layer and performing preliminary data verification (such as data format verification, data range verification, data integrity verification, etc.) and sorting (such as data classification, data storage format conversion, etc.). The data analysis module uses advanced data processing technology (such as data cleaning, preprocessing, feature extraction, data mining, machine learning, deep learning and other complex and precise operations) to conduct in-depth analysis of the data. The strategy formulation module formulates comprehensive, scientific and forward-looking energy management strategies including equipment operation power adjustment strategy, equipment operation time adjustment strategy, energy allocation priority strategy, equipment maintenance strategy, energy procurement strategy, etc. based on the data analysis results. These strategies can be updated and optimized in real time according to the dynamic changes in user needs and the real-time fluctuations in energy supply. The equipment control module sends accurate and efficient control instructions to the equipment layer based on the strategy to ensure the stable operation of the system and the realization of energy management goals. At the same time, it has instruction caching, instruction resending and instruction execution feedback mechanisms to ensure the reliable execution of control instructions. (4) User layer: It includes a feature-rich, easy-to-use mobile application and smart control panel. Together, they form a convenient channel for users to interact with the energy management system in all aspects. Users can receive various practical information pushed by the system (such as real-time energy consumption reminders, personalized energy-saving suggestions, equipment failure warnings, energy usage report push, system upgrade notifications, etc.), and send a variety of personalized instructions to the system (such as equipment control instructions, energy management strategy adjustment instructions, personalized setting instructions, etc.), to achieve in-depth participation, autonomous control and personalized customization of smart home energy management. At the same time, it supports multi-user management functions. Different users can have independent accounts and personalized settings. The user layer has a user operation log recording function, which is convenient for users to query operation history and system troubleshooting.

3. The smart home energy management optimization method under the Internet of Things environment according to claim 1 is characterized in that: In the device intelligent identification and classification management step (S1), the classified device types include but are not limited to lighting equipment, household appliances, energy storage equipment, security equipment, entertainment equipment, etc., and the energy management strategy for each type of equipment can be flexibly adjusted by the user according to his or her own needs within the preset strategy parameter range. The adjustment methods include manually entering parameter values, selecting preset parameter templates, automatically generating parameter adjustment suggestions based on user behavior habits and confirming by the user, etc.

4. The smart home energy management optimization method under the Internet of Things environment according to claim 1 is characterized in that: In the real-time energy monitoring and data analysis step (S2), the sensors include temperature sensors, humidity sensors, light sensors, human infrared sensors, smoke sensors, air quality sensors, water flow sensors and other sensors, and the collected data include power consumption, power consumption, power consumption time, power consumption waveform, ambient temperature, ambient humidity, light intensity, personnel activity location and trajectory, smoke concentration, air quality indicators, water flow and water-using equipment information of each device. Data analysis includes using cluster analysis, association rule mining, decision tree analysis, neural network algorithm and other data mining algorithms to analyze historical energy consumption data to find out energy consumption patterns in different seasons, different months, different date types, different time periods, different family areas, different personnel combinations, different equipment combinations, energy consumption abnormalities and complex relationships between energy consumption influencing factors, and predict future energy demand trends at different time scales, time points for the occurrence of energy consumption peaks and valleys, and corresponding energy consumption type distributions.

5. The smart home energy management optimization method under the Internet of Things environment according to claim 1 is characterized in that: In the step (S3) of the intelligent energy scheduling and optimization algorithm, the energy supply situation includes the real-time dynamic division of the peak and valley electricity price periods of the power grid, the real-time power generation power, power generation, power generation efficiency and remaining power of renewable energy power generation equipment, the current power, charging and discharging status, charging and discharging power, charging and discharging efficiency and health status of the energy storage equipment, and the user needs include the multi-level setting of equipment use priority, the quantitative indicators of comfort requirements, the setting of energy-saving targets, and the setting of cost control targets. The optimization algorithm comprehensively formulates an energy allocation plan based on these factors, and the plan can be dynamically adjusted in seconds according to the real-time changes in energy supply and user needs. The adjustment methods include real-time recalculation of the allocation plan, automatic adjustment of the allocation strategy based on preset rules, and instant modification of the allocation plan according to real-time user intervention instructions.

6. The smart home energy management optimization method under the Internet of Things environment according to claim 1 is characterized in that: In the user interaction and personalized setting step (S4), the mobile phone application provides visualized energy consumption charts (such as bar charts, line charts, pie charts, etc.), curves (such as energy consumption trend curves, power change curves, etc.), three-dimensional models (such as household energy consumption distribution models, etc.) and statistical data (such as power consumption and electricity charges classified by device type, region, and time). Users can perform one-click energy-saving operations (such as one-click to start energy-saving mode, one-click to shut down all non-critical devices, etc.) through the intelligent control panel. Personalized settings also include setting device priorities (prioritizing devices according to user-defined rules), setting energy usage reminder thresholds (power thresholds, electricity charge thresholds, etc., automatically reminding users when the thresholds are reached), creating energy management rules (automatically executing device operations or adjusting energy management strategies according to specific conditions), etc. The system gives priority to powering key devices when energy is tight according to the priority, sends reminder information to users in a timely manner according to the energy usage reminder threshold, and automatically executes corresponding energy management operations according to the energy management rules.

7. The smart home energy management optimization method under the Internet of Things environment according to claim 1 is characterized in that: In the device firmware upgrade and remote management step (S5), the system regularly monitors the status of the device, and the monitoring period can be customized by the user. When a device failure or abnormality is found, an alarm message is sent to the user (including SMS, push notification, sound and light alarm, email notification, etc.) and fault diagnosis suggestions are provided (including fault cause analysis, possible solutions, maintenance instructions, etc.). The firmware upgrade can be automatically pushed (based on the automatic detection and update rules pre-set by the user) or manually selected by the user (the user manually triggers the upgrade operation on the mobile phone application or smart control panel). During the upgrade process, the firmware file is encrypted for transmission, integrity checked and version compatibility checked to ensure data security, stable operation and functional compatibility of the device during the upgrade process. After the upgrade is completed, the device is automatically functionally tested to ensure that the device can operate normally after the upgrade and the energy management related functions are updated and optimized.

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