Artificial intelligence home management system

Abstract

The invention discloses an artificial intelligence home management system, which relates to the technical field of smart home and comprises a data acquisition module, a behavior analysis module, an environment sensing module, a cooperative control module and an energy consumption optimization module. According to the artificial intelligence home management system provided by the invention, through multi-source data fusion and cross-equipment collaborative decision, the intelligent management level of a home scene is remarkably improved, user behavior data can be deeply analyzed, a dynamic behavior model can be constructed, and a user habit mode can be accurately identified and predicted, so that the initiative and adaptability of equipment control are realized, and the user experience is improved. The system comprehensively senses environmental parameters, constructs an environmental state map, provides accurate environmental background information for a cooperative control strategy, ensures the accuracy of an equipment control strategy, combines equipment operation data with a user behavior scene, generates an energy consumption optimization scheme, ensures the comfort of a user, realizes high efficiency and energy conservation, and improves the user experience. And user privacy leakage is effectively prevented, and potential faults are early warned.

Description

Technical Field

[0001] This invention relates to the field of smart home technology, specifically to an artificial intelligence home management system. Background Technology

[0002] With the rapid development of IoT and AI technologies, smart home systems are evolving towards greater intelligence and automation. These systems integrate various sensors, communication modules, and control units to achieve networked management and intelligent control of home devices, aiming to provide users with a more convenient, comfortable, and safe living environment. Currently, smart home systems on the market can achieve basic functions such as remote device control and environmental parameter monitoring, but there is still room for improvement in areas such as multi-device collaborative decision-making, user behavior learning, and energy consumption optimization management. More efficient intelligent management solutions are urgently needed to meet the increasingly diverse needs of users.

[0003] However, existing smart home technologies still face many challenges in practical applications. For example, most systems lack the ability to deeply analyze user behavior data, making it difficult to accurately identify user habit patterns and make dynamic predictions, resulting in insufficient initiative and adaptability in device control. In terms of environmental perception, the fusion and processing of multi-dimensional environmental parameters is not comprehensive enough, failing to construct a complete environmental state map, which in turn affects the accuracy of collaborative control strategies. Furthermore, device energy consumption optimization schemes often do not fully integrate user behavior scenarios with device operating data, making it difficult to achieve high efficiency and energy saving while ensuring user comfort. Additionally, the mechanisms for data transmission security and device anomaly detection in some systems need improvement. To address these issues, we propose an artificial intelligence-based home management system. Summary of the Invention

[0004] To address the aforementioned technical issues, an AI-powered home management system is provided. This solution resolves the problems of insufficient in-depth analysis of user behavior data in smart home systems, making it difficult to dynamically predict user habits; incomplete integration and processing of environmental parameters, affecting the accuracy of collaborative control; insufficient integration of scenarios and data in energy consumption optimization, resulting in low energy efficiency; and the need for improvements in data transmission security and device anomaly detection mechanisms.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] An AI-powered home management system includes: a data acquisition module, a behavior analysis module, an environmental perception module, a collaborative control module, and an energy consumption optimization module;

[0007] The data acquisition module is used to collect user behavior data, home appliance operation data, and environmental parameter data.

[0008] The behavior analysis module is electrically connected to the data acquisition module and is used to build a dynamic behavior model based on user behavior data and identify user habit patterns.

[0009] The environmental sensing module is used to monitor environmental parameters in real time and generate an environmental status map.

[0010] The collaborative control module is electrically connected to the behavior analysis module and the environmental perception module, and generates a collaborative control strategy for the device based on the dynamic behavior model and the environmental state map.

[0011] The energy consumption optimization module is used to generate an energy consumption optimization scheme by combining equipment operating data and control strategies;

[0012] The system achieves intelligent management of home scenarios through multi-source data fusion and cross-device collaborative decision-making.

[0013] Preferably, the data acquisition module includes a multi-source sensor array and a data preprocessing unit;

[0014] The multi-source sensor array is deployed in the home environment and includes pressure sensors, infrared sensors, temperature and humidity sensors and air quality sensors to collect user activity trajectories, device usage frequency, ambient temperature and humidity and air quality indicators in different areas.

[0015] The data preprocessing unit performs spatiotemporal alignment and noise filtering on the collected data, eliminates the differences in sampling frequencies between different sensors through timestamp unification, reduces environmental noise interference by using the Kalman filter algorithm, and generates a standardized multidimensional dataset, providing a unified data foundation for subsequent analysis.

[0016] Preferably, the behavior analysis module adopts a hierarchical learning architecture, with the bottom layer using a long short-term memory network to extract temporal features from user behavior sequences and capture the user's activity patterns at different times.

[0017] Middle management focuses on key behavioral patterns through attention mechanisms, including but not limited to: the order of device usage after waking up, and behavioral differences between weekdays and weekends;

[0018] A dynamic behavior model is built at the top level. This model is based on the probability distribution of historical behavior data, updates the changing trends of user behavior patterns in real time, and predicts the probability distribution of user behavior in a short period of time in the future.

[0019] Preferably, the environmental perception module includes an environmental state map construction unit, which divides the real-time monitored environmental parameters into spatial, temporal, and parameter dimensions.

[0020] The spatial dimensions are divided according to home areas, generating heat maps of environmental parameters for each area;

[0021] Analyze parameter change trends over time to identify cyclical environmental change patterns; establish environmental parameter correlation models from a parameter perspective.

[0022] By constructing a 3D map, a panoramic state representation of the home environment can be achieved, providing environmental background information for collaborative control strategies.

[0023] Preferably, the collaborative control module adopts a multi-objective optimization decision-making mechanism, with user comfort, equipment energy consumption and system response efficiency as optimization objectives, and establishes a multi-objective function that automatically adjusts according to the user's current behavior scenario;

[0024] In the decision-making process, user needs are first predicted based on a dynamic behavior model, and then candidate control strategies are generated by combining the environmental state map. The candidate strategies are evaluated through a multi-objective function, and the strategy with the best comprehensive score is selected for execution.

[0025] Preferably, the energy consumption optimization module includes an equipment energy efficiency map construction unit and an intelligent scheduling unit;

[0026] The equipment energy efficiency map construction unit establishes energy efficiency models for different equipment under different operating conditions based on equipment operation data, and marks high energy consumption periods and inefficient operation modes.

[0027] The intelligent scheduling unit uses a task priority scheduling algorithm based on the energy consumption optimization scheme to implement peak-shifting control for non-emergency electrical equipment, while ensuring that normal use by users is not affected and reducing energy consumption.

[0028] Preferably, it also includes a user interaction module, which comprises a natural language processing unit and a context-aware interactive interface;

[0029] The natural language processing unit supports multi-turn dialogue and intent understanding, and converts user voice commands into device control tasks.

[0030] The context-aware interactive interface dynamically adjusts the displayed content based on the current environmental state and user behavior scenario.

[0031] Preferably, it also includes a security protection module, which includes a device abnormal behavior detection unit and a data encryption transmission unit;

[0032] The device abnormal behavior detection unit establishes a baseline for normal device operation and uses the isolated forest algorithm to identify abnormal operating modes of the device, including: sudden current changes and abnormal vibrations, and provides timely warnings of potential faults.

[0033] The data encryption transmission unit performs end-to-end encryption on the collected user behavior data and device operation data, and uses the national cryptographic SM4 algorithm to ensure data transmission security and prevent user privacy leakage and malicious attacks.

[0034] Preferably, it also includes an adaptive learning module, which is based on a federated learning framework, extracts features from user behavior data at local edge nodes, and only uploads model update parameters to the cloud server to avoid leakage of raw data;

[0035] The cloud server aggregates model parameters from multiple edge nodes, iteratively optimizes the global model, and then distributes the optimized model to each edge node, enabling the system to adaptively learn different user behavior patterns.

[0036] Preferably, it also includes an extended interface module, which comprises a standardized protocol conversion unit and a device access management unit;

[0037] The standardized protocol conversion unit supports the conversion of multiple communication protocols, including but not limited to: ZigBee, Wi-Fi, and Bluetooth;

[0038] The device access management unit provides a visual device management interface, allowing users to add or delete devices and manage permissions and configure functions for connected devices.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0040] The AI-powered home management system proposed in this invention significantly improves the intelligent management level of home scenarios through multi-source data fusion and cross-device collaborative decision-making. It can deeply analyze user behavior data, construct dynamic behavior models, accurately identify and predict user habit patterns, thereby achieving proactive and adaptive device control. The system comprehensively perceives environmental parameters, constructs an environmental state map, and provides accurate environmental background information for collaborative control strategies, ensuring the accuracy of device control strategies. Combining device operation data with user behavior scenarios, it generates energy consumption optimization schemes, achieving high efficiency and energy saving while ensuring user comfort. It also emphasizes data transmission security and device anomaly detection, employing end-to-end encryption and the isolated forest algorithm to effectively prevent user privacy leaks and warn of potential faults. Through an adaptive learning module, the system can continuously optimize the global model, improving its adaptive learning ability to different user behavior patterns, providing users with a more convenient, comfortable, and safe living environment. Attached Figure Description

[0041] Figure 1 This is a system framework diagram of the present invention;

[0042] Figure 2 This is a system workflow diagram of the present invention. Detailed Implementation

[0043] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0044] Reference Figure 1 As shown, an AI-powered home management system aims to achieve intelligent management of the home environment by integrating advanced data acquisition, analysis, sensing, control, and optimization technologies. The system comprises multiple core modules, each collaborating with each other. Utilizing multi-source data fusion and cross-device collaborative decision-making mechanisms, it enhances user comfort, reduces energy consumption, and ensures safety.

[0045] The overall system architecture is based on edge computing and cloud computing frameworks, deployed on local edge nodes in the home, and works in collaboration with cloud servers to support real-time response and long-term learning capabilities.

[0046] The data acquisition module, as the foundation of the system, is responsible for acquiring raw data from the home environment. This module includes a multi-source sensor array and a data preprocessing unit. The sensor array is deployed in various areas of the home, such as the living room, bedroom, and kitchen. It includes pressure sensors to monitor the user's activity trajectory in different areas, such as detecting user movement through changes in floor pressure; infrared sensors to capture the frequency of device use, such as detecting the status of appliance switches; temperature and humidity sensors to measure ambient temperature and humidity parameters in real time; and air quality sensors to detect indicators such as PM2.5 and CO2 to ensure a healthy indoor environment.

[0047] The data preprocessing unit standardizes the collected raw data. First, it performs spatiotemporal alignment, eliminating differences in sampling frequencies between different sensors through timestamp unification. For example, it aligns the 10 samples per second from the infrared sensor with the 1 sample per second from the temperature and humidity sensor to a unified time base, and uses interpolation algorithms to fill in missing points. Next, it performs noise filtering, employing a Kalman filter algorithm to reduce environmental noise interference. The Kalman filter is based on a state-space model and consists of two steps: prediction and update. In the prediction step, the state estimate... Through the state transition matrix and the previous state The calculation yields the following formula:

[0048]

[0049] In the formula, This is the prior state estimate at time step k (the current state predicted based on information from time step k-1). This is the state transition matrix (which describes how the system state changes from time step k-1 to k, and is defined by the system model). This is the posterior state estimate at time step k-1 (the state estimate at the previous time step). This is the control input matrix (used to map external control inputs to weight matrices for state changes). For control input vectors (external input control signals, such as user commands or system adjustments);

[0050] In the update step, actual observations are incorporated. and observation matrix Calculate Kalman gain And update the status using the following formula:

[0051]

[0052]

[0053] In the formula, Kalman gain (used to adjust the weights of the predictions and balance the confidence levels of the predictions and observations). This is to estimate the covariance (the uncertainty or error covariance matrix of the predicted state) in the prior art. The observation matrix (describes how the system state is mapped to the observations). This is the transpose of the observation matrix (matrix transpose operation). To observe the noise covariance, This is the posterior state estimate at time step k (the updated state after fusing observations). These are the actual observations (raw data obtained from the sensors).

[0054] These steps generate a standardized multidimensional dataset that covers user behavior, device operation, and environmental parameters, providing a unified data foundation for subsequent modules and ensuring data consistency and reliability.

[0055] The behavior analysis module is directly connected to the data acquisition module and is responsible for building dynamic models based on user behavior data and identifying user habit patterns.

[0056] This module employs a hierarchical learning architecture. The bottom layer uses a Long Short-Term Memory (LSTM) network to extract temporal features from user behavior sequences. LSTM is a recurrent neural network specifically designed for processing time-series data. Its core structure includes an input gate, a forget gate, and an output gate, effectively capturing long-term dependencies.

[0057] In the specific implementation, the input sequence, such as the user's activity timestamp, uses a forget gate to determine how much previous state information to retain; the input gate controls the input of new information; the output gate generates the current output; the cell state update formula is:

[0058]

[0059] In the formula, This represents the current cell state (the long-term memory of the LSTM). Output for the forget gate. This refers to the state of the unit at the previous moment (the previous long-term memory). For input gate output, The weight matrix represents the unit state. This is the bias vector for the cell state. The state was hidden in the previous moment. This is the current input vector;

[0060] The final hidden state is:

[0061]

[0062] In the formula, Hide the current state. Output gate output;

[0063] This layer extracts user patterns across different time periods, such as morning activity peaks. The middle layer focuses on key behavioral patterns through an attention mechanism; attention weights are calculated based on query, key, and value vectors. This allows the model to focus on analyzing specific behaviors, such as the order of device usage after waking up or differences between weekdays and weekends, identifying high-frequency patterns. The top layer constructs a dynamic behavioral model, adjusting it in real-time using a Bayesian update method based on the probability distribution of historical behavioral data. During model initialization, a Gaussian mixture model is used to fit the behavioral distribution, with the probability density function as follows:

[0064]

[0065] In the formula, It is the probability density function (the probability distribution of user behavior data points). This represents the number of Gaussian distributions (the number of components in the mixture model). It is a mixed weight. and These are the mean and covariance; during real-time updates, the distribution parameters are corrected using new observation data, such as by using the expectation-maximization algorithm to optimize and predict the probability distribution of users' short-term future behavior, such as predicting the probability of turning on the air conditioner after get off work, ensuring that the model adapts to changing trends.

[0066] The environmental sensing module focuses on real-time monitoring of environmental parameters and generating environmental status maps, providing a panoramic view of the home environment. This module includes an environmental status map construction unit, which divides parameters into spatial, temporal, and parameter dimensions. The spatial dimension is divided by home area, generating heatmaps of environmental parameters for each area. Methods include interpolation based on sensor data, such as using the inverse distance weighting method to calculate values ​​for unmonitored points, with the formula:

[0067]

[0068] In the formula, For interpolation of the estimated points (unmonitored locations), The number of known monitoring points (sensor deployment points). For the first The weight of each monitoring point (based on distance calculation) For the first The actual value of each monitoring point It's distance. These parameters are power-law parameters, generating temperature and humidity heatmaps to visualize spatial changes. The time dimension analyzes parameter change trends and identifies periodic patterns, such as using Fourier transform to detect daily / weekly cycles and extracting trends like morning humidity increases. The parameter dimension establishes correlation models, calculating relationships between parameters using Pearson correlation coefficients to identify correlations such as between temperature and air conditioning power consumption. A 3D map construction integrates these dimensions, outputting a panoramic state representation to provide background information for the control module, such as identifying priority cooling areas in hot weather.

[0069] The collaborative control module connects the behavior analysis module and the environmental perception module, generating collaborative control strategies for devices based on dynamic behavior models and environmental state maps. This module employs a multi-objective optimization decision-making mechanism, with optimization objectives including user comfort, device energy consumption, and system response efficiency. First, multi-objective functions are established, such as comfort, energy consumption, and response efficiency.

[0070] The decision-making process is divided into steps: predicting user needs based on dynamic behavior models, such as predicting when users will return home; generating candidate strategies by combining environmental state maps, such as turning on the air conditioner and air purifier at the same time; evaluating strategies through multi-objective functions, using a weighted sum method to synthesize scores, with weights automatically adjusted according to user scenarios, such as focusing on efficiency on weekdays; and finally selecting the strategy with the best score for execution, such as prioritizing energy-saving mode, to ensure real-time response.

[0071] The energy consumption optimization module combines equipment operating data and control strategies to generate optimization schemes, reducing overall energy consumption. This module includes an equipment energy efficiency map construction unit and an intelligent scheduling unit. The equipment energy efficiency map unit builds an energy efficiency model based on operating data, such as the energy efficiency ratio of air conditioners, and marks high-energy-consumption periods (such as peak electricity price periods) and inefficient modes (such as standby power consumption). The intelligent scheduling unit uses a task priority scheduling algorithm to achieve peak-shifting control: first, equipment is classified, with emergency equipment such as refrigerators kept running, and non-emergency equipment such as washing machines delayed from starting; the scheduling algorithm is based on a priority queue, and the formula is:

[0072]

[0073] In the formula, This is the priority value. Derived from behavioral models, The data comes from energy efficiency maps; during execution, heuristic algorithms such as greedy strategies are used to allocate time periods to ensure that user experience is not affected, for example, by running dishwashers during periods of low electricity prices to reduce peak loads.

[0074] The user interaction module supports natural interaction, including a natural language processing unit (NLP) and a context-aware interface. The NLP processes voice commands, including speech recognition and text conversion using a recurrent neural network (RNN) for encoding; intent understanding employs a sequence labeling model, such as a bidirectional LSTM, to output control tasks; and supports multi-turn dialogue by managing context through a memory network. The context-aware interface dynamically adjusts its displayed content based on the current environment and behavioral scenario; for example, a simplified interface is displayed in sleep mode, and the UI is updated using rendering algorithms.

[0075] The security protection module ensures system security and includes an abnormal device behavior detection unit and a data encryption transmission unit. The abnormal behavior detection unit establishes a baseline for device behavior, such as the normal current range; it uses an isolated forest algorithm to identify anomalies: first, random trees are generated, and each tree is constructed using random segmentation features; anomaly scores are based on path length, detecting sudden current changes or abnormal vibrations and providing timely warnings. The data encryption transmission unit uses the national standard SM4 algorithm for end-to-end encryption: the encryption process is processed in rounds, each round including nonlinear and linear transformations, and key expansion generates subkeys; ensuring secure data transmission and preventing privacy leaks.

[0076] The adaptive learning module is based on a federated learning framework to achieve distributed learning: local edge nodes extract features from user behavior data and use convolutional neural networks; only model update parameters, such as gradient values, are uploaded to the cloud; the cloud aggregates parameters from multiple nodes and uses a weighted average formula; after iteratively optimizing the global model, it is distributed to the edge nodes to achieve adaptive learning and avoid leakage of raw data.

[0077] The extended interface module supports device integration and includes a standardized protocol conversion unit and a device access management unit. The protocol conversion unit supports conversions to ZigBee, Wi-Fi, Bluetooth, etc., using a middleware layer to parse protocol headers, with conversion formulas such as data packet reconstruction. The device access management unit provides a graphical interface, supports users adding and deleting devices, and uses a role-based access control model for permission management, such as defining user permission levels.

[0078] The entire system achieves intelligent management through modular collaboration: data collection provides the foundation, behavior analysis predicts user intent, environmental perception monitors the background, collaborative control formulates strategies, energy consumption optimization reduces costs, user interaction enhances the experience, security protection ensures reliability, adaptive learning continuously improves, and extended interfaces enhance compatibility. During system deployment, edge nodes handle real-time tasks, while the cloud performs global optimization to ensure efficient operation. Through this integration, the system has been validated in real-world home scenarios, significantly improving quality of life and energy efficiency while maintaining privacy and security. In the future, it can be expanded to more device types and scenarios to adapt to the evolving needs of smart homes.

[0079] Reference Figure 2 As shown, the usage process of this invention is as follows:

[0080] The system first collects user behavior, equipment operation, and environmental parameter data through a multi-source sensor array in the data acquisition module. After standardization by the data preprocessing unit, the behavior analysis module uses a hierarchical learning architecture to build a dynamic behavior model and predict user habits.

[0081] The environmental perception module monitors environmental parameters in real time and generates a state map. The collaborative control module combines the model and the map to generate a multi-objective optimization strategy for collaborative equipment control.

[0082] The energy consumption optimization module generates optimization schemes based on equipment operating data and control strategies. Meanwhile, each module is protected by safety safeguards and adaptive learning modules, enabling intelligent management.

[0083] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. An artificial intelligence-based home management system, characterized in that, include: The system includes a data acquisition module, a behavior analysis module, an environmental perception module, a collaborative control module, and an energy consumption optimization module. The data acquisition module is used to collect user behavior data, home appliance operation data, and environmental parameter data. The behavior analysis module is electrically connected to the data acquisition module and is used to build a dynamic behavior model based on user behavior data and identify user habit patterns. The environmental sensing module is used to monitor environmental parameters in real time and generate an environmental status map. The collaborative control module is electrically connected to the behavior analysis module and the environmental perception module, and generates a collaborative control strategy for the device based on the dynamic behavior model and the environmental state map. The energy consumption optimization module is used to generate an energy consumption optimization scheme by combining equipment operating data and control strategies; The system achieves intelligent management of home scenarios through multi-source data fusion and cross-device collaborative decision-making.

2. The artificial intelligence home management system according to claim 1, characterized in that, The data acquisition module includes a multi-source sensor array and a data preprocessing unit; The multi-source sensor array is deployed in the home environment and includes pressure sensors, infrared sensors, temperature and humidity sensors and air quality sensors to collect user activity trajectories, device usage frequency, ambient temperature and humidity and air quality indicators in different areas. The data preprocessing unit performs spatiotemporal alignment and noise filtering on the collected data, eliminates the differences in sampling frequencies between different sensors through timestamp unification, reduces environmental noise interference by using the Kalman filter algorithm, and generates a standardized multidimensional dataset to provide a unified data foundation for subsequent analysis.

3. The artificial intelligence home management system according to claim 1, characterized in that, The behavior analysis module adopts a hierarchical learning architecture. The bottom layer uses a long short-term memory network to extract temporal features from user behavior sequences and capture the activity patterns of users at different times. Middle management focuses on key behavioral patterns through attention mechanisms, including but not limited to: the order of device usage after waking up, and behavioral differences between weekdays and weekends; A dynamic behavior model is built at the top level. This model is based on the probability distribution of historical behavior data, updates the changing trends of user behavior patterns in real time, and predicts the probability distribution of user behavior in a short period of time in the future.

4. The artificial intelligence home management system according to claim 1, characterized in that, The environmental perception module includes an environmental state map construction unit, which divides the real-time monitored environmental parameters into spatial, temporal, and parameter dimensions. The spatial dimensions are divided according to home areas, generating heat maps of environmental parameters for each area; Analyze parameter change trends over time to identify cyclical environmental change patterns; establish environmental parameter correlation models from a parameter perspective. By constructing a 3D map, a panoramic state representation of the home environment can be achieved, providing environmental background information for collaborative control strategies.

5. The artificial intelligence home management system according to claim 1, characterized in that, The collaborative control module adopts a multi-objective optimization decision-making mechanism, with user comfort, equipment energy consumption and system response efficiency as optimization objectives, and establishes a multi-objective function that automatically adjusts according to the user's current behavior scenario. In the decision-making process, user needs are first predicted based on a dynamic behavior model, and then candidate control strategies are generated by combining the environmental state map. The candidate strategies are evaluated through a multi-objective function, and the strategy with the best comprehensive score is selected for execution.

6. The artificial intelligence home management system according to claim 1, characterized in that, The energy consumption optimization module includes an equipment energy efficiency map construction unit and an intelligent scheduling unit; The equipment energy efficiency map construction unit establishes energy efficiency models for different equipment under different operating conditions based on equipment operation data, and marks high energy consumption periods and inefficient operating modes. The intelligent scheduling unit uses a task priority scheduling algorithm based on the energy consumption optimization scheme to implement peak-shifting control for non-emergency electrical equipment, while ensuring that normal use by users is not affected and reducing energy consumption.

7. The artificial intelligence home management system according to claim 1, characterized in that, It also includes a user interaction module, which contains a natural language processing unit and a context-aware interactive interface; The natural language processing unit supports multi-turn dialogue and intent understanding, and converts user voice commands into device control tasks. The context-aware interactive interface dynamically adjusts the displayed content based on the current environmental state and user behavior scenario.

8. The artificial intelligence home management system according to claim 1, characterized in that, It also includes a security protection module, which contains a device abnormal behavior detection unit and a data encryption transmission unit; The device abnormal behavior detection unit establishes a baseline for normal device operation and uses the isolated forest algorithm to identify abnormal operating modes of the device, including: sudden current changes and abnormal vibrations, and provides timely warnings of potential faults. The data encryption transmission unit performs end-to-end encryption on the collected user behavior data and device operation data, and uses the national cryptographic SM4 algorithm to ensure data transmission security and prevent user privacy leakage and malicious attacks.

9. The artificial intelligence home management system according to claim 1, characterized in that, It also includes an adaptive learning module, which is based on a federated learning framework. This module extracts features from user behavior data at local edge nodes and only uploads model update parameters to the cloud server to avoid leakage of raw data. The cloud server aggregates model parameters from multiple edge nodes, iteratively optimizes the global model, and then distributes the optimized model to each edge node, enabling the system to adaptively learn different user behavior patterns.

10. An artificial intelligence home management system according to claim 1, characterized in that, It also includes an extended interface module, which contains a standardized protocol conversion unit and a device access management unit; The standardized protocol conversion unit supports the conversion of multiple communication protocols, including but not limited to: ZigBee, Wi-Fi, and Bluetooth; The device access management unit provides a visual device management interface, allowing users to add or delete devices and manage permissions and configure functions for connected devices.

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