Intelligent Optimization Method for Multi-Screen Interaction in Smart Home Based on AI Services

Abstract

The present invention discloses an intelligent optimization method for multi-screen interaction in a smart home based on AI services, which relates to the technical field of smart homes; it includes: device identification and classification: identifying all screen devices in the home through wireless network sensing technology and classifying them according to device characteristics; user behavior analysis: collecting operation data of users on different screens and analyzing the user's behavior pattern using machine learning algorithms; content recommendation system: recommending content suitable for different screens using a deep learning model based on user behavior analysis and device characteristics; interaction optimization algorithm: an interaction optimization algorithm based on multi-screen interaction. The present invention realizes an intelligent optimization system for multi-screen interaction in a smart home by adopting artificial intelligence technologies, including support vector machines, hidden Markov models, convolutional neural networks, reinforcement learning, etc.; this system can automatically identify various screen devices in the home and classify and manage them according to device performance and usage environment.

Description

Technical Field

[0001] The present invention relates to the technical field of smart homes, and particularly to an intelligent optimization method for multi-screen interaction in a smart home based on AI services. Background Art

[0002] With the popularization of smart home devices, the number of screen devices in the home has been increasing, such as smartphones, tablets, TVs, and smart home appliances. The interaction and data sharing among these devices have become increasingly important, but at the same time, it has also brought challenges in management complexity and user experience consistency. Traditional home management systems often lack effective multi-device collaboration and management mechanisms, are unable to provide personalized services according to user behaviors and preferences, and have efficiency problems in resource allocation and content display.

[0003] After retrieval, a patent with the Chinese patent application number CN202311449229.9 discloses a real-time interaction method and related device based on multi-screen collaboration. The method includes: constructing a first multi-screen collaboration network based on a collaboration interaction center and multiple target screens and determining a picture distribution channel; performing encoding and sorting processing to obtain operation encoding and sorting data; performing real-time interaction response, generating an interaction picture data stream and performing distribution and adaptive rendering to obtain a target rendered interaction picture; obtaining response delay data and performing feature extraction to obtain multiple response delay feature values, performing picture feature calculation to obtain multiple picture feature values; performing vector encoding and vector fusion to obtain a target feature vector; inputting the target feature vector into a multi-screen collaboration network analysis model for multi-screen collaboration network analysis and network parameter optimization to obtain a second multi-screen collaboration network. The real-time interaction method in the above patent has the following deficiencies: the function of analyzing and understanding user behaviors is insufficient, and it may not be able to achieve equally precise personalized services. Summary of the Invention

[0004] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose an intelligent optimization method for multi-screen interaction in a smart home based on AI services.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] An intelligent optimization method for multi-screen interaction in a smart home based on AI services, including:

[0007] Device identification and classification: identifying all screen devices in the home through wireless network sensing technology and classifying them according to device characteristics;

[0008] User behavior analysis: collecting operation data of users on different screens and analyzing the user's behavior patterns using machine learning algorithms;

[0009] Content recommendation system: Based on user behavior analysis and device characteristics, a deep learning model is used to recommend content suitable for different screens;

[0010] Interaction optimization algorithm: An interaction optimization algorithm based on multi-screen interaction, including minimizing touch transfer latency and visual consistency calibration;

[0011] Dynamic resource allocation: According to the current network condition and device performance, dynamically adjust the resource allocation strategy to ensure a smooth multi-screen interaction experience;

[0012] Among them, in the interaction optimization algorithm, the problem of minimizing touch transfer latency is solved by a mixed integer linear programming method, and the formula is as follows:

[0013]

[0014]

[0015]

[0016] Among them, I represents the set of all touch operations, J represents the set of all screen devices, c ij represents the cost of transferring operation i to screen j, and x ij is a decision variable indicating whether to transfer operation i to screen j.

[0017] As a preferred embodiment of the present invention: It specifically includes the following steps:

[0018] S1: The device identification and classification module uses a support vector machine algorithm to quickly and accurately classify devices;

[0019] S2: The user behavior analysis module uses a hidden Markov model to predict the potential needs of users;

[0020] S3: The content recommendation system analyzes user preferences through a convolutional neural network and provides personalized content recommendations in combination with a collaborative filtering algorithm;

[0021] S4: The dynamic resource allocation module uses a reinforcement learning framework and continuously adjusts the resource allocation strategy through the Q-learning algorithm to adapt to different network environments and user needs;

[0022] In the device identification and classification, it specifically includes:

[0023] S11: Signal acquisition and preprocessing, using a wireless sensor network to collect wireless signal data of all screen devices in the home, and filtering and enhancing the signals to reduce the interference of environmental noise;

[0024] S12: Feature extraction, extracting key features from the wireless signals, and using principal component analysis to reduce the feature dimension and improve the classification efficiency;

[0025] S13: Model training and classification. Use the support vector machine or random forest algorithm to train the device classification model, optimize the model parameters through cross-validation, and ensure high-accuracy device identification;

[0026] S14: Real-time device monitoring. Monitor the addition of new devices or the removal of existing devices in real time, and automatically update the device list.

[0027] As a preferred embodiment of the present invention: The user behavior analysis module uses the hidden Markov model to predict the potential needs of users, and its formula is:

[0028]

[0029] Where:

[0030] X = (X 1 , X 2 , …, X n ) is the observed user behavior sequence;

[0031] Y = (Y 1 , Y 2 , …, Y n ) is the hidden state sequence;

[0032] P(Y) is the prior probability of the hidden state sequence;

[0033] P(Y i |Y i-1 ) is the state transition probability, indicating the probability of transitioning from the previous state Y i-1 to the current state Y i ;

[0034] P(X i |Y i ) is the emission probability, indicating the probability of observing the behavior X i under the given hidden state Y i ;

[0035] As a preferred embodiment of the present invention: The content recommendation system analyzes user preferences through a convolutional neural network and provides personalized content recommendations in combination with the collaborative filtering algorithm, specifically including:

[0036] S21: User profile construction. Collect the browsing history, preference settings, and feedback information of users on each screen, and use the user data to construct a personalized user profile;

[0037] S22: Content analysis. Perform metadata analysis on available content resources, extract keywords, types, and rating features, and use natural language processing technology to analyze content descriptions to enhance content understanding;

[0038] S23: Implement the recommendation algorithm, combine collaborative filtering and content-based recommendation algorithms to generate a personalized content recommendation list, use A / B testing to evaluate the effectiveness of different recommendation strategies, and optimize the recommendation quality;

[0039] S24: Provide feedback on the recommendation results, track the click-through rate and viewing time of the recommended content by users, collect feedback information, and adjust the recommendation algorithm according to the feedback to achieve continuous optimization;

[0040] The dynamic resource allocation specifically includes:

[0041] S31: Monitor the network status, real-time monitor the bandwidth usage and latency level of the home network, and identify network congestion and device performance bottlenecks;

[0042] S32: Forecast the resource requirements, analyze the applications and content currently running on each screen, predict the resource requirements in the short term, and use time series analysis to predict the changes in network load;

[0043] S33: Implement a resource scheduling algorithm, implement a priority-based resource scheduling algorithm to ensure that critical tasks can obtain resources first, and use reinforcement learning to adjust the resource allocation strategy to minimize latency and maximize user satisfaction;

[0044] S34: Evaluate the scheduling effect, evaluate the impact of the resource scheduling strategy on the multi-screen interaction experience, and adjust the strategy to adapt to changes in network conditions and user needs.

[0045] As a preferred embodiment of the present invention: it further includes:

[0046] Intelligent space recognition and analysis: By using deep learning algorithms, more accurate space recognition and analysis of multi-screen devices in the home environment are carried out; this step can identify the position, size of each screen and its relative relationship with other devices;

[0047] In-depth user behavior modeling: Combine the hidden Markov model and long short-term memory network to deeply model the switching behavior and content preferences of users between different screens; this can help the system better understand the user's behavior pattern and predict the user's potential needs;

[0048] Multi-screen content collaborative filtering: Use collaborative filtering algorithms and deep learning technologies to provide cross-screen content recommendations for users; by analyzing the historical activity data of users on multiple screens, the system can recommend the most suitable content for the current screen and context;

[0049] Dynamic resource scheduling optimization: Adopt a reinforcement learning framework to dynamically optimize the resource allocation between different screens; according to the current network conditions and the resource requirements of each screen, the resource allocation strategy is adjusted in real time to ensure a smooth user experience.

[0050] As a preferred embodiment of the present invention: the in-depth modeling of user behavior specifically includes:

[0051] Behavior data collection, collecting the operation logs of the user on different screens, including click, swipe, and screen switching behaviors, and synchronously collecting the user's physiological data;

[0052] Pattern recognition, applying the Hidden Markov Model and Long Short-Term Memory Network to analyze the user's behavior patterns and identify the user's emotional state and attention distribution;

[0053] Behavior prediction, predicting the user's future behaviors and potential needs based on a deep learning model to provide proactive services;

[0054] Model iterative optimization, continuously iteratively optimizing the behavior model according to user feedback and behavior changes, and using transfer learning and incremental learning techniques to maintain the timeliness of the model.

[0055] As a preferred embodiment of the present invention: in the in-depth modeling of user behavior, the Hidden Markov Model and Long Short-Term Memory Network are combined to handle the long-term dependence problem in time series data. The specific formula is:

[0056] i t = σ(W xi x t + W hi h t-1 + W ci C t-1 + b i )

[0057] f t = σ(W xf x t + W hf h t-1 + W cf c t-1 + b f )

[0058]

[0059] o t = σ(W xo x t + W ho h t-1 + W co c t + b o )

[0060]

[0061] where i t is the state of the input gate, f tis the state of the forget gate, o t is the state of the output gate, x t is the input at time t; h t is the hidden state at time t; c t is the cell state at time t; W is the weight matrix and b is the bias term; σ and tanh are activation functions.

[0062] As a preferred embodiment of the present invention: It further includes:

[0063] Interactive feedback learning mechanism: Introduce simulated annealing and genetic algorithms, design an interactive feedback learning mechanism, and allow the system to self-adjust and optimize according to user feedback; Through the interaction between the user and the system, the system can continuously learn and adapt to the user's preferences;

[0064] Adaptive interface layout adjustment: Utilize tabu search and ant colony algorithms to develop an adaptive interface layout adjustment algorithm; This algorithm can automatically adjust the content layout according to the screen characteristics and user preferences, providing a more comfortable visual experience;

[0065] Algorithm performance self-evaluation and optimization: Introduce automated machine learning techniques to perform performance self-evaluation and optimization on various intelligent algorithms used in the system; Through continuous testing and feedback loops, the system can automatically adjust algorithm parameters to adapt to the changing usage environment and user needs.

[0066] As a preferred embodiment of the present invention: In the algorithm performance self-evaluation and optimization, it specifically includes:

[0067] Performance monitoring, real-time monitor the performance metrics of each algorithm in the system, and use dashboards and alert mechanisms to notify system administrators in a timely manner of performance degradation;

[0068] Automated optimization, apply automated machine learning techniques to automatically adjust algorithm parameters, and use Bayesian optimization and genetic algorithms to find the optimal parameter combination;

[0069] Simulation testing, test the performance of the algorithm in various edge cases through a simulation environment, and use chaos engineering methods to evaluate the robustness of the system;

[0070] Continuous learning and updating, adopt online learning and incremental learning techniques to enable the system to adapt to new data and new trends, and regularly update the system's knowledge base and decision rules.

[0071] As a preferred embodiment of the present invention: In the adaptive interface layout adjustment, the pheromone update formula of the ant colony algorithm is specifically:

[0072] τ ij (t + 1) = (1 - ρ)·τ ij (t) + Δτ ij

[0073] Among them, τ ij (t + 1) is the value of the pheromone concentration on path ij at time t + 1, ρ is the pheromone evaporation rate, and Δτ ij is the pheromone increment on path ij.

[0074] The beneficial effects of the present invention are as follows:

[0075] 1. By adopting artificial intelligence technologies, including support vector machines, hidden Markov models, convolutional neural networks, reinforcement learning, etc., the present invention realizes a smart home multi-screen interaction intelligent optimization system; this system can automatically identify various screen devices in the home and classify and manage them according to device performance and usage environment.

[0076] 2. Through in-depth analysis and understanding of user behavior, the system can predict the potential needs of users and recommend appropriate content to different devices accordingly; at the same time, the system also optimizes the interaction response and resource allocation between devices to ensure the consistency and smoothness of the user experience when switching and applying between different devices.

[0077] 3. The present invention also includes advanced functions such as intelligent space recognition and analysis, in-depth user behavior modeling, multi-screen content collaborative filtering, and dynamic resource scheduling optimization, further enhancing the intelligent level of the system and the user interaction experience. By introducing technologies such as interactive feedback learning mechanisms, adaptive interface layout adjustment, algorithm performance self-evaluation and optimization, the system can continuously self-optimize and adapt to the changing needs of users and environmental changes, maintaining long-term service quality and efficiency.

[0078] 4. The present invention effectively solves the technical challenges of multi-screen interaction and management in the smart home environment, significantly improves the operation convenience of users, the accuracy of personalized content recommendation, and the overall interaction experience, providing important technical support and innovative ideas for the future development trend of smart home. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 is a flowchart of the smart home multi-screen interaction intelligent optimization method based on AI services proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0080] The technical solutions of this patent will be further described in detail below in conjunction with the specific embodiments.

[0081] The embodiments of this patent will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below by referring to the drawings are exemplary and are only used to explain this patent and should not be construed as a limitation of this patent.

[0082] Example 1:

[0083] An intelligent optimization method for multi-screen interaction in a smart home based on AI services, including:

[0084] Device identification and classification: Identify all screen devices in the home through wireless network sensing technology and classify them according to device characteristics;

[0085] User behavior analysis: Collect operation data of users on different screens and analyze the user's behavior patterns using machine learning algorithms;

[0086] Content recommendation system: Based on user behavior analysis and device characteristics, use a deep learning model to recommend content suitable for different screens;

[0087] Interaction optimization algorithm: An interaction optimization algorithm based on multi-screen interaction, including minimizing touch transfer latency, visual consistency calibration, etc.;

[0088] Resource dynamic allocation: Dynamically adjust the resource allocation strategy according to the current network conditions and device performance to ensure a smooth multi-screen interaction experience;

[0089] Among them, in the interaction optimization algorithm, the problem of minimizing touch transfer latency is solved by a mixed integer linear programming method, and the formula is as follows:

[0090]

[0091]

[0092]

[0093] Among them, I represents the set of all touch operations, J represents the set of all screen devices, c ij represents the cost of transferring operation i to screen j, and x ij is a decision variable indicating whether to transfer operation i to screen j.

[0094] Specifically, it includes the following steps:

[0095] S1: The device identification and classification module uses a support vector machine algorithm to classify devices quickly and accurately;

[0096] S2: The user behavior analysis module uses a hidden Markov model to predict the user's potential needs;

[0097] S3: The content recommendation system analyzes user preferences through a convolutional neural network and provides personalized content recommendations in combination with a collaborative filtering algorithm;

[0098] S4: The resource dynamic allocation module adopts a reinforcement learning framework and continuously adjusts the resource allocation strategy through the Q-learning algorithm to adapt to different network environments and user requirements.

[0099] Among them, in the device identification and classification, it specifically includes:

[0100] S11: Signal acquisition and preprocessing. Use a wireless sensor network to collect the wireless signal data of all screen devices in the home, and perform filtering and enhancement processing on the signals to reduce the interference of environmental noise.

[0101] S12: Feature extraction. Extract key features from the wireless signals, such as signal strength, signal-to-noise ratio, device response time, etc., and use principal component analysis to reduce the feature dimension and improve the classification efficiency.

[0102] S13: Model training and classification. Use the support vector machine or random forest algorithm to train the device classification model, and optimize the model parameters through cross-validation to ensure high-accuracy device identification.

[0103] S14: Real-time device monitoring. Real-time monitor the addition of new devices or the removal of existing devices, and automatically update the device list.

[0104] Among them, the user behavior analysis module uses the hidden Markov model to predict the potential needs of users, and its formula is:

[0105]

[0106] Where:

[0107] X = (X 1 , X 2 , …, X n ) is the observed user behavior sequence (such as clicks, swipes, etc.);

[0108] Y = (Y 1 , Y 2 , …, Y n ) is the hidden state sequence (such as the implicit needs of users);

[0109] P(Y) is the prior probability of the hidden state sequence;

[0110] P(Y i |Y i-1 ) is the state transition probability, indicating the probability of transitioning from the previous state Y i-1 to the current state Y i ;

[0111] P(X i |Y i ) is the emission probability, indicating the probability of emitting under the given hidden state Y iThe probability of observing behavior X below i of.

[0112] Among them, the content recommendation system analyzes user preferences through a convolutional neural network and provides personalized content recommendations by combining a collaborative filtering algorithm, specifically including:

[0113] S21: Construction of user portraits, collecting the browsing history, preference settings, and feedback information of users on each screen, and using user data to construct personalized user portraits;

[0114] S22: Content analysis, performing meta-data analysis on available content resources, extracting features such as keywords, types, and ratings, and using natural language processing technology to analyze content descriptions to enhance content understanding;

[0115] S23: Implementation of recommendation algorithms, combining collaborative filtering and content-based recommendation algorithms to generate a list of personalized content recommendations, and using A / B testing to evaluate the effects of different recommendation strategies and optimize the recommendation quality;

[0116] S24: Feedback on recommendation results, tracking the click-through rate and viewing time of users on recommended content, collecting feedback information, and adjusting the recommendation algorithm according to the feedback to achieve continuous optimization.

[0117] Among them, the dynamic resource allocation specifically includes:

[0118] S31: Network condition monitoring, real-time monitoring of the bandwidth usage and latency level of the home network, and identifying network congestion and device performance bottlenecks;

[0119] S32: Resource demand prediction, analyzing the applications and content currently running on each screen, predicting the resource demand in the short term, and using time series analysis to predict changes in network load;

[0120] S33: Resource scheduling algorithm, implementing a priority-based resource scheduling algorithm to ensure that critical tasks obtain resources first, and using reinforcement learning to adjust the resource allocation strategy to minimize latency and maximize user satisfaction;

[0121] S34: Evaluation of scheduling effects, evaluating the impact of resource scheduling strategies on the multi-screen interaction experience, and adjusting the strategy to adapt to changes in network conditions and user needs.

[0122] Embodiment 2:

[0123] The intelligent optimization method for multi-screen interaction in a smart home based on AI services. In this embodiment, the following improvements are made on the basis of Embodiment 1: It further includes:

[0124] Intelligent Space Recognition and Analysis: By adopting deep learning algorithms, more accurate space recognition and analysis are carried out on multi-screen devices in the home environment; this step can identify the position, size of each screen and its relative relationship with other devices;

[0125] In-depth User Behavior Modeling: Combining the Hidden Markov Model and Long Short-Term Memory Network, in-depth modeling is carried out on the switching behavior and content preferences of users between different screens; this can help the system better understand the user's behavior patterns and predict the user's potential needs;

[0126] Multi-Screen Content Collaborative Filtering: Using collaborative filtering algorithms and deep learning technologies, cross-screen content recommendations are provided for users; by analyzing the historical activity data of users on multiple screens, the system can recommend the most suitable content for the current screen and situation;

[0127] Dynamic Resource Scheduling Optimization: Adopting a reinforcement learning framework, the resource allocation between different screens is dynamically optimized; according to the current network conditions and the resource requirements of each screen, the resource allocation strategy is adjusted in real time to ensure a smooth user experience.

[0128] Among them, the in-depth user behavior modeling specifically includes:

[0129] Behavior Data Collection, collecting the operation logs of users on different screens, including behaviors such as clicks, swipes and screen switching, and synchronously collecting the physiological data of users, such as eye movements and facial expressions, to obtain a deeper emotional state;

[0130] Pattern Recognition, applying the Hidden Markov Model and Long Short-Term Memory Network to analyze the user's behavior patterns and identify the user's emotional state and attention distribution;

[0131] Behavior Prediction, predicting the user's future behavior and potential needs based on the deep learning model, and providing proactive services, such as automatically displaying recipes on the kitchen screen when the user turns to the kitchen;

[0132] Model Iterative Optimization, continuously iteratively optimizing the behavior model according to user feedback and behavior changes, and adopting transfer learning and incremental learning technologies to maintain the timeliness of the model.

[0133] Among them, in the in-depth user behavior modeling, the Hidden Markov Model and Long Short-Term Memory Network are combined to handle the long-term dependence problem in time series data. The specific formula is:

[0134] i t =σ(W xi x t +W hi h t-1 +W ci C t-1 +b i )

[0135] f t = σ(W xf x t + W hf h t-1 + W cf c t-1 + b f )

[0136]

[0137] o t = σ(W xo x t + W ho h t-1 + W co c t + b o )

[0138]

[0139] where i t is the state of the input gate, f t is the state of the forget gate, o t is the state of the output gate, x t is the input at time t; h t is the hidden state at time t; c t is the cell state at time t; W is the weight matrix, b is the bias term; σ and tanh are activation functions.

[0140] Example 3:

[0141] Intelligent optimization method for multi-screen interaction in a smart home based on AI services. In this example, the following improvements are made on the basis of Example 1: It further includes:

[0142] Interactive feedback learning mechanism: Introduce simulated annealing and genetic algorithms to design an interactive feedback learning mechanism that allows the system to self-adjust and optimize according to user feedback; through the interaction between the user and the system, the system can continuously learn and adapt to the user's preferences;

[0143] Adaptive interface layout adjustment: Utilize tabu search and ant colony algorithms to develop an adaptive interface layout adjustment algorithm; this algorithm can automatically adjust the content layout according to screen characteristics and user preferences to provide a more comfortable visual experience;

[0144] Algorithm performance self-evaluation and optimization: Introduce automated machine learning techniques to perform performance self-evaluation and optimization on each intelligent algorithm used in the system; through continuous testing and feedback loops, the system can automatically adjust algorithm parameters to adapt to the changing usage environment and user requirements.

[0145] Among them, in the self-evaluation and optimization of the algorithm performance, it specifically includes:

[0146] Performance monitoring, which monitors the performance indicators of each algorithm in the system in real time, such as accuracy, response time, and resource consumption, and uses a dashboard and an alarm mechanism to notify the system administrator in a timely manner of performance degradation;

[0147] Automated optimization, which applies automated machine learning techniques to automatically adjust algorithm parameters and uses Bayesian optimization and genetic algorithms to find the optimal parameter combination;

[0148] Simulation testing, which tests the performance of the algorithm in various edge cases through a simulation environment and uses chaos engineering methods to evaluate the robustness of the system;

[0149] Continuous learning and updating, which adopts online learning and incremental learning techniques to make the system adapt to new data and new trends, and regularly updates the knowledge base and decision rules of the system.

[0150] Among them, in the adaptive interface layout adjustment, the pheromone update formula of the ant colony algorithm is specifically:

[0151] τ ij (t + 1) = (1 - ρ)·τ ij (t) + Δτ ij

[0152] Among them, τ ij (t + 1) is the value of the pheromone concentration on path ij at time t + 1, ρ is the pheromone evaporation rate, and Δτ ij is the pheromone increment on path ij.

[0153] As described above, only the specific preferred embodiments of the present invention are provided, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and all should be covered within the protection scope of the present invention.

Claims

1. A smart home multi-screen interactive intelligent optimization method based on AI service, characterized in that: include: Device identification and classification: Through wireless network sensing technology, all screen devices in the home are identified and classified according to device characteristics; User behavior analysis: Collect user operation data on different screens and use machine learning algorithms to analyze user behavior patterns; Content recommendation system: Based on user behavior analysis and device characteristics, a deep learning model is used to recommend content suitable for different screens; Interaction optimization algorithm: Interaction optimization algorithm based on multi-screen interaction, including minimization of touch transmission delay and visual consistency calibration; Dynamic resource allocation: Dynamically adjust resource allocation strategies based on current network conditions and device performance to ensure a smooth multi-screen interactive experience; Among them, in the interactive optimization algorithm, the problem of minimizing touch transfer delay is solved by the mixed integer linear programming method, and the formula is as follows: Among them, I represents the set of all touch operations, J represents the set of all screen devices, and c ij represents the cost of delivering operation i to screen j, x ij is a decision variable, indicating whether to pass operation i to screen j; Also includes: Intelligent spatial recognition and analysis: By using deep learning algorithms, more accurate spatial recognition and analysis of multiple screen devices in the home environment can be performed; this step can identify the location, size and relative relationship of each screen to other devices; Deep modeling of user behavior: Combining hidden Markov models and long short-term memory networks, users’ switching behaviors and content preferences between different screens are deeply modeled; this can help the system better understand user behavior patterns and predict users’ potential needs; Multi-screen content collaborative filtering: Use collaborative filtering algorithms and deep learning technology to provide users with cross-screen content recommendations; by analyzing the user's historical activity data on multiple screens, the system can recommend the most suitable content for the current screen and context; Dynamic resource scheduling optimization: The reinforcement learning framework is used to dynamically optimize resource allocation between different screens. The resource allocation strategy is adjusted in real time according to the current network status and resource requirements of each screen to ensure a smooth user experience. The user behavior in-depth modeling specifically includes: Behavioral data collection: collect user operation logs on different screens, including clicks, slides, and screen switching behaviors, and simultaneously collect user physiological data; Pattern recognition, using hidden Markov models and long short-term memory networks to analyze user behavior patterns and identify users’ emotional states and attention distribution; Behavior prediction: predicting users’ future behaviors and potential needs based on deep learning models and providing proactive services; Model iterative optimization: Continuously iterate and optimize the behavior model based on user feedback and behavior changes, and use transfer learning and incremental learning techniques to maintain the timeliness of the model; In the user behavior deep modeling, the hidden Markov model and the long short-term memory network are combined to deal with the long-term dependency problem in time series data. The specific formula is: i t =σ(W xi x t +W hi h t-1 +W ci c t-1 +b i ) f t =σ(W xf x t +W hf h t-1 +W cf c t-1 +b f ) o t =σ(W xo x t +W ho h t-1 +W co c t +b o ) Among them, i t is the state of the input gate, f t is the state of the forget gate, o t is the state of the output gate, x t is the input at time t; h t is the hidden state at time t; c t is the cell state at time t; W is the weight matrix, b is the bias term; σ and tanh are activation functions.

2. The AI ​​service-based smart home multi-screen interactive intelligent optimization method according to claim 1 is characterized in that: The specific steps include: S1: The device identification and classification module uses the support vector machine algorithm to quickly and accurately classify devices; S2: User behavior analysis module uses hidden Markov model to predict users' potential needs; S3: The content recommendation system analyzes user preferences through convolutional neural networks and provides personalized content recommendations in combination with collaborative filtering algorithms; S4: The dynamic resource allocation module adopts a reinforcement learning framework and continuously adjusts the resource allocation strategy through the Q-learning algorithm to adapt to different network environments and user needs; The device identification and classification specifically includes: S11: Signal acquisition and preprocessing: using wireless sensor networks to collect wireless signal data from all screen devices in the home, filtering and enhancing the signals to reduce interference from environmental noise; S12: Feature extraction, extracting key features from wireless signals and using principal component analysis to reduce feature dimensions and improve classification efficiency; S13: Model training and classification: Use support vector machine or random forest algorithm to train device classification model, optimize model parameters through cross-validation, and ensure high accuracy of device identification; S14: Real-time device monitoring: real-time monitoring of the addition of new devices or the removal of existing devices, and automatic updating of the device list.

3. The AI ​​service-based smart home multi-screen interactive intelligent optimization method according to claim 2 is characterized in that: The user behavior analysis module uses a hidden Markov model to predict the user's potential needs, and its formula is: in: X=(X1,X2,…,X n ) is the observed user behavior sequence; Y=(Y1,Y2,…,Y n ) is the hidden state sequence; P(Y) is the prior probability of the hidden state sequence; P(Y i |Y i-1 ) is the state transition probability, indicating the transition from the previous state Y i-1 Transfer to current state Y i probability; P(X i |Y i ) is the emission probability, indicating that in a given hidden state Y i The behavior X is observed i probability.

4. The AI ​​service-based smart home multi-screen interactive intelligent optimization method according to claim 2 is characterized in that: The content recommendation system analyzes user preferences through convolutional neural networks and provides personalized content recommendations in combination with collaborative filtering algorithms, specifically including: S21: User portrait construction, collecting users’ browsing history, preference settings and feedback information on each screen, and using user data to build personalized user portraits; S22: Content analysis: metadata analysis of available content resources, extraction of keywords, types, and rating features, and use of natural language processing technology to analyze content descriptions and enhance content understanding; S23: Recommendation algorithm implementation, combining collaborative filtering and content-based recommendation algorithms to generate personalized content recommendation lists, using A / B testing to evaluate the effects of different recommendation strategies and optimize recommendation quality; S24: Feedback on recommendation results: tracking the click rate and viewing time of users on recommended content, collecting feedback information, and adjusting the recommendation algorithm based on the feedback to achieve continuous optimization; The dynamic allocation of resources specifically includes: S31: Network status monitoring, real-time monitoring of home network bandwidth usage and latency levels, identifying network congestion and device performance bottlenecks; S32: Resource demand prediction: analyzing the applications and content currently running on each screen, predicting short-term resource demand, and using time series analysis to predict network load changes; S33: Resource Scheduling Algorithm: Implement a priority-based resource scheduling algorithm to ensure that critical tasks get resources first, and use reinforcement learning to adjust resource allocation strategies to minimize latency and maximize user satisfaction. S34: Scheduling effect evaluation: evaluate the impact of resource scheduling strategies on the multi-screen interactive experience and adjust strategies to adapt to changes in network conditions and user needs.

5. The AI ​​service-based smart home multi-screen interactive intelligent optimization method according to claim 1, characterized in that: Also includes: Interactive feedback learning mechanism: Introducing simulated annealing and genetic algorithms, designing an interactive feedback learning mechanism that allows the system to self-adjust and optimize based on user feedback; Through the interaction between the user and the system, the system can continuously learn and adapt to the user's preferences; Adaptive interface layout adjustment: Develop an adaptive interface layout adjustment algorithm using taboo search and ant colony algorithm; this algorithm can automatically adjust the content layout according to screen characteristics and user preferences to provide a more comfortable visual experience; Self-evaluation and optimization of algorithm performance: Introduce automated machine learning technology to perform self-evaluation and optimization of the performance of each intelligent algorithm used in the system; through continuous testing and feedback loops, the system can automatically adjust algorithm parameters to adapt to the ever-changing usage environment and user needs.

6. The AI ​​service-based smart home multi-screen interactive intelligent optimization method according to claim 5 is characterized in that: The algorithm performance self-assessment and optimization specifically includes: Performance monitoring, which monitors the performance indicators of each algorithm in the system in real time, and uses dashboards and alert mechanisms to promptly notify system administrators of performance degradation; Automated optimization, applying automated machine learning techniques to automatically adjust algorithm parameters, using Bayesian optimization and genetic algorithms to find the optimal parameter combination; Simulation testing: testing the performance of the algorithm in various edge cases through a simulated environment, and using chaos engineering methods to evaluate the robustness of the system; Continuous learning and updating, using online learning and incremental learning techniques to adapt the system to new data and new trends, and regularly updating the system's knowledge base and decision rules.

7. The AI ​​service-based smart home multi-screen interactive intelligent optimization method according to claim 6, characterized in that: In the adaptive interface layout adjustment, the ant colony algorithm pheromone update formula is specifically: t ij (t+1)=(1-ρ)·τ ij (t)+Δτ ij Among them, τ ij (t+1) is the value of the pheromone concentration on path ij at time t+1, ρ is the pheromone evaporation rate, Δτ ij is the pheromone increment on path ij.

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