Dynamic scene-oriented smart home adaptive control system and method

Abstract

The invention provides a dynamic scene-oriented smart home adaptive control system and method, and belongs to the technical field of smart home control. Comprising a scene recognition module, a compatibility evaluation module and a consistency control module, the scene recognition module comprises a data acquisition module, a user behavior analyzer, a power consumption scene (EUS) database and a scene recognition algorithm, and the compatibility evaluation module comprises electric appliance operation state recognition and a compatibility evaluation algorithm. The consistency control module comprises a control strategy generation, feedback and optimization module, and the scene recognition module, the compatibility evaluation module and the consistency control module are used in cooperation. According to the invention, through efficient data acquisition and processing, real-time identification of user behaviors and environment changes is realized, so that the intelligent level and the living comfort level are improved. In combination with user demand analysis and multi-device coordination control, the system can optimize resource utilization and provide personalized and convenient home experience.

Description

Technical Field

[0001] The present invention provides a smart home adaptive control system and method for dynamic scenes, belonging to the technical field of smart home control. Background Art

[0002] In actual operation, smart home systems are easily affected by uncertainties such as changes in user demand and fluctuations in environmental conditions, which causes the power service effect to deviate from expectations. The main reason for this phenomenon is that the existing control strategies are often managed based on preset fixed scenarios and are difficult to adapt to dynamically changing application environments.

[0003] There are many urgent problems to be solved in the prior art in the adaptive control of smart homes. First, there is a lack of a method that can uniformly characterize various application scenarios, which makes it difficult for smart homes to automatically optimize and control electrical appliances across multiple application scenarios. Secondly, the prior art lacks a method that can accurately evaluate the degree of adaptation between the operating status of electrical appliances and user needs and the actual environment, which makes it difficult for the system to accurately trigger consistency control. Furthermore, the existing control methods make it difficult for smart homes to maintain high compatibility and meet the personalized needs of users in a dynamically changing environment. Finally, the prior art also lacks an effective simulation system for comprehensively simulating the process of smart homes providing power services by controlling multiple electrical appliances in a time-varying environment. These deficiencies seriously restrict the ability of smart home systems to provide high-quality, personalized power services in complex and changing environments.

[0004] To this end, the present invention proposes a smart home adaptive control system and method for dynamic scenarios. By introducing the concept of electricity usage scenarios and a compatibility evaluation mechanism, automatic adaptation and optimized control are achieved in different application scenarios, effectively improving the flexibility, intelligence and user satisfaction of the smart home system. In the proposed method, the smart home control process is decoupled into three main links: scene recognition, compatibility evaluation and consistency control, which correspond to perception, decision-making and execution processes respectively. Among them, scene recognition uses the concept of electricity usage scenarios (EUS) to uniformly represent various application scenarios; compatibility evaluation reflects the degree of adaptation between the operating status of electrical appliances and user needs and the actual environment; consistency control is based on the compatibility evaluation results, and adjusts the operating status of electrical appliances to maintain a high system compatibility to solve the above-mentioned problems. Summary of the invention

[0005] The present invention provides a smart home adaptive control system and method for dynamic scenarios, which realizes real-time recognition of user behavior and environmental changes through efficient data collection and processing, thereby improving the level of intelligence and living comfort. Combined with user demand analysis and multi-device coordinated control, the system can optimize resource utilization and provide a personalized and convenient home experience.

[0006] In order to solve the above problems, the present invention proposes a technical solution: a smart home adaptive control system and method for dynamic scenes, including a scene recognition module, a compatibility evaluation module and a consistency control module, the scene recognition module includes a data acquisition module, a user behavior analyzer, an electricity usage scenario (EUS) database, and a scene recognition algorithm, the compatibility evaluation module includes electrical appliance operation status recognition and a compatibility evaluation algorithm, the consistency control module includes a control strategy generation, feedback and optimization module, and the scene recognition module, the compatibility evaluation module and the consistency control module are used in combination.

[0007] Furthermore, the specific usage of the scene recognition module is as follows:

[0008] The data acquisition module collects environmental data in real time from various sensors, including temperature, humidity, and light intensity. The user behavior analyzer uses the human infrared device to record whether there is anyone in the room.

[0009] Preprocess the collected raw data, including cleaning, filtering, and normalizing the data to improve the quality and consistency of the data, and convert the preprocessed data into feature vectors that can be used for scene recognition;

[0010] Use the scene recognition algorithm to analyze the feature vector and select the application scene that should be run according to the current time;

[0011] The identified scenario is matched with the standard electricity usage scenario (S-EUS) in the electricity usage scenario (EUS) database to determine the current electricity usage scenario (C-EUS). If the identified scenario does not completely match the predefined electricity usage scenario, the scenario recognition module outputs the identified electricity usage scenario to the compatibility evaluation module as input for subsequent compatibility evaluation and consistency control.

[0012] Furthermore, the compatibility evaluation module obtains the current operating status data of various electrical appliances; obtains the current environmental data, including indoor temperature, humidity, and light intensity; based on the operating status of the electrical appliances, user needs and environmental data, uses a compatibility evaluation algorithm to calculate the compatibility score between the current power usage scenario and the actual operating status. The compatibility score reflects the degree of adaptation between the operating status of the electrical appliances and user needs and the actual environment.

[0013] Furthermore, the control strategy is generated based on the compatibility evaluation results. The consistency control module generates a corresponding control strategy. If the compatibility score is low, the operating status of the electrical appliance is adjusted according to the current power consumption scenario and its time series data, including adjusting the air conditioning temperature, light brightness, and humidifier humidity; the consistency control module sends control instructions to various electrical equipment through the control center of the smart home system to achieve coordinated control of multiple devices; after executing the control strategy, the feedback and optimization module collects actual operating data and user feedback for optimizing scene recognition, compatibility evaluation and consistency control algorithms to improve the system's adaptability and user satisfaction.

[0014] Furthermore, the compatibility evaluation module reflects the degree of compatibility between the operating state of the appliance and user needs and the actual environment.

[0015] Furthermore, the consistency control module adjusts the operating state of the electrical appliance based on the compatibility evaluation result to maintain a high system compatibility.

[0016] Due to the adoption of the above technical solution, the beneficial effects of the smart home adaptive control system and method for dynamic scenes of the present invention are as follows:

[0017] 1. Intelligent scene recognition: Through efficient data collection and processing methods, the system can identify different user behaviors and environmental changes in real time, thereby achieving rapid adaptation to dynamic scenes and improving the intelligence level of the home environment.

[0018] 2. Accurate capture of user needs: The system can analyze users’ historical behavior data and preferences to achieve personalized needs analysis, so that users’ actual needs in different scenarios can be accurately understood and met.

[0019] 3. Efficient compatibility assessment: Combining the operating status of appliances, user needs and environmental data, the system can flexibly calculate the compatibility score to ensure that the operating status of appliances is highly matched with actual needs, thereby improving energy efficiency and living comfort.

[0020] 4. Self-optimization capability: Through the feedback and optimization module, the system can continuously adjust and optimize scene recognition, compatibility assessment and consistency control algorithms according to actual operation data and user feedback, thereby improving the system's adaptive capability and user satisfaction, making the overall home experience more intelligent and convenient.

[0021] 5. Multi-device coordinated control: The consistency control module can generate reasonable control strategies to achieve coordinated control of multiple smart devices in the house, so that the devices can collaborate efficiently, avoid waste of resources, and improve the user experience. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present invention are described clearly and completely below with reference to examples. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] The present invention provides a smart home adaptive control system and method for dynamic scenes: comprising a scene recognition module, a compatibility evaluation module and a consistency control module, wherein the scene recognition module comprises a data acquisition module, a user behavior analyzer, an electricity usage scenario (EUS) database, and a scene recognition algorithm, the compatibility evaluation module comprises an electrical appliance operation state recognition and a compatibility evaluation algorithm, the consistency control module comprises a control strategy generation, feedback and optimization module, and the scene recognition module, the compatibility evaluation module and the consistency control module are used in coordination.

[0024] The specific usage of the scene recognition module is as follows:

[0025] The data acquisition module collects environmental data in real time from various sensors, including temperature, humidity, and light intensity. The user behavior analyzer uses the human infrared device to record whether there is anyone in the room.

[0026] Preprocess the collected raw data, including cleaning, filtering, and normalizing the data to improve the quality and consistency of the data, and convert the preprocessed data into feature vectors that can be used for scene recognition;

[0027] Use the scene recognition algorithm to analyze the feature vector and select the application scene that should be run according to the current time;

[0028] The identified scenario is matched with the standard electricity usage scenario (S-EUS) in the electricity usage scenario (EUS) database to determine the current electricity usage scenario (C-EUS). If the identified scenario does not completely match the predefined electricity usage scenario, the scenario recognition module outputs the identified electricity usage scenario to the compatibility evaluation module as input for subsequent compatibility evaluation and consistency control.

[0029] The compatibility evaluation module obtains the current operating status data of various electrical appliances; obtains the current environmental data, including indoor temperature, humidity, and light intensity; based on the operating status of the electrical appliances, user needs and environmental data, uses a compatibility evaluation algorithm to calculate the compatibility score between the current power usage scenario and the actual operating status. The compatibility score reflects the degree of adaptation between the operating status of the electrical appliances and user needs and the actual environment.

[0030] The control strategy is generated based on the compatibility evaluation results. The consistency control module generates the corresponding control strategy. If the compatibility score is low, the operating status of the electrical appliance is adjusted according to the current power consumption scenario and its time series data, including adjusting the air conditioning temperature, light brightness, and humidifier humidity; the consistency control module sends control instructions to various electrical equipment through the control center of the smart home system to achieve coordinated control of multiple devices; after executing the control strategy, the feedback and optimization module collects actual operating data and user feedback for optimizing scene recognition, compatibility evaluation and consistency control algorithms to improve the system's adaptability and user satisfaction.

[0031] The compatibility evaluation module reflects the degree of adaptation between the operation status of the appliance and the user's needs and the actual environment.

[0032] The consistency control module adjusts the operation state of the electrical appliance based on the compatibility evaluation result to maintain a high system compatibility.

[0033] The electricity consumption scenario EUS uniformly represents various application scenarios through four dimensions: energy, society, environment and time. Its mathematical expression is as follows:

[0034]

[0035] In the formula, the electricity usage scenario (EUS) describes the power service constraints and planning tasks of the smart home through four dimensions: energy dimension Ene, social dimension Soc, environmental dimension Env and time dimension Tim. A set of parameters representing the energy dimension, including the total power demand of the room P S and the working status of each electrical appliance Soc = {Hab i ,Act i} represents the parameter set of social dimension, covering the user's personalized preference Hab i and behavior patterns i , this information may come from user settings, daily life rules or professional advice; Env = {s P ,[T min ,T max ],[H min ,H max ], K} represents the parameter set of the environmental dimension, taking into account the indoor occupant state s P , Temperature range [T min ,T max ] and humidity range [H min ,H max ] and other factors; Tim = {Pla i} represents the parameter set of the time dimension, Pla irepresents the i-th valid period. Through this multi-dimensional parameter set, EUS can comprehensively and accurately describe the operating requirements and constraints of smart homes in various scenarios. This unified characterization method provides a flexible and operational framework for smart home systems, enabling the system to automatically adapt and optimize control in different application scenarios, thereby improving the flexibility, intelligence and user satisfaction of the system.

[0036] The standard electricity usage scenario (S-EUS) in the present invention is a representative scenario obtained by clustering analysis of the historical EUS time series, which serves as an important basis for optimizing and managing the operating status of electrical appliances.

[0037] As a comprehensive, flexible and operational framework, EUS enables different EUSs to be used in different application scenarios. On a daily basis, the EUS time series exhibits stability, which is due to the regularity of users' daily electricity consumption behavior, resulting in similar EUS time series on the same type of day. For example, EUS time series on weekdays usually have similar patterns.

[0038] By performing cluster analysis on the historical EUS time series, several cluster centers, namely S-EUS, can be extracted. The mathematical expression of S-EUS is as follows:

[0039]

[0040] Where, F(*) represents the method of obtaining the cluster center of the historical EUS time series, such as the K-Means clustering algorithm; EUS S Represents a collection of historical EUS time series, including Multiple EUS time series; S-EUS i represents the i-th standard electricity consumption scenario, i∈[1,N S ],N S is the optimal number of clusters.

[0041] The S-EUS time series can be used as the basis for the smart home system to automatically optimize and control the operating status of electrical appliances, effectively reducing the complexity of the smart home in the process of automatic operation in multiple scenarios. For example, the unsupervised clustering method K-Means can be used to determine the optimal number of clusters, that is, the number of S-EUS types. Arranging the various types of S-EUS in chronological order and naming them in combination with life experience can obtain various specific scenarios.

[0042] To ensure that the system can adapt to the changing user needs and operating environment, the present invention proposes an adaptive parameter optimization method based on user feedback. This method uses the feedback data from the user's interaction with the appliance to adjust the expected value of each parameter in the S-EUS.

[0043] U-This method is based on the following assumptions:

[0044] (1) Necessity: User demand is time-varying, and the expected values ​​of parameters in S-EUS are also time-varying.

[0045] (2) Interactivity: Users are the recipients and feedback providers of power services. When the system misoperates or fails to meet expectations, users will manually adjust the values ​​of parameters such as the operating status of the appliance and the set temperature. These behaviors will be captured and learned by the system, which will then optimize the expected values ​​of each parameter.

[0046] (3) Regularity: User needs remain relatively stable over a certain period of time, which means that in the process of optimizing S-EUS, although the initial parameter settings may not fully adapt to user needs, by continuously collecting user feedback and adjusting the expected values ​​of the parameters, the system can gradually adapt the expected values ​​of the parameters to user needs.

[0047] For state parameters (such as the start and stop status of electrical appliances), the adaptive adjustment formula is as follows:

[0048]

[0049] represents the weight of the i-th parameter at the k-th optimization; a is the weight adjustment coefficient, a>0; r is the ratio of the number of manual adjustments by the user to the number of automatic adjustments by the smart home; Δw k is the weight change during the kth optimization. g(F * ,f * ) is a function that returns the index of the element in a collection that is equal to a specific value.

[0050] From the weight adjustment mechanism, it can be seen that when the user automatically adjusts the operating state of the smart home appliance, S,sol If you are not satisfied, manually adjust to f S,adj When the system will reduce f S,sol The weight of f S,adj The more such user interactions occur, the greater the reduction or increase of the two, which not only improves the system's responsiveness to users' repeated operations, but also helps improve the efficiency and accuracy of the decision-making process.

[0051] Finally, the system selects the state with the largest weight as the expected value of the parameter:

[0052]

[0053] For interval parameters (such as temperature and humidity), the adaptive adjustment formula is as follows:

[0054]

[0055] In the formula, is the weight of the i-th element after the k-th optimization; f I,adj and f I,sol They are the values ​​manually adjusted by the user and the values ​​automatically adjusted by the system; is the median of the interval. Since the interval type parameters are affected by the interval upper bound f in S-EUS I,up and the lower bound f I,low The median of the interval is used because of the limitation of As the basis for adjusting the upper or lower bound of the interval.

[0056] Through the adaptive parameter optimization method, the smart home system can continuously adjust and optimize its control strategy based on user feedback, so as to better adapt to changes in user needs. As the number of interactions between users and smart homes increases, the system will adapt to the actual needs of users and automatically optimize the weight distribution of parameter expectations. This improves the system's understanding of user preferences and enhances the adaptability of smart home systems in complex and changing environments.

[0057] Smart home compatibility reflects the degree of adaptation between the operating status of electrical appliances and user needs and the actual environment, and serves as the triggering basis for consistency control. If the compatibility is lower than the preset threshold, the system will start consistency control and improve compatibility by adjusting the operating status of electrical appliances, thereby achieving efficient use of electrical energy and providing personalized power services.

[0058] Traditional compatibility evaluation methods mainly include distance-based and correlation-based methods. For the former, there are methods such as relative distance to the evaluation index and Wasserstein distance; for the latter, there are methods such as Pearson correlation coefficient and Kendall rank correlation coefficient.

[0059] However, these methods have obvious limitations in the smart home environment:

[0060] It is impossible to accurately capture the dynamic changes of the smart home environment: that is, it is impossible to distinguish whether the changes in compatibility are caused by the real-time environment or user needs.

[0061] Failure to fully consider users’ expectations and personalized needs for electricity services: Failure to accurately reflect how differences in various parameters in the standard electricity consumption scenario (S-EUS) affect changes in compatibility.

[0062] To this end, the present invention proposes a compatibility evaluation method based on electricity usage scenario (EUS), and defines compatibility as the sum of spatial compatibility and temporal compatibility.

[0063] Compatibility Definition

[0064] The present invention defines compatibility as:

[0065]

[0066] Where: C C-EUS,t Indicates the compatibility of the current electricity usage scenario (C-EUS) at time t; It represents the spatial compatibility at time t, which is used to measure the similarity between EUSs in adjacent time periods and can reflect the changes in parameters such as indoor occupant status, temperature, and humidity; It represents the temporal compatibility at time t and measures the difference between C-EUS and S-EUS (representing user needs), thereby making the system more sensitive to user needs.

[0067] Time compatibility assessment method

[0068] The calculation steps of time compatibility are as follows:

[0069] (1) Rearrange the parameters in S-EUS:

[0070] Since EUS covers parameters in multiple dimensions, and S-EUS, as a subset of EUS, cannot be directly compared with C-EUS, it is necessary to reorder the M state-type and interval-type parameters contained in S-EUS to form a new sequence in is the i-th parameter of S-EUS.

[0071] (2) Extraction of relevant parameters in C-EUS:

[0072] Refer to the parameters contained in P, extract the corresponding parameters from C-EUS to form a new sequence of length M in is the i-th parameter of C-EUS.

[0073] (3) Calculation time compatibility:

[0074]

[0075] In the formula, f C (S-EUS t ,C-EUS t ) represents a method for calculating C-EUS t and S-EUS t This method adopts the exponential function form, the purpose of which is to amplify the influence of the differences between different parameters on the overall compatibility; A1 represents the weight coefficient of time compatibility, which is used to adjust the importance of time compatibility in the overall compatibility evaluation; α1 is the attenuation coefficient in the system; x 1,i For C-EUS t and S-EUS tis the score item of the i-th parameter; is the weight of the i-th parameter; I I is a collection of interval parameters, including temperature and humidity; I S It is a collection of state parameters, including the indoor occupant status and the start / stop status of electrical appliances, etc.

[0076] To keep the same order of magnitude as the temporal compatibility, the present invention expresses the spatial compatibility as:

[0077]

[0078] Where: f A (C-EUS t ,C-EUS t-1 ) is used to calculate C-EUS t and C-EUS t-1 A2 is the spatial compatibility weight coefficient, and A1+A2=1; α2 is the attenuation coefficient; x2 is C-EUS t and C-EUS t-1 The similarity between them is measured by the Pearson correlation coefficient, and its value range is mapped to [0, 1].

[0079] Control timing trigger mechanism

[0080] Compatibility is the basis for triggering consistency control, but its threshold cannot be used directly as a criterion. On the one hand, personalized power demand will inevitably make the threshold vary greatly, making it difficult to unify the standard; on the other hand, the time-varying operating environment further increases the complexity of setting a unified compatibility threshold.

[0081] To this end, the present invention uses a compatibility change value that is dynamically adjusted according to actual conditions as the basis for triggering control to address the above problems. When the compatibility change value exceeds the limit, consistency control will be triggered:

[0082]

[0083] Where: C C-EUS,t Indicates compatibility at time t; C C-EUS,t-1 Indicates compatibility at time t-1; This consistency control trigger mechanism based on the change value can sensitively respond to the dynamic changes of the real-time environment and user needs, and alleviate the negative impact of time-varying on the smart home automatic control system.

[0084] Overall system architecture

[0085] The consistency control system proposed in this invention aims to optimize the operating status of electrical appliances in the room and achieve the control target under the premise of meeting user needs. It mainly includes the following modules:

[0086] (1) Appliance status assessment module: responsible for real-time assessment of the operating status and active power of each appliance. This module can be integrated into the automatic control system or implemented through a dedicated intelligent agent corresponding to each appliance.

[0087] (2) Control trigger module: triggers the control timing based on the compatibility change value.

[0088] (3) EUS tracking model building module: The EUS tracking model is built based on the latest electrical appliance operating status, active power and other operating information.

[0089] (4) Model solving and execution module: solves the EUS tracking model and executes the solution to make the current electricity consumption scenario (C-EUS) highly adaptable to user needs and operating environment.

[0090] This system significantly improves the response speed of the system by decoupling the evaluation and control processes, and is particularly suitable for time-varying smart home environments.

[0091] The present invention divides common household appliances into two categories according to their control characteristics:

[0092] (1) Two-state appliances: including lamps, humidifiers, water dispensers, etc., which have only two states: on and off. The relationship between their operating state and active power can be expressed as:

[0093]

[0094] In the formula, represents the jth operating state of the i-th two-state appliance, 0 represents off and 1 represents on; Indicates the active power corresponding to the operating state; → Indicates the mapping relationship between the operating state and the active power.

[0095] (2) Multi-state appliances: Inverter air conditioners (IACs) are representative of multi-state appliances. Considering the impact of the set temperature on the IAC operating state, the IAC operating state is described as:

[0096]

[0097] Where: Indicates the jth operating state of the ith IAC, 0 means off, 1 means on; Indicates the active power corresponding to the operating state; Indicates that the set temperature Under this condition, the active power obtained by the evaluation method; N I Indicates the ratio of the set temperature interval length to the adjustment step length.

[0098] For two-state electrical appliances, the active power in the on state is equal to its rated power. For IAC, the present invention adopts a method based on a first-order equivalent thermal parameter model to evaluate the active power corresponding to the set temperature.

[0099] In order to ensure that the smart home system maintains high compatibility in a dynamic environment and meets personalized user needs, the present invention proposes an EUS tracking model. The objective function and constraints of the model are as follows:

[0100] Objective function:

[0101] max(C C-EUS )

[0102] Constraints:

[0103]

[0104]

[0105] Where: C C-EUS Indicates the compatibility of the current power usage scenario; I EA,1 and I EA,2 I represents the set of two-state electrical appliances and multi-state electrical appliances (such as IAC); i,S represents the operating state set of the i-th electrical appliance; P S,S-EUS represents the expected room active power in the standard electricity usage scenario (S-EUS); ΔP S,max Indicates the maximum allowable active power deviation; and Indicates whether the jth operating state of the i-th appliance in the two-state appliance and the multi-state appliance is selected, 1 indicates selected, and 0 indicates unselected; f cl and f op These are the parameters that must be used to turn off or on the appliance in S-EUS. S-EUS 、Env S-EUS and Tim S-EUS Respectively represent the social, environmental and time dimension constraint functions in S-EUS; T In and H In Respectively represent indoor temperature and humidity; T In ,max , T In,min , H In,max and H In,min Respectively represent the upper and lower limits of indoor temperature and humidity.

[0106] The model maximizes system compatibility by selecting the optimal combination of appliance operating states, while taking into account room power balance, appliance operation mutual exclusivity, and social, environmental, and temporal constraints. The model can be solved using commercial solver software or an exhaustive method based on the characteristics of the problem.

[0107] Through the compatibility-driven consistency control system proposed in the present invention, smart homes can adapt to dynamically changing environments more flexibly, provide efficient and personalized power services, and achieve efficient use of electrical energy.

[0108] Through Example 1 and Example 2, the coordination relationship among the scene recognition module, compatibility assessment module and consistency control module is explained.

[0109] Example 1

[0110] Get up in the morning:

[0111] Scene recognition: The sensor detects that the indoor temperature is 20°C, the light gradually increases, and the human infrared sensor detects the user getting up.

[0112] The scene recognition module recognizes that the current scene is "getting up in the morning";

[0113] Compatibility assessment: The compatibility assessment module obtains the current operating status of electrical appliances (such as light brightness, air conditioning temperature, audio volume, etc.), analyzes the user's historical waking habits, determines user needs (such as comfortable temperature, soft lighting, gentle music, etc.), and calculates the compatibility score between the current power usage scenario and the actual operating status;

[0114] Consistency control: Based on the compatibility assessment results, the consistency control module generates a control strategy to adjust the lighting brightness to a soft mode, the air conditioning temperature to 22°C, and the audio system to play soft music. The system sends control instructions to the lighting, air conditioning, and audio equipment through the control center to achieve coordinated control of multiple devices.

[0115] Example 2

[0116] Night rest:

[0117] Scene recognition: The sensor detects that the indoor temperature is 25°C, the light gradually weakens, and the human infrared sensor detects that the user is ready to rest. The scene recognition module recognizes that the current scene is "night rest";

[0118] Compatibility evaluation: The compatibility evaluation module obtains the current operating status of the appliance (such as light brightness, air conditioning temperature, humidifier humidity, etc.), analyzes the user's historical rest habits, determines the user's needs (such as comfortable temperature, soft lighting, quiet environment, etc.), and calculates the compatibility score between the current power usage scenario and the actual operating status;

[0119] Consistency control: Based on the compatibility assessment results, the consistency control module generates a control strategy to adjust the light brightness to dim mode, the air conditioning temperature to 23°C, turn off the humidifier, and lower the audio volume. The system sends control instructions to the lights, air conditioners, humidifiers, and audio equipment through the control center to achieve coordinated control of multiple devices.

[0120] The present invention and its implementation methods are described above, and such description is not restrictive. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design structures and embodiments similar to the technical solution without creativity, which should all fall within the protection scope of the present invention.

Claims

1. A smart home adaptive control system and method for dynamic scenes, comprising a scene recognition module, a compatibility evaluation module and a consistency control module, wherein the scene recognition module comprises a data acquisition module, a user behavior analyzer, an electricity usage scenario (EUS) database, and a scene recognition algorithm, the compatibility evaluation module comprises an electrical appliance operating state recognition and a compatibility evaluation algorithm, and the consistency control module comprises a control strategy generation, feedback and optimization module, and the scene recognition module, the compatibility evaluation module and the consistency control module are used in conjunction with each other.

2. According to the smart home adaptive control system and method for dynamic scenes described in claim 1, the specific method of using the scene recognition module is as follows: The data acquisition module collects environmental data in real time from various sensors, including temperature, humidity, and light intensity. The user behavior analyzer uses the human infrared device to record whether there is anyone in the room. Preprocess the collected raw data, including cleaning, filtering, and normalizing the data to improve the quality and consistency of the data, and convert the preprocessed data into feature vectors that can be used for scene recognition; Use the scene recognition algorithm to analyze the feature vector and select the application scene that should be run according to the current time; The identified scenario is matched with the standard electricity usage scenario (S-EUS) in the electricity usage scenario (EUS) database to determine the current electricity usage scenario (C-EUS). If the identified scenario does not completely match the predefined electricity usage scenario, the scenario recognition module outputs the identified electricity usage scenario to the compatibility evaluation module as input for subsequent compatibility evaluation and consistency control.

3. The smart home adaptive control system and method for dynamic scenes according to claim 1, characterized in that: The compatibility evaluation module obtains the current operating status data of various electrical appliances; obtains the current environmental data, including indoor temperature, humidity, and light intensity; based on the operating status of the electrical appliances, user needs and environmental data, uses a compatibility evaluation algorithm to calculate the compatibility score between the current power usage scenario and the actual operating status. The compatibility score reflects the degree of adaptation between the operating status of the electrical appliances and user needs and the actual environment.

4. The smart home adaptive control system and method for dynamic scenes according to claim 1, characterized in that: The control strategy is generated based on the compatibility evaluation results. The consistency control module generates the corresponding control strategy. If the compatibility score is low, the operating status of the electrical appliance is adjusted according to the current power consumption scenario and its time series data, including adjusting the air conditioning temperature, light brightness, and humidifier humidity; the consistency control module sends control instructions to various electrical equipment through the control center of the smart home system to achieve coordinated control of multiple devices; after executing the control strategy, the feedback and optimization module collects actual operating data and user feedback for optimizing scene recognition, compatibility evaluation and consistency control algorithms to improve the system's adaptability and user satisfaction.

5. The smart home adaptive control system and method for dynamic scenes according to claim 1, characterized in that: The compatibility evaluation module reflects the degree of adaptation between the operation status of the appliance and the user's needs and the actual environment.

6. The smart home adaptive control system and method for dynamic scenes according to claim 1, characterized in that: The consistency control module adjusts the operation state of the electrical appliance based on the compatibility evaluation result to maintain a high system compatibility.

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